Reports from the Heart of the Machine
Editors: Xiao Zhou, Chen Ping
A total of 10 PhD students received NVIDIA Graduate Scholarships for the 2022-2023 academic year.
For two decades, the NVIDIA Graduate Scholarship Program, open to applicants worldwide, has been dedicated to supporting students working on THE GPU. Now, NVIDIA has announced the recipients of the Graduate Scholarship for the 2022-2023 academic year.
The 2022-2023 academic year NVIDIA Graduate Fellowships award a total of 10 PhD students involved in GPU computing research, each receiving up to $50,000.
These researchers work at the forefront of GPU computing, including projects in deep learning, robotics, computer vision, computer graphics, architecture, circuits, high-performance computing, life sciences, and programming systems.
Bill Dally, Nvidia's Chief Scientist, once said, "Our fellowship recipients are some of the most talented graduate students in the world who are working on some of the most important issues in computer science, and we are excited to support their research."
Recipient of the 2022-2023 Academic Year Graduate Scholarship
The 10 PhD students who received NVIDIA Graduate Fellowships for the 2022-2023 academic year were from Stanford University, The University of Texas at Austin, the University of Washington, the Massachusetts Institute of Technology, Carnegie Mellon University, the University of California, Santa Barbara, Harvard University, and Cornell University.
Davis Rempe, Stanford University
Personal homepage: https://davrempe.github.io/
Davis Rempe, a Ph.D. student in computer science at Stanford University, studied under Professor Leonidas Guibas and is primarily researching modeling 3D motion to solve pose estimation, shape reconstruction, and motion prediction to enable intelligent systems to understand dynamic 3D objects, people, and scenes.
Davis Rempe interned at NVIDIA, Adobe, and Snap. He received his bachelor's degree from the University of Nebraska at Lincoln with a minor in physics.
University of Texas at Austin Hao Chen
Personal homepage: https://baloneymath.github.io/
Hao Chen is a PhD student in electrical and computer engineering at the University of Texas at Austin, where he studied under Professor David Z. Pan and conducted research in the UTDA Lab. Hao Chen's primary research direction is the development of next-generation VLSI (Very Large Scale Integrated Circuit) physical synthesis tools that enable the generation of high-quality layouts at advanced manufacturing nodes, especially in analog/mixed-signal circuits.
Hao Chen won third prize in the ICCAD 2018 CAD competition and third prize in the ISPD 2018 Initial Details Routing competition. Hao Chen interned at Synopsys and NVIDIA Research.
University of Washington Mohit Shridhar
Personal homepage: https://mohitshridhar.com/
Mohit Shridhar is a PhD student at the University of Washington under Professor Dieter Fox. Prior to that, he was a researcher at the National University of Singapore. Shridhar's main interests are human-computer interaction, computer vision, natural language processing, and machine learning. Its main contribution is to connect language to vision-based robot perception and action, where representations of vision and language are learned through concrete interactions rather than static data sets.
Mohit Shridhar received his B.A. in Computer Engineering from the National University of Singapore, and during his undergraduate studies, Shridhar spent a year at Stanford University and interned at a YCombinator AR startup.
MIT Sai Praveen Bangaru
Personal homepage: https://people.csail.mit.edu/sbangaru/
Sai Praveen Bangaru is a PhD student at the Massachusetts Institute of Technology under Professor Fredo Durand. It also works closely with Professor Tzu-Mao Li and Professor Jonathan Ragan-Kelley. His main research interests are micromixable simulation methods and, more specifically, microrepresent rendering and its applications in computer vision and perception. Currently, Topics that Bangaru is actively working on include: programmable differentiated compilers, real-time path tracing, neural rendering, and physics-based modeling.
Prior to that, Bangaru spent 6 months as a researcher at CMU and interned at INRIA.
University of California, Santa Barbara, Shlomi Steinberg
Personal homepage: https://ssteinberg.xyz/
Shlomi Steinberg is a PhD student at the University of California, Santa Barbara, under the tutelage of Professor Lingqi Yan. Steinberg's main research interests lie somewhere between computer graphics and computational electrodynamics. Steinberg's main research goal is to develop theoretical, analytical models, and computational tools for physical light transport.
Sneha Goenka, Stanford University
Personal homepage: https://stanford.edu/~gsneha/
Sneha Goenka is a ph.D. fourth-year student in the Department of Electrical Engineering at Stanford University under the supervision of Professor Mark Horowitz. Sneha Goenka's research interests include computer architecture and computational biology, focusing on exploring genomic analysis pipelines through hardware-software co-design, enabling ultra-rapid diagnosis of genetic diseases and accelerating large-scale comparative genomic analysis.
Yufei Ye, Carnegie Mellon University
Personal homepage: https://www.ri.cmu.edu/ri-people/judy-yufei-ye/
Yufei Ye is a PhD student at carnegie Mellon University's Robotics Institute, whose research interests are in building agents that can perceive physical interactions between objects, understand the results of interactions with the physical world, and predict the potential effects of specific interactions.
University of California, Santa Barbara, Yuke Wang
Personal homepage: https://www.wang-yuke.com/
Yuke Wang is a Ph.D. student in the Department of Computer Science at the University of California, Santa Barbara, under the supervision of Assistant Professor Yufei Ding. Yuke Wang's research interests include high-performance computing and deep learning algorithms, working on new algorithms and system-level design optimizations to accelerate deep learning workloads such as deep neural networks and graph neural networks.
Harvard University Deng Yuntian
Yuntian Deng is a PhD student at Harvard University and graduated from Tsinghua University with a bachelor's degree. His research interests are in developing scalable, controllable, and interpretable natural language generation methods using deep generative models with potential applications in long text generation.
Cornell University Zekun Hao
Personal homepage: http://www.cs.cornell.edu/~zekun/
Zekun Hao is a fourth-year PhD student at Cornell University under the supervision of Professor Serge Belongie. Zekun Hao's research interests include machine learning and its applications in computer vision and computer graphics, with a particular interest in developing unsupervised models that assist in the creation of 2D/3D content.
In addition, five PhD students were nominated for the 2022-2023 Academic Year Nvidia Graduate Scholarship:
The University of Hong Kong, Enze Xie
Gokul Swamy, Carnegie Mellon University
Stanford University Hong-Xing (Koven) Yu
Suyeon Choi, Stanford University
Yash Sharma, University of Tübingen
Reference links: https://blogs.nvidia.com/blog/2021/12/14/graduate-fellowship-awards-2/
ETH Zurich DS3Lab: Building data-centric machine learning systems
The DS3Lab Lab at ETH Zurich is comprised of Assistant Professor Ce Zhang and 16 PhD and postdoctoral fellows, Ease.ML project: how to design, manage, and accelerate data-centric machine learning development, operation, and operation processes, and ZipML: Designing efficient and scalable machine learning systems for new hardware and software environments.
From December 15th to December 22nd, 11 guests from the DS3Lab Lab at ETH Zurich will share 6 sessions: Building Data-Centric Machine Learning Systems, as follows: