Tokyo's Mori Art Museum Features an AI Art Installment Utilizing Work from Kahlert School of Computing Faculty
In the bustling Roppongi district in Tokyo's Minato ward lives the Mori Art Museum, a contemporary art space nestled in the 54-story Roppongi Hills Mori Tower.
A recent exhibit at Mori Art Museum entitled "MACHINE LOVE: Video Game, AI and Contemporary Art" contained approximately 50 works of contemporary art utilizing game engines, AI, and virtual reality (VR). Among the works exhibited was German American artist Diemut Strebe's "El Turco/Living Theater", featuring code written by Kahlert School of Computing Assistant Professor Ben Greenman.
The piece presents two character puppets on screen. The puppets speak out loud as their lips move in sync, and their words appear on screen like a chat history. One puppet portrays an inventor of smart home devices being interviewed by the other puppet. However, the course of the conversation can change, and each performance is unique.
One puppet is controlled by a human. Another puppet is controlled by Anthropic’s Claude AI. The audience is faced with a challenge: which puppet is AI, the inventor or the interviewer? Does it matter?
Behind the scenes, this project combines several technologies: including Claude API, Azure text to speech, Amazon speech to text, and Unreal Audio to Face. The piece uses the Racket programming language, developed by Kahlert School Professor Matthew Flatt, to synchronize these different technologies in an event-based framework. For example, Audio to Face can sleep until Claude has written the next part of its puppet's script.
Select performances of "El Turco/Living Theater" are available on the artist's YouTube channel.
Greenman would like to extend a special thanks to Varun Shankar for providing access to a machine for software development.
Recognizing Those Who Build a Vibrant Technical Community
Special Awards Honor Computing Professionals for Impactful Service
ACM, the Association for Computing Machinery, recognized five individuals with awards for their exemplary service to the computing field on May 28, 2025. Representing diverse areas, the 2024 award recipients were selected by their peers for building a vibrant community that benefits both their colleagues and the broader society. This year’s awardees drove advancements in computer science curriculum, cyberinfrastructures, computer science education, and assistive robotics. They were formally recognized at ACM’s annual awards banquet on June 14, 2025, in San Francisco.
Manish Parashar, Professor, University of Utah, receives the ACM Distinguished Service Award for service and leadership in furthering the transformative impact of computer and computational science on science and engineering.
Parashar’s record of service includes leadership at the National Science Foundation (NSF), where he developed NSF’s strategic vision for a national cyberinfrastructure, as well as at the White House’s Office of Science and Technology Policy (OSTP), where he developed the Future Advancement Computing Ecosystem Strategic Plan (FACE). For ACM, Parashar served two terms as editor-in-chief of ACM Transactions on Autonomous and Adaptive Systems (ACM TAAS), and has led steering, organizing and programming committees for numerous ACM conferences.
The ACM Distinguished Service Award is presented on the basis of value and degree of services to the computing community. The contribution should not be limited to service to the Association but should include activities in other computer organizations and should emphasize contributions to the computing community at large.
Dan Garcia, Teaching Professor, UC Berkeley, and Brian Harvey, Teaching Professor Emeritus, UC Berkeley, receive the Karl V. Karlstrom Outstanding Educator Award for their advocacy of and advances in education to bring the beauty and joy of computing to all students, especially those from historically underrepresented communities.
Together Garcia and Harvey have been instrumental in expanding computer science education, most notably through the development of the Beauty and Joy of Computing (BJC) curriculum, which began as a national pilot for the CSforALL movement. A key part of this effort was Snap!, a blocks-based programming language on which Harvey collaborates with principal developer Jens Mönig. Subsequently Garcia and Harvey and BJC co-PI Tiffany Barnes went on to expand BJC’s reach by training over 1,000 teachers, offering the curriculum in Spanish, and developing a middle school version, BJC Sparks. Importantly, the BJC Course at Berkeley is the only EECS course to exceed 50% female enrollment, and once exceeded 70%.
The Karl V. Karlstrom Outstanding Educator Award is presented annually to an outstanding educator who is appointed to a recognized educational baccalaureate institution. The recipient is recognized for advancing new teaching methodologies; effecting new curriculum development or expansion in Computer Science and Engineering; or making a significant contribution to the educational mission of ACM. Those with 10 years or less teaching experience are given special consideration. A prize of $10,000 is supplied by Pearson Education.
Judith Gal-Ezer, Professor Emerita, Open University of Israel, receives the Outstanding Contribution to ACM Award in recognition of her sustained contributions to computer science education policy and research and, more broadly, to the ACM Europe Council.
Gal-Ezer has been an internationally recognized leader in computing education. For her accomplishments, she has received the ACM Karl V. Karlstrom Outstanding Educator Award as well as the ACM SIGCSE Award for Outstanding Contribution to Computer Science Education. Gal-Ezer has been very active in the ACM Europe Council and its sub-committees. She represents ACM Europe in the Informatics for All (I4All) coalition—a collaboration between ACM Europe, Informatics Europe, CEPIS and IFIP. This ambitious initiative was created to promote informatics education in primary and secondary schools across Europe. The sustained advocacy of I4All has been instrumental in the European Commission’s decision to prioritize informatics education at all stages of the curriculum.
The Outstanding Contribution to ACM Award recognizes outstanding service contributions to the Association. Candidates are selected based on the value and degree of service overall and may be given to up to three individuals each year.
Maja Matarić, Professor, University of Southern California, receives the ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics for pioneering socially assistive robotics (SAR) for improving wellness and quality of life for users with special needs.
Over the past two decades, Matarić has been the leading figure in the field of socially assistive robotics. These robots are designed to gain insights into the drivers of human behavior related to overcoming challenges. The goal of this field is to provide people with personalized assistance to enhance their abilities in areas such as convalescence, rehabilitation, training, and education. Socially assistive robotics is an interdisciplinary field which emphasizes co-design and user participation throughout the development process. Her research is aimed at major challenges, including post-stroke rehabilitation, cognitive and social skills training for children with autism spectrum disorders, cognitive and physical exercises for Alzheimer’s patients, study support for students with ADHD, and personalized therapy interventions for students with anxiety and/or depression.
The ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics recognizes an individual or group who has made a significant contribution through the use of computing technology. It is given once every two years, assuming that there are worthy recipients. The award is accompanied by a prize of $5,000.
About ACM
ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers, and professionals to inspire dialogue, share resources, and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.
Mary Hall Elected Vice Chair of Computing Research Association Board of Directors
Mary Hall Elected Vice Chair of Computing Research Association Board of Directors
Goldman Sachs U of U Former Intern Panel - April 3
Event Details
April 3, 2025
5:00 PM- 6:00 PM
Warnock Enginerring Building (WEB) room 1250
Please join us for the U of U Former Intern Panel. This event will provide you an opportunity to learn more about our businesses, network with Goldman Sachs professionals and learn more about ourSummer program opportunities with Goldman Sachs.
This event is open to all sophomore engineering students as well as incoming new analysts and summer analysts.
Goldman Sachs is where exceptional people build extraordinary careers. We hire people with diverse skill sets, interests, and backgrounds – and we provide them with the hands-on experience to business challenges and opportunities to learn firsthand from the very best.
If you are someone who thrives on excellence, join us at our upcoming event to learn more about Goldman Sachs and our long-standing apprenticeship culture.
We look forward to meeting you.
NSF-Simons CosmicAI Institute Seminar Series with Varun Shankar - March 26
Event Information
March 26, 2025
12:00 PM – 1:00 PM
Evans Conference Room, Warnock Engineering Building (WEB) Room 3780
Zoom Access
Meeting ID: 958 8067 9001
Passcode: 255836
Structure-Preserving, Low-Parameter, Interpretable, Operator Learning for Surrogate Modeling with Varun Shankar (Assistant Professor, Kahlert School of Computing)
Scientific machine learning (SciML) is a relatively new scientific discipline that weds scientific computing and high performance computing with carefully designed machine learning (ML) techniques. In the context of astrophysics, SciML has been applied to galaxy classification and identification, outlier detection, and uncertainty quantification.
Within SciML, operator learning is a rapidly emerging and powerful new paradigm for surrogate modeling across engineering and the sciences, with recent successes in climate modeling, material design, and carbon sequestration problems (to name a few). In this talk, I will present a unified framework that encompasses many operator learning paradigms and use this to present three advancements in operator learning: (1) the Kernel Neural Operator (KNO), a generalization of the Fourier neural operator that allows for greater flexibility in kernel choices and for local spatial adaptivity while inherently using far fewer trainable parameters; (2) the ensemble DeepONet, a generalization to Deep Operator Networks that enables the incorporation of spatial adaptivity directly into a set of basis functions; and (3) a new operator learning paradigm based on kernel approximation that analytically preserves the divergence free property and requires minimal training, all while achieving state-of-the-art performance on incompressible flow problems.
We argue that operator learning has the potential to positively impact astrophysics through trustworthy, rapid, and interpretable surrogate models for multiscale simulations of magnetohydrodynamics (MHD) and numerical general relativity (GR), and for inverse problems such as physical parameter estimation.
Goldman Sachs: Leadership and Economic Insights with Rob Kaplan - March 24
Event Information
March 24, 2025
2:00 PM - 3:00 PM
Rick and Marian Warner Auditorium in the Robert H. and Katharine B. Garff Building
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Join Goldman Sachs on Monday, March 24 to hear unique insights on leadership and the economy from Rob Kaplan - Vice Chairman of Goldman Sachs and former CEO of the Federal Reserve Bank of Dallas.
Goldman Sachs is where exceptional people build extraordinary careers. We hire people with diverse skill sets, interests, and backgrounds - and we provide them with the hands-on experience to business challenges and opportunities to learn firsthand from the very best.
Register through the University of Utah Handshake page here.
Announcing the Inaugural Kahlert Impact Prize Honorees
We are proud to recognize the inaugural recipients of the Kahlert Impact Prize.
The Kahlert School of Computing offers the Kahlert Impact Prize to two graduate students who, whether through research or service, show a track record of success and a compelling story of the high impact of their work. Honorees receive a scholarship of $2,000 each.
Amit Samanta
PhD Student
Amit works in the area of system design and implementation. His recent work has focused on serverless computing platforms, which are often deployed by large cloud services. Cloud workloads demand massive resources, and they are often dynamic and unpredictable. Amit’s contributions improve resource utilization while targeting performance and sustainability metrics.
Amit has published many papers at top systems conferences, earning him multiple awards. He has collaborated with industry professionals on some of his work. A key novelty is the deployment of scheduling algorithms that consider cutting-edge technologies like persistent or disaggregated memory. More recently, the carbon footprint of cloud platforms has come under scrutiny – Amit’s ongoing work explores carbon-aware and sustainability-aware network routing schemes. Amit has a long track record of service to his research community, including engagement in program committees, artifact evaluation committees, and event organizing.
Maitrey Mehta
PhD Candidate
Maitrey works to expand the impact of AI to low-resourced languages. While most recent large language model advancements (like ChatGPT) are evident for English, progress in other languages has languished. Maitrey has focused on his native language of Gujarati, with hopes that it provides a roadmap to extend AI technologies to the many other languages spoken by the world’s population.
Maitrey’s vision is to give every human the right to interact with technology in one’s native language. To achieve this, he focuses on a key ingredient for developing this technology: data. Data is the fuel that powers modern LLMs, and there is an unfortunate data disparity across languages. His research aims to find efficient methods to close this resource gap. Maitrey contributed the first semantically annotated dataset in the Gujarati language that also captures cultural nuances. Subsequently, this dataset has been used to create dependency treebanks and other basic language tools like parsers and taggers. Maitrey has collaborated with industry and other groups on campus. He has helped the research community by serving on program committees and through mentorship roles. Among many talks on AI, he has also presented to an audience of veteran business owners at the 7th Annual Utah Veteran Business Conference in 2023.
UCBPC Computing Industry Panel—February 11
Tomorrow (February 11) at 3:30 PM, UCBPC hosts their Computing Industry Panel in Marriott Library Room 1120. Join us for an engaging discussion with professionals from top tech companies and academia. We hope to see you there!
2025 Data Science & AI Day Sees Over 200 Attendees, Keynote Speaker from Nvidia
Friday, January 24, in the A. Ray Olpin Student Union Ballroom. 229 students, faculty members, University staff members, and attendees from the general public gathered in the A. Ray Olpin Student Union ballroom on Friday, January 24, for The Utah Center for Data Science’s annual Data Science & AI day.
Keeping Us Posted
The event began with 10 research poster presentations from current students sharing their knowledge with peers and professors alike. Simultaneously, partners with University Career Success hosted a Data Science & AI Career Fair, connecting students with professionals across the Silicon Slopes as they prepare to enter the workforce.
Keynote Speaker: Professor Dieter Fox of Nvidia & U Washington
The public has witnessed huge advances in generative artificial intelligence, including large language models, chatbots, and image and video generation tools. How has this progress impacted robotics?
This is the question that Dieter Fox, Senior Director of Robotics Research at Nvidia and University of Washington Professor, captivated the attendees while addressing during his keynote. Professor Fox identified large scale data as a primary ingredient to recent advances in generative AI. He then proposed several directions for generating large scale data for robot learning, focusing on his work in using large scale, parallelized simulation as the primary tool to enable massive data generation. He also discussed how this could be combined with human demonstrations. His keynote concluded by demonstrating exciting recent advances achieved using these techniques and gave his thoughts on how neural network architectures should evolve to further their use in robotics.
Research Highlights
The afternoon session culminated with research highlights from across the University who have practically applied data science & AI within their work.
Assistant Professor Ziad Al-Halah's (Kahlert School of Computing) section "AI in Computer Vision" regarded spatial features in the audio-visual medium.
Associate Professor Xiaoyue Cathy Liu's (Civil & Environmental Engineering) section "DS of Human Mobility" discussed AI applications for traffic patterns and safety.
Research Assistant Professor Shiqi Yu (Physics and Astronomy) closed out the day's presentations with her "ML for Astronomy" section.
Guest Speaker: Proximal Causal Inference with Text Data talk by Katie Keith
When: Monday, January 27, 2–3:30pm
Where: WEB 3780 (Evans Conference Room)
Talk Title: Proximal Causal Inference with Text Data
Speaker’s Bio: Katherine (Katie) Keith is currently an Assistant Professor in the Computer Science department at Williams College in Massachusetts. Her research interests are at the intersection of natural language processing, causal inference, and computational social science. Previously, she was a Postdoctoral Young Investigator at the Allen Institute for Artificial Intelligence, and she graduated with a PhD from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. She has been a co-organizer of the First Workshop on Causal Inference and NLP, co-organizer of the NLP+CSS Workshops at EMNLP at NAACL, and was a recipient of a Bloomberg Data Science PhD fellowship.
Talk Abstract:
Causal inference underlies many important policy decisions and interventions. For example, clinicians must decide how to prescribe medications to patients, central bank committees must decide how to change interest rates, and online platform administrators must decide how to moderate users. In the absence of a randomized controlled trial, one can turn to observational (non-experimental) data to estimate causal effects. In this setting, a primary obstacle to unbiased causal effect estimation is confounding variables, variables that affect both the treatment (e.g., which medication) and the outcome (e.g., an aspect of patient health). In many applications, a rich, unstructured source of confounding variables is text data: notes from electronic health records (EHRs) detail patients’ personal and medical histories, newspaper articles document national and international events, and online platforms host exchanges of users’ written opinions. By expanding observational causal estimation methods that can incorporate natural language processing (NLP) and text data, analysts may be able to make inferences in a wider range of settings.
Recent text-based causal methods attempt to mitigate confounding bias by estimating proxies of confounding variables that are partially or imperfectly measured from unstructured text data. These approaches, however, assume analysts have supervised labels of the confounders given text for a subset of instances, a constraint that is sometimes infeasible due to data privacy or annotation costs. In this work, we address settings in which an important confounding variable is completely unobserved. We propose a new causal inference method that uses two instances of pre-treatment text data, infers two proxies using two zero-shot models on the separate instances, and applies these proxies in the proximal g-formula. We prove, under certain assumptions about the instances of text and accuracy of the zero-shot predictions, that our method of inferring text-based proxies satisfies identification conditions of the proximal g-formula while other seemingly reasonable proposals do not. To address untestable assumptions associated with our method and the proximal g-formula, we further propose an odds ratio falsification heuristic that flags when to proceed with downstream effect estimation using the inferred proxies. We evaluate our method in synthetic and semi-synthetic settings—the latter with real-world clinical notes from MIMIC-III and open large language models for zero-shot prediction—and find that our method produces estimates with low bias. We believe that this text-based design of proxies allows for the use of proximal causal inference in a wider range of scenarios, particularly those for which obtaining suitable proxies from structured data is difficult.