cv

Basics

Name Abhishek Sharma
Label PhD Candidate
Email abhisheksharma@g.harvard.edu
Summary PhD Candidate at Harvard University working on decision-focused models, representation learning, and reinforcement learning with applications in healthcare.

Work

  • 2024.05 - 2024.08
    Student Researcher
    Google Research
    Worked on foundation modeling efforts for waveform data in healthcare. Proposed a new self-supervised learning method to learn interpretable representations.
  • 2022.05 - 2022.08
    Research Intern
    Mitsubishi Electric Research Labs (MERL)
    Built a density model of time-to-destination for better uncertainty quantification, applied to elevator scheduling.
  • 2020.09 - Present
    Graduate Researcher
    Harvard University
    Worked on decision-focused models, representation learning, and reinforcement learning for healthcare. Developed methods for feature selection using prediction-focused mixture models and reward transfer in model-based RL.
  • 2019.05 - 2019.08
    Machine Learning Intern
    Qualcomm
    Applied sequence modeling to system-on-chip design and coreset selection for training data compression.
  • 2019.05 - 2020.08
    Graduate Researcher
    University of Massachusetts Amherst
    Proposed a state-space model using variational autoencoders to model patient trajectories in electronic health records.
  • 2015.01 - 2017.01
    Co-founder
    Blaffer
    Virtual reality experiences for real estate and hospitality.

Education

  • 2020.09 - 2025.05
    PhD
    Harvard University
    PhD Candidate
  • 2018.09 - 2020.05
    M.S.
    University of Massachusetts Amherst
    Computer Science
  • 2011.09 - 2016.05
    B.Tech.
    Indian Institute of Technology, Madras
    Engineering

Awards

Skills

Programming Languages/Frameworks
Python
PyTorch
JAX
TensorFlow
Machine Learning
Probabilistic Modeling
Deep Learning
Reinforcement Learning
Representation Learning
Statistics
Foundation Models
Self-Supervised Learning

Languages

English
Fluent
Hindi
Native

Interests

Reinforcement Learning
Safe Policy Improvement
Model-based RL
Decision-Focused Models
Representation Learning
Self-Supervised Learning
Foundation Models
Healthcare Applications of Machine Learning