Embedded Machine Learning Engineer
Computer-vision detection and tracking for UAV autonomy. Working as a Kumrah engineer embedded with the Drone Leaf team, applying quantization and hardware-aware optimization so perception runs onboard within the airframe's latency, memory and power budget.
Advanced Research and Innovation Center (ARIC)
Visiting Researcher
Quantization-centric ANN-to-SNN conversion — studying distribution-aware and hardware-aware optimization to enable efficient, low-latency inference on neuromorphic hardware. This is the basis of the MSc thesis.
Department of Computer Science & Engineering
Graduate Research Assistant
Research on distribution-aware and hardware-aware neural network quantization for low-latency, energy-efficient inference on microcontroller-class devices.
Department of Computer Science & Engineering
Graduate Teaching Assistant
Supported eight undergraduate CSE courses across software, hardware and networks — leading lab sessions, guiding students through programming and debugging, grading with detailed feedback, and proctoring exams.
Department of Computer Science & Engineering
Undergraduate Research Assistant
Developed and deployed TinyML models on microcontroller platforms including the Sony Spresense for low-latency, resource-efficient edge inference, contributing to published machine learning research.
eBrain Lab
Visiting Researcher
Developed and validated optimizations for the TinyCL continual-learning hardware accelerator, including approximate multipliers and performance benchmarking, focused on embedded vision for autonomous mobile robots.
Automation and Industrial IT Intern
SCADA and industrial control systems across upstream and downstream operations. Preventive maintenance on SCADA cabinets, remote Cisco switches and ICS laptops, supporting network device integration, reliability and OT security.
Participant — Nuclear Innovation & Technology Summer School