Alex Renda

Email:	[email protected]
Twitter:	@alex_renda_
GitHub:	@alexrenda
LinkedIn:	@alexrenda
Google Scholar:	Alex Renda

About

I graduated from MIT with a PhD in EECS in 2024. My research studied machine learning as an abstraction to help programmers develop complex systems, 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.

Education

Ph.D. student in EECS, MIT CSAIL. 2018-2024.
Thesis: Programming with Neural Surrogates of Programs.
Worked on learning-based systems and efficient neural networks.
Advised by Michael Carbin.

S.M. in Electrical Engineering and Computer Science, MIT. 2020.
Thesis: 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.

Publications

CoMEt: x86 Cost Model Explanation Framework.
Isha Chaudhary, Alex Renda, Charith Mendis, and Gagandeep Singh.
MLSys, 2024. Paper. Bibtex.

Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs.
Alex Renda, Yi Ding, and Michael Carbin.
OOPSLA, 2023. Paper. Bibtex. Code. Presentation.

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.

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.

Workshop Papers

Can LLMs Generate Random Numbers? Evaluating LLM Sampling in Controlled Domains.
Alex Renda*, Aspen Hopkins*, and Michael Carbin.
Sampling and Optimization in Discrete Space Workshop, ICML, 2023. Paper. Bibtex. Code.

Renamer: A Transformer Architecture Invariant to Variable Renaming.
Zachary Ankner, Alex Renda, and Michael Carbin.
Machine Learning for Systems Workshop, NeurIPS, 2023. Bibtex.

The Effect of Data Dimensionality on Neural Network Prunability.
Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, and Tian Jin.
I Can’t Believe It’s Not Better Workshop, NeurIPS, 2022. Paper. Bibtex.

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.

Drafts

A Theory of Equivalence-Preserving Program Embeddings.
Logan Weber, Jesse Michel, Alex Renda, Saman Amarasinghe, and Michael Carbin.
2023. Paper.

Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression.
Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, and Henry Hoffmann.
2022. Paper.

Academic Service

NeurIPS 2023 — Reviewer

ICML 2023 — Reviewer

ICLR 2023 — Reviewer

PLDI 2023 — Social Events Co-Chair

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)

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

6.1100 (formerly 6.035) - Computer Language Engineering. Teaching Assistant. MIT. Spring 2023.

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.

Industry Experience

Spring 2021: Consultant at ReadySet

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