Winners of the 2024 Henry Ford II Scholar Award Announced
The Henry Ford II Scholar Award is awarded annually to engineering students with the best academic record at the end of the third year of undergraduate study. This year, nine third-year undergraduate students have received the award: Cloris Cheng, Ethan Z. Dawn, Rachael Kim, Markus P. Lendermann, Miguel O. Liu-Schiaffini, JS Paul, Pablo Backer Peral, Dominic M. Phung, and Ayush Varshney.
Chun Xin (Cloris) Cheng is a third-year undergraduate majoring in Computer Science, and is interested in machine learning problems in optimization and their applications in accelerating scientific discovery. She has worked with Yisong Yue, Professor of Computing and Mathematical Sciences, and Adam Wierman, the Carl F. Braun Professor of Computing and Mathematical Sciences; Director, Information Science and Technology, on different research projects during her time at Caltech. This summer, Cloris will be completing an off-campus SURF at Princeton University on applying generative modeling to small molecule discovery. She plan to pursue a PhD in computer science and further expand her research interests. She is advised by Professor Adam Wierman.
Ethan Z. Dawn's academic interests lie in statistics and data science, particularly their applications in economics and finance, as well as computer algorithms and software development. He chose Caltech for its dedication to theoretical and mathematical rigor, and is pursuing studies in computer science, economics, and data science. Some highlights of his academic journey include exploring game theory and mechanism design and learning about their real-world applications in networks and routing games. This summer, Ethan will intern as a software engineer at The Voleon Group, working with the Machine Learning Operations team to optimize ML research workflows on a high-performance cloud-based platform. He is advised by Adam Blank, Teaching Professor of Computing and Mathematical Sciences; Academic Director, First-Year Success Research Institute, Kota Saito, Professor of Economics, and Babak Hassibi, the Mose and Lillian S. Bohn Professor of Electrical Engineering and Computing and Mathematical Sciences.
Subin (Rachael) Kim is interested in computer science applications in finance, mainly financial modeling and strategy development through ML. Her experience with BEM option at Caltech has led her to pursue a technical career in finance, and this summer she will be working as a Dev intern at Virtu Financial in New York. She plans to either pursue a master's degree in financial engineering/machine learning or continue her technical career in finance post-graduation. She is advised by Professor Yisong Yue and Jakša Cvitanić, the Richard N. Merkin Professor of Mathematical Finance.
Markus P. Lendermann is interested in the development of computer operating systems. He employs a first-principles approach, applying a strong fundamental understanding of the underlying systems to software engineering and optimization problems. Over the past two summers, he helped develop kernels for electric vehicles and storage servers. In the coming summer, he will be working as a software engineer intern at Jane Street. Outside of school, Markus enjoys building, modifying and racing cars on the track. His advisor is Adam Blank, Teaching Professor of Computing and Mathematical Sciences; Academic Director, First-Year Success Research Institute.
Miguel O. Liu-Schiaffini's research interests broadly lie in machine learning and its scientific applications. Over the past three years, he has worked in the lab of Anima Anandkumar, the Bren Professor of Computing and Mathematical Sciences, in developing the methods and applications of neural operators, a class of deep learning models that can be used to accelerate physics simulations. He has worked on using neural operators to model turbulent fluid flows and for forecasting tipping points of complex systems (like Earth's climate) far in advance. His advisor is Yisong Yue, Professor of Computing and Mathematical Sciences.
Jean Sebastien (JS) Paul is dual majoring in Computer Science and Biology. This year he has worked for Niles Pierce, Professor of Applied and Computational Mathematics and Bioengineering, on engineering CRISPR/Cas9 conditional guide RNAs, and he has started working for Lulu Qian, Professor of Bioengineering, on DNA molecular oscillators. This summer he will SURF in Professor Xiaojing Gao's lab at Stanford, researching the use of Language Models in humanizing gene-activating proteins as a potential therapeutics platform. JS is interested in the intersection of Synthetic and Systems Biology and Molecular Programming. After graduation, he plans to pursue a PhD within these fields and hopes to one day become a physician scientist.
Pablo Backer Peral chose to pursue electrical engineering at Caltech because of its broad scope, providing a strong foundation for research in areas ranging from machine learning applications to optics. His research trajectory at Caltech began in the lab of Pietro Perona, the Allen E. Puckett Professor of Electrical Engineering, where he worked on developing machine learning algorithms to automatically classify mice behavior from interaction videos and predict their neural activity based on those behaviors. For the past year, he has been a part of Ali Hajimiri's CHIC Lab, investigating and designing quantum optoelectronic circuits and systems. He will continue this work through a SURF this summer. After Caltech, he hopes to expand his research interests abroad through a Fulbright scholarship, ultimately leading to a Ph.D. He is advised by Ali Hajimiri, the Bren Professor of Electrical Engineering and Medical Engineering; Co-Director, Space-Based Solar Power Project.
Dominic Phung discovered a passion for programming in elementary school, starting with game development and then evolving into game hacking and reverse engineering by middle school. He has engineered novel exploits in popular games, developed several games, and shared his insights through numerous forum posts. With a love for applied computer science, he has explored diverse fields from designing clean cross-platform UI/UX to developing code obfuscation techniques. He looks forward to refining existing software to enhance user experience and efficiency while also creating innovative applications through his own ventures. He is advised by Changhuei Yang, the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering; Investigator, Heritage Medical Research Institute.
Ayush Varshney is passionate about leveraging AI to address the grand challenges of our time. Drawn to Caltech's culture of ambitious interdisciplinary research, he has explored the applications of AI in neural decoding and protein binding. Currently, he is working with Katie Bouman, Assistant Professor of Computing and Mathematical Sciences, Electrical Engineering and Astronomy; Rosenberg Scholar; Investigator, Heritage Medical Research Institute, on expressive variational inference for inverse problems. This summer, Ayush will intern at Goldman Sachs as a quantitative strategist and plans to pursue a PhD in machine learning. Outside academics, he enjoys playing alto saxophone and performs with the Caltech Wind Orchestra. Ayush is advised by Adam Blank, Teaching Professor of Computing and Mathematical Sciences; Academic Director, First-Year Success Research Institute.