My resume can also be found as a PDF and on LinkedIn
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
- 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, (editor: Dibya Ghosh), et al.
PDF (Last updated 01/2017)
- Development of a Computationally Optimized Model of Cancer-induced Angiogenesis through Specialized Cellular Mechanics
Intel ISEF 2015. Arxiv
Flexible Deep Reinforcement Learning
Randomized Community Segmentation in Graphs
- 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
- 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
Head Undergraduate Student Instructor for UC Berkeley Statistics (Jan 2017 - Present)
Lead Developer at Preminon Inc (Dec 2016 - Present)
Research Assistant at Lawrence Berkeley National Laboratory (June 2016 - Present)
- Working with < cloaked > in deployment of machine learning toolkits for alternative architectures
Course Developer at UC Berkeley (June 2016 - Dec 2016)
- See Randomized Community Segmentation in Graphs in Research above for more details.
Visualization Lead for the Berkeley Institute for Data Science (Feb 2016 - June 2016)
- Worked with Ani Adhikari and Jason Zhang to develop and design curriculum for Statistics 140:
Probability for Data Science.
- Co-wrote the prob140 library with Jason Zhang, a data science library geared toward probability
theory written for Prob140 in Python. The library supports graphical visualization and computational tools for finite, infinite, joint,
and continuous probability distributions as well as Markov Chains and other random processes.
- Prototyped and fleshed out labs, projects, and other instructional material
- 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
Course Development Assistant for Data 8 at UC Berkeley (Jan 2016 - May 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.
- 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, Spring 2017, Fall 2017
- 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)