Events
Qualifying ExamFrom Biological to Artificial Learning: Computational Frameworks for Interpreting Representational Change |
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Tuesday, March 03, 2026, 02:00pm |
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Speaker: Jack Klawitter
Bio
Location : CoRE 301
Committee:
Professor Konstantinos Michmizos
Professor Dimitris Metaxas
Professor Casimir Kulikowski
Professor James Abello
Event Type: Qualifying Exam
Abstract: Learning is a defining characteristic of intelligent systems, yet our understanding of how it unfolds differs across biological and artificial domains. Neuroscience has revealed that learning is accompanied by continual reorganization of neural representations. Conversely, machine learning has largely focused on optimizing performance under static assumptions. This disconnect limits our ability to interpret learning as a dynamic process that links internal representations to behavior. My research seeks to address this by developing computational frameworks that treat representational change as a central object of study. This work emphasizes the interpretability of learning dynamics, alignment with biological principles, and the development of methods that reveal how representations reorganize over time. By examining learning in both neural and artificial systems, I identify shared structural signatures and demonstrate how computational models can serve as probes of plasticity. This perspective positions learning as a process that can be interpreted, compared, and tracked across systems, tasks, and timescales, contributing to an understanding of how stable behavior emerges from dynamic representations, with implications for machine learning, neuroscience, and the design of adaptive intelligent systems.
Organization:
Contact Professor Konstantinos Michmizos
Zoom Link: https://rutgers.zoom.us/my/jck241?pwd=bGtzMlI5aHpJN3BNRDViamNvYld0UT09