Amogh Patankar



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Hi, I'm Amogh! I am a 2nd year M.S. student studying Computer Science, with an AI/ML specialization at the University of California, San Diego. I'm currently working as a AI Applications Development intern at AMD, on the RyzenAI team working on their Neural Processing Unit (NPU)

I've previously worked as a generative AI intern at Marvell, and as a data science researcher at the Stanford University School of Medicine, where I worked under Dr. Eric Gross, Dr. Latha Palaniappan, and Jin Long. I've also worked at Amazon Web Services as a software development intern on their AI-powered automatic speech recognition team, Lex ASR.

I graduated with a B.S. in Data Science from the University of California, San Diego in 2023. For more details, check my CV or shoot me an email!

I enjoy playing and watching football, basketball and cricket, as well as hiking, and (very competitively) playing board games whenever I can!

Experiences

AI Applications Development Intern
September 2024 - December 2024

  • Developing software for AMD RyzenAI Neural Processing Unit (NPU); specifically, performance optimization engineering and competitive benchmarking with Apple, Nvidia, and Qualcomm products.
  • Optimizing AMD hardware to efficiently execute generative AI workloads, and enabling hardware to achieve optimal performance and power efficiency. Workloads include transformers, large language models, convolutional neural networks (CNN), and diffusion models.
  • Optimization and benchmarking techniques could be applied to other CPU and GPU products (AMD Radeon, Instinct, EPYC).

Data Scientist Intern, Marvell Inc.
Jun 2024 - September 2024

  • Used generative pretrained transformer (GPT) models to generate synthetic data, leveraging parameter efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA).
  • Implemented reinforcement learning (RL) algorithms like q-learning, and deep q-networks (DQN), as well as deep learning methods for DSP parameter optimization.
  • Integrated large language models (LLM) and retrieval augmented generation (RAG) to automate hardware modeling process.
  • Developer tools include AWS S3, EC2, and Sagemaker, as well as Tableau, Streamlit and SnowflakeDB.

Researcher, Stanford University School of Medicine
June 2023 - June 2024

  • Developed statistical packages for multiple biomedical research teams in Python and R for data analysis. Packages composed of chi-squared and Fisher tests amongst other statistical utilities.
  • Led research teams mentored by Dr. Gross and Dr. Palaniappan for analyzing opioid mortality and glycemic control in Type-2 diabetics.
  • Opioid research is published in the British Journal of Anaesthesia and diabetes research is in preprint at Journal of Asian Health.

Software Development Engineering Intern, Amazon Web Services
June 2022 - Sept 2022

  • Improved latency of AWS Lex ASR (Automatic Speech Recognition) Services and AWS DataHub when used by conversational AI models by recommending and implementing architectural changes.
  • Enabled compliant storage of critical and non-critical customer data in DataHub by designing and enhancing Lex ASR and AWS DataHub schemas using AWS S3, Kinesis, and Lambda.
  • Optimized ASR Service to allow faster resolution of customer requests, by up to ∼75%, using AWS CloudWatch to track and analyze metrics.

Research Intern, Scripps Research Translational Institute
Jun 2021 - Aug 2021

  • Developed an R library to estimate genetic regulatory variation using a confidence interval estimation method.
  • Implemented various statistical concepts like binomial distributions and parametric bootstrapping, and applied them to data from the Genotype Tissue Expression Project (GTEx).

Firmware Embedded Engineering Intern, Inphi Corporation (acq. Marvell Inc.)
July 2019 - Sept 2019

  • Developed Python modules and a user interface to create and display data based on test options and feature selection.
  • Developed firmware in C++ to parse data, removing duplicates based on timestamp & hex value, per modular architecture.

Education

Jacobs School of Engineering at University of California, San Diego
M.S., Computer Science and Engineering, AI/ML Concentration
Sept 2023 - Present

Halicioglu Data Science Institute at University of California, San Diego
B.S., Data Science
Sept 2020 - Jun 2023