Neuromorphic Computing and Hardware-Aware Machine Learning Systems

• Efficient LLM inference — sparsity-aware algorithms and hardware acceleration (Processing-in-Memory / racetrack memory)
• Biologically-inspired sequence and time-series modeling for anomaly detection and prediction
• Processing-in-Memory (PIM) architectures and hardware-software co-design
• Sparse and efficient deep neural network design
• Neuromorphic computing and spintronic memory systems

Ph.D., Electrical Engineering, University of South Florida, 2026