Hire Me!

I’m interested in internship opportunities in deep learning research, data science, and quantitative analysis. I am available for the next internship cycle (2021 summer).  Send me an email if you think I am a good match for your company. My resume is available upon request.

Selected Coursework

Undergraduate
Combinatorial Analysis (18.211), Statistics for Applications (18.650), Theory of Computation (18.404), Data and Politics (17.831), Macroeconomics (14.02).

Advanced Undergraduate
Abstract Algebra (18.701-2), Design and Analysis of Algorithms (6.046), Computational Cognitive Science (6.804), Software Construction (6.031).

Graduate:
Machine Learning (6.867), Machine Learning Augmented Algorithms (6.890), Advanced Natural Language Processing (6.864), Cryptography (6.875), Algorithmic Lower Bounds (6.892).

Misc
Philosophy of Religion (24.05), Metaphysics (24.280), Intro to Acting (21M.600), How to Construct a Language (24.917).

Work Experience

Undergraduate Researcher, MIT CSAIL, Cambridge, MA (Feb 2020-)
PI:
Dr. Regina Barzilay
I work on understanding the connection between interpretability and robustness of deep learning models. I investigate techniques to train the deep learning models that are robust to adversarial and cross-domain distribution shift by using human-like causal factors. To put it simply,  I train the models to ‘think like humans’ so that they retain their performance under adverse conditions just like humans.

Software Engineering Intern, Facebook Inc, Seattle, WA (Jun 2020-Aug 2020)
I mathematically analyzed an ML model’s loss function to understand the tradeoff between performance and latency. Then I used those insights to cache the model with 44% latency reduction.

Software Engineering Intern, Kensho Technologies, New York City, NY (Jun 2019 – Aug 2019)
Data Science Intern, QuantCo Inc, Cambridge, MA (Jun 2018-Aug 2018)
Remote Research Assistant, University of California, Irvine. (June 2016-June 2017)
PI: Dr. Wayne Hayes.

Volunteer Experience

Data Analyst, National Data Analytics Task Force, ICT Division, Govt. of Bangladesh (Apr 2020-May 2020)
I developed Bayesian hierarchical models to understand the dynamics of the COVID-19 pandemic in Bangladesh and the effectiveness of various intervention policies for Bangladeshi rural and urban settings.

Journal and Conference Publications
  1. Adib Hasan, Po-Chien Chung and Wayne Hayes. Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8. PLoS ONE, 2017.
Books & Articles
  1. Adib Hasan, Ahmed Zawad Chowdhury and Joydip Saha. An Introduction to Combinatorics (Part 1, Part 2). Dimik Publications, 2019. I’ve also open-sourced the book’s template.
  2. Adib Hasan and Thanic Nur Samin. The Method of Indirect Descent (Part II). Crux Mathematicorum, volume 44, number 4, April 2018.
  3. Adib Hasan. The Method of Indirect Descent (Part I). Crux Mathematicorum, volume 44, number 3, March 2018.
Software and Packages
  1. Smart Heap, Machine learning augmented binary heap that predicts future operations and optimizes average cost per operation accordingly. It turned out to be 40% faster than classical binary heap.
  2. LaTeXbangla, a package that simplifies the process of writing Bangla documents in LaTeX.
  3. Faye: Fast querying in large biological network databases in presence of noise.