CV

General Information

Full Name Hitesh Sapkota
Languages English, Nepali

Education

  • 2017-2023
    PhD in Computer Science
    Rochester Institute of Technology, Rochester, NY, USA
    • Developed Distributionally Robust Optimization (DRO) based Bayesian Multiple Instance Learning technique for Anomaly Detection resulting.
    • Developed Deep Reinforcement Learning technique for Partial Sketch based Image Retrieval.
    • Developed Evidential Openset Detection techniques considering Imbalance Classes and Few Shot Learning Setting.
    • Developed Distributionally Robust Ensemble of Sparse Networks for better Calibration, Debiased, and Openset Detection Performance.
    • Developed Generalized Focal Loss Ensemble of Low-Rank Networks for Calibrated Visual Question Answering.
  • 2012 - 2015
    BE in Electrionics and Communication
    Institute of Engineering (IOE) Pulchowk, Lalitpur, Nepal
    • Performed vehicle number plate detection as a final year project using neural network.
    • Financial support from Ncell for excellent academic performance.

Experience

  • 2023 DEC - PRESENT
    Applied Scientist II
    Amazon, Hardware-Product Integrity, Sunnyvale, CA, USA.
    • Devising LLMs that can assist engineers in the hardware manufacturing process by providing solutions to the various challenging questions.
  • 2022 May - 2022 Aug
    Applied Scientist Intern
    Amazon, Hardware-Product Integrity, Sunnyvale, CA, USA.
    • Designed and Implemented embedding adaptation using attention-based architecture in gas sensor technologies.
    • Performance improvement of 6% over the existing baseline.
  • 2021 Jun - 2021 Aug
    Applied Scientist Intern
    AWS, Support, Seattle, WA, USA.
    • Designed ML models to detect AWS service failures (issues) early before impacting customers significantly.
    • Performance improvement of 4% over the existing baseline.

Honors and Awards

  • 2022
    • KDD Travel Award
  • 2017
    • RIT Ph.D. Merit Scholarship
  • 2014
    • Ncell Scholarship and Excellence Award

Academic Interests

  • Anomaly Detection.
    • DRO based Bayesian Multiple Instance Learning.
    • Bayesian Nonparametirc Video Paritition Techniques.
    • Multiple Instance Active Learning Techniques.
  • Openset Detection.
    • DRO based Evidential Learning under Class Imbalanced Setting.
    • DRO based Evidential Learning for Biased/Spurious Correlation Setting.
    • Fairness AI.
  • Reinforcement Learning.
    • Partial Sketch based Imange Retrieval using Double Exploration.
    • Dense Object Detection using Evidential Q-Learning.
  • Network Sparisification.
    • Calibrated Sparse DNN using Distributionally Robust Ensemble of Lottery Tickets.
    • Generalized Focal Loss Ensemble of Multimodal Sparse Network for Visual Question Answeing.
  • Generative AI.
    • Sparsified Generative AI models that are sparse, uncertainty aware, and unbiased.

Other Interests

  • Hobbies: Reading, Hiking, Traveling.