Alex Renda

Grad Student @ MIT CSAIL
Programming Systems & Machine Learning

Email:	[email protected]
Twitter:	@alex_renda_
GitHub:	@alexrenda
LinkedIn:	@alexrenda
Google Scholar:	Alex Renda
Office:	MIT 32G-738, Cambridge MA 02139

About

I'm interested in using machine learning as an abstraction to help write complex programs, with the goal of making it easier to write programs that are hard or even impossible to write by hand.

Here is a full CV.

News

July, 2021	Programming with Neural 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.

Publications

Programming with Neural Surrogates of Programs.
Alex Renda, Yi Ding, and Michael Carbin.
Onward!, 2021. Paper. Bibtex. Code. Presentation.
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates.
Alex Renda, Yishen Chen, Charith Mendis, and Michael Carbin.
MICRO, 2020. Paper. Bibtex. Code. Presentation.
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, and Saman Amarasinghe.
Workshop on Systems for ML, NeurIPS, 2019. Paper. Bibtex.
Comparing Rewinding and Fine-tuning in Neural Network Pruning.
Alex Renda, Jonathan Frankle, and Michael Carbin.
ICLR, 2020. Paper. Bibtex. Code. Presentation.
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, and Michael Carbin.
IISWC, 2019. Paper. Bibtex. Code.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks.
Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin.
ICML, 2019. Paper. Bibtex. Code.
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, and Adrian Sampson.
SysML, 2018. Paper. Extended Draft. Bibtex. Code.

Honors

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

Academic Service

PLDI 2023 — Social Events Co-Chair
ICLR 2023 — Reviewer
OOPSLA 2022 — Artifact Evaluator / External Review Committee
ECOOP 2022 — Artifact Evaluator / External Review Committee
ICLR 2022 — Reviewer
POPL 2022 — Artifact Evaluator
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)

Institutional Service

PLSE Seminar Co-Coordinator — Spring 2021 - present
PLSE Coffee Chat Co-Coordinator — Fall 2020 - present
PLSE Lunch Co-Coordinator — Fall 2019 - Spring 2020, Fall 2021 - present
EECS GAAP Mentor — Fall 2020, Fall 2021
Fast ML Reading Group Coordinator — Fall 2019 - Spring 2020

Invited Talks

January, 2022 — NEC Labs Europe — Programming with Neural Surrogates of Programs
March, 2021 — MIT PLSE Seminar — Learned x86 Cost Models: Steps Towards a Learned Compiler Backend
November, 2020 — Facebook AI Compiler Group — Learned x86 Cost Models: Steps Towards a Learned Compiler Backend
July, 2021 — OctoML — DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates

Teaching

CS 4120 - Introduction to Compilers. Teaching Assistant. Cornell University. Spring 2018.
CS 2112 - Object Oriented Programming and Data Structures - Honors. Consultant. Cornell University. Fall 2015, Fall 2016.

Education

Ph.D. student in EECS, MIT CSAIL. 2018-present.
Working on learning-based systems and efficient neural networks.
Advised by Michael Carbin.
S.M. in Electrical Engineering and Computer Science, MIT. 2020.
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Worked on efficient neural networks.
Advised by Michael Carbin.
B.S. (Summa Cum Laude) in Computer Science with Honors, with a minor in Linguistics, Cornell University. 2018.
Worked on programming abstractions for natural language and intelligent systems as an undergraduate member of the Capra group.
Advised by Adrian Sampson.

Industry Experience

Summer 2020: MLSys Research 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