Gabriel Matos

Gabriel Matos is a graduate student working on experimental particle physics at Nevis Laboratories, Columbia University, advised by John Parsons. He works in the ATLAS Experiment at CERN's Large Hadron Collider, exploring the intersection of machine learning and particle physics, in particular in the search for beyond the Standard Model physics. He has contributed to the development of anomaly detection techniques in this setting, and currently works on a search for axion-like particles decaying to pairs of collimated photons. Outside of physics, Gabriel enjoys running, hiking, photography, and is an avid moviegoer.

Mihir Singhal

Mihir Singhal is a graduate student at UC Berkeley and is advised by Venkatesan Guruswami and Jelani Nelson. He works on various problems in theoretical computer science with a combinatorial or probabilistic flavor. Much of his work has been in streaming algorithms and other models of sublinear computation.

Navid Eslami

Navid Eslami is a PhD student at the University of Toronto, advised by Niv Dayan. His work focuses on designing data structures tailored to database systems, with an emphasis on randomized and sketching data structures, such as Bloom filters. His space-efficient and theoretically-grounded designs enable database systems to provide better and more robust performance-cost tradeoffs. He proves the optimality of his data structures through lower bounds, while also leveraging the power and speed of modern hardware to the fullest when implementing them. Navid enjoys studying math and complexity theory in general, loves cooking, and does calisthenics.

Noah Amsel

Noah Amsel is a graduate student at New York University, advised by Christopher Musco and Joan Bruna. He studies algorithms for machine learning, with the goal of using mathematics to explain machine learning problems, fill gaps in our understanding, and improve our methods. Numerical linear algebra and approximation theory are major themes in his work. He has lately been focused on algorithms for training neural networks.

Rui Pan

Rui Pan is a PhD candidate at Princeton where he is advised by Ravi Netravali. His research lies at the intersection of systems and machine learning, with a recent focus on software infrastructure and algorithms for efficient large language model inference. In particular, he studies how to co-design novel models (e.g., hybrid LLMs, reasoning LLMs, diffusion LLMs) and the underlying runtimes that serve them, ensuring they scale and perform efficiently for emerging generations of AI innovations. Outside of research, he enjoys playing soccer and table tennis with friends.

Seyoon Ragavan

Seyoon Ragavan is a PhD student at MIT and is advised by Vinod Vaikuntanathan. He is broadly interested in cryptography and quantum algorithms, and enjoys leveraging insights from algebra and number theory to improve the efficiency — both theoretical and practical — of algorithms and cryptographic protocols. His most recent target was efficient private information retrieval, which enables a user to access an entry in a remote server-hosted database without revealing the entry to the server. In his free time, Seyoon can be found playing the mridangam (a South Indian classical percussion instrument), at a coffee shop, or at the local AMC.

Soham Ghosh

Soham Ghosh studies mathematics at University of Washington and is advised by Max Lieblich and Farbod Shokrieh. His research is in two broad facets of arithmetic geometry. The first is the study of the arithmetic and geometry of varieties arising from problems related to Brauer groups. The second involves studying Fourier-Mukai transforms in non-archimedean geometry using tropical methods.

Sukjun Hwang

Sukjun Hwang is a PhD candidate at CMU, advised by Albert Gu. His research aims to develop dynamic architectures for foundation models that selectively compress and retain information from each input, enabling unsupervised discovery of meaningful units without hand-crafted heuristics. This improves efficiency and scalability by allocating compute and memory where they matter most. Outside of research, Sukjun enjoys exploring new coffee beans and brewing espresso.

Yeyuan Chen

Yeyuan Chen is a graduate student in EECS at University of Michigan, advised by Mahdi Cheraghchi. He is broadly interested in theoretical computer science, in particular coding theory and graph theory problems with algebraic flavor. His recent projects focus on algebraic coding theory and spectral graph theory. His favorite puzzle game is Bean and Nothingness, made by another coding theorist with her math PhD friends.