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.
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!
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!
A still from video footage of Team 17 demonstrating their design, Personalizing Physical Therapy via Muscular Feedback.
On Friday, April 24th, CIS students gathered to present their projects at the Spring 2020 Senior Design Alumni Presentations.
Due to the current COVID-19 crisis and its subsequent effects, “gathered” took on a virtual connotation: Seniors were asked to attend using video call platform Zoom.
CIS Associate Professor Ani Nenkova facilitated the event, and despite a few technical difficulties, Penn perseverance prevailed. Robert Zajac, one of 14 panelists, believes that the presentations shone bright although communication during these final months of development must have been undoubtedly more difficult.
“Ordinarily you would have a more human element when seeing the project in person,” said Zajac. “But I think the understanding among everyone is that we have to just do the best we can and try extra hard to have empathy for each other.”
Students were separated into breakout rooms (or sessions) on the call, with 3-4 teams presenting in each session for a group of assigned panelists. The seniors utilized PowerPoints to demonstrate their work via frontend and backend analysis, algorithm and tech evaluation, and planned improvements.
Team members included Jack Buttimer, Matthew Kongsiri and Siyuan Liu (Team 7) introduced an interface they called Newsfluence, which hopes to help users recognize their own biases by allowing them to see how media outlets are comparatively reporting. Studies that the group encountered suggested that those who are most susceptible to fake news are “tech illiterate,” so their most prioritized metrics included interface friendliness, intuitiveness and seamlessness.
Like Newsfluence, many of the groups have working models of the projects currently available.
The Online OH Queue project – winner in the social impact category and created by team members Steven Bursztyn, Christopher Fischer, Monal Garg, Karen Shen and Marshall Vail (Team 19) – has already been effectively helping students and faculty schedule electronic office hours during this global crisis.
“It is rare in this class to have a project deployed and used by 1000+ people before the end of the Spring semester,” said Nenkova via email. “This year is a first for that.”
Zajac, who received both his BSE and MSE in Computer Science in 2019, sat in on the presentation of a document reader called CoParse. Group members Jacob Beckerman, Josh Doman, Sarah Herman and James Xue (Team 5) posed the question, “What if we could navigate contracts like we navigate the web?” and replied with an app that allowed for just that. The project was awarded the panel’s technical sophistication honor.
“CoParse was a smart legal document reader that used natural language processing to build a rich representation of documents,” said Zajac. “It was impressive because the team received interest from industry in trying the product, which is not always common for senior design projects.”
Zajac, currently a Software Engineer with company Two Sigma, imparts words of wisdom to future presenters:
“Start your presentation with the problem you’re solving and explain why it’s meaningful! If we don’t first agree on why the problem is meaningful, we can’t begin to talk about the technical solution.”
The complete list of winners is as follows:
Technical sophistication: Team 5 (Jacob Beckerman, Josh Doman, Sarah Herman and James Xue): CoParse: Performance-enhancing document reader for legal contracts
Creativity: Team 14 (Alexander Chea, Yi Ching, Vijay Ramanujan, Anelia Valtchanova, Leon Wu): Gamifying Physical Therapy Using Virtual Reality
Societal impact: Team 19 (Steven Bursztyn, Christopher Fischer, Monal Garg, Karen Shen, Marshall Vail): Online OH Queue
Alumni’s choice: Team 16 (Suyog Bobhate, Tsz Lam, William Sun, Zeyu Zhao, Zhilei Zheng): Data Synchronization
Click HERE for a full list of participants and panelists
