Current Students:

• Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds
  MH Uddin, S Baidya
  arXiv preprint arXiv:2505.22779, 2025
  [PDF] | [View on GitHub]


• Large Language Models in the IoT Ecosystem--A Survey on Security Challenges and Applications
  K Khatiwada, J Hopper, J Cheatham, A Joshi, S Baidya
  arXiv preprint arXiv:2505.17586, 2025
  [PDF] | [View on GitHub]


• A Hierarchical Optimization Framework Using Deep Reinforcement Learning for Task-Driven Bandwidth Allocation in 5G Teleoperation
  N Golmohammadi, MM Rayguru, S Baidya
  arXiv preprint arXiv:2505.15977, 2025
  [PDF] | [View on GitHub]

Current External Student Collaborators:

• Yu-Jen Ku (PhD, ECE, UCSD)
  


• Basar Kutukcu (PhD, ECE, UCSD)
  


• Bryse Flowers (PhD, ECE, UCSD)
  

• System Optimizations for Edge/Cloud Computing
  


• Building Simulators for Autonomous Systems with Digital Twin
  


• Multi-modal Sensor Fusion for Connected and Autonomous Vehicles


• Adaptive Robust and Efficient Computing and Communications for UAVs


• Design Space Exploration for AI Applications on Embedded Systems


• Adaptive 360 degree Video Streaming and AR/VR


• Smart and Connected Healthcare IoT

• Collaborative Perception over Vehicular Edge Computing
  Developing efficient task partitioning algorithms and real-time multi-sensor fusion for improved collaborative vision in smart transportation systems. Features a prototype testbed with smart vehicles and edge computing nodes.
  [View Project Details]


• Design Space Exploration for ML Applications
  Conducting design-space exploration for software-hardware co-design of application-driven adaptive computing systems, focusing on optimizing power, area, and speed while maintaining high accuracy and low latency.
  [View Project Details]


• Resilient Computation for UAV Systems
  Developing "Hydra", an architecture for flexible sensing-analysis-control pipelines over autonomous airborne systems, introducing the concept of information autonomy for edge-assisted UAV applications.
  [View Project Details]


• Renewable Energy-driven Vehicular Edge Computing
  Creating machine learning models to predict small cell operation time based on communication traffic, computing demand, and environmental factors, maximizing renewable energy utilization.
  [View Project Details]


• Robust UAV Communications
  Designing a robust multi-path communication framework for UAV systems, featuring a fully open-sourced integrated UAV-network simulator called "FlyNestSim".
  [View Project Details]


• Software-Defined Edge Computing
  Developing a content and computation-aware communication control framework based on SDN paradigm, using extended Berkeley Packet Filter (eBPF) for efficient multi-streaming.
  [View Project Details]


• Cognitive Interference Control
  Creating a cognitive interference control framework for heterogeneous local access networks in urban IoT systems, optimizing transmission patterns for improved throughput and accuracy.
  [View Project Details]

1. Sujit Dey, Professor, University of California San Diego


2. Anand Raghunathan, Professor, Purdue University


3. Marco Levorato, Associate Professor, University of California, Irvine


4. Truong Nguyen, Professor, University of California San Diego


5. Dinesh Bharadia, Assistant Professor, University of California San Diego


6. Xinyu Zhang, Associate Professor, University of California San Diego


7. Sameer Singh, Assistant Professor, University of California, Irvine


8. Bhaskar Krishnamachari, Professor, University of Southern California


9. Ravi Prakash, Professor, University of Texas at Dallas


10. Aakanksha Chowdhery, Machine Learning Engineer, Google Brain

• Adaptive Computation Partitioning in Edge Computing Systems
  M. H. Uddin, S. Dey, and S. Sabur, "IEEE Conference on Edge Computing", 2024.
  [PDF] | [View on GitHub]


• Digital Twin for Autonomous Vehicle Testing
  S. Sabur, A. Chowdhery, and B. Krishnamachari, "IEEE Transactions on Intelligent Transportation Systems", 2023.
  [PDF] | [View on GitHub]


• Multi-modal Sensor Fusion for Connected Autonomous Vehicles
  Y. Ku, S. Sabur, and T. Nguyen, "IEEE Internet of Things Journal", 2023.
  [PDF] | [View on GitHub]



For a complete list of publications, please visit our publications page.