I am a U.S. Citizen with a Ph.D. in Computer Engineering from UC Santa Barbara, passionate about building the next generation of AI computing systems.
My work sits at the intersection of machine learning, computer architecture, and systems engineering. I am interested in AI hardware-software co-design, efficient inference, accelerator architectures, CUDA optimization, and scalable infrastructure for large-scale AI.
My doctoral research spans diffusion models, probabilistic machine learning, and energy-efficient accelerator design, with a focus on translating machine learning algorithms into practical, high-performance computing systems.
I am excited by opportunities to help shape the future of AI infrastructure through close integration of algorithms, software, and hardware.
Ph.D., Electrical and Computer Engineering, UC Santa Barbara (UCSB)
M.S., Electrical and Computer Engineering, UC Santa Barbara (UCSB)
B.E. (Hons.), Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, India
Efficient LLM inference and diffusion models
Probabilistic, physics-inspired machine learning
Hardware-aware Monte Carlo sampling and optimization solvers
Energy-based and physics-informed models
LLM and diffusion model hardware-software co-design
Heterogeneous accelerators (GPU/FPGA/sMTJ/CMOS/CPU)
Designing and prototyping probabilistic circuits
Analog/RF and digital ASIC design (Cadence, Spice, Verilog)
Device-circuit co-design with sMTJs and CMOS
