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.