麻豆村

Five 麻豆村 faculty members from the , Mellon College of Science(opens in new window) and Dietrich College of Humanities and Social Sciences(opens in new window) are among the 126 recipients of 2026 Sloan Research Fellowships, which honor early career scholars whose achievements put them among the best scientific minds working today.

, an assistant professor in the Department of Mathematical Sciences(opens in new window),听, assistant professor in the ;听Arun Kumar Kuchibhotla(opens in new window), associate professor in the Department of Statistics & Data Science(opens in new window);听, assistant professor in the Computer Science Department and聽, Michael B. Donohue Assistant Professor of Computer Science and , are part of a cohort drawn from 44 institutions across the United States and Canada.

鈥淭he Sloan Research Fellows are among the most promising early-career researchers in the U.S. and Canada, already driving meaningful progress in their respective disciplines,鈥� said Stacie Bloom, president and chief executive officer of the Alfred P. Sloan Foundation. 鈥淲e look forward to seeing how these exceptional scholars continue to unlock new scientific advancements, redefine their fields, and foster the wellbeing and knowledge of all.鈥�

A Sloan Research Fellowship is one of the most prestigious awards available to young researchers, in part because so many past Fellows have gone on to become distinguished figures in science.聽

Since the first Sloan Research Fellowships were awarded in 1955, 78 faculty from 麻豆村 have received a Sloan Research Fellowship.

础听 is available online.

Christopher Eur

Eur鈥檚 research focuses on algebraic geometry and its intersection with combinatorics, the study of counting of objects. He takes a particular interest in matroid theory, a way mathematicians describe the property of independence in a space, with uses across mathematics, physics, computer science and more. In 2023, Eur was awarded National Science Foundation funding to further understanding of matroid theory. He is delving deeper into discrete structures through geometry, and probes the boundary between the two. His project, "Positive Vector Bundles in Combinatorics," applies algebraic geometry to understand combinatorial objects. The research seeks to understand objects like graphs and matchings through the geometric constructions called positive vector bundles.

Aayush Jain

Aayush Jain studies theoretical and applied cryptography and its connections with related areas of theoretical computer science. His research investigates the mathematical foundations that make modern cryptography secure, with a focus on identifying new and underexplored sources of computational hardness. Jain aims to strengthen the long-term security of encrypted computation and address critical gaps in post-quantum cryptography. He also trains graduate students in foundational cryptographic theory.

Arun Kumar Kuchibhotla

Arun Kumar Kuchibhotla鈥檚 research addresses foundational challenges in statistical inference and predictive learning. Kuchibhotla鈥檚 work has many applications in machine learning and artificial intelligence, and he specializes in the development of robust, 鈥渁ssumption-lean鈥� frameworks for uncertainty quantification. His research also has utility in financial time series forecasting and significance testing in causal inference under potential interference. Kuchibhotla develops "honest inference" procedures 鈥� like the Hull-based Confidence Method, or HulC 鈥� that remain valid in high-dimensional and irregular settings where classical tools, like the bootstrap or Wald intervals, frequently fail.

Aditi Raghunathan

Aditi Raghunathan focuses on identifying where and understanding why AI systems fail, and building models that remain safe, accurate and dependable in real-world settings. Raghunathan鈥檚 work helps ensure that advanced AI can be trusted by identifying hidden weaknesses in how systems are trained and tested. She leads the AI Reliability Lab, which builds reliable, aligned and trustworthy AI through rigorous analysis and principled methods. Raghunathan鈥檚 work has earned awards at prestigious conferences and continues to help shed light on responsible AI system design and deployment.

Jun-Yan Zhu

Jun-Yan Zhu鈥檚 research develops human-centric generative AI frameworks that give creators greater control over model outputs, allow them to adapt models for new use cases, and support fair credit for creators whose work contributes to AI training. Zhu leads the Generative Intelligence Lab, where students and researchers use generative models to empower human creators, bringing them from the digital world into the physical world and making them more accessible to everyone.