Grad Student @ MIT CSAIL.
Programming Systems & Machine Learning.
I'm interested in using machine learning as an abstraction to help write complex programs.
I want to make it easier to write programs that are hard or even impossible to write by hand.
I also want to make machine learning more efficient, to be able to incorporate learning into more programs.
Here is a full CV
|July, 2021 ||Programming with Surrogates of Programs accepted at Onward! 2021.|
|July, 2020 ||DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates accepted at MICRO 2020.|
|June, 2020 ||I'm spending the summer at OctoML, working on reducing training time for neural network approximations.|
|April, 2020 ||Comparing Rewinding and Fine-tuning in Neural Network Pruning published at ICLR 2020. MIT News. IBM Research blog.|
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates.
Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin.
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning.
Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous, Fatima Zohra Benhamida, Alex Renda, Jonathan Elliott Frankle, Michael Carbin, Saman Amarasinghe.
Workshop on Systems for ML, NeurIPS 2019.
Comparing Rewinding and Fine-tuning in Neural Network Pruning.
Alex Renda, Jonathan Frankle, Michael Carbin.
Oral presentation (<2% of submitted papers).
BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models.
Yishen Chen, Ajay Brahmakshatriya, Charith Mendis, Alex Renda, Eric Atkinson, Ondřej Sýkora, Saman Amarasinghe, Michael Carbin.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks.
Charith Mendis, Alex Renda, Saman Amarasinghe, Michael Carbin.
Best Paper award at the ML for Systems workshop at ISCA 2019.
Programming Language Support for Natural Language Interaction
Alex Renda, Harrison Goldstein, Sarah Bird, Chris Quirk, Adrian Sampson.
NSF GRFP Honorable Mention, 2020
Best Paper award for Ithemal at the ML for Systems workshop at ISCA 2019
MIT Great Educators Fellowship, 2018-2019
Cornell University: Summa Cum Laude with Honors, 2018
- OOPSLA 2021 — Artifact Evaluator
- NeurIPS 2021 — Reviewer
- ICML 2021 — Reviewer
- ASPLOS 2021 — Artifact Evaluator
- ICLR 2021 — Reviewer (Outstanding Reviewer)
- AAAI 2021 — Emergency Reviewer
- NeurIPS 2020 — Reviewer
- ICML 2020 — Reviewer (Top 33% Reviewer)
- PLSE Seminar Co-Coordinator — Spring 2021–present
- PLSE Coffee Chat Co-Coordinator — Fall 2020–present
- EECS GAAP Mentor — Fall 2020, Fall 2021
- PLSE Lunch Co-Coordinator — Fall 2019–Spring 2020
- Fast ML Reading Group Coordinator — Fall 2019–Spring 2020
CS 4120 — Introduction to Compilers.
Teaching Assistant. Cornell University, Spring 2018.
CS 2112 — Object Oriented Programming and Data Structures - Honors.
Consultant. Cornell University, Fall 2016, Fall 2015.
Summer 2020: MLSys Intern at OctoML
Summer 2018: Software Engineering Intern at Two Sigma
Summer 2017: Software Engineering Intern at Two Sigma
Summer 2016: Software Engineering Intern at Facebook
Summer 2014: System Validation Intern at Tesla