The researchers driving AIM's work, organized by pillar. Research summaries are drawn from public Texas State faculty profiles and lab pages. Select any avatar to open that faculty member's Texas State profile in a new tab.
OK
University Distinguished Professor of Computer Science. Research in behavioral AI via eye tracking, biometrics, and XR security and health assessment. Contributes to Pillar 3 (Multimodal, Human-Centered & Behavioral AI).
Safe deployment, edge AI, secure cyber-physical systems, robotics, autonomous driving, and physical AI — from verification through real-world deployment.
Secure and cognitive AI for cyber-physical systems and smart health; trustworthiness via human–AI synergy.
Software engineering for trustworthy AI; responsible AI engineering focused on design, verification, and evaluation of safety and trust in AI systems.
Hardware-aware lightweighting and adaptive learning for on-device AI.
Also Pillar 2Network security, adversarial machine learning, and resilient distributed systems.
Autonomous driving, drone swarms, goal recognition, AI planning, and game theory applied to real-world robotics.
Physics-informed ML, generative models for discovery, GPU optimization, green AI, fault-tolerant training, and AI–numerical methods fusion — pushing the boundaries of scientific discovery.
Scientific ML, generative AI for metamaterials and inverse design, symbolic regression, physics-informed machine learning, and reinforcement learning.
GPU/multicore acceleration of irregular algorithms including graph ML, high-speed data compression, and HPC performance optimization.
Compiler optimizations and auto-tuning for HPC; ML-based code improvements for energy-efficient AI training.
TI
Representation learning, test-time adaptation, time- and data-efficient generative modeling, and ML-based modeling of large-scale heterogeneous computing systems.
Sustainable HPC, green AI, and energy-aware optimizations for large-scale AI model training and inference.
Physics-informed neural network surrogates for oxide memristor design; curvature-aware spectral optimization for large-scale PINNs, implicit neural representations, and transformers; automated synthesis and verification of mission-critical software and cyber-physical multi-agent systems.
Distributed system and algorithm optimizations for scalable AI.
Also Pillar 1NLP, XR, biometrics, smart health, wearables, immersive media, and affective computing — the department's flagship pillar with the deepest critical mass.
OK
Behavioral AI via eye tracking, biometrics, XR security, and health assessment.
Multimodal NLP, emotion and behavior prediction, and mental health applications.
VM
ML and computer vision for smart health, affective computing, physiological time-series, and AR/VR interaction.
JT
Scalable multimodal and multisource data analysis and multimedia vision; NAI Senior Member.
AN
AI/ML on wearables for smart health, smartwatch-based fall detection and risk analysis, synthetic cross-modal data generation, and IoT middleware for edge ML.
ML-driven video quality metrics, QoE in immersive media, visual attention models, and AR/VR/360° video processing with no-reference quality assessment.
AI-driven adaptive XR systems, human–computer interaction, cognitive modeling for immersive environments, and interactive visualization.
Historical depth, mentorship, and global visibility spanning all pillars.
Over 50 years in AI research including expert systems, fault diagnosis, and speech recognition; founder and leader of the IEA/AIE international conference series.
Research summaries are drawn from public Texas State faculty profiles, lab pages, and department news as of June 2026. Avatars show each faculty member's initials in their pillar color; official headshots can be dropped in to replace them. Faculty without a confirmed individual profile URL currently link to the CS faculty directory — exact profile links can be supplied to complete them.