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.
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.
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!
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)!
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.
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."
