Seed Grant Awards 2025

• Ruzica Piskac, Professor, Computer Science
• Daniel C. Esty, Hillhouse Professor of Environmental Law and Policy, School of the Environment and Yale Law School
• John W Emerson, Adjunct Professor and Director of Graduate Studies, Statistics and Data Science
• Zachary Wendling, Research Director, Yale Center for Environmental Law and Policy
• Timos Antonopoulos, Research Scientist and Lecturer, Computer Science 
• Topher Allen, Research Assistant, Yale College ‘27

Project: The Environmental Performance Index (EPI), created by Yale and Columbia University, has been the top sustainability scorecard for 25 years, assessing 180 countries with 58 indicators. However, it struggles with data limitations. Our research will use AI to uncover hidden data relationships, leveraging Large Language Models and symbolic reasoning, to improve environmental policy. This approach will enhance how nations collect and use data, discovering new proxies for key sustainability issues like clean water access and waste management, ultimately advancing environmental science and policymaking.

• Jacob Musser, Assistant Professor, Biology
• Thomas Biederer, Professor, Yale Medical School Department of Neurology
• Kejue Jia, Associate Research Scientist, Biology
• Roy Zang, Biology

Project: The evolution of the brain is one of the great mysteries of animal biology. A pivotal step was the emergence of synapses — tiny chemical connections that allow nerve cells to communicate. Our project will develop an AI language model that integrates protein sequences and cellular data to predict how synapses were first assembled and later modified over the course of evolution. This work will shed new light on the deep history and extraordinary diversity of animal brains.

• Andre Levchenko, Professor, Biomedical Engineering 
• Murat Gunel, Professor Neurosurgery, Neuroscience, and Genetics; Chair Neurosurgery
• Karthik Desingu, Graduate Student, Biomedical Engineering

Project: Predicting cancer outcomes is crucial, especially for aggressive cancers like glioblastoma, which has a median survival of 1.5 years. Current tests are imprecise. Our project will use AI to create a precise, patient-specific prognostic test by integrating diverse clinical, genetic, and phenotypic data, improving predictions and patient care quality.

• Alex Wong, Assistant Professor, Computer Science
• Alison Sweeney, Associate Professor, Ecology and Evolutionary Biology and Physics

Project: We will develop a solution for land-, water-, and carbon-efficient algal biomass production using sunlight, inspired by giant clams. The Sweeney group found these clams optimize sunlight conversion through dynamically organized microalgae arrays. Using AI-assisted computer vision, we will study the clams’ adaptations to light changes. This knowledge will help create highly efficient engineered materials for biomass production, potentially reducing land use significantly. Our goal is to track the microalgae positions with high precision, mimicking the clams’ efficiency under varying light conditions.

• Tianyu Zhu, Assistant Professor, Chemistry
• Yu He, Assistant Professor, Applied Physics
• Ke Liao, Postdoc, Chemistry
• Christian Venturella, Graduate Student, Chemistry
• Jinming Yang, Graduate Student, Applied Physics

Project: We aim to develop an AI framework to predict the spectroscopic and superconducting properties of complex quantum materials, focusing on copper oxide superconductors (cuprates). Traditional physics methods struggle with these materials’ strong electron interactions. Our project integrates advanced spectroscopy, quantum simulations, and deep learning. Using a physics-informed model, we will predict electron spectra to uncover superconducting phases. We will also create a unique dataset for AI training, aiding in discovering new superconductors and advancing materials research.

• Marc Hammarlund, Professor, Genetics and Neuroscience
• Smita Krishnaswamy, Associate Professor, Computer Science and Genetics
• Rex (Zhitao) Ying, Assistant Professor, Computer Science

Project: Before it is translated into protein, RNA goes through complex processing steps like splicing and editing.  We do not full understand how these steps are controlled.  Our project will create advanced AI models to study regulation of RNA processing by combining data about RNA and cell context.  These models will aid biologists in decoding how cells control RNA processing.

• Earl Bellinger, Assistant Professor, Astronomy 
• Daisuke Nagai, Professor, Physics and Astronomy
• Lu Lu, Assistant Professor, Statistics and Data Science

Project:  We explore how artificial intelligence, especially deep learning, can improve the solving of eigenvalue problems, which are essential in fields like quantum mechanics, structural engineering, and stellar astrophysics. By overcoming current AI limitations, we aim to boost scientific research across many areas. This advancement will help us use physics-informed neural operators to solve these problems more accurately and comprehensively, speeding up discoveries and innovations in science.

• Brian Scassellati, Professor, Computer Science, Mechanical Engineering and Materials Science
• Dylan Gee, Associate Professor, Psychology

Project: We propose using a social robot to help teens (ages 12-14) with anxiety disorders develop deep breathing skills to reduce stress. Unlike previous tests, this robot will be deployed in homes as an autonomous system, providing real-time support and enhancing therapy interactions. Our team combines AI and anxiety treatment expertise to explore the broader impact of such technologies on mental health.

• Chin Siang Ong, Assistant Professor, Surgery
• Harlan Krumholz, Harold H. Hines, Jr. Professor of Medicine (Cardiology) and Professor in the Institute for Social and Policy Studies, and of Investigative Medicine and of Public Health
• Arman Cohan, Assistant Professor, Computer Science
• Eric Schneider, Associate Professor, Surgery and Chronic Disease Epidemiology
• Prashanth Vallabhajosyula, Associate Professor, Cardiac Surgery

Project: We propose SurgiMind, an AI framework to enhance surgical practice by reducing non-surgical workload, improving clinical reasoning, and supporting high-stakes decisions. Unlike general AIs like ChatGPT, SurgiMind will integrate reliable medical sources and model uncertainty to aid surgeons and cardiologists. Over 18 months, the project will develop, evaluate, and create open-access tools to improve surgical AI.

• Liza Comita, Professor, Yale School of Environment
• Arman Cohan, Assistant Professor, Computer Science 
• Mitch Horn, Senior Research Data Support Analyst, Yale School of the Environment
• Fabian Michelangeli, Abess Senior Curator of Tropical Botany, New York Botanical Garden
• Luke Browne, Associate Research Scientist, Yale School of the Environment
• Nohemi Huanca-Nunez, Associate Research Scientist, Yale School of Environment
• María Alejandra Gallegos Koyner, Graduate Student, Yale School of Environment

Project: Biodiversity loss is speeding up, but we lack tools to track plant species in places like tropical forests. Most plant species there are rare, so current image-based AI falls short. We will teach AI using detailed written descriptions of plants to recognize traits from photos, improving species identification for better conservation efforts.

• Claus Horn, Research Scientist, Biomedical Informatics and Data Science
• Xianjun Dong, Associate Professor, Neurology
• Akiko Iwasaki, Professor, Immunobiology, Biology and Epidemiology
• Smita Krishnaswamy, Associate Professor, Genetics and Computer Science
• Lu Lu, Assistant Professor, Department of Statistics and Data Science
• Rex Ying, Assistant professor, Department of Computer Science

Project: Deep neural networks are transforming many fields, yet their potential in scientific discovery remains largely untapped. Fields like astrophysics, biochemistry, genomics, and neuroscience generate vast, complex data requiring advanced AI systems. These systems must process irregular data, build dynamic representations, integrate domain knowledge, generate and test hypotheses, design experiments, learn continuously, update iteratively, and provide interpretable insights. Yale’s strengths across various departments and access to advanced facilities create an ideal setting to lead this frontier. This workshop will unite AI experts and scientists at Yale to discuss recent AI advances, foster collaborations, and explore transformative AI applications in science.

• William Oh, Professor Internal Medicine, Director Precision Medicine Yale Cancer Center  
• Sanjay Aneja, Assistant Professor, Radiology 
• Maryam Lustberg, Professor, Internal Medicine
• Meina Wang, Research Scientist, Medical Oncology and Hematology

Project: The one-day workshop “AI in Cancer: Advancing Precision Medicine through Interdisciplinary Innovation” will unite scientists, clinicians, and trainees from Yale Cancer Center, Smilow Cancer Hospital, and the broader Yale community. It aims to explore AI applications in cancer research and care. Featuring thought leaders, the workshop will foster collaboration, spark new research ideas, and equip participants to integrate AI into precision oncology. Sessions focus on diagnosis, genomics, decision support, patient monitoring, workflow optimization, and AI challenges. The event includes lectures, panel discussions, and presentations, aiming to build a collaborative network, identify future projects, and guide AI integration into cancer care at Yale.

• Suresh Mohan, Assistant Professor Otolaryngology, Surgery
• Edouard Aboian, Associate Professor Vascular Surgery, Surgery
• Xenophon Papademetris, Professor, Biomedical Informatics
• Kiran Turaga, Professor, Surgical Oncology
• Matt Harrington, Software Development Lead Healthcare AI, Epic
• Tom Yosick, Product Lead, Epic
• Ricarda Tomlin, Research Support Manager, Surgery
• Muhammad Munshi, Medical Student ‘27, Yale School of Medicine

Project: AI has the potential to revolutionize surgical care, but implementation barriers exist. Our half-day workshop, Implementing AI in Surgical Care Delivery: From No-Code Prototypes to Clinical Deployment, addresses these barriers. Yale surgeons, AI specialists, IT experts, and regulatory professionals will collaborate to develop actionable AI solutions. Three $5,000 AI credits will be awarded to facilitate immediate prototyping, with proposals judged post-workshop. This initiative aims to boost interdisciplinary collaboration, prototype development, and regulatory understanding, enhancing Yale’s leadership in AI-driven clinical innovation and patient care.

• Daphne Brooks, Professor, African American Studies
• Toni-Leslie James, Professor, Head of Design and Costume Design
• Eric Glover, Assistant Professor, English and African American Studies
• Jonathan Square, Assistant Professor, Visual Culture, Parsons School of Design 
• Ruha Benjamin, Professor, African American Studies, Princeton 
• Rikki Byrd, Assistant Professor, Visual Culture Studies, University of Texas at Austin
• Amanda Awanjo, Project Manager, The Black Bibliography Project 
• Brad Mindich, Founder and CEO, Inveniem
• Elizabeth Way, Associate Curator Costume, Museum at the Fashion Institute of Technology
• Rebecca Pietri, MFA ‘27, Costume Design

Project: “Memory Machines: Reimagining Black Performance and Costume Archives through Digital Humanities” is a one-day workshop exploring how AI can ethically preserve and reinterpret Black performance legacies, focusing on costumes. Bringing together scholars, artists, technologists, and archivists, this event addresses how traditional archives often misclassify or erase Black creative work. Grounded in Black feminist theory and performance studies, the workshop will develop ethical metadata design and AI tools to support legacy preservation. Key outputs include a white paper, visual toolkit, and research collective, positioning Yale as a leader in equitable digital infrastructure and archival innovation.

• Timos Antonopoulos, Research Scientist and Lecturer, Computer Science
• Julián Posada, Assistant Professor, American Studies
• Peter Crumlish, Dwight Hall at Yale, Executive Director
• Jennifer Imamura, Poorvu Center, Assistant Director, Teaching Development and Initiatives
• Johnny Scafidi, Dwight Hall at Yale, Director of Community Outreach and Engagement
• Topher Allen, Research Assistant, Yale College ‘27

Project:  We envision a unique symposium to tackle the exclusion of Majority World perspectives in AI, leading to models with reduced global relevance. By collaborating with New Haven’s diverse local communities, we aim to integrate frameworks from critical data studies into AI development, ensuring our models are representative and globally accurate. This initiative will lead to new standards for human-centered AI development, improving AI’s reliability worldwide and empowering all nations to shape technology aligned with their values.

• Robert Frank, Professor, Linguistics
• Dana Angluin, Professor, Computer Science 
• Ruzica Piskac, Professor, Computer Science
• Arman Cohan, Assistant Professor, Computer Science
• R. Thomas McCoy, Assistant Professor, Linguistics
• Timos Antonopoulos, Research Scientist and Lecturer, Computer Science
• Lena Strobl, Post-doctoral Fellow, Linguistics

Project: Recent AI advances are driven by Neural Sequence Models (NSMs), which play a key role in natural language processing, image generation, and multimodal interactions. However, the theoretical foundations of NSMs lag behind their practical successes. This interdisciplinary workshop aims to bridge this gap by developing a theoretical framework to understand NSMs’ expressivity, computational capabilities, and trainability. Experts from linguistics, computer science, logic, machine learning, and industry will collaborate to foster deep and innovative exploration, leveraging academic networks for interdisciplinary synergy.

• Ilker Yildirim, Assistant Professor, Psychology
• Robert Frank, Professor, Linguistics
• Julian Jara-Ettinger, Associate Professor, Psychology
• Tyler Brooke-Wilson, Assistant Professor, Philosophy and Cognitive Science
• Alex Lew, Assistant Professor, Computer Science
• Tom McCoy, Assistant Professor, Linguistics
• Andre Wibisono, Assistant Professor, Computer Science

Project:  We aim to address the “efficiency gap” between human intelligence and AI. By hosting a workshop with experts from Yale and beyond, we will explore ways to make AI more like human intelligence in learning, perception, planning, and reasoning. We’ll focus on novel approaches beyond traditional AI. This initiative will foster collaborations across various disciplines to shape the future of AI.

• Daniel C. Esty, Hillhouse Professor of Environmental Law and Policy, School of the Environment and Yale Law School
• Ruzica Piskac, Professor, Computer Science 
• John W. Emerson, Adjunct Professor and Director of Graduate Studies, Statistics and Data Science
• Zachary Wendling, Research Director, Yale Center for Environmental Law and Policy
• Timos Antonopoulos, Research Scientist and Lecturer, Computer Science
• Topher Allen, Research Assistant, Yale College ‘27

Project: We will host two workshops to explore how AI can improve the Environmental Performance Index (EPI) by Yale and Columbia, which tracks sustainability metrics. Current EPI data on waste and water management is often incomplete or outdated. By bringing together AI experts, environmental scientists, and policymakers, we’ll explore using AI tools to enhance and automate data collection. This will help create more accurate global sustainability indicators and impact effective environmental policymaking.