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Featured Students

AI Month, NSF Fellowship, & Enhancing Language Models

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/

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Students

Penn HCI & NLP Students Awarded NSF GRFP

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.

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Faculty

Recognizing Joe Devietti: Faculty Advising Award Recipient

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!

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Faculty Featured

Advancing Quantum Computing: NSF Career Award Fuels Innovative Research

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.

To learn more about quantum computing, visit: Penn Center for Quantum Information, Engineering, Science and Technology (QUIEST)

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Faculty

Aaron Roth Awarded 2023 Hans Sigrist Prize

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.

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Featured

Grace Murray Hopper: Pioneering the Digital Frontier

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.”

Oral History of Captain Grace Hopper, Angeline Pantages
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Faculty

In the Spotlight: Mingmin Zhao and Building a Bridge Between Machine Learning and Monitoring Health

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.

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Programs

Women In Data Science Conference

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 House
DATS students getting excited for the conference!
Keynote Speaker: Aimee Johnson, Former CMO of Zillow with Mary Purk Executive Director, Wharton AI & Analytics for Business
Susan Davidson, Weiss Professor of Computer and Information Science, University of Pennsylvania
Julia Stoyanovich Associate Professor of Computer Science & Engineering and of Data Science, Director of the Center for Responsible AI, New York University
High School Students from Penn’s Summer Data Science Program
Sukanya Joshi MSE Data Science Student
Women in Data Science Conference Co-Chairs, Susan Davidson, Mary Purk, and Linda Zhao
Sukanya 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.

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Faculty

In the Spotlight: Osbert Bastani and Integrating Machine Learning into Real-world Settings

Osbert Bastani, Assistant Professor in the Computer and Information Science Department in the School of Engineering, University of Pennsylvania

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!

Game
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Programs

The ASSET Center: Enabling Trust Between AI and its User

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.

Rajeev Alur, Zisman Family Professor and inaugural director of ASSET

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 that is used in competitions
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!