Tag: penn engineers

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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 Students Awarded 2024 NSF GRFP

Four students from the University of Pennsylvania have been awarded the prestigious NSF Graduate Research Fellowship Program (GRFP) for 2024. Three of these awardees, Lena Armstrong, Emmanuel Suarez Acevedo, and Stephan Xie, will be heading to other universities for their PhDs, while Edward Zhang will continue his doctoral studies at Penn. Here are their achievements and reflections on this milestone.

Lena Armstrong (HCI)

Lena Armstrong, who works with Danaë Metaxa, will begin her PhD in Computer Science at Harvard this fall. Reflecting on her achievement, Armstrong said, “I feel excited and honored to have received the NSF Graduate Research Fellowship. This award allows me greater access to resources and opportunities to further my research, advocacy, and teaching goals. My aim is to change the narrative of who gets to shape our digital future and create more inclusive experiences in computer science.”

Armstrong’s research focuses on algorithmic fairness, particularly in employment and housing. She has co-authored several papers, including “Navigating a Black Box: Students’ Experiences and Perceptions of Automated Hiring” and “The Silicon Ceiling: Auditing GPT’s Race and Gender Bias in Hiring.”

Danaë Metaxa praised Armstrong, stating, “Lena is a star, one of the most motivated and creative undergraduates I have worked with. Her departure is bittersweet, but I look forward to seeing all she will accomplish at Harvard.”

Emmanuel Suarez Acevedo (Formal Methods, Verification, and Programming Languages)

Emmanuel Suarez Acevedo, advised by Stephanie Weirich, is heading to Cornell for his PhD. On receiving the fellowship, Acevedo remarked, “I was excited and grateful for the support from fellow students and professors at Penn. The GRFP will allow me to focus on my dissertation topic without the worry of finding funding, enabling me to continue working towards broadening participation in computer science.”

Acevedo recently worked on an extension to an intermediate language for static tracking of program behaviors, collaborating with students from Penn and the University of Michigan. This work aims to improve compiler optimizations.

Stephanie Weirich commented, “Emma is heading to Cornell’s PhD program next year and will intern with Kuen-Bang Hou Favonia at the University of Minnesota this summer. He has worked with me on several projects, including enhancing the Call-by-Push-Value type system and mechanizing proofs in Agda.”

Stephan Xie (Machine Learning)

Stephan Xie, who has been working with Aaron Roth, will pursue his PhD in Machine Learning at Carnegie Mellon University. Xie shared his excitement, saying, “Receiving the GRFP was a huge honor. It gives me more confidence in my research direction and opens up many possibilities as I begin my PhD.”

Xie’s research focuses on advancing machine learning decision-making and discovery in real-world applications. He has worked on projects exploring unbiased predictions and improving the reliability of deep learning.

A project I worked on last year with my advisor Prof. Aaron Roth and graduate students Georgy Noarov and Ramya Ramalingam was along this vein, exploring how we could make “unbiased predictions” that lead to strong decision-making guarantees, i.e. low swap regret for all decision-makers. This also led to a number of applications in online combinatorial optimization, extensive-form games, and uncertainty quantification.

Aaron Roth expressed his enthusiasm for Xie’s work, stating, “It was great fun working with Stephan. Together with my PhD students, we wrote what turned out to be my favorite paper in some time. I’m looking forward to following his progress at CMU.”

Edward Zhang (Computer Vision)

Edward Zhang, who will continue his PhD at Penn, expressed his gratitude for receiving the fellowship. “I am incredibly grateful and honored to have received this opportunity. I hope to leverage these funds to take charge of my own research direction,” Zhang said.

Zhang plans to use the funding to investigate the use of visual computing to solve real-world problems in disciplines like medicine, public health, and public policy. He aims to deploy these solutions in economically sustainable and scalable ways.

Live Mas 2024 Renewal Submission – Edward Zhang
A recent project: Ego-Exo4D – Here is an early stage data collection photo. I credit this project for helping me develop my practical knowledge about computer vision and research in general.

Congratulations to all the 2024 NSF GRFP awardees! This is a well-deserved achievement, and we look forward to seeing the remarkable contributions you will make during your PhDs!

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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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Faculty

PACT, Summer 2023

Rajiv Gandhi, Director of PACT

“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.”

Created by the PACT team, https://algorithmicthinking.org/

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.

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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.

Categories
Students

The C.I.S. Blog Presents- “DALL-E Art Gallery”

University of Pennsylvania, by Beeple (prompt generated by Yuxin Meng)

So far in this semester, our department blog has talked about the growths in our labs and our faculty members careers. We have touched on the incredible seminars that have been taking place as well as the exciting research projects that our students are involved in. The blog’s overall purpose is to showcase the technological strides that Penn Engineering is making as well as the significant academic achievements of our faculty and student body. The C.I.S. blog is also a platform that strives to implement humanity and relatability to those who are a part of the Penn community and those outside of the University.

As many of you may know, DALL-E 2 developed by OpenAI was launched in April of this year and has just recently been made available to anyone. You get an allowance of free credits that enable you to type in a detailed description of anything that comes to mind and the machine learning models generate digital images that reflect the prompt. We were interested in the combination between artificial intelligence and the ability to generate realistic images using human ingenuity.

DALL-E is a great example of AI and human collaboration working to break barriers and expand horizons through artistic creativity. This platform also gives people the ability to play and be as out there and imaginative as they want. All in all, DALL-E gives us the opportunity to have fun and explore our hobbies, interests, and studies in the form of art. To showcase this AI system and what it can do I had asked all students from the C.I.S. department, from Undergrad to Ph.D., to send in the descriptions that they prompted and to have fun with it.

With that being said, this Gallery of DALL-E generated art was made possible by some of our wonderful students in the C.I.S. Department!

Ani Petrosyan, she/her
Computer Science major, 2026

Purple mountains with Armenian waterfalls

"I am an international student from Armenia. It's a very mountainous country, with waterfalls and wonderful nature. One fun fact about me: most of the dreams I see have purple color in them, so I am dreaming of my country and seeing it in purple.
A.
B.
Edward Hu, he/him
Ph.D. in Robot Learning

A. Bob Ross in the style of Picasso uncanny unreal engine

"Bob Ross is an iconic painter, so I would like AI painters to pay homage to him."

B. Darth Vader cooking in Hell's Kitchen

"Hell's Kitchen is one of my favorite shows. I think Darth Vader's past with high heat and pressure scenarios would make him an excellent contestant."
A.
B.
C.
Yuxin Meng, she/her
MCIT, 2024

A. Hacker, another dimension, digital art

"This one was intended to be "software engineer..." or "coding in..." but these keywords seemed to be less instructive compared to "hacker". I wanted to see us working on the same thing in another universe."

B. Bionic sheep, blueprints

"Love the book: Do Androids Dream of Electric Sheep? Electric sheep in the book look like real sheep, so I guess bionic is proper."

C. In lab, machine reading brain, codes on computer science, digital, by Beeple

"Not so much what I pictured in my mind. I've been obsessed with Pantheon (science fiction drama) recently. Briefly, I expected a picture of machine scanning human brain as code."
Rotem Dror, she/her
Postdoc in the Cognitive Computation Group

Two computers compete in a running competition

"I needed an image for my job talk presentation that would show two models competing who are going to be state of the art. My research involves developing statistical methods for comparing NLP models to determine which is better."
A.
B.
C.
D.
Hannah Gonzalez, she/her
MSE and BSE in Computer Science, 2023

A. A red fox surfing The Great Wave off Kanagawa by Katsushika Hokusai

B. Huskies sledding in Alaska by Monet

C. Macro 35mm film photography of a floating otter wearing a space suit with the Van Gogh "Starry Night" painted background

D. An Andy Warhol style painting of a corgi winking
Gaoxiang Luo, he/him
Ph.D. in Machine Learning

"I generated an image using my hobbies as keywords: cat, guitar, and latte art. I was very impressed and surprised that the AI considered the cat element as latte art!"