My resume can also be found as a PDF and on LinkedIn

Education

UC Berkeley (2015 - 2019)
  • Pursuing triple major in Computer Science , Statistics, and Mathematics .
  • GPA: 4.0 / 4.0
  • Dean's List from Fall 2015 - present
  • Relevant Courses: Machine Learning, Convex Optimization, Artificial Intelligence, Algorithms, Computer Architecture, Randomized Algorithms, Data Structures . Full list here

Publications

  • Divide-and-Conquer Reinforcement Learning
    Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine.
    NIPS Deep Reinforcement Learning Symposium 2017. Arxiv
  • Theory Meets Data
    Ani Adhikari, Dibya Ghosh, et al.
    PDF (Last updated 01/2017)
  • Development of a Computationally Optimized Model of Cancer-induced Angiogenesis through Specialized Cellular Mechanics
    Dibya Ghosh
    Intel ISEF 2015. Arxiv

Research

Flexible Deep Reinforcement Learning
  • Working under Sergey Levine in the Berkeley AI Research Lab (BAIR)
  • Modifying deep reinforcement learning algorithms to learn robust behaviour under unstable initial states and flexible policies to extend new behaviour
Randomized Community Segmentation in Graphs
  • Working under the supervision of Dr. Benjamin Brown at Lawrence Berkeley National Laboratory (Brown Lab)
  • Focused on determining higher-order interactions between variables through Random Forest ensembles and randomized partitioning algorithms.
  • Designing and implementing a stochastic distributed community detection algorithm to ascertain structure in network data

Experience

Undergraduate Student Instructor for UC Berkeley Statistics (Jan 2017 - Present)
  • Serving as a Undergraduate Student Instructor for Statistics 140: Probability for Data Science.
  • Teaching 2 30-person discussion sections weekly and administering quizzes
  • Co-designing labs,projects, and other instructional material with another staff member
Lead Developer at Preminon Inc (Dec 2016 - Present)
  • Working with < cloaked > in deployment of machine learning toolkits for alternative architectures
Undergraduate Researcher at Lawrence Berkeley National Laboratory (June 2016 - Present)
  • See Randomized Community Segmentation in Graphs in Research above for more details.

Previously

Course Developer at UC Berkeley (June 2016 - Dec 2016)
  • Worked with Ani Adhikari and Jason Zhang to develop and design curriculum for pilot offering of Stat 140 (see above)
  • Designed build system and pipeline for prototyping HW assignments and deployment to students
  • Worked closely with Jupyter and Gradescope to optimize student submission of assignments
Visualization Lead for the Berkeley Institute for Data Science (Feb 2016 - June 2016)
  • Data Science Community Visualization for the Annual Ecosystem Report @ BIDS
  • Applying unsupervised learning techniques to find graph clusters and identify communities of and topics in data science research.
  • Managed the front-end development with D3.JS and AngularJS
  • Responsbile for integration with internal pipeline through Flask, Mongo DB, and MySQL.
Course Development Assistant for Data 8 at UC Berkeley (Jan 2016 - May 2016)
  • Developing the backend for materials and datasets in use for the pioneering Data Science class (500 students)
  • Responsibilities involved exploring appropriate datasets for course materials, running preliminary statistical analyses of datasets, designing examples to include in the textbook, Computational and Inferential Thinking, and developing exercises to include in practice sets or assignments.

Awards and Honors

  • 3rd Place worldwide at Intel International Science Fair 2015 in Bioinformatics / Computational Biology
  • Dean's Honor List (top 4% at UC Berkeley) for Fall 2015, Spring 2016, Fall 2016
  • 1st Place in Pacman AI competition for CS 188 in Summer 2016
  • Robert J. Kraft Award for Freshmen (awarded to ~ 300 people in the 9000-person freshman class)
  • Intel Award for Computer Science (best CS related project at CCCSEF)
  • Chevron Award for Innovation (best project at CCCSEF)