Amitash Nanda

Amitash Nanda

Ph.D. Student

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About Me

I am a second-year Ph.D. Student in the Department of Electrical and Computer Engineering specializing in Machine Learning and Data Science at the University of California San Diego. I graduated from UCSD with a Master’s in Electrical and Computer Engineering, specializing in Intelligent Systems, Robotics, and Control, in Spring 2023. I am a part of the Boolean Lab, and my advisors are Dr. Debashis Sahoo (advisor) and Dr. Bill Lin (co-advisor). I am also a Graduate Student Researcher at Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center, working under Kevin Gott and Hannah Ross.

I have over two years of professional experience as a Software Engineer specializing in research and development. During my time at companies like Accenture Research and Teradata US (Technology Innovation Office and Optimizer Development Team), I worked on proof-of-concepts and contributed to product development. I designed, built, and configured software applications to align with business processes. My responsibilities included code development and creating software plugins and features. Additionally, I analyzed millions of data points to build machine learning models, pipelines, and operations.

Research Interests: Distributed Machine Learning, Generative-AI, Vision-AI, DNN/LLM Optimization, Software & Systems, AI/ML Systems, Bioinformatics

I develop novel optimization techniques for deep neural networks (DNNs) and transformer-based models, focusing on efficient model architectures and scalable learning frameworks. My work involves designing quantization, pruning, and fine-tuning strategies to enhance the efficiency and performance of vision and language models. Additionally, I explore distributed learning frameworks to address challenges in decentralized and dynamic data environments. My research integrates software and systems design and leverages data science methodologies to build scalable AI solutions. Additionally, it facilitates efficient training, fine-tuning, and deployment of models in real-world applications spanning bioinformatics, computer vision, and natural language processing.

I’m a member of the Boolean Lab and a student researcher at the National Energy Research Scientific Computing Center Research.

Boolean Lab NERSC

Personal Life:

🏠 Hometown: Rourkela, Odisha, India

🙋 Activities: San Diego Cricket League Player, Acting, Gaming, Youtube/Twitch Streaming

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portfolio

publications

2018

  1. Raghavendra Vedula, Amitash Nanda, Sai Sankar Gochhayat, Asutosh Hota, Rishav Agarwal, Sanjay K Reddy, Sandeep Mahapatra, Keshab Kishor Swain, Siddharth Das, “Computer Vision Assisted Autonomous Intra-Row Weeder,” In 2018 International Conference on Information Technology (ICIT), IEEE, December 2018, pp. 79-84.  

2019

  1. Amitash Nanda, Keshab Kishor Swain, K Sanjay Reddy, “Real-Time Internal Inspection of Pontoons of Floating Roof Tank using a Mobile Robot.,” In International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),vol. 8, no. 5, May 2019.  

2020

  1. Amitash Nanda, Keshab Kishor Swain, K Sanjay Reddy, Rishav Agarwal, “sTransporter: An Autonomous Robotics System for Collecting Fresh Fruit Crates for the betterment of the Post Harvest Handling Process,” In Proceedings of the 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, March 2020, pp. 577-582.  
  2. Raghavendra Vedula, Amitash Nanda, Keshab Kishor Swain, Siddharth Das, Mihir Narayan Mohanty, “Plant Sustainability Monitoring Using Unmanned Aerial Vehicle,” In Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (ICDSMLA), Springer Singapore, May 2020, pp 1175–1183.  

2022

  1. Dharanidhar Dang, Amitash Nanda, Bill Lin, Debashis Sahoo, “NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines,” Preprint arXiv:2208.03500, arXiv, August 2022.    
  2. Amitash Nanda, and Deepak Ahire, “An Autonomous Intelligent System to Leverage the Post-harvest Agricultural Process Using Localization and Mapping,” In Proceedings of the Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, Springer, Singapore, May 2022.  

2023

  1. Amitash Nanda, “Novel Vision-AI Techniques for Morphological Discovery in System Biology,” In eScholarship Open Access, University of California and ProQuest Dissertations & Theses Global, May 2023.  

2024

  1. Amitash Nanda, Sree Bhargavi Balija, and Debashis Sahoo, “CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models,” Preprint arXiv:2412.03599, arXiv, December 2024.    
  2. Sree Bhargavi Balija, Amitash Nanda, and Debashis Sahoo, “Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain,” In Proceedings of the AAAI Symposium Series (AAAI), May 2024.  

2025

  1. Amitash Nanda, Md Kamal Hossain Chowdhury, Hannah Ross, and Kevin Gott, “Exploring Dynamic Load Balancing Algorithms for Block-Structured Mesh-and-Particle Simulations in AMReX,” In Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration (PEARC ‘25), ACM, New York, NY, USA, Article 5, 9 pages, July 2025.
     
  2. Amitash Nanda, Sree Bhargavi Balija, and Debashis Sahoo, “FedNAMs: Performing Interpretability Analysis in Federated Learning Context,” Preprint arXiv:2506.17466, arXiv, June 2025.    
  3. Sree Bhargavi Balija, Amitash Nanda, and Debashis Sahoo, “Decoding Federated Learning: The FedNAM+ Conformal Revolution,” Preprint arXiv:2506.17872, arXiv, June 2025.    
  4. Mahdi Behroozikhah, Soni Khandelwal, Amitash Nanda, Atishna Samantaray, Arya Prabhudesai, Dharanidhar Dang, Debashis Sahoo, “Expression gradient of cancer suppressor gene using Vision-AI,” Manuscript in Preparation.
  5. Amitash Nanda, Dharanidhar Dang, Sophia Carpinelli, Courtney Tindle, Saptarshi Sinha, Pradipta Ghosh, Debashis Sahoo, “OrgaTuring: Accelerating Organoid Discovery with Vision-AI,” Manuscript in Preparation.
  6. Amitash Nanda, Sree Bhargavi Balija, Debashis Sahoo, “CHAI-KTQ: CHAI-KTQ: A Novel Framework for Scalable LLMs and Efficient Inference,” Manuscript in Preparation.
  7. Amitash Nanda, H M Zabir Haque, Debashis Sahoo, “Leveraging High-Performance Computing for Spatial Transcriptomic Identification of CDX2 Genes in Intestinal Crypts Using Deep Neural Network,” Manuscript in Preparation.

talks

teaching

CS 432/532 - Web Science

  Catalog Description: Provides an overview of the World Wide Web and associated decentralized information structures, focusing mainly on the computing aspects of the Web: how it works, how it is used, and how it can be analyzed. Students will examine a number of topics including: web architecture, web characterization and analysis, web archiving, Web 2.0, social networks, collective intelligence, search engines, web mining, information diffusion on the web, and the Semantic Web. Prerequisites: A grade of C or better in CS 361 and CS 330. Read more

CS 625 - Data Visualization

  Catalog Description: This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course. Read more

CS 725/825 - Information Visualization & Data Analytics

  Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more

CS 800 - Research Methods

  Catalog Description: Introduction to research methods in computer science. Topics include academic publishing, academic writing, literature reviews, responsible conduct of research, and presenting research results. Research faculty will present overviews of their research and how research is conducted in their labs. Read more

CS 725/825 - Information Visualization & Data Analytics

  Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more

CS 625 - Data Visualization

  Catalog Description: This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course. Read more

CS 725/825 - Information Visualization & Data Analytics

Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more

CS 625 - Data Visualization

  Catalog Description: This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course. Read more

CS 725/825 - Information Visualization & Data Analytics

Catalog Description: This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets. Prerequisite: CS 625 (Data Visualization) Read more