Hariri Institute FY26 Junior Faculty Fellows and Graduate Student Fellows
Hariri Institute for Computing fellows are competitive awards granted to early-career faculty and exemplary doctoral students who are pursuing innovative computationally-driven research with the potential for high impact.
For 2026, the Institute has awarded five Junior Faculty Fellowships and five Graduate Student Fellowships to scholars across Boston University. The cohort spans research areas including bioinformatics, computer science, global health, electrical and computer engineering, linguistics, marketing, speech, language, and hearing sciences, and surgery.
The Institute supports our junior faculty and doctoral student fellows by contributing to their research developments and connecting them with one another to lead Institute-sponsored events. These fellowships are three-year appointments.
Learn more about our 2026 Junior Faculty Fellows and Graduate Student Fellows below.
2026 Junior Faculty Fellows
Our 2026 Junior Faculty Fellows are:
Meredith Brooks, PhD, Assistant Professor, Global Health, School of Public Health
Meredith Brooks’ research innovatively applies computational methods to address pressing global health challenges, particularly in tuberculosis (TB) epidemiology and implementation science. Brooks’s research integrates advanced computational techniques—such as multi-armed bandit (MAB) algorithms—with real-world public health data to optimize resource allocation in community-based TB screening programs
Wei-Lun Chao, PhD, Associate Professor, Electrical & Computer Engineering, College of Engineering
Wei-Lun Chao’s research advances computational computer vision, machine learning, and robust perception for autonomous driving and other data‐intensive applied domains. His program addresses foundational and practical challenges in learning with imperfect, limited, and distribution‐shifting data, federated and personalized learning, vision‐and‐language problems, and interpretable AI.
Boqing Gong, PhD, Assistant Professor, Computer Science, College of Arts & Sciences
Gong’s research focuses on generalization, efficiency, and the visual analytics of objects, scenes, human activities, and their relationships—with particular emphasis on developing AI models that can perform effectively across different domains and conditions. He has quickly established himself as a leading researcher in this field, making significant contributions to several key areas, including domain adaptation, video summarization, adversarial attack and defense, and multimodal video understanding.
Jacob Nudel, MD, Assistant Professor, Surgery, Chobanian & Avedisian School of Medicine
Jacob Nudel treats clinical decision-making as a computational challenge, using decision and cost-effectiveness analysis to model outcomes. His scholarship transforms traditional surgical intuition into a rigorous, data-driven framework. He is currently identifying opportunities for AI adoption to improve everything from revenue capture to patient safety at the bedside.
Paul Stillman, PhD, Assistant Professor, Marketing, Questrom School of Business
Paul Stillman’s research focuses on how individuals navigate conflicts among goals, values, and attention. A central contribution of his work is the development of computational models of dynamic psychological processes. Stillman models decision-making as an evolving system, using high-frequency behavioral data (such as mouse-tracking and temporal choice trajectories) to infer latent cognitive states.
2026 Graduate Student Fellows
Our 2026 Graduate Student Fellows are:
Tianle Chen, PhD Student, Computer Science, College of Arts & Sciences, (Advised by Professor Deepti Ghadiyaram).
Tianle Chen’s current research focuses on addressing the stability, interpretability, and reliability of AI. Chen takes advantage of data-driven methodologies in order to solve his research questions, and he leveraged these methodologies in order to research generative model reliability and interpretability in multimodal AI.
Rosalie Gendron, PhD Student, Speech, Language, and Hearing Sciences, Sargent College of Health and Rehabilitations Sciences, (Advised by Professor Frank Guenther).
Rosalie Gendron’s doctoral research centers on advancing and applying a neurocomputational framework of speech motor control to derive individualized, quantitative parameters from behavioral and neural data. Her work emphasizes the implementation and validation of a reduced form computational model, designed to capture core control mechanisms.
Ilker Isik, PhD Student, Electrical & Computer Engineering, College of Engineering, (Advised by Professor Wenchao Li).
Ilker Isik’s research is centered around neuro-symbolic AI, which combines data-driven learning with symbolic computation to build more robust, data-efficient, and interpretable AI systems. Isik’s work sits at the intersection of machine learning, formal methods, and domain-specific applications.
Emily Kim, PhD Student, Bioinformatics, Faculty of Computing & Data Sciences, (Advised by Professors Jennifer Bhatnagar and Daniel Segre).
Emily Kim’s research leverages computational modeling, multiomics data analysis, and synthetic ecology to address the disruption of urban tree microbiomes. Kim aims to develop synthetic microbial community (SynCom) treatments for newly planted urban trees by creating an AI-based computational pipeline to analyze biological data on urban and rural tree microbiomes.
Yulu Qin, PhD Student, Linguistics, Graduate School of Arts & Sciences, (Advised by Professor Najoung Kim).
Yulu Qin’s research is at the intersection of linguistics and artificial intelligence, especially focusing on what kind of linguistic knowledge and capabilities can be learned from experience alone versus what has to be provided innately, and what can be gained above and beyond what the models can learn from text alone.