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 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.
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.
