Hung-Hsun (Hans) Yu

Hung-Hsun (Hans) Yu is a PhD student at Princeton, advised by Noga Alon and Zeev Dvir. His research is in the areas of incidence geometry and extremal combinatorics. Recently he has been focusing on the application of the polynomial method in incidence geometry, and in particular the ‘Joints Problem’ and its connections to other problems in extremal combinatorics.

Joakim Færgeman

Joakim Færgeman is advised by Sam Raskin at Yale. His research is concerned with geometric aspects of the Langlands program. Concretely, he studies symmetries between the geometry of curves and the representation theory of algebraic groups, often using the language of category theory. When not doing mathematics, Joakim can be found hiking and doing martial arts.

Kevin Ren

Kevin Ren is pursuing a PhD at Princeton University, advised by Assaf Naor. His work draws inspiration from analysis, combinatorics, and geometry to tackle fundamental problems in metric and fractal geometry. Last year, he solved the longstanding Furstenberg set conjecture. Currently, he is working on metric embedding problems with connections to approximation algorithms in graph theory. Kevin likes hiking, running, and playing the piano.

Lichen Zhang

Lichen Zhang is a graduate student at MIT, advised by Jonathan Kelner. His research focuses on optimization and sketching algorithms. Specifically, he is designing fast algorithms with provable guarantees for large, non-convex machine learning problems, such as training deep, over-parameterized neural networks, matrix completion and kernel SVM. He utilizes tools from modern optimization to develop an error-robust framework for these problems, which can in turn be accelerated via sketching algorithms.

Louis Golowich

Louis Golowich is a PhD student at UC Berkeley and is advised by Venkatesan Guruswami. His research focuses on designing new ways to protect quantum information against errors via designing new quantum error-correcting codes and decoding algorithms. Such errors arise inherently in quantum devices, and present one of the main challenges in realizing the potential of large-scale quantum computation. Louis also works to understand the interesting combinatorial and topological objects that underlie these codes.

Mesut Çalışkan

Mesut Çalışkan is a student at Johns Hopkins in the Physics and Astronomy department, advised by Marc Kamionkowski and Emanuele Berti. His research aims to enhance our understanding of the nature of dark matter by constraining the density profiles and abundances of low-mass dark-matter structures, known as dark-matter subhalos, across the Universe. For this objective, he studies these subhalos' interactions with the gravitational waves emitted by massive black hole binaries. Additionally, he is interested in probing the cosmic expansion rate using strongly lensed gravitational waves and exploring the epoch of helium reionization using the cosmic microwave background and the large-scale structure in the Universe. Mesut likes road cycling and playing the electric guitar in his free time.

Philip Easo

Philip Easo is a PhD student at Caltech and is advised by Tom Hutchcroft. His research in discrete probability has been focused on a general theory for the percolation phase transition on finite transitive graphs. This bridges the Erdős–Rényi model and percolation on infinite graphs, which were historically studied by distinct groups, and sheds new light on open questions about the latter. Philip feels most at home in the mountains, especially hiking or skiing.

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 an algorithmic approach to design and analyze ways to aggregate the preferences of individuals in order to make collective decisions. Common examples include choosing the winner of an election, or preferential matching problems like school choice. His goal is to design simple and effective algorithms for these problems. 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.

Rena Chu

Rena Chu is a PhD student studying analytic number theory at Duke University, advised by Lillian Pierce. She thinks about additive and multiplicative character sums, including both their intrinsic properties and applications to other problems. Currently, she is working on bounding short multiplicative character sums evaluated at homogeneous polynomials, where the methods involved apply results from algebraic geometry as well as ideas from additive combinatorics. She loves capturing the beauty of this world through photography and is also learning to paint with watercolors.

Rohan Yadav

Rohan Yadav is pursuing a PhD in parallel programming systems at Stanford University. He is advised by Alex Aiken and Fredrik Kjolstad. Rohan’s work aims to make high performance parallel computing accessible to the everyday programmer. He has worked on compilation techniques for distributed dense and sparse tensor computations, and recently has focused on enabling the efficient composition of distributed programs. Outside of work, he is an avid tennis player, skier, weightlifter and cook.

Sharut Gupta

Sharut Gupta is an MIT graduate student, advised by Stefanie Jegelka. Her work in representation learning mainly focuses on building robust and generalizable machine learning systems under minimal supervision. She enjoys working on out-of-distribution generalization, self-supervised learning, causal inference, and representation learning. In addition to her research, she enjoys playing squash and baseball, watching sports, and walking around the city. She has also won several national championships in baseball and softball.

Surya Mathialagan

Surya Mathialagan is a PhD student at MIT where she is advised by Vinod Vaikuntanathan and Virginia Vassilevska Williams. Surya’s research focuses on fine-grained complexity and cryptography. Recently she has been focusing on problems in theoretical cryptography involving oblivious RAMs, memory checking and succinct proofs. Her research addresses the following questions: How can we maintain integrity and privacy when outsourcing a large database to an untrusted cloud storage? Similarly, how can we efficiently verify the correctness of a large computation delegated to a remote cloud server? In her free time, Surya enjoys drawing, playing the guitar, and dancing.

Yonathan Fisseha

Yonathan Fisseha is a graduate student at the University of Michigan, advised by Todd Austin and Jean-Baptiste Jeannin. His work in hardware description languages focuses on formalizing emerging HDLs and relating them to algebraic structures, for example Kleene Algebra and its extensions. He believes that this will improve the theorem proving experience for hardware engineers and complement the current model checking based verification workflow.

Zongyi Li

Zongyi Li is a PhD student at Caltech, advised by Anima Anandkumar. Zongyi's research focus is neural operators, a novel generalization of deep learning methods to operators mapping between infinite-dimensional function spaces. In particular, the Fourier neural operator model has shown state-of-the-art performance with a 1000x speedup in learning the turbulent Navier-Stokes equations, as well as promising applications in weather forecasting and carbon capture simulation.