My research aims to make fundamental algorithmic contributions to human-centered machine intelligence (ML) and to promote scientific advancement in trustworthy artificial intelligence (AI) and address pressing challenges related to trustworthy and responsible AI, including privacy-preservation, explainability, and fairness. To ensure sustainable human-AI teaming, I further investigate fundamental challenges in AI-based decision-making and examine factors of trust that support effective decision-making between humans and AI systems. This endeavor highly draws from interdisciplinary collaborations in health sciences, social sciences, and learning sciences, and leads to interdisciplinary scientific contributions.
keywords
Affective computing, health analytics, human-computer interaction, trustworthy AI
CSCI 5622 - Machine Learning
Primary Instructor
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Spring 2024
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
CSCI 6402 - Issues and Methods in Cognitive Science
Primary Instructor
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Fall 2023
Interdisciplinary introduction to cognitive science, examining ideas from cognitive psychology, philosophy, education, and linguistics via computational modeling and psychological experimentation. Includes philosophy of mind; learning; categorization; vision and mental imagery; consciousness; problem solving; decision making, and game-theory; language processing; connectionism. No background in Computer Science will be presumed. Same as EDUC 6504 and LING 6200 and PHIL 6310 and PSYC 6200 and SLHS 6402.
LING 6200 - Issues and Methods in Cognitive Science
Primary Instructor
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Fall 2023
Interdisciplinary introduction to cognitive science, examining ideas from cognitive psychology, philosophy, education, and linguistics via computational modeling and psychological experimentation. Includes philosophy of mind; learning; categorization; vision and mental imagery; consciousness; problem solving; decision making, and game-theory; language processing; connectionism. No background in computer science will be presumed. Recommended prerequisite: at least one course at the 3000-level or higher in CSCI, LING, PHIL, or PSYC. Same as CSCI 6402 and EDUC 6504 and PHIL 6310 and PSYC 6200 and SLHS 6402.
PHIL 6310 - Issues and Methods in Cognitive Science
Primary Instructor
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Fall 2023
Interdisciplinary introduction to cognitive science, examining ideas from cognitive psychology, philosophy, education, and linguistics via computational modeling and psychological experimentation. Includes philosophy of mind; learning; categorization; vision and mental imagery; consciousness; problem solving; decision making, and game-theory; language processing; connectionism. No background in computer science will be presumed. Recommended prerequisite: at least one course at the 3000-level or higher in CSCI, LING, PHIL, or PSYC. Same as CSCI 6402 and EDUC 6504 and LING 6200 and PSYC 6200 and SLHS 6402.
PSYC 6200 - Issues and Methods in Cognitive Science
Primary Instructor
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Fall 2023
Interdisciplinary introduction to cognitive science, examining ideas from cognitive psychology, philosophy, education, and linguistics via computational modeling and psychological experimentation. Includes philosophy of mind; learning; categorization; vision and mental imagery; consciousness; problem solving; decision making, and game-theory; language processing; connectionism. No background in computer science will be presumed. Same as CSCI 6402 and EDUC 6504 and LING 6200 and PHIL 6310 and SLHS 6402.
