Hannah Lawrence

Hannah Lawrence is an MIT graduate student, advised by Ankur Moitra and Tess Smidt. Hannah develops mathematically-grounded methods for both enforcing and exploiting the structure in representations learned by neural networks. Much of her work focuses on developing new methods and analyses for equivariance, the practice of enforcing known physical symmetries (such as permutations and rotations) in deep learning pipelines. She loves running, baking, and live music, and is excited to run the Tokyo marathon this year!

Hong-Xing ''Koven'' Yu

Hong-Xing “Koven” Yu is a graduate student at Stanford, advised by Jiajun Wu. His research focuses on creating interactive virtual 4D worlds that respond dynamically to human imagination and actions, making immersive digital environments accessible to everyone. His approach combines generative AI with the physical structures of the world, including knowledge from computer graphics, physics, and language, to enable meaningful interactions with these virtual worlds.

Johannes Wagner

Johannes Wagner is a UC Berkeley PhD student, advised by Heather Gray. Johannes works as part of the ATLAS experiment at the Large Hadron Collider, where he is studying interactions of the Higgs boson with other fundamental particles. His research is focused on improving our identification of charm quarks with machine learning as well as designing statistical analysis techniques to measure the Higgs to charm coupling. Outside of this work, he enjoys traveling and weightlifting.

John Bostanci

John Bostanci studies computer science at Columbia University, advised by Henry Yuen. His research is focused on studying the powers and limitations of quantum computers, and discovering the implications of both. Some specific problems he is interested in include: constructing quantum money and related primitives from reasonable assumptions, exploring the mathematical foundations of quantum cryptography, and designing efficient algorithms for learning from quantum data. John will be running the NYC marathon in 2025!

Lichen Zhang

Lichen Zhang is an MIT student where he is advised by Jonathan Kelner. Lichen’s research focuses on designing efficient algorithms for machine learning problems with provable guarantees. This includes both establishing theoretical guarantees for widely used heuristics and developing practical algorithms inspired by theoretical insights. Recently, he has been leveraging tools from quantum computing, differential privacy, and spectral graph theory to develop fast theoretical and practical machine learning algorithms.

Prasanna Ramakrishnan

Prasanna Ramakrishnan is a graduate student at Stanford, advised by Moses Charikar and Li-Yang Tan. His research in computational social choice uses the lens of approximation algorithms to find simple and effective ways for groups of individuals with preferences to make collective decisions. Motivating examples include choosing the winner of an election, or matching problems like school choice. In his free time, he loves to play tennis, board games, and music. His favorite instrument is the steel pan, which was invented in Trinidad and Tobago, his home country.

Sam Goldstein

Sam Goldstein studies physical cosmology at Columbia University. He is advised by J. Colin Hill, and he works at the intersection of theoretical and observational cosmology. His research focuses on using the cosmic microwave background (CMB) and large-scale structure to probe fundamental physics, with a particular interest in studying the origin, evolution, and composition of the universe. Sam also likes playing guitar and bouldering.

Seyoon Ragavan

Seyoon Ragavan is a PhD student in the computer science department at MIT, advised by Vinod Vaikuntanathan. He is broadly interested in cryptography, particularly its intersection with quantum information. He seeks to understand the extent of the security threat posed by future quantum computers if used by malicious eavesdroppers, by designing improved quantum algorithms for problems such as integer factorisation. He has also worked on designing encryption schemes with security properties that require the honest users to have access to quantum computers. In his free time, Seyoon enjoys playing the mridangam (a South Indian classical percussion instrument) and watching films in theatres.

Spencer Compton

Spencer Compton is a PhD student at Stanford, where he is advised by Tselil Schramm and Gregory Valiant. His research hopes to understand how algorithms can best extract signal from data, viewed through the lens of theoretical computer science and information theory. He enjoys thinking about algorithm design, evidence of computational hardness, and the structure that makes certain statistical tasks tractable. He spends much of his free time at coffee shops, pizzerias, and tennis courts.

Yiyun Liu

Yiyun Liu is PhD candidate at the University of Pennsylvania where he is advised by Stephanie Weirich. Yiyun studies how dependent types and dependency tracking can be combined to produce type systems that are both more expressive and easier to use. He uses the Rocq proof assistant to formally verify the systems he designs, and he looks for ways to reduce the effort of mechanization, making difficult proofs about dependent types more accessible. In his free time, Yiyun works on hobby projects related to home networking.