Overview
A key goal of CSoI is to support increased diversity within the disciplines of the Science of Information by providing a broad range of opportunities to students, post-docs, and faculty who are members of underrepresented groups. We aim to design supportive pathways that will broaden participation of individuals in research and education initiatives within CSoI.
Through our Channels Program we provide multiple educational, research and mentoring opportunities for K-12, undergraduates, graduate students, post-docs, and faculty to enable them to succeed and flourish in this dynamic area of scientific endeavor. We also strive to build successful partnerships with other programs who promote the recruitment and retention of underrepresented groups to STEM disciplines.
Meet Our Team
Deepak KumarAssociate Director
Professor of Computer Science, Adjunct Faculty
Philosophy, Nueral and Behavioral Sciences
Bryn Mawr College peopleWeb site |
Research
- Artificial Intelligence
- BDI Architectures
- Robotics
- Cognitive Science
- Computational Linguistics
- AI Education
- Evolutionary Computation
- Programming Paradigms
- Computer uses in Education
Todd ColemanAssistant Director for Diversity
Associate Professor
Electrical and Computer Engineering and Neuroscience
University of California, San Diego peopleWeb site |
Research
Dr. Coleman's research interests include information theory, operations research, and computational neuroscience. The main thrust of his research is centered around understanding the interplay between systems engineering principles (information theory and control theory) and neuroscience. Multi-disciplinary Application of Feedback Information-theoretic Principles
- Information theory of timing channels
- Use of directed information as a statistical measure of causality
- Interpretation of optimal feedback communication schemes from stochastic control and dynamical systems viewpoints
- Examining the role of reversible Markov chains in maximizing directed information with implications in feedback communications, decentralized control, and thermodynamics
- Unification of Systems Engineering Principles with Neuroscience
- Use of mathematical and applied probability models to understand how sensory processing, perception and learning, and decision-making are performed with a common neural substrate in simple organisms
- Use of directed information as a statistical measure of causality in ensemble neural recordings
- Statistical inference in point process models of how information is encoded in neural spike trains
- Statistical signal processing to estimate intent in brain machine interfaces
- Application of feedback information theory and decentralized control to the design of high-performance brain-machine interfaces.
