As we begin the 2024-25 academic year, CIS is thrilled to welcome Joel Ramirez from Stanford to our teaching faculty. He will be contributing to the CIS 2400 and 1100 courses, further enhancing our program’s academic strength. Over the past six years, Penn has made over 25 faculty hires, continuing its incredible growth. This year, we are also excited about the near completion of Amy Gutmann Hall, a new space dedicated to data science and engineering collaboration, which will host key research centers and foster innovation across multiple fields.
In addition to new spaces, Penn is launching the Penn Advanced Research Computing Center (PARCC), offering cutting-edge GPU and CPU resources for advanced research. CIS faculty are involved in groundbreaking research projects, including AI-enabled medical treatments and reducing data center energy consumption. These initiatives showcase the department’s leadership in tackling real-world challenges with innovative solutions.
This fall also marks the launch of the first Ivy League Bachelor’s degree in Artificial Intelligence, with 35 students enrolled and more courses in development. Leadership updates include Joe Devietti extending his role as Undergraduate Chair and Anindya De stepping in as Graduate Chair, both bringing fresh ideas to the department. With these exciting developments, CIS is poised for another year of growth, innovation, and academic excellence.
Jacob Gardner, Assistant Professor in Computer and Information Science, first encountered machine learning during a high school internship predicting hurricanes in his native North Carolina. Today, Gardner applies machine learning to scientific research rather than weather prediction. His goal is to develop AI tools that can enhance fields like drug discovery, similar to how the electron microscope transformed science. He aims to create tools that help scientists work faster and more effectively, offering new insights into complex problems.
At Penn Engineering, Gardner’s research focuses on using AI to optimize machine learning algorithms for scientific applications. His work has garnered significant recognition, including a CAREER Award from the National Science Foundation. Collaborating with colleagues at the Perelman School of Medicine, Gardner envisions creating a “self-driving lab” that automates the discovery of therapeutic molecules for drug-resistant bacteria and diseases like cancer. This vision involves AI generating molecular structures, robots synthesizing them, and testing them for effectiveness against diseases.
Gardner’s research group explores a wide range of applications for AI, from theoretical optimization problems to tools that could revolutionize different scientific fields. As a mentor, Gardner encourages his students to pursue projects they are passionate about, believing that excitement drives productivity. His lab’s research, which spans multiple domains, reflects the power and versatility of AI in solving real-world problems. Gardner is currently recruiting graduate students for Fall 2025 to join his lab’s cutting-edge research.
In celebration of AI Month, we are thrilled to extend our congratulations to Andrew Zhu, under the guidance of Chris Callison-Burch, for his remarkable achievement in receiving the prestigious 2024 NSF Graduate Research Fellowship Program (GRFP). Andrew’s dedication and innovative vision have earned him this esteemed recognition, marking a significant milestone in his academic journey.
Andrew’s research endeavors are poised to address a critical challenge in the realm of Language Model Machines or Large Language Models (LLMs), where despite their formidable capabilities, they often grapple with the issue of ‘forgetting’ when confronted with complex inquiries or tasks spanning extensive timelines. For instance, while humans effortlessly navigate through multi-step queries, LLMs encounter hurdles in retaining the original context across successive stages of information retrieval. Inspired by this gap, Andrew’s previous work, FanOutQA, shed light on the limitations of current models, showcasing the vast barrier between human accuracy and machine performance.
High-level diagram showing the settings in the FanOutQA benchmark: the LLM (robot) must answer a complex question by breaking it down (bottom). There are 3 settings we test LLMs in: closed-book (the LLM cannot use any search tools, only what it was trained on), open-book (the LLM may search Wikipedia), and evidence-provided (the LLM is given the Wikipedia pages containing the answers and must extract the correct answers).
Central to Andrew’s proposed solution is the concept of recursive delegation, a groundbreaking approach aimed at endowing LLMs with the ability to allocate intricate tasks to subordinate LLMs. This hierarchical structure allows for the decomposition of complex problems into more manageable segments, with each ‘child’ LLM equipped to handle its designated subtask. Through this cascading delegation, Andrew envisions a synergistic collaboration among LLMs, comparable to a relay race where each participant contributes to achieving the final objective.
In laying the groundwork for his research, Andrew has developed Kani, a versatile framework designed to facilitate seamless interaction between LLMs and human-written code. This innovative tool empowers researchers to experiment with different LLM architectures while interfacing with Python functions effortlessly. By democratizing access to such tools, Andrew aims to catalyze progress in the field and foster a collaborative ecosystem of exploration and discovery.
Screenshot of an early proof-of-concept recursive delegation system I developed this year. In this example, it’s helping make travel plans for a Japan trip and looking at Shinkansen (bullet train) travel times on the internet. Each node (in the top right) is an independent LLM. (green=in progress, yellow=waiting, gray=completed, blue=selected)The same as above, but with the code needed to actually use Kani. It’s designed to be easy to pick up while allowing experienced developers to customize the library extensively.An overview of what the Kani system is comprised of. It manages the chat history and provides it and user-written functions to underlying LLMs (engines) with a common interface.
Andrew’s advisor, Chris Callison-Burch, effectively summarizes the significance of his work, stating,
“Andrew Zhu’s research is at the cutting edge of programming languages, natural language processing, and artificial intelligence. His groundbreaking open-source software package, Kani, elegantly solves a major weakness of large language models (LLMs) like ChatGPT by enabling them to write code and call functions. Kani seamlessly integrates LLMs into Python programs, allowing developers to write functions that can be called by the language model. This game-changing approach combines the strengths of AI and traditional programming, enabling LLMs to interweave natural language generation with complex computations.
Andrew’s work has the potential to revolutionize how we develop AI systems, paving the way for more powerful and versatile AI applications that go beyond ChatGPT’s current abilities of understanding and generating natural language to also include the abilities to create and manipulate code.”
Chris Callison-Burch
As Andrew embarks on this ambitious journey, his work holds the promise of transforming the landscape of artificial intelligence, ushering in a new era of collaboration and problem-solving. We eagerly anticipate the insights and advancements that will emerge from his pioneering efforts, underscoring the transformative potential of AI in shaping our collective future. Once again, congratulations to Andrew Zhu on this well-deserved recognition, and here’s to a future marked by innovation, collaboration, and boundless possibilities in the realm of AI.
References:
[1] Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024. FanOutQA: Multi-Hop, Multi-Document Question Answering for Large Language Models. Arxiv preprint, in submission at ACL 2024. https://arxiv.org/abs/2402.14116
[2] Andrew Zhu, Liam Dugan, Alyssa Hwang, and Chris Callison-Burch. 2023. Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 65–77, Singapore. Association for Computational Linguistics. https://aclanthology.org/2023.nlposs-1.8/
Four students from the University of Pennsylvania have been awarded the prestigious NSF Graduate Research Fellowship Program (GRFP) for 2024. Three of these awardees, Lena Armstrong, Emmanuel Suarez Acevedo, and Stephan Xie, will be heading to other universities for their PhDs, while Edward Zhang will continue his doctoral studies at Penn. Here are their achievements and reflections on this milestone.
Lena Armstrong (HCI)
Lena Armstrong, who works with Danaë Metaxa, will begin her PhD in Computer Science at Harvard this fall. Reflecting on her achievement, Armstrong said, “I feel excited and honored to have received the NSF Graduate Research Fellowship. This award allows me greater access to resources and opportunities to further my research, advocacy, and teaching goals. My aim is to change the narrative of who gets to shape our digital future and create more inclusive experiences in computer science.”
Danaë Metaxa praised Armstrong, stating, “Lena is a star, one of the most motivated and creative undergraduates I have worked with. Her departure is bittersweet, but I look forward to seeing all she will accomplish at Harvard.”
Emmanuel Suarez Acevedo (Formal Methods, Verification, and Programming Languages)
Emmanuel Suarez Acevedo, advised by Stephanie Weirich, is heading to Cornell for his PhD. On receiving the fellowship, Acevedo remarked, “I was excited and grateful for the support from fellow students and professors at Penn. The GRFP will allow me to focus on my dissertation topic without the worry of finding funding, enabling me to continue working towards broadening participation in computer science.”
Acevedo recently worked on an extension to an intermediate language for static tracking of program behaviors, collaborating with students from Penn and the University of Michigan. This work aims to improve compiler optimizations.
Stephanie Weirich commented, “Emma is heading to Cornell’s PhD program next year and will intern with Kuen-Bang Hou Favonia at the University of Minnesota this summer. He has worked with me on several projects, including enhancing the Call-by-Push-Value type system and mechanizing proofs in Agda.”
Stephan Xie (Machine Learning)
Stephan Xie, who has been working with Aaron Roth, will pursue his PhD in Machine Learning at Carnegie Mellon University. Xie shared his excitement, saying, “Receiving the GRFP was a huge honor. It gives me more confidence in my research direction and opens up many possibilities as I begin my PhD.”
Xie’s research focuses on advancing machine learning decision-making and discovery in real-world applications. He has worked on projects exploring unbiased predictions and improving the reliability of deep learning.
A project I worked on last year with my advisor Prof. Aaron Roth and graduate students Georgy Noarov and Ramya Ramalingam was along this vein, exploring how we could make “unbiased predictions” that lead to strong decision-making guarantees, i.e. low swap regret for all decision-makers. This also led to a number of applications in online combinatorial optimization, extensive-form games, and uncertainty quantification.
Aaron Roth expressed his enthusiasm for Xie’s work, stating, “It was great fun working with Stephan. Together with my PhD students, we wrote what turned out to be my favorite paper in some time. I’m looking forward to following his progress at CMU.”
Edward Zhang (Computer Vision)
Edward Zhang, who will continue his PhD at Penn, expressed his gratitude for receiving the fellowship. “I am incredibly grateful and honored to have received this opportunity. I hope to leverage these funds to take charge of my own research direction,” Zhang said.
Zhang plans to use the funding to investigate the use of visual computing to solve real-world problems in disciplines like medicine, public health, and public policy. He aims to deploy these solutions in economically sustainable and scalable ways.
Live Mas 2024 Renewal Submission – Edward ZhangA recent project: Ego-Exo4D – Here is an early stage data collection photo. I credit this project for helping me develop my practical knowledge about computer vision and research in general.
Congratulations to all the 2024 NSF GRFP awardees! This is a well-deserved achievement, and we look forward to seeing the remarkable contributions you will make during your PhDs!
Four PhD students from Penn’s Human-Computer Interaction (HCI) and Natural Language Processing (NLP) groups have been awarded the prestigious NSF Graduate Research Fellowship Program (GRFP) for 2024. These awards recognize and support outstanding graduate students in STEM disciplines. Three of the awardees, Ro Encarnación, Hita Kambhamettu, and Princess Sampson, are pioneering research in HCI, while a fourth student, Andrew Zhu, has made significant strides in NLP.
During AI Month, the CIS Blog highlighted Andrew’s research and accomplishments in a previous article. For more details on Zhu’s awards, click here!
Here are their achievements and reflections on this milestone.
Ro Encarnación
Advised by Danaë Metaxa, Ro Encarnación has been focusing on community-engaged public interest technology policy and design. “I took a big risk leaving the workforce to pursue the kind of work I’m interested in. Receiving the award provided validation that I’m on the right track,” said Encarnación. They are currently working on a project under review on emergent auditing behavior in social media.
Metaxa praises Encarnación’s work, noting, “Ro’s recent work has studied how users interrogate the algorithmic systems they interact with in their everyday lives. Her awarded GRFP proposed to build systems for high schoolers and their families to navigate the high-stakes and unequal setting of high school selection. Ro’s experience as a TechCongress fellow positions her to produce impactful research.”
Hita Kambhamettu
Under the guidance of Andrew Head, Hita Kambhamettu is advancing research at the confluence of human-AI interaction and biomedical informatics. Reflecting on the fellowship, she shared, “When I verified that I had indeed received the fellowship, I was overcome with a profound sense of excitement and immense gratitude. This achievement is due to the steadfast support from my advisor, collaborators, peers, and family.”
Kambhamettu plans to develop intelligent systems that help patients make informed health decisions and create explainable machine learning methods for medical research. She recently completed a project introducing “traceable text” to enhance the understanding of AI-generated medical summaries. Head commended her contributions, saying, “Hita is designing human-AI interfaces to serve as just-in-time translators of health records. With the NSF fellowship, she will build on her strong research foundation to re-envision patients’ relationships with their health information.”
Princess Sampson
Princess Sampson, also advised by Danaë Metaxa, expressed their honor in receiving the NSF-GRFP alongside her peers. She highlighted the collective effort, “We formed a weekly check-in group, broke the application into distinct deliverables, and developed a timeline to hold each other accountable. One win would have been a win for all of us, but it’s a joy and a privilege to win together.”
Sampson aims to empower users, especially from marginalized communities, in their interactions with digital ad targeting systems. They are currently working on a system for collaborative, value-driven ad-blocking. Metaxa lauded her dedication, stating, “Princess has been working on tools for empowering users in the context of digital ad targeting systems. Her paper published in FAccT last year was very well-received, and I look forward to seeing the rest of her PhD develop.”
Advisor Reflections
Danaë Metaxa expressed immense pride in the students, saying, “All of us in the Penn HCI group are incredibly proud of our NSF GRFP awardees this year. Ro, Princess, and Hita are all founding members of the HCI group, our first cohort of students; it’s inspiring and affirming to see their success.”
Andrew Head shared similar sentiments about Kambhamettu’s achievements and future potential, “Hita is making great inroads in her research agenda. With the NSF fellowship, she will continue to advance her work and positively impact the field of biomedical informatics.”
These students’ successes highlight the collaborative and supportive environment at Penn HCI and NLP, paving the way for innovative research with real-world impact.
Joseph Devietti, serving as Associate Professor and Undergraduate Curriculum Chair in the CIS Department, has recently been honored with the Ford Motor Company Award for Faculty Advising. This accolade underscores his unwavering commitment to guiding students towards achieving their academic, professional, and personal aspirations.
Specializing in computer architecture and programming languages, particularly in streamlining multiprocessor programming through advancements in computer architectures and parallel programming models, Devietti brings a wealth of expertise to his role.
Having obtained a BSE in computer science and a BA in English from the University of Pennsylvania in 2006, Devietti furthered his education with MS and PhD degrees in computer science and engineering from the University of Washington in 2009 and 2012, respectively.
Devietti’s dedication extends beyond the classroom, as evidenced by his investment in mentoring student projects. His willingness to provide access to his lab’s resources for academic exploration and projects, coupled with his ability to break down and explain the complicated parts of a technical topic, has garnered admiration from students.
Each year, the Penn Engineering undergraduate student body meticulously selects recipients of these awards, acknowledging individuals for their commitment to teaching, mentorship, and advocacy on behalf of students.
Reflecting on his achievement,
“I felt honored to join so many colleagues from CIS who have been previous winners. I’ll try to continue to live up to the high standards we’ve set for advising!”
Joe Devietti
Congratulations to Joe Devietti on this well-deserved honor!
Assistant Professor Gushu Li has been honored with the prestigious NSF Career Award, signaling a groundbreaking venture into the world of quantum computing. The central challenge Gushu is tackling revolves around creating programming systems that can support larger-scale quantum computers. Imagine our current systems as tailor-made for small prototypes, limiting the true potential of these advanced quantum machines.
The focus of Gushu Li’s research is on enhancing the way we write and execute programs for quantum computers. The obstacle lies in the fact that existing systems are like specialized tools for small tasks. They are not equipped to handle the complex demands of larger-scale quantum computers. To address this, Gushu is essentially upgrading the software that controls quantum computers, making it smarter and more adaptable.
The NSF Career Award doesn’t just fuel groundbreaking research but also enables Gushu Li to expand the team. New graduate students will be brought in to contribute to related projects and help build the proposed software improvements. Beyond the technical aspects, Gushu is also committed to sharing knowledge and raising awareness. The award will support the creation of two courses focused on quantum computing. They won’t just be for tech enthusiasts, but for anyone interested in understanding this cutting-edge technology.
Additionally, Gushu Li plans to be part of existing outreach programs. Introducing quantum computing concepts to students at various levels is important to expose young minds to all academic options. The interdisciplinary nature of this research, blending computer science, math, physics, and engineering, underscores its significance in advancing our understanding of how we can manipulate the very building blocks of the universe. Gushu and the team are on a path to making meaningful contributions, thanks to the support provided by the NSF Career Award.
Aaron Roth, a Professor of Computer Science and Cognitive Science at the University of Pennsylvania, has been awarded the prestigious Hans Sigrist Prize by the University of Bern. This award, endowed with 100,000 Swiss francs, recognizes Roth’s pioneering work in the field of fair algorithms and his efforts to incorporate social norms into algorithmic decision-making processes.
Roth’s research, spanning over 15 years, has focused on critical issues such as algorithmic fairness and differential privacy. Algorithmic fairness is concerned with ensuring that algorithms do not perpetuate biases against specific demographic groups, particularly in areas like job applications. Roth highlights the importance of careful algorithm design to avoid unintentional bias based on historical data.
Differential privacy, another key aspect of Roth’s work, involves developing mathematical methods to analyze large datasets while preserving individual privacy. By combining data in a way that prevents the identification of specific individuals, Roth’s research contributes to the responsible use of data, particularly in fields like clinical studies.
The Hans Sigrist Prize committee commended Roth’s outstanding contributions, emphasizing that his work not only benefits society but also helps individuals maximize the benefits of data science while mitigating negative side effects.
Roth’s research aligns with the current initiatives at the University of Bern, which seeks to address digitalization in an interdisciplinary manner and as part of social transformation. The university’s focus on projects that transcend traditional faculty boundaries, as evident in the Bern Data Science Initiative BeDSI, aligns with Roth’s vision for tackling challenges in data science.
Christiane Tretter, Professor at the Mathematical Institute of the University of Bern and Chair of the 2023 Hans Sigrist Prize Committee, highlights the significance of Roth’s work in bridging the perceived gap between fairness and algorithms. She notes that Roth’s achievements send a positive signal to the university and beyond, emphasizing the need for increased attention to topics like fairness and algorithms.
Roth, when asked about his plans for the prize money, expresses the importance of building a research community in the area. He recognizes the value of foundation funds, such as those from the Hans Sigrist Foundation, in supporting this goal and advancing research in algorithmic fairness and privacy. Roth’s commitment to creating awareness and addressing challenges in data analysis underscores the transformative potential of his work in shaping ethical and responsible data science practices.
“Picture a room full of students hard at work on math problems. Some draw graphs on the chalkboard, testing out algorithms. Others shuffle complicated algebraic expressions, trying to simplify a summation. Still, others stare intently at a piece of paper, trying to find the one necessary final lemma to complete a proof or to understand a recent result.
The students are all working hard, but they are also having fun, as the air is ripe with the excitement of discovery. These are not college students, though they are studying at a college campus, and learning material that is not normally taught until the undergrad or even the graduate level. Rather, these are mostly high school students, spending their summer learning and enjoying themselves, studying theoretical computer science.”
The Program in Algorithmic and Combinatorial Thinking (PACT), ran by Rajiv Gandhi (Professor of CIS @ Rutgers-Camden/part-time Lecturer in CIS @ UPenn), is partially supported by the National Science Foundation. It is a five-week intensive course that teaches students about the mathematics and algorithms fundamental to the computer science field. There were two groups running simultaneously. In the “Beginner’s Group”, students learned the “Mathematical Foundations of Computer Science” while students in the advanced group studied “Advanced topics in Algorithm Design”.
“During Summer 2023, PACT was run in hybrid mode, i.e., there were some students who did the program in person and others did it online. The in-person program had 11 students (from MA, NY, NJ, China) and the rest (more than 150) did the program online — students from various countries (Australia, Canada, China, India, Kazakhstan, Morocco, Nigeria, Rwanda, S. Korea, Switzerland, US). The youngest student in the program had finished grade 5. Most others were high school students and some were in college.”
Rajiv Gandhi
Rajiv Gandhi established the PACT for high school students in 2010. Since 2011, the program has taken place at Princeton University and has been attracting students from the U.S. and internationally. The program has grown from a handful of students to nearly 200 students each year. PACT gives students the ability to be exposed to advanced topics in computer science at a young age. Rajiv has also worked closely with and mentored students in India at schools whose students have not typically gone on to graduate study.
Many PACT students over the years have pursued PhDs in Computer Science as well as other STEM fields. Several of our students and alumni at Penn Engineering participated in PACT when they were in high school. Students including Chris Jung and Ezra Edelman (currently, a student of Surbhi Goel) are a few examples. The incredible thing about PACT is the students that the program is able to reach in various parts of the world. This can lead these young people to better opportunities and help these students build strong careers.
This past summer, the program was held at the University of Pennsylvania for the first time. For Summer 2024, Rajiv Gandhi is hoping to make the in-program component larger. Due to the success of PACT and his dedication to inspiring his students, Rajiv was presented the 2022 ACM-SIGACT Distinguished Service Award.
SI Neg. 83-14878. Date: na. Grace Murray Hopper at the UNIVAC keyboard, c. 1960. Grace Brewster Murray: American mathematician and rear admiral in the U.S. Navy who was a pioneer in developing computer technology, helping to devise UNIVAC I. the first commercial electronic computer, and naval applications for COBOL (common-business-oriented language). Credit: Unknown (Smithsonian Institution)
Early Years and Academic Prowess
Born in 1906 in the bustling heart of New York City, Grace Murray Hopper emerged as a trailblazing figure in the world of computer science. Her academic journey began at Vassar College, where she graduated in 1928 with degrees in Mathematics and Physics. Hopper furthered her education with a Master’s in Mathematics from Yale in 1930, later becoming a pioneering force in the male-dominated field.
Service in the U.S. Navy during World War II
During World War II, Hopper’s commitment to service led her to join the U.S. Navy after the Pearl Harbor bombing. Trained intensively at the Midshipmen’s School for Women at Smith College, she was assigned to the Bureau of Ships Computation Project at Harvard University. Here, she worked on the IBM Automatic Sequence Controlled Calculator (MARK 1), contributing to top-secret calculations vital for the war effort, such as rocket trajectories and anti-aircraft gun tables.
Revolutionizing Computer Science: FLOW-MATIC and COBOL
Post-war, Hopper continued her groundbreaking work, transitioning to the burgeoning field of computer science. In 1953, she proposed the revolutionary idea of writing programs in words rather than symbols. This concept led to the creation of FLOW-MATIC in 1956, the first programming language using word commands and heralded as a “user-friendly language.” Her groundbreaking efforts culminated in the development of COBOL (Common Business Oriented Language) in 1959, a milestone that revolutionized programming and became the most extensively used computer language globally by the 1970s.
Legacy Beyond the Screen: Teaching and Consulting
US Navy computer ctrs. Commodre Grace Hopper in her office.(Photo by Cynthia Johnson/Getty Images)
Despite the challenges faced by women in her era, Hopper’s legacy endured. She turned down a full professorship at Vassar to continue her work with computers, eventually becoming a senior consultant at the Digital Equipment Corporation until her passing in 1992. Grace Hopper’s influence extended beyond her technological contributions, as she also dedicated herself to teaching and mentoring future generations of computer scientists at institutions such as the Moore School of Electrical Engineering at Penn and George Washington University. Her indelible mark on the digital landscape and commitment to education continue to inspire generations in the ever-evolving world of computing.
“I think we consistently…underestimate what we can do with computers if we really try.”
In the past year, the Department of Computer and Information Science has welcomed an unprecedented number of academic professionals to join Penn’s faculty. One of the Assistant Professor’s who has joined both CIS and ESE this past Fall is Mingmin Zhao, an MIT graduate with a PhD focusing on building wireless sensing systems with artificial intelligence.
The collaboration between CIS and a number of departments at Penn is what encouraged Zhao to further his research and teaching career here.
“Penn provides a fertile ground for interdisciplinary research not only within the CIS department but also with other departments, including ESE, medical school, nursing school, etc.” said Zhao, “I am very excited about collaborating with people at Penn and working on highly-impactful interdisciplinary research.”
Zhao’s research interests include building wireless sensing systems that can capture a human’s functionality through physical surfaces. He explains that his research “uses machine learning to interpret and analyze wireless reflections to detect humans through walls, track their movements, and recognize their actions, enabling a form of x-ray vision.”
“Through-Wall Human Pose Estimation Using Radio Signals” Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, Dina Katabi, Massachusetts Institute of Technology
With these wireless sensing systems, he has also developed a way for healthcare professionals to track a person’s functions including sleep, respiration, and heart rate. “These technologies allow us to continuously and without contact monitor people’s health without wearable sensors or physical contact with the user.” In the startup he joined after graduating, Zhao stated that they are building upon his own research to “work with pharmaceutical companies to run clinical trials in people’s homes.”
“Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture” Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi, MIT & Massachusetts General Hospital
When asked about what made him passionate about the work that he does, Zhao explained that he is passionate about developing sensing tech that focuses on better understanding humans and their wellbeing.
“New sensing technologies (e.g., contactless monitoring of physiological signals) could help doctors understand various diseases and how patients are doing after taking medications,” said Zhao. “They could enable new digital health and precision medicine solutions that improve people’s life.”
Mingmin Zhao is currently teaching CIS 7000 focusing on wireless mobile sensing and building AIoT (Artificial-intelligence Internet of Things) systems. He is looking forward to educating his students to apply what they have learned in building “hardware-software systems” to solving practical problems that can impact the world.
On Friday, February 3rd, the fourth annual Women in Data Science Conference was held in Perry World House welcoming speakers from Zillow, TikTok, and Party City. For the first time since the pandemic, attendees joined us on campus for in-person talks showcasing the latest advances in data science, speaker Q&A sessions, and networking opportunities.
The C.I.S. Department was represented by Weiss Professor of Computer and Information Science, Susan Davidson, MSE Data Science Student, @sukanyajoshi, and NYU Computer Science and Engineering and Data Science Associate Professor, Julia Stoyanovich.
Thank you to all of the Wharton and Penn Engineering Students who attended this incredible conference!
The WIDS 2023 Conference was held at UPenn’s Perry World HouseDATS students getting excited for the conference!Keynote Speaker: Aimee Johnson, Former CMO of Zillow with Mary Purk Executive Director, Wharton AI & Analytics for BusinessSusan Davidson, Weiss Professor of Computer and Information Science, University of PennsylvaniaJulia Stoyanovich Associate Professor of Computer Science & Engineering and of Data Science, Director of the Center for Responsible AI, New York UniversityHigh School Students from Penn’s Summer Data Science ProgramSukanya Joshi MSE Data Science StudentWomen in Data Science Conference Co-Chairs, Susan Davidson, Mary Purk, and Linda ZhaoSukanya Joshi and Staci Kaplan, DATS Program Manager
A celebrated interdisciplinary event, WiDS @ Penn welcomed academic, industry, and student speakers from across the data science landscape to celebrate its diversity, both in subject matter and personnel.
University of Pennsylvania, by Beeple (prompt generated by Yuxin Meng)
So far in this semester, our department blog has talked about the growths in our labs and our faculty members careers. We have touched on the incredible seminars that have been taking place as well as the exciting research projects that our students are involved in. The blog’s overall purpose is to showcase the technological strides that Penn Engineering is making as well as the significant academic achievements of our faculty and student body. The C.I.S. blog is also a platform that strives to implement humanity and relatability to those who are a part of the Penn community and those outside of the University.
As many of you may know, DALL-E 2 developed by OpenAI was launched in April of this year and has just recently been made available to anyone. You get an allowance of free credits that enable you to type in a detailed description of anything that comes to mind and the machine learning models generate digital images that reflect the prompt. We were interested in the combination between artificial intelligence and the ability to generate realistic images using human ingenuity.
DALL-E is a great example of AI and human collaboration working to break barriers and expand horizons through artistic creativity. This platform also gives people the ability to play and be as out there and imaginative as they want. All in all, DALL-E gives us the opportunity to have fun and explore our hobbies, interests, and studies in the form of art. To showcase this AI system and what it can do I had asked all students from the C.I.S. department, from Undergrad to Ph.D., to send in the descriptions that they prompted and to have fun with it.
With that being said, this Gallery of DALL-E generated art was made possible by some of our wonderful students in the C.I.S. Department!
Ani Petrosyan, she/her
Computer Science major, 2026
Purple mountains with Armenian waterfalls
"I am an international student from Armenia. It's a very mountainous country, with waterfalls and wonderful nature. One fun fact about me: most of the dreams I see have purple color in them, so I am dreaming of my country and seeing it in purple.
A.B.
Edward Hu, he/him
Ph.D. in Robot Learning
A. Bob Ross in the style of Picasso uncanny unreal engine
"Bob Ross is an iconic painter, so I would like AI painters to pay homage to him."
B. Darth Vader cooking in Hell's Kitchen
"Hell's Kitchen is one of my favorite shows. I think Darth Vader's past with high heat and pressure scenarios would make him an excellent contestant."
A.B.C.
Yuxin Meng, she/her
MCIT, 2024
A. Hacker, another dimension, digital art
"This one was intended to be "software engineer..." or "coding in..." but these keywords seemed to be less instructive compared to "hacker". I wanted to see us working on the same thing in another universe."
B. Bionic sheep, blueprints
"Love the book: Do Androids Dream of Electric Sheep? Electric sheep in the book look like real sheep, so I guess bionic is proper."
C. In lab, machine reading brain, codes on computer science, digital, by Beeple
"Not so much what I pictured in my mind. I've been obsessed with Pantheon (science fiction drama) recently. Briefly, I expected a picture of machine scanning human brain as code."
Rotem Dror, she/her
Postdoc in the Cognitive Computation Group
Two computers compete in a running competition
"I needed an image for my job talk presentation that would show two models competing who are going to be state of the art. My research involves developing statistical methods for comparing NLP models to determine which is better."
A.B.C.D.
Hannah Gonzalez, she/her
MSE and BSE in Computer Science, 2023
A. A red fox surfing The Great Wave off Kanagawa by Katsushika Hokusai
B. Huskies sledding in Alaska by Monet
C. Macro 35mm film photography of a floating otter wearing a space suit with the Van Gogh "Starry Night" painted background
D. An Andy Warhol style painting of a corgi winking
Gaoxiang Luo, he/him
Ph.D. in Machine Learning
"I generated an image using my hobbies as keywords: cat, guitar, and latte art. I was very impressed and surprised that the AI considered the cat element as latte art!"
This year’s Halloween celebration for Penn Engineering was a great success! Held in Quain Courtyard this past Monday, October 31, students, staff, and faculty gathered for trick or treating, hot apple cider and hot chocolate, and a costume contest. There was a great turn out of students coming in between classes and during their lunch break. In addition, incredibly decorated tables from each department covered the courtyard and there was wonderful staff participation. Even some faculty members came out in costume! It was a great experience interacting with students for some spooky and relaxing fun!
Scroll down to see the department tables and awesome costumes that came out for another fun year of Halloween festivities!
Many students and faculty alike may recognize the face above as Osbert Bastani. Well that’s because this Assistant Professor is not a new member of the Penn Engineering team. Osbert joined the Computer and Information Science Department as a Research Assistant Professor in 2018 specializing in programming languages and machine learning.
“Penn has a great group of faculty working on interesting research problems, and they are all incredibly supportive of junior faculty. I’ve been fortunate enough to collaborate with Penn CIS faculty in a range of disciplines, from programming languages to NLP to theory, and I hope to have the chance to collaborate with many more.” (Osbert Bastani)
Osbert actually began his research career in programming languages. This major challenge in this research is “verifying correctness properties for software systems deployed in safety-critical settings.” He explains that because machine learning is progressively being incorporated into these systems, it has become a greater challenge facing verification. In his research, he is tackling this overarching question; “How can one possibly hope to verify that a neural network guiding a self-driving car correctly detects all obstacles?” While there has been progress made in trustworthy machine learning, there is still a long road ahead to finding solid solutions.
His enthusiasm in working with the Ph.D. students on various topics and research projects is what he has looked forward to most as he entered into this new role in his teaching career at the start of this Fall semester. Since the school year began, he has been teaching Applied Machine Learning (CIS 4190/5190) with Department Chair, Zachary Ives. When asked about how the semester is going Osbert replied:
“I’ve been very fortunate to have strong students with very diverse interests, meaning I’ve had the opportunity to learn a great deal from them on a variety of topics ranging from convex duality for reinforcement learning to graph terms in linear logic. An incoming PhD student and I are now learning about diffusion models in deep learning, which are really exciting!” (Osbert Bastani)
While teaching, Osbert is also involved in several research projects that are dealing with trustworthy machine learning within real-world settings. One project that raises several questions about fairness and interpretability includes “building a machine learning pipeline to help allocate limited inventories of essential medicines to health facilities in Sierra Leone.” In addition, during a summer internship at Meta, one of Osbert’s students has been in the process of “developing deep reinforcement learning algorithms that can learn from very little data by pretraining on a huge corpus of human videos.”
Osbert Bastani wears many hats in the CIS Department. Not only is he involved in teaching and research projects with students, he is also a member of several groups within the department. Those include PRECISE, PRiML, PLClub, and the ASSET Center and he encourages all students to attend the seminars that each club holds and get the opportunity to learn about research in their areas or outside of their own.
Just as Osbert works to problem solve within the classroom and in his research, he does just about same outside of work as well! He expresses that he is an avid board game player and frequents the restaurant just down the street from Penn called “The Board and Brew”. He and his wife have played through the restaurant’s entire collection of the game “Unlock!”. The Board and Brew has great food and several hundred games to choose from. It is highly recommended by Osbert himself!
As the School of Engineering and Applied Science grows, so do the rooms that house all of the creative minds within our departments. Just in time for the Fall 2022 semester, two labs that are a part of the C.I.S. Department and are located in the Moore Building have gotten a makeover this past summer and the faculty and students are thrilled. The Distributed Systems Laboratory (DSL) in Moore 100 and The SIG Center for Computer Graphics in Moore 103 have both undergone renovations for more student space to collaborate, create and liven up the space!
Cheryl Hickey, the Administrative/Event Coordinator in C.I.S., collaborated with the directors of the labs and a design team. She was tasked with choosing the furniture, color scheme, and layout that would best suit the space and the students. The Distributed Systems Lab received all new desks as well as a redone kitchen area for faculty and students. Jonathan M. Smith, Olga and Alberico Pompa Professor of C.I.S. said, “The DSL renovation allow for maximum flexibility in placement of student desks and experiments. Moving the network drops into the DSL machine room freed up space in the main areas for scholarly interaction and increased physical security.” The lab now encourages students to work together within the space better than ever before.
The Distributed Systems Laboratory (Moore Hall 100)
“The DSL renovation is nicely done and timely. I know many of our students really appreciate the new space. The new desks are superb. These days, when I walk past DSL, I’m pleased to see many students interacting with each other. The vibrancy and bustle are back in DSL! Our DSL seminars have also been packed with faculty and students listening to each other’s research. Nothing beats in-person interactions, and the best ideas happen when students talk and iterate through ideas in close proximity.” (Boon Thau Loo, Director of DSL)
Students have noticed the changes to the DSL lab as well since they began the semester just over a month ago. Jess Woods, a C.I.S. PhD being advised by Sebastian Angel, stated that, “It’s nice having our own functional cabinets/lockers. There are more desks and less clutter, which seems to encourage more people to come to campus to work, which makes the environment more collaborative and productive”. Having a space post-Covid that is inviting and open for all students to take advantage of is incredibly positive for the department.
While a part of the SIG Center for Computer Graphics is fully renovated as of this past summer, the lab is still continuing to grow. New technologies are in the works in addition to a couple of new faculty members who will be officially joining the department this coming Spring 2023 semester. Incoming assistant professor in Computer Graphics, Lingjie Liu, stated that there will be many upgrades to the space and equipment including GPU clusters and data storage space in support of research development.
Upon her arrival to Penn Engineering, Lingjie will “we will also purchase and set up a multi-view volumetric camera capture stage for capturing human motions and human-object interactions with multiple synchronized cameras (about 40 cameras).” This set of equipment will be an addition to the labs’ Vicon 16-camera motion capture system.
“The research focus of the new SIG Lab will be developing Artificial Intelligence (AI) technologies for perceiving, understanding, and interacting with 3D objects, people, and environments. Specifically, we will pursue three research goals: (1) High-quality reconstruction of real-world scenes from sparse RGB (camera) images; (2) Photo-realistic image synthesis of real-world scenes with 3D control; and (3) Large-scale 3D scene generation for 3D machine learning tasks.
Lingjie states that the SIG Lab’s new research focus will be “developing Artificial Intelligence (AI) technologies for perceiving, understanding, and interacting with 3D objects, people, and environments.” There are three research goals that the lab will pursue which include:
High-quality reconstruction of real-world scenes from sparse RGB (camera) images
Photo-realistic image synthesis of real-world scenes with 3D control
Large-scale 3D scene generation for 3D machine learning tasks.
“We will approach these goals by designing new algorithms that incorporate AI advances into classical computer graphics methods. We believe that with the new research focus and the facilities upgrade, we will create new research achievements and continue the success of our SIG Lab.”
The SIG Center for Computer Graphics (Moore Hall 103)
The finished and fully furnished side of the SIG lab will now be filled with another incoming assistant professor of C.I.S. and ESE, Mingmin Zhao‘s students. “Working with the renovation and designer team was a pleasure, and the new lab is absolutely gorgeous. I am excited to attract and recruit more students and spend time with them in the new lab.”
While touring SIG lab earlier this month, there were several students hard at work and raved about how much they enjoy spending time in the space. One PhD student who is being advised by Mingmin, Gaoxiang Luo, said, “The renovation of Moore 103B is way beyond my expectation. The workspace shelves allow us to store electronic components, while the wall pegboard allows us to hang tools. While the space is large enough for us to conduct wireless experiments using radar with a certain distance, the ESD flooring further ensures our safety. It’s also worth mentioning that I love the ergonomic height-adjustable desk and chair, which is particularly useful for our health as we work with our computer frequently nowadays.”
It is incredible to see the impact that this renovation has had on the students and faculty. The department is looking forward to seeing the work and successes that come out of these two labs for this new generation of students in Penn Engineering!
Picture this: you’re getting ready to watch a movie on Netflix, popcorn in hand, and several films pop up that have been curated just for you. What are you going to do: choose one from the list recommended by the underlying AI algorithm, or worry about how this list was generated and whether you should trust it? Now, think about when you are at the doctors’ office and the physician decides to consult an online system to figure out what dosage of medicine you as the patient should take. Would you feel comfortable having a course of treatment chosen for you by artificial intelligence? What will the future of medicine look like where the doctor is not involved at all in making the decision?
This is where the ASSET Center comes into play. This initiative, led by the C.I.S. Department in Penn Engineering, to focuses on the trustworthiness, explainability, and safety of AI-systems. The faculty members and students who are a part of this Center have tasked themselves with finding ways to achieve this trust between an AI-system and a user. Through new collaborations between departments all throughout Penn, innovative research projects, and student engagement, ASSET will be able to unlock AI abilities that have never been achieved before.
I recently spoke with Rajeev Alur, Zisman Family Professor in the C.I.S. Department and inaugural director of ASSET. He elaborated on our Netflix example to explain the trust between an AI-system and a user and when it is absolutely critical for the adoption of AI by society. Based on movies and shows that the user watches, Netflix is able to give several recommendations, and it is the user’s choice as to whether they will go for something new. While the recommendations may be decent picks to the user “there is no guarantee or assurance that what they are recommending is foolproof or safe”, says Rajeev. Although AI is found to be useful in the case of choosing what to watch, the user needs a higher level of assurance with the system in more critical applications. An example of this could be when a patient is receiving treatment from a doctor. This high assurance can become important in two cases. One is when the system is completely autonomous, or what is called a “closed loop system,” and the other case is when the system is making a recommendation to a physician who decides what course of action to take. For this latter case, the AI does not make the decision directly, but its recommendation may still be highly influential. In many clinical settings, there are AI-systems already in place that dole out courses of treatment that best suits the patient, and a physician consults and tweaks these choices. What ASSET is looking to implement in the medical field are autonomous AI-systems that are trustworthy and safe in their decision making for the users.
“The ultimate goal is to create trust between AI and its users. One way to do this is to have an explanation and the other one is to have higher guarantees that this decision the AI-system is making is going to be correct,” Rajeev explains.
Collaborations
For ASSET to succeed, the center must nurture connections throughout Penn Engineering and beyond its walls. Within C.I.S., machine learning and AI experts are working together with faculty members in formal methods and programming languages to come up with tools and ideas for AI safety. Outside of C.I.S., Rajeev explains that robotics faculty in ESE and MEAM are interested in designing control systems and software that uses AI techniques in the Center. Going beyond Penn Engineering, ASSET is dedicated to making connections with Penn’s Perelman School of Medicine. “There is a great opportunity because Penn Medicine is right here and there are lots of exciting projects going on. They all want to use AI for a variety of applications and we have started a dialogue with them…This will all be a catalyst to having new research collaborations”, says Rajeev.
Research Projects
F1Tenth Racing Car
In keeping with the idea of autonomous AI that was discussed earlier, one of ASSET’s flagship projects is called Verisig. The goal of this project involving the collaborative efforts of Rajeev Alur, Insup Lee, and George Pappas, is to “give verified guarantees regarding correct behaviors of AI-based systems” (Rajeev Alur). In a case study being performed by the Center, researchers have verified the controller of an autonomous F1/10 racing car to check the design and safety of the controller so that the car is guaranteed to avoid collisions. The purpose of this project is to further understand assured autonomy; if a controller of a small car can be found trustworthy and safe, these methods may eventually be generalized and used in AI applications within the medical field.
How to get involved
The best way for students to get involved with ASSET is engaging in the center’s Seminar Series. They happen every Wednesday in Levine 307 from noon to 1pm, and the great thing about them is that any Penn student can join. There are incredible speakers lined up through the Fall and Spring semesters this school year, so instead of turning on Netflix and letting the system choose your next bingeworthy show, join ASSET every Wednesday for exciting talks about creating safe and trustworthy AI!
What happens when AI goes wrong? Probably not the Terminator or the Matrix – despite what Hollywood suggests – but rather, something that could still harm a human, such as a self-driving car that gets into an accident, or an algorithm that discriminates against certain people. Fortunately Penn has innovative researchers like Eric Wong, who build tools to make sure AI works correctly!
You may have already seen Eric on campus or perhaps teaching his advanced graduate class. Just like the Class of 2026 who are quickly learning their way around Levine Hall, Eric is one of the C.I.S. Department’s newest faculty members. An Assistant Professor who works in Machine Learning, Eric is a Carnegie Mellon Ph.D. graduate and a former MIT post-doctoral researcher in the Computer Science and Artificial Intelligence Lab.
As this semester is in full swing, Eric Wong is busy at work teaching course 7000-05: Debugging Data and Models. When asked what he is looking forward to most about teaching in Penn Engineering, Eric stated,
“One of the key skills that students will learn is how to tinker with AI systems in order to debug and identify their failure modes. I’m excited to see the new ways in which Penn Engineering students will break AI systems, as well as the innovations they come up with to repair them!”
The initiatives that Penn Engineering has launched in recent times are what drew Eric to the C.I.S. Department, specifically the ASSET Center. “Penn Engineering is well-situated to ensure that the tools and systems we develop as computer scientists actually satisfy the needs and requirements of those that want to use them.”, said Eric. He will be one of many faculty members working with ASSET to develop reliable and trustworthy AI-systems which coincides with his own research.
Some of Eric’s specialized interests in this field include “verifying safety properties of an AI-system, designing interpretable systems, and debugging the entire AI pipeline (i.e. the data, models, and algorithms).” His research goals are working towards debugging AI-systems so that the user is able to understand the decision process of a system and learn how to inspect its defects. Eric is also interested by the interdisciplinary work of connecting these methods to other fields outside of engineering. Collaborators in medicine, security, autonomous driving, and energy would “ensure that the fundamental methods we develop are guided by real-world issues with AI reliability.”
As AI is being developed and deployed at a rapid rate, Eric worries that, “it is only a matter of time before the ‘perfect storm’ induces a catastrophic accident for a deployed AI system.” In teaching methods of debugging AI-systems, he strives to give his students the tools and knowledge toward building safer and more trustworthy AI for the future. He hopes that with his research and teachings in the classroom, students take the time to “critically examine their own system” before sending them out into the world.
When Eric is not spending time making sure AI-systems are at the top tier of trustworthiness and reliability, he enjoys trying to recreate the recipes of meals that he orders at restaurants. Trying to “reverse engineer its creation process” is harder than it might seem. Eric mentioned that, “It does not always look the same as the original, nor does it always taste as good, but sometimes it works!”. Maybe someday that too will be something an AI can do (correctly)!
The 2022-2023 academic year has kicked off last week and summer has officially come to a close. We would like to welcome back returning Computer and Information Science students as well as the Class of 2026! Penn Engineering is excited to have you on board.
With a new school year comes new changes for Penn Engineering and the CIS department. In the past year, we have hired an exceptional number of faculty, brought in new research initiatives, renovated our spaces, and broke ground on a state-of-the-art facility. We are also incredibly thankful to be able to see so many faces in person this year as the circumstances surrounding Covid-19 continues to improve and in-person activities can commence.
Faculty
Several brand new assistant professors have joined the department this semester, while the rest will arrive in January and next Fall. Rejoining the CIS department, in a new role as an Assistant Professor, is Osbert Bastani who develops innovative techniques for programming and building software that incorporate machine learning components. Two new faces to Penn Engineering features Danaë Metaxa, who works in areas of human-computer interaction and communications, and Eric Wong, who works on robust and reliable machine learning.
“We are delighted to welcome an unprecedented 10 new assistant professors arriving over the next year. Each brings new innovations to the curriculum and more opportunities to get involved in undergraduate research projects.”, says Zachary Ives, Chair of the CIS Department.
Each new faculty member entering into Computer and Information Science has arrived to break barriers and help our students grow.
Space
As our department is growing, our spaces have been transforming as well. This summer, the Distributed Systems Lab (DSL) and the SIG Lab for Computer Graphics on the first floor of Moore have both undergone major renovations. On the second floor of Levine Hall, the brand new Penn Human-Computer Interaction Lab, led by Andrew Head and Danaë Metaxa, just opened.
“We couldn’t be more excited for the start of this year and the official launch of our group. We’re looking forward to teaching Penn’s first Human-Computer Interaction courses at all levels, welcoming our first cohort of PhD students and opening our physical space- the HCI Lab- in Levine 255. We welcome interested students to reach out to us!” -Danaë Metaxa, Assistant Professor, CIS Dept, University of Pennsylvania.
In addition to our growth in space, Penn Engineering is not stopping there. The construction of Amy Gutmann Hall has begun during the summer. The creation of new space, study rooms, and research labs is anticipated to be opening in September 2024.
Research Initiatives
Since the launch of this past year’s interdisciplinary research initiative, Innovation in Data Engineering and Science (IDEAS), the CIS Department announced the ASSET Center directed by Rajeev Alur. ASSET (AI-enabled Systems: Safe, Explainable, Trustworthy) focuses on implementing tools and science to guarantee AI systems do exactly what they are designed to do. Getting students involved in this new initiative is a top priority for the Center.
“The best way to get involved is to join our seminars. It’s every Wednesday at noon and we have a great line-up of speakers. There is a number of faculty from our department, other Penn faculty, and also outside speakers. Some of the topics will include applications to healthcare, explainablility, and safety.” -Rajeev Alur, Director of ASSET, University of Pennsylvania.
With every new faculty member, space renovation, and research initiative; all of these things are implemented to give students the best opportunities for success. We have another exciting year just beginning at Penn Engineering. Let’s make it count.
Stevens University Professor Duncan Watts, founder of the CSS Lab
The Wharton School and the University of Pennsylvania are delighted to announce the expansion of the Penn Media Accountability Project (PennMAP), an interdisciplinary, nonpartisan research project dedicated to enhancing media transparency and accountability. Its growth is made possible by a new leadership gift from Richard Jay Mack, W’89.
“Our goal at PennMAP is to detect and expose biased, misleading, and otherwise problematic content in media from across the political spectrum and spanning television, radio, social media, and the broader web,” says Duncan Watts, the Stevens University Professor and a Wharton professor of Operations, Information and Decisions who leads PennMAP. Watts also holds faculty appointments in the Annenberg School of Communication and in the Department of Computer and Information Science in the School of Engineering and Applied Science. He is a Faculty Fellow of Analytics at Wharton, the preeminent and first business school center focused on research, teaching, and corporate partnerships around analytics and their application in business, non-profits, and society.
“Clearly this is an ambitious goal that requires a substantial investment in research infrastructure as well as building collaborations with a diverse set of partners,” says Watts. “Richard Mack’s generous gift will allow us to significantly accelerate our efforts and increase our impact both in terms of research and the public conversation on these important issues. We are tremendously grateful for his support.”
PennMAP is a product of the University’s Computational Social Science Lab (CSSLab), a joint venture of the School of Engineering and Applied Science, the Annenberg School for Communication, and the Wharton School. The Lab seeks novel, replicable insights into societally relevant problems by applying computational methods to large-scale data.
On Friday, October 29, each department of the School of Engineering and Applied Science gathered together on the West Towne Lawn in the spirit of Halloween celebration!
Students, staff and faculty were able to stop by each department’s station for delicious treats and candy, Penn Engineering swag, and fun Halloween stickers and toys.
Scroll down for some amazing photos of the day, which also included a photo backdrop and Halloween tunes!
Other CIS-affiliated Halloween events include:
The Penn Society of Women Engineers Meet and Greet – Levine Lobby, October 29, 4-5pm Come take a break from studying, meet other students and enjoy some arts and crafts and insomnia cookies.
CIS Faculty and Postdoctoral Fellow Halloween TGIF – Quain Courtyard, October 29, 5-8pm There will be a Halloween Costume Contest with gift card prizes for winners! There will also be a pumpkin carving event, food, and an expanded selection of alcoholic and non-alcoholic drinks.
Last year during the peak of the COVID-19 pandemic in the US, testing and contact tracing failed to quell the spread. Many circumstances — including a decades-old underfunding of state health departments, and slow workforce build – have contributed to this outcome.
However, according to Department of Computer and Information Science Professor Andreas Haeberlen, one of the main reasons contact tracing wasn’t relatively more successful is simple: people don’t’ feel comfortable sharing their information.
“It’s really scary to think of people knowing all the things that you type in your phone,” said Haeberlen. “Like what you’ve had for breakfast, or your medical information, or where you’ve been all day or who you’ve met. All of that data is super super sensitive.”
Haeberlen, whose research centers distributed systems, networking, security, and privacy, believes that differential privacy could be the solution.
“Differential privacy is a way to purpose private information so that you can really guarantee that somebody can’t later learn something sensitive from this information,” said Haeberlen. “[It] has a very solid mathematical foundation.”
The National Institute of Science and Technology defines differential privacy in terms of mathematical qualification. “It is not a specific process, but a property that a process can have,” said NIST on their website. “For example, it is possible to prove that a specific algorithm ‘satisfies’ differential privacy.”
And so we might assert that, if an analysis of a database without Joe Citizen’s individual data and an analysis of a database with Joe Citizen’s individual data yield indistinguishable results, then differential privacy is satisfied. “This implies that whoever sees the output won’t be able to tell whether or not Joe’s data was used, or what Joe’s data contained,” said NIST on their site.
Haeberlen insists that, with widespread application of differential privacy, user trust is not only no longer a barrier, but that it is not necessarily required. Surrendering our sensitive information to large corporations such as Apple would no longer require a leap of faith.
Building the tools
A popular industry standard of cybersecurity involves adding imprecision into results to purposefully skew them, and thus protect individual user data. Challenges to this application, according to Haeberlen, include the ongoing debate among experts about whether it satisfies differential privacy specifications, and its lack of scalability.
“Fuzzi: A Three-Level Logic for Differential Privacy,” a paper by Haeberlen and fellow researchers Edo Roth, Hengchu Zhang, Benjamin C. Pierce and Aaron Roth, is one of many of Haeberlen’s oeuvre that focuses on developing tools that can do the work for us. The paper presents a prototype called Fuzzi, whose top level of operational logic “is a novel sensitivity logic adapted from the linear-logic-inspired type system of Fuzz, a differentially private functional language,” according to the abstract.
Essentially, a researcher would input data into the tool, define what that data means, and specify what data output they’re searching for. The tool would be able to state if that output satisfies differential privacy specifications, and, if not, what amount of imprecision would need to be added in order to meet specifications.
“The way that we did that was by baking differential privacy into a programming language,” said Haeberlen. “As a practitioner you don’t have to understand what differential privacy is, you also don’t have to be able to prove it.”
In the world of science, imprecision usually means error and gross miscalculation. However, in the more specific realm of differential privacy, imprecision equals security.
“Imprecision is good because it causes the adversary to make mistakes,” said Haeberlen. In this case, the “adversary” is any person or system trying to gain access to sensitive information.
All tools developed by Haeberlen and his team have been made available under open-source license, and companies such as Uber and Facebook are currently releasing data sets using differential privacy.
School of Engineering and Applied Science Dean Vijay Kumar, President Amy Gutmann, Trustee and naming donor Harlan M. Stone, and Penn Engineering Board Chair Rob Stavis at the October 1, 2021 groundbreaking for Amy Gutmann Hall to be located on the northeast corner of 34th and Chestnut Streets. Courtesy of University of Penn Almanac site
On Friday, October 1, 2021, the University of Pennsylvania’s School of Engineering and Applied Science held a groundbreaking ceremony for its new data science building and unveiled the building’s official name, Amy Gutmann Hall, honoring Penn’s President. Amy Gutmann is the eighth and longest-serving President in Penn’s history, leading the University since 2004; her term will conclude at the end of this academic year.
Amy Gutmann Hall will serve as a hub for cross-disciplinary collaborations that harness expertise, research, and data across Penn’s 12 schools and numerous academic centers. Upon completion, it will centralize resources that will advance the work of scholars across a wide variety of fields while making the tools and concepts of data analysis more accessible to the entire Penn community.
“I am thrilled Penn Engineering’s new data science building will honor Dr. Gutmann’s remarkable legacy at Penn,” said Vijay Kumar, the Nemirovsky Family Dean of Penn Engineering. “Her Penn Compact and the principles of inclusion, innovation, and impact influenced the school’s strategic priorities from which the plan for a data science building emerged. This revolutionary new facility will create a centralized home for data science research and provide collaborative and accessible space for our faculty and students, as well as the Philadelphia community.”
The 116,000-square-foot, six-floor building will be located at the northeast corner of 34th and Chestnut Streets. Planned academic features include a data science hub, the translational and outreach arm of Penn Engineering in the area of data science and artificial intelligence; research centers for new socially aware data science methodologies and novel, bio-inspired paradigms for computing; and laboratories that will develop data-driven, innovative approaches for safer and more cost-effective health care.
The impressive building is the design of executive architects Lake/Flato, with KSS Architects serving as associate architects. The building’s architecture will signify the future and the dynamic shift from the traditional to the digital. The facility is planned to be the first mass timber building in Philadelphia and will be designed sustainably.
Construction will begin in spring 2022 and is slated for completion in 2024.
“Our central mission is to integrate methods and ways of thinking from the computational and social sciences in the service of real-world applications,” said Lab Director and Stevens University Professor in the Department of Computer Science Duncan Watts. “We are also dedicated to building research infrastructure to support mass collaboration around shared data, and to facilitate open, transparent, and replicable science. We have a great team and a great set of initial projects. I’m really looking forward to seeing what we can do.”
Some of those projects include a set of interactive data dashboards that utilize demographic and mobility data around COVID-19 to help inform decisions, as well as a project “dedicated to enhancing media transparency and accountability at the scale of the entire information ecosystem,” according to the Lab site.
Among the team who will help bring the mission to fruition are Associate Research Scientist Homa Hosseinmardi, Research Data Engineer Yingquan Li, and the Lab’s Executive Director, Valery Yakubovich.
As a former professor at ESSEC Business School, Yakubovich is excited to bring his management expertise to the Lab.
“Creating a hub for cutting-edge research at the intersection of social science and high-tech requires genuine intrapreneurship, open and secure digital organization, and a functionally diverse team of staff speaking in one language,” said Yakubovich. “Under the auspices of three professional schools—each as renowned as it is independently-minded—this task is especially challenging but equally rewarding.”
CIS looks forward to the exciting work the Lab will produce. Visit the CSS Lab site, and be sure to check our blog for important updates and research findings.
Humans have never been more connected to one another, though the speed with which we can share with one another has its drawbacks. For example, the spread of COVID-19, as well as misinformation about it, have both been facilitated by our highly connected online and in-person networks. Fortunately, the branches of mathematics known as information theory and network theory can help us to understand how both systems work and how to control them.
NSF CAREER Award recipient Shirin Saeedi Bidokhti, Assistant Professor in Electrical and Systems Engineering, will use the grant to conduct research on both online social networks and COVID-19 contact tracing networks. As case studies, these real-word examples will inform networked systems’ theoretical foundations, as well as the design of learning and decision-making algorithms that help us to make sense of them. She will also use the funding to develop a new course module that brings information and network theory into practice for undergraduate students at Penn.
Using a combination of tools from information theory, network theory and machine learning, Saeedi Bidokhti aims to narrow the gap between theory and practice through algorithm-informed real-time data sampling, estimation and inference in networked systems. Her work aims to produce smarter algorithms that can extract information, infer about these systems, and ultimately provide more precise control of them.
While such algorithms are already improving our ability to understand complex networks, there is always a tradeoff that needs to be considered when it comes time to use that information.
“In information extraction, knowing when to sample with real-time data makes a difference, says Saeedi Bidokhti. “It helps us to know if we should act now or wait to sample, facing the tradeoff of gathering the most information while minimizing costs to most efficiently control the system.”
Sponsored non-profit organisation Very Large Database Endowment Inc., the award focuses on the cumulative lifetime work of the researcher. Davidson was specifically honored “for groundbreaking work in the areas of data integration, data provenance and her efforts in cross-disciplinary research, namely bridging databases and biology.”
“Really it was more that I was one of the early people to help define what interesting topics, there were in bioinformatics,” said Davidson.
The former Department Chair of CIS wrote an award acceptance speech titled “It’s not just Cookies and Tea” that blended the focal points of her life’s work — data integration, provenance and concurrencies — with personal life. The two are often inextricable.
“I talked about my parents and how they influenced where I am today: that was provenance,” said Davidson. “I talked about how i’ve built programs to recruit, retain and promote women in engineering, computer science. You have to integrate, as well as have cookies and tea.”
Davidson’s advocacy for other women, both within the engineering field and without, has also been a defining facet of her professional career. The Founder of Advancing Women in Engineering (AWE) at Penn was hoping her speech would also serve as a point of motivation.
“I was also really trying to encourage other women, “said Davidson. “I know that it’s been extremely hard for for women with young children during the pandemic.”
The Women in Database Research Award is one of many presented at the annual VLDB Conference, this year hosted in hybrid format, August 16-20 in Copenhagen, Denmark. According to the VLDB site, "this series is perhaps the most international (in terms of participation, technical content, organization, and location) among all comparable events."
Professors Susan Davidson and Boon Thau Loo have been awarded the 2021 Ruth and Joel Spira Awards for Excellence in Teaching. Sponsored by the Spira co-founded Lutron Electronics in 2019, the award specifically recognizes outstanding faculty within the C.I.S. department at Penn, and has corresponding awards at universities across the country.
Susan Davidson
Penn Engagement Days: Engineering In 100 Seconds: Susan Davidson Courtesy of Penn NSOAI YouTube
For Professor Davidson, the Spira Award is the first teaching award she has received in her nearly 40-year career.
“I think it’s especially meaningful because it’s difficult for women in STEM fields,” said Davidson. “Women in STEM fields tend not to rate as highly as their counterparts, because of a certain amount of gender bias.”
The Founder of SEAS’ Advancing Women in Engineering (AWE) received her Spira Award “for her critical role in defining our initiatives in data science and databases, and especially for the outstanding job she has done teaching CIS 545 and 550,” according to Department Chair Zack Ives.
Davidson says that it was Professor Loo who pushed her to revamp her CIS 550 (Introduction to Database and Information Systems), and reform it so it could become a part of the MCIT Online curriculum. Doing so required the course to be broken down into smaller, punchier segments: more frequent quizzes, a normally 90-minute lecture efficiently split into bite-sized, twelve-minute fragments.
“It was Boon who basically talked me into it, by saying how much it had improved his course,” said Davidson. “The argument that he used was that his teaching ratings had jumped up quite a bit as a result of that.”
Right in the middle of recording the different aspects of CIS 550, fine-tuning and taking a closer look at how to make it a more immersive experience for students, work-from-home was imposed due to COVID-19. Without knowing it, Professor Davidson was preparing for a complete online transition.
“That’s the second reason I’m very thankful to Boon. he convinced me to do this and gave me the impetus to improve the course and, by doing so, I was very well prepared for the teaching during the pandemic,” said Davidson. “I know the students really appreciated the quality of the recordings: that’s recognition to the online MCIT staff and the program and how well they are able to produce or help us produce our lecture segments.”
Boon Thau Loo
“Boon Thau Loo – Programming Network Policies by Examples: Platform, Abstraction, and User Studies” Courtesy of NetPL YouTube
Professor Boon Thau Loo holds his colleague and fellow Spira Award winner in the highest regard as well.
“Anytime you got an opportunity to win an award with Susan that’s a great honor,” said Loo. “She’s always the gold standard for me as far as being a good teacher, being very dedicated to teaching.”
According to Professor Ives, the Associate Dean for SEAS Grad Programs “was recognized for his superb teaching and mentoring of students, both inside and beyond the classroom. Students praise his clarity of explanations, his passion and expertise, and his positivity.”
Professor Loo serves as an inspiration for those with a calling to teach, but who must overcome personal obstacles in order to excel in that calling. He confesses that he did not start his career as an effective teacher: he is not naturally a good public speaker, and his initial Penn course student reviews were horrible.
“I remember my first CIS 505 was a complete disaster. I don’t have a tremendous stage presence,” said Loo. “As a clueless Assistant Professor, it took awhile for me to learn how to teach. I tried incorporate a more personal touch, get to know the students well.”
Professor Loo’s main classroom philosophies boil down to practicality: he emphasizes the importance of group work and communication, and insists a complete educational experience means getting your hands dirty.
“You cannot learn operating systems just by reading a textbook. Students have to learn by doing,” said Loo. “I’m a big proponent of project-based learning. I don’t think, especially in software systems, you can learn just in isolation, by reading a textbook or from PowerPoint.”
Pronounced “wisdom,” the WSDM (Web Search and Data Mining) Conference is one of many presented by ACM (Association for Computing Machinery), and “publishes original, high-quality papers related to search and data mining on the Web and the Social Web, with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance,” according to their site.
The paper’s motivation stems from a years-old debate in the fields of communication, marketing and sociology: do ordinary folk have the power to spread ideas in media? When Malcolm Gladwell’s “The Tipping Point,” released in 2000, asserted that a very specific portion of regular people were the most effective at spreading and magnifying ideas and products, Professor Watts took up academic arms.
“I had been arguing against this idea for some time,” said Watts. “Not that some people are not more influential than others, but just that there was any sort of magical effect, that you could sort of find some ordinary person and they would somehow trigger this massive cascade that would that would change the world. Which is really sort of the the promise of this book, and why everybody loved it so much. “
Professor Watts and team approached the debate with a foundational scientific perspective: if certain people are more influential, then computer science should be able to predict it.
“If it’s true that certain types of people, for whatever reason, happened to be disproportionately influential in the world and disproportionately good at getting other people to listen to them and to change their minds about some issue, you can do pretty well predicting how many retweets someone’s going to get just by looking at how many followers they have,” said Watts.
Right before the paper was published, Professor Watts recalls that mega influencer Kim Kardashian, with roughly 1 million followers, was charging around $10,000 to mention a product in one tweet. The paper proposal offers that focusing on one influencer with a huge amount of followers is not necessarily the most efficient strategy.
“Maybe you want to pay your $10,000 but you would rather find 1,000 people who have 1,000 followers each,” said Watts. “And they might do it for free. Or they might do it for $1. So then you pay $1,000, and you still reach a million people.”
The three main findings of the paper are as follows: 1. It is nearly impossible to predict, with accuracy, the efficacy of influence 2. To the extent that one could predict it, “it’s all baked into the past success of the person who seeds the information, and most of it is just how many followers you have,” said Watts
And the 3rd:
“Under a broad range of conditions, you’re actually better off going with a large number of people who have not that many followers, then a small number of people with a large number of followers,” said Watts. “And I think each of those findings has sort of reverberated.”
When Associate Professor Joseph Devietti was an undergrad in the Department of Computer and Information Science almost 20 years ago, the pace and scale of the department was drastically different.
“Everything has just gotten so professionalized and competitive. Computing has kind of exploded, across campus,” said Devietti. “Things like the second major from the college is really exciting. To be able to give people other ways into computer science, without having to be an engineer and take physics. Follow that kind of rigid path.”
Now the coding aficionado has come full circle as he takes on the role of CIS Undergrad Chair.
“I think the undergrads that we have at Penn, even back when I was here a long time ago, were really strong,” said Devietti. “I’m glad I don’t have to compete with the undergrads that are here now.”
After majoring in both English and Computer and Information Science at Penn, Professor Devietti went on to get both his Master’s and his Ph.D. in Computer Science and Engineering at the University of Washington. With a slew of honors, awards and publications under his belt, CIS Department Chair Zack Ives also notes he is “renowned for his research in using both hardware and software techniques to simplify multiprocessor programming, [and] has also been a successful entrepreneur and an amazing mentor to many undergraduate, Master’s, and PhD students.”
Professor Andreas Haeberlen, whose shoes Professor Devietti will be stepping into, did wonders while serving as chair.
“One of the things I found inspiring about what Andreas had done in his time as undergrad chair was that he had helped a lot with kind of smoothing out these internal business processes,” said Devietti.
In addition to digitizing many paper processes, Professor Ives says Haeberlen also “led curriculum reform across our multiple degree programs [and] personally developed important infrastructure, including the waitlist system that allows us to manage student demand in a fair way.”
With returning to campus and the subsequent readjustment as a top priority, and the nearly 1,000 undergrad students currently enrolled in CIS, Professor Devietti believes the key lies in continuing to focus on efficiency.
“We need to try to streamline things as much as possible,” said Devietti. “I’ve been talking with the advising staff. There are other kinds of opportunities to just help things work more smoothly.”
