Welcome

I am an Assistant Professor of Information Technology at Lawrence Technological University. My teaching and research focus on artificial intelligence, machine learning, reinforcement learning, and data-driven methods, with applications in intelligent transportation, unmanned aerial vehicles (UAVs), smart communities, and business analytics.

My research combines machine learning algorithms, graph neural networks, multi-agent reinforcement learning, and spatiotemporal data analytics to address complex real-world challenges. Current projects include traffic flow analysis and congestion detection via GPS trajectory mining, reinforcement learning for adaptive traffic signal control, graph neural networks for transportation analytics, and UAV technologies for mission planning and aerial monitoring.

I lead the MIND Lab (Machine Intelligence and Data Laboratory), where graduate and undergraduate students conduct research in intelligent transportation systems, UAVs, mobility analytics, and AI-driven decision-making. Students are involved throughout the research process—from formulating research questions and developing algorithms to publishing their work and presenting at conferences.

As an educator, I believe the best learning happens when theory meets practice. I aim to create engaging course experiences that prepare students to think critically and tackle real problems. Through mentoring undergraduate and graduate students in the lab and in the classroom, I enjoy building curiosity and a genuine interest in research.

If you are a prospective student interested in joining the MIND Lab, or a researcher or industry partner interested in collaboration, I would be happy to hear from you.

Latest News

  • May 2026 — Congratulations to Chaitanya Chodi from the MIND Lab on graduating from LTU!
  • April 2026 — Our paper, GPS Trajectory-Based Traffic Congestion Detection and Classification Using Delaunay Triangulation and Graph Neural Networks, was published in IEEE Access. [Link]
  • April 2026 — Received the Best Presentation Award at the IEEE Southeast Conference AI Summit for our poster, Automated Congestion Detection and Cause Classification via Delaunay Triangulation and Graph Neural Networks on GPS Trajectories.
  • April 2026 — Presented the poster HAGTNet: Hierarchical Attention-based Graph Transformer Network for GPS Trajectory-Based Traffic Flow States Detection at the IEEE Southeast Conference AI Summit.
  • March 2026 — Became a member of the AI + Emerging Technologies Advisory Board.
  • December 2025 — Congratulations to Tej Acharya from the MIND Lab on graduating from LTU!
  • December 2025 — Congratulations to Rinkuben Patel from the MIND Lab on graduating from LTU!
  • May 2025 — Delivered an invited presentation at the Building Michigan Communities Conference (Lansing, MI): AI for Everyone: Building Smart, Sustainable, and Connected Communities with Machine Learning.
  • April 2025 — Received the CoBIT Researcher Award at LTU.
  • April 2025 — Organized and presented the Data Strategy Workshop for industry leaders at LTU: Building a Data Strategy: Unlocking the Value of Data for Business Growth.
  • April 2025 — Our paper, The Next Frontier: Using Emerging Technology to Overcome the Challenges of the U.S. Housing Industry, was accepted for publication in the Proceedings of the ARCC 2025 International Conference.
  • April 2025 — Presented Integrating Graph Neural Networks and Delaunay Triangulation for Traffic Congestion Prediction at LTU Research Day.
  • April 2024 — Student poster Unveiling Housing Insights: Exploring Community Feedback Using the Apriori Algorithm presented at LTU Research Day.
  • April 2024 — Student poster Adaptive Traffic Control: SARSA Reinforcement Learning for Dynamic Urban Mobility presented at LTU Research Day.
  • April 2024 — Student poster Insights into Road Safety: A Study of Holland City’s Crash Trends presented at LTU Research Day.