The Siebel Scholars Foundation has awarded Siebel Scholars fellowships to six Princeton University graduate students: Sotiris Apostolakis, Kyle Genova, Wei Hu, John Li and Divyarthi Mohan in computer science; and Joseph Hamill in chemical and biological engineering.
Now in its 20th year, the Siebel Scholars program annually recognizes exceptional students from the world’s leading graduate schools of business, computer science, energy science and bioengineering. The 92 distinguished students of the Class of 2021 join past Siebel Scholars classes to form a professional and personal network of more than 1,500 scholars, researchers and entrepreneurs. The program brings together diverse perspectives from business, science and engineering to influence the technologies, policies, and economic and social decisions that shape the future.
“Every year, the Siebel Scholars continue to impress me with their commitment to academics and influencing future society. This year’s class is exceptional, and once again represents the best and brightest minds from around the globe who are advancing innovations in healthcare, artificial intelligence, the environment and more,” said Thomas M. Siebel, chair of the Siebel Scholars Foundation.
Founded in 2000 by the Thomas and Stacey Siebel Foundation, the Siebel Scholars program awards grants to 16 universities in the United States, China, France, Italy and Japan. Following a competitive review process by the deans of their respective schools on the basis of outstanding academic achievement and demonstrated leadership, the top graduate students from 27 partner programs are selected each year as Siebel Scholars and receive a $35,000 award for their final year of studies.
Sotiris Apostolakis is a Ph.D. student in computer science whose research focuses on compilers, program analysis and automatic parallelization. His goal is to make harnessing the full power of parallel architectures accessible to all programmers, regardless of their programming expertise. To that end, he has developed automatic parallelization and analysis frameworks that address longstanding problems in the field. He has done internships at Facebook and Intel, working on binary code analysis. Before joining Princeton, he earned his diploma in electrical and computer engineering at the National Technical University of Athens, Greece.
Kyle Genova is a Ph.D. student in computer science whose research in computer vision focuses on how to represent and render three-dimensional shapes to make new neural network-based algorithms possible. This work has been recognized by the Conference on Computer Vision and Pattern Recognition with oral and spotlight presentations, as well as a best paper nomination, and one paper has over 300,000 YouTube views. As an intern at Google, his research led to two Google Open Source projects, a differentiable rendering algorithm that was later implemented by TensorFlow Graphics, and three Google patent filings. He has been awarded the GRFP fellowship by the National Science Foundation, a by Princeton University, and a graduate student teaching award by the computer science department. Prior to beginning his Ph.D. work, he received a B.A. in computer science from Cornell University.
Joseph Hamill is a Ph.D. student in chemical and biological engineering (CBE) whose research sheds light on the degradation pathways that limit the lifetimes of perovskite solar cells, an emerging photovoltaic technology. He has published three first-author papers and several co-author publications. A member of the CBE graduate student committee since 2015, he co-organized the graduate student symposium in 2018. He served as president of the SEAS Graduate Engineering Council in the 2016-17 academic year, and organized numerous social events for SEAS graduate students. He received his B.S. in chemical and biomolecular engineering from North Carolina State University, graduating summa cum laude. He received a National Defense Science and Engineering Graduate Fellowship.
Wei Hu is a Ph.D. student in computer science whose current research interest is in the theoretical foundation of modern machine learning — in particular, on obtaining theoretical understandings of the optimization and generalization mysteries in deep learning, as well as using theoretical insights to design practical and principled machine learning algorithms. Besides deep learning theory, his research papers also contribute to the areas of representation learning, online learning, optimization, and computing in data streams. Previously, he obtained his B.E. in Computer Science from Tsinghua University, where he was a member of Yao Class. He has also spent time as a research intern at Google and Microsoft.
John Li is a second-year master’s student in computer science with interests in programming languages and verification. He is currently working on CertiCoq, a project which aims to build a proved-correct compiler for a dependently typed functional language. He is developing a framework for automatically generating large parts of compiler optimization passes and their correctness proofs from high-level specifications. He received a B.A. in neuroscience from Princeton in 2019. As an undergraduate, he interned as a researcher in neuroscience labs at Princeton and proved one of CertiCoq’s optimization passes correct. Before starting his master’s degree, he spent a summer at HRL Laboratories as a research intern in the assured autonomy team.
Divyarthi Mohan, a Ph.D. student in computer science, is broadly interested in algorithms and algorithmic game theory. She studies simplicity in algorithmic economics and when simple mechanisms give “good” outcomes. Her research focus is on multi-item mechanism design, and on developing the first subexponential approximation scheme in this line of work. She is also interested in social learning and understanding the effects of various simple dynamics. She received a SEAS Award for Excellence in 2019 and graduate student teaching award from the computer science department in 2018. She obtained her M.Sc. in theoretical computer science from The Institute of Mathematical Sciences in Chennai, India, and completed undergraduate studies in mathematics at the Indian Statistical Institute in Bangalore.
(This story was adapted from the Siebel Scholars Foundation’s announcement.)