Bio

I am a Ph.D. student in the Computer Science department at the Donald Bren School of Information and Computer Sciences at the University of California Irvine, working with Prof. Amir M. Rahmani, Prof. Ramesh Jain, and Prof. Nikil Dutt.

I specialize in causal inference, generative AI, and large language models for health applications. My research has been published in top-tier venues like IEEE Medicine & Biology and Elsevier Smart Health.

I have expertise in Python, R, SQL, PyTorch, TensorFlow, and LangChain, leveraging these tools to develop machine learning models and AI-driven applications. Passionate about entrepreneurship, I aim to drive innovation in AI, technology, and healthcare.

nnagesh1[at]uci[dot]edu  /  Resume  /  Google Scholar  /  LinkedIn  /  Github  / 

Nitish Nagesh Profile Picture
Relevant Publications
  • Nitish Nagesh et al. “Assessment Of AI/ML Approaches For Qualitative Analysis In Obstructive Sleep Apnea”, American Thoracic Society, San Francisco, 2025.
  • Mahyar Abbasian, Nitish Nagesh et al. “Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients”, IEEE Medicine and Biology Conference, 2024.
  • Zhongqi Yang, Nitish Nagesh et al. “ChatDiet: Empowering personalized nutrition-oriented food recommender chatbots through an LLM-augmented framework.”, Elsevier Smart Health, 2024.
  • Ajan Subramanian, Nitish Nagesh et al. “Long Term Remote Patient Monitoring Reduces Blood Pressure in Patients with Stage II Hypertension.”, American Heart Association, Chicago, 2024.
  • Nitish Nagesh, Iman Azimi et al. “Towards Building Deep Personal Lifestyle Models using Multimodal N-of-1 Data.”, 29th International Conference on Multimedia Modeling, Norway, 2023.
Work Experience
Graduate Research Scientist, UC Irvine Institute for Future Health
Irvine, CA | June 2022 - Present

  • Build novel LLM-augmented causally fair synthetic data generation pipeline using Python, R to reduce bias in real-world health datasets; first-author publication in progress.
  • Assisted in conceptualizing an LLM-powered framework for a diet recommendation chatbot using health data, achieving 92% effectiveness; co-authored publication in Elsevier Smart Health Journal.
  • Integrated dietary guidelines and nutrition calculation tools into LLM-based health agents, enabling explainable diet risk assessments; co-authored publication in IEEE Medicine and Biology Conference.
  • Developed a causal inference framework using Python for a 3-year N-of-1 observational dataset to analyze the effect of caffeine on heart rate variability; first-author paper in computing conference.

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    Research Data Scientist Intern, ResMed
    San Diego, CA | June 2024 - September 2024

  • Built LLM-powered topic modeling framework to analyze quantitative data for 6,000 sleep apnea patients using LLMs and LangChain in Python, improving therapy personalization.
  • Collaborated with an interdisciplinary team of 5 researchers to refine product claims related to pressure and wakefulness in sleep apnea patients; first-author abstract in American Thoracic Society 2025.

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    Data Scientist Intern, iHealth Labs
    Sunnyvale, CA | June 2023 - September 2023

  • Led de-identification, aggregation, and analysis of data for 12,000 patients in a remote patient monitoring program, adhering to HIPAA guidelines, resulting in an academia-industry partnership.
  • Collaborated with a cross-functional team to develop personalized interventions for diabetes and hypertension management; co-authored abstract and poster at American Heart Association 2024.

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    Applied Machine Learning Intern, UC Irvine Institute for Future Health
    Irvine, CA | September 2021 - May 2022

  • Implemented an image segmentation pipeline for a retina dataset using deep learning frameworks such as TensorFlow, Keras, SciPy achieving 31% accuracy for optic disk and 8% for fovea segmentation.
  • Classified the Fashion-MNIST dataset using convolutional neural networks (CNN) in Python, achieving 95.88% training accuracy and 93% test accuracy after hyperparameter tuning and cross-validation.

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    Software Engineer, Qualcomm
    Austin, TX | March 2021 - August 2021

  • Developed a Python tool to parse 5,000 logs from the Qualcomm AI accelerator, reducing cycle time by 3x and lowering upstream production costs.
  • Triaged and debugged failures in three ML accelerator SDKs through feature engineering and model evaluation, resulting in a 10% performance improvement across internal benchmarks.

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    Projects

    PhdList, Irvine, CA
    December 2024 - Present

    • Created a consolidated PhD openings database on GitHub to improve information access for prospective students, contributing to a more diverse applicant pool.

    FoodMoodAlly, Irvine, CA
    January 2023 - December 2023

    • Led planning, coordination, and development of AI-driven food recommendation systems to improve mood of pregnant women & new mothers, leveraging Python, Xcode, Trello, and Git.
    • Winner: Product Design Competition (1st out of 17 teams), Mental Health Hackathon $3,500 (1 of 30).
    Awards
    • Entrepreneur Award in Computer Science ($5,000) – Awarded annually to one student for outstanding innovation and entrepreneurial impact.
    • Fellowship to Commercialize Research ($2,500) – Selected to translate academic research into real-world applications.
    • Fellowship to Coach Student Entrepreneurs ($5,000) – Mentored and guided emerging student-led ventures during MS & PhD at UC Irvine.
    • Best Outgoing Student (Ranked 1st of 70) – Recognized for all-round excellence during BS at R V College, India.
    • Academic Excellence Award (Ranked 2nd of 70) – Achieved the highest overall GPA in BS program.
    • Most Innovative Thesis Award (Ranked 2nd of 70) – Recognized for exceptional research contributions.
    Leadership
    • Lead Mentor, AI Club @ UCI – Initiated the “How to Apply to Grad School” series for 75+ students, reviewed 15+ personal statements, leading to 5+ admission offers from CMU, UC Berkeley, etc.
    • DEI Representative, CS Dept @ UCI – Ally for women in tech (GHC ‘23 scholar), peer mentor for 5 underrepresented students, and led a panel of 50+ attendees on ethical and societal challenges in computing.