Summary
Amitash Nanda is a Ph.D. candidate in Electrical and Computer Engineering at UC San Diego who combines a decade of engineering experience with deep research in distributed ML, DNN/LLM optimization, and AI systems. He has transitioned from industry R&D—building robotics testbeds and large-scale data pipelines at Accenture and Teradata—to high-performance scientific computing at Berkeley Lab and NERSC, where he accelerated protein-design workflows and developed hybrid load-balancing algorithms. Amitash designs quantization, pruning, and fine-tuning strategies for efficient vision and language models, contributes to MLCommons’ MLPerf Inference efforts, and explores Edge-AI and energy-efficient distributed training. He brings a strong systems perspective to bioinformatics and medical imaging projects in the Boolean Lab, including real-time device integration and lightweight CNN deployments on microcontrollers. Known for turning research prototypes into usable software (including a published PyPI package and HPC-AI speedups), he bridges algorithmic innovation with practical, deployable ML systems.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Technology - BTech, Instrumentation and Electronics Engineering, Bachelor of Technology - BTech, Instrumentation and Electronics Engineering at Odisha University of Technology and Research
Doctor of Philosophy - PhD, Machine Learning and Data Science, Doctor of Philosophy - PhD, Machine Learning and Data Science at UC San Diego Jacobs School of Engineering
Matriculation and Intermediate, Science, Matriculation and Intermediate, Science at DEMS, Rourkela
Nanodegree, AI Programming with Python, Nanodegree, AI Programming with Python at Udacity
English, Hindi, Odia