Department of Computer Science · Texas State University
AIM

Center for Artificial Intelligence and Machine Learning

Department of Computer Science · Texas State University
Research Center

Advancing AI research, education, and innovation at Texas State

AIM is the Department of Computer Science's hub for artificial intelligence and machine learning — uniting faculty across pillars of excellence in trustworthy autonomous systems, scientific machine learning, and human-centered AI to address national priorities in health equity, scientific discovery, safe deployment, and societal impact.

Explore the Pillars Meet the Faculty
3
Pillars of Excellence
20
Core CS Faculty
7
Collaboration Clusters
$10M+
Collaborative Grant Target
Mission & Vision

AI anchored in Computer Science, projected university-wide

A hub for discovery and impact

The Center for Artificial Intelligence and Machine Learning (AIM) unites faculty expertise across pillars of excellence — spanning trustworthy autonomous systems, scientific machine learning, human-centered AI, and high-performance computing — to drive discoveries that address national priorities.

By anchoring AI expertise within Computer Science and projecting it university-wide, AIM positions Texas State as a distinctive contributor to the national AI research ecosystem.

Texas State students examining a VR headset and eye-tracking hardware in an AIM Center lab
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Reach beyond the core

Beyond its core pillars, AIM extends its reach through seven External Collaboration Clusters that potentially cover the needs of hundreds of faculty across Texas State's colleges — fostering interdisciplinary research teams and elevating the university's competitive position for center-scale federal grants.

This structure creates a university-wide ecosystem for AI integration, targeting $10M+ in collaborative grants and addressing pressing societal challenges.

Part I

Pillars of Excellence

AIM's core research strengths within Computer Science. Each pillar is organized around a thematic area with demonstrated faculty depth, external funding, and national relevance. Faculty may contribute to more than one pillar where their expertise creates natural bridges.

Pillar 1

Trustworthy AI & Autonomous Systems

Software engineering for AI, verifiable deep learning, secure cyber-physical systems, cognitive AI safety, robustness in deployment, multi-agent and robotic systems, motion planning, deep reinforcement learning for drones and vehicles, swarm intelligence, and physical AI.

6Faculty · NSF CISE, DoD, NIH
Pillar 2

Scientific ML, AI & HPC for Science

Physics-informed ML, predictive, causal, and generative models for scientific discovery, surrogate modeling of multiphysics systems, AI–numerical methods fusion, representation learning, parallel and GPU optimizations, fault-tolerant large-scale ML training, and energy-efficient HPC.

7Faculty · DOE, NSF AI for Science
Pillar 3

Multimodal, Human-Centered & Behavioral AI

Natural language processing, affect and emotion recognition, computer vision for human behavior, eye-tracking and biometrics, smart health and pervasive computing, AR/VR human-state assessment, wearable AI, and immersive media processing.

7Faculty · NIH, NSF SCH, AHRQ
Cross-Cutting Advisory

Foundational AI Advisory & Heritage

Expert systems, fuzzy logic, applied AI across domains, and long-standing international conference leadership (the IEA/AIE series, founded by Texas State faculty). This cross-cutting role provides historical depth, mentorship, and global visibility to all pillars — anchored by Dr. Moonis Ali's five decades in applied AI research.

* Dr. Chul-Ho Lee contributes to both Pillar 1 and Pillar 2.

Part II

External Collaboration Clusters

Faculty outside Computer Science whose research can be enhanced through AI and machine learning. Organized thematically and aligned with AIM's pillars, these partnerships extend the center's impact university-wide — representative of over 1,000 faculty across seven colleges.

1

Scientific & Engineering Applications

Aligned with Pillar 2

Natural sciences, engineering, and quantitative fields involving complex data modeling and simulation — supported by physics-informed ML, generative models, and HPC optimization.

DOE · NSF
2

Health & Human Performance

Aligned with Pillar 3

Diagnostics, behavioral health, and physiological monitoring — enhanced by multimodal AI, wearable ML, and generative AI for data synthesis.

NIH · NSF SCH
3

Social & Behavioral Sciences

Aligned with Pillar 3

Human behavior, society, and policy — where AI aids data mining, sentiment analysis, network modeling, and ethical decision-making.

NSF SBE · NEH
4

Business, Economics & Organizational Studies

Aligned with Pillar 1

Analytics, management, and economics — benefiting from AI in decision-making, NLP for business intelligence, and trustworthy AI systems.

NSF CISE · Industry
5

Education & Leadership

Aligned with Pillar 3

Teaching, leadership, and adult education — where AI enhances educational technology, personalized learning, and assessment.

NSF STEM Ed · DoEd
6

Environmental & Agricultural Sciences

Aligned with Pillar 2

Sustainability, agriculture, and geography — benefiting from AI in remote sensing, climate modeling, and resource optimization.

USDA · EPA
7

Arts, Communication & Creative Industries

Aligned with Pillar 3

Creative fields where AI supports digital media, immersive technology, and content analysis.

NEA · Industry
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University-Wide Ecosystem

All Colleges

Together these clusters represent hundreds of non-CS faculty with synergistic potential — targeting $10M+ in collaborative grants and interdisciplinary teams addressing societal challenges.

Strategic Positioning

Critical mass, aligned with federal priorities

Texas State's AI portfolio — bolstered by the new MS in Artificial Intelligence, active NSF CAREER awards, NSF Smart and Connected Health grants, Department of Energy awards including the university's only DOE Early Career award, NAI recognitions, and HPC infrastructure — demonstrates critical mass across the center's pillars.

A distinctive, faculty-driven hub

Pillar 3 offers a highly cohesive, fundable cluster as the center's flagship. Pillar 2 aligns directly with the Department of Energy's Genesis Mission for AI, HPC, and quantum integration. Pillar 1 consolidates trustworthy AI with autonomous systems, covering the full lifecycle from verification through real-world physical deployment.

To achieve national prominence, two to three strategic hires are recommended in areas such as foundation models and large language models, distributed ML, or ethical XR — positioning AIM for center-scale proposals including NSF AI Institute planning grants and NIH U-grants.

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Recent Scholarship

Selected Publications, 2023–2026

A selection of recent work from AIM faculty across all three pillars, in reverse-chronological order. This is a representative sample compiled from public sources; for each researcher's complete and current record, follow the Google Scholar links on the faculty page.

  1. 2026 R. Suvvari, … & A. H. H. Ngu. "Dual-Stream Transformer with Kalman-Based Sensor Fusion for Wearable Fall Detection." Big Data and Cognitive Computing, 10(3), 90. Pillar 3
  2. 2025 A. Yasmin, T. Mahmud, S. T. Haque, S. Alamgeer & A. H. H. Ngu. "Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study." Sensors, 25(17), 5249. Pillar 3
  3. 2025 A. H. Ngu et al. "Enhancing Wearable Fall Detection System via Synthetic Data." Sensors, 25(15), 4639. Pillar 3
  4. 2025 A. Dey, N. Antony, … & T. Z. Islam. "ModelX: A Novel Transfer Learning Approach Across Heterogeneous Datasets." ACM Int'l Symposium on High-Performance Parallel and Distributed Computing (HPDC). Pillar 2
  5. 2025 E. Fefey & T. Z. Islam. "Optimizing Deep Learning Inference on Heterogeneous Edge Devices: An ILP-Based Rate-Monotonic Scheduling Approach." IEEE Cloud Summit — Best Paper Award. Pillar 2
  6. 2025 A. Fallin, N. Azami, S. Di, F. Cappello & M. Burtscher. "Fast and Effective Lossy Compression on GPUs and CPUs with Guaranteed Error Bounds." IEEE Int'l Parallel and Distributed Processing Symposium (IPDPS). Pillar 2
  7. 2025 W. A. Fallin, N. Azami & M. Burtscher. "Efficient Lossless Compression of Scientific Floating-Point Data on CPUs and GPUs." ACM Int'l Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS). Pillar 2
  8. 2025 B. De, K. Blekos, V. Pikoulis, D. Kosmopoulos & V. Metsis. "Geometric Knowledge Distillation via Procrustes Analysis for Efficient Motion Sequence Classification." IEEE Int'l Conf. on Digital Signal Processing (DSP). Pillar 3
  9. 2025 M. Shebaro, L. J. Rusnak, M. Burtscher & J. Tešić. "GraphC: Parameter-Free Hierarchical Clustering of Signed Graph Networks." Pillar 2 Pillar 3
  10. 2025 R. Podorozhny. "Curvature-Aware Optimization via Chebyshev Polynomials of the Second Kind for Deep Learning." SIAM Conference on Optimization, Edinburgh. Pillar 2
  11. 2024 S. T. Haque, M. Debnath, A. Yasmin, T. Mahmud & A. H. H. Ngu. "Experimental Study of LSTM and Transformer Models for Fall Detection on Smartwatches." Sensors, 24(19), 6235. Pillar 3
  12. 2024 X. Li, M. Sakevych, G. Atkinson & V. Metsis. "BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis." Bioengineering, 11(4), 299. Pillar 3
  13. 2024 H. Irani & V. Metsis. "Enhancing Time-Series Prediction with Temporal Context Modeling: A Bayesian and Deep Learning Synergy." FLAIRS Conference. Pillar 3
  14. 2024 B. A. Burtchell & M. Burtscher. "Using Machine Learning to Predict Effective Compression Algorithms for Heterogeneous Datasets." Data Compression Conference (DCC). Pillar 2
  15. 2024 N. Azami, R. Lawson & M. Burtscher. "LICO: An Effective, High-Speed, Lossless Compressor for Images." Data Compression Conference (DCC). Pillar 2
  16. 2024 Y. Liu, N. Azami, A. Vanausdal & M. Burtscher. "Indigo3: A Parallel Graph Analytics Benchmark Suite for Exploring Implementation Styles and Common Bugs." ACM Trans. on Parallel Computing, 11(3). Pillar 2
  17. 2024 J. Jacobson, M. Burtscher & G. Gopalakrishnan. "HiRace: Accurate and Fast Data Race Checking for GPU Programs." Int'l Conf. for High Performance Computing, Networking, Storage and Analysis (SC). Pillar 2
  18. 2024 B. K. De, M. Sakevych & V. Metsis. "The Impact of Data Augmentation on Time-Series Classification Models: An In-Depth Study with Biomedical Data." AI in Medicine (Springer). Pillar 3
  19. 2024 A. Gueroudji, C. Phelps, … & T. Z. Islam. "Performance Characterization and Provenance of Distributed Task-Based Workflows on HPC Platforms." IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS). Pillar 2
  20. 2023 T. Ramadan, A. Lahiry & T. Z. Islam. "Novel Representation Learning Technique Using Graphs for Performance Analytics." IEEE Int'l Conf. on Machine Learning and Applications (ICMLA). Pillar 2
  21. 2023 O. Komogortsev et al. Recent work on eye-movement biometrics, gaze prediction, and privacy-preserving gaze signals (IEEE VR / TIFS and related venues). Pillar 3

Compiled from publicly available sources (publisher pages, DBLP, and faculty websites). It is a representative selection rather than a complete bibliography, and some entries are abbreviated. Faculty not yet represented above — including Dr. Shibbir Ahmed, Dr. Tsz-Chiu Au, Dr. Aniruddha Bora, Dr. Mina Guirguis, Dr. Chul-Ho Lee, Dr. Veronica Perez-Rosas, Dr. Apan Qasem, Dr. Heena Rathore, Dr. Mylene Queiroz de Farias, Dr. Isayas Berhe Adhanom, Dr. Kecheng Yang, Dr. Ziliang Zong, and Dr. Moonis Ali — have active records reachable via their Google Scholar links on the faculty page.

Explore the people behind AIM

Meet the faculty driving research across all three pillars and the foundational advisory role — with links to their Texas State profiles and current work.

View Faculty Directory