Teaching & Mentoring
Courses, interdisciplinary perspectives, and research-led learning.
CS 691 — Special Topics: Neurosymbolic Artificial Intelligence
Spring 2026 · Department of Computer Science · The University of Alabama
This graduate special-topics course explored the integration of symbolic reasoning and contemporary machine learning, with particular emphasis on large language models and knowledge-grounded AI systems.
Topics
01 · FOUNDATIONS
Symbolic Knowledge Representation
First-order logic (FOL), predicates, quantifiers, inference, ontologies, knowledge graphs, and formal representation of domain knowledge.
02 · INTEGRATION
Neural–Symbolic Architectures
Hybrid neural and symbolic approaches, knowledge-infused learning, constraint-guided reasoning, and integration of structured knowledge with learned representations.
03 · LANGUAGE MODELS
LLMs and Knowledge-Grounded Reasoning
Combining large language models with knowledge graphs, logical constraints, retrieval, symbolic planning, and evidence-based reasoning.
04 · APPLICATIONS
Trustworthy and Applied Neurosymbolic AI
Explainability, causal and process-aware reasoning, evaluation of hybrid systems, and applications in healthcare and decision support.
Guest Lectures
Nigel Glenday
Guest Lecturer · Spring 2026
Raj Krishnan
Guest Lecturer · Spring 2026
Research and Publication Outcomes
Research developed in connection with the course’s themes includes the following coauthored work with Himanshu Tripathi.
How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
NeSy 2026 · Accepted conference paper
From Protocol to Practice: Graded Sepsis Bundle Compliance and Actionable Insights from Real-World ICU Data
medRxiv · 2026 · Research preprint
Fall 2026
CS 696 — Graduate Research Mentoring
3 credit hours · The University of Alabama
Spring 2027
CS 483 / CS 583 — Computational Foundations of Machine Learning
The University of Alabama · Scheduled for Spring 2027