MIT PLSE Seminar

 
The MIT programming languages and software engieering community hosts a weekly seminar series with talks from MIT and outside researchers. The seminar is currently running on Zoom, with talks held on Thursdays at 4pm-5pm Eastern, and is open to anyone (at MIT or otherwise). For the Zoom link and to stay up-to-date with announcements subscribe to the plse-seminar mailling list.

Contact the organizers (plse-seminar-organizers@csail.mit.edu) with questions or slot reservations.

Upcoming Talks

Date Speaker Affiliation Topic Other links
5/9/2022 Eric Koskinen Stevens Institute of Technology Programming with Commutativity veracity-lang.org
TBD Charles Yuan MIT CSAIL Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs. POPL 2022

Past Talks

Date Speaker Affiliation Topic Other links Recording
2/24/2022 Michael Greenberg Stevens CS Formal Support for the POSIX Shell POPL 2020
PLOS 2021
HotOS 2021
Link
2/17/2022 Nikos Vasilakis MIT CSAIL Automated, Correct Parallelization of Shell Programs ICFP 2021
HotOS 2021
EuroSys 2021
Link
2/9/2022 Amanda Liu MIT CSAIL Verified Tensor-Program Optimization Via High-Level Scheduling Rewrites POPL 2022 Link
2/2/2022 Chuchu Fan MIT AeroAstro Fast, optimal, and guaranteed safe synthesis for autonomous systems Link
8/5/2021 Feras Saad MIT CSAIL SPPL: Probabilistic Programming with Fast Exact Symbolic Inference PLDI 2021
GitHub
Link
7/22/2021 and MIT CSAIL Systematically Differentiating Parametric Discontinuities SIGGRAPH 2021 Link
7/15/2021 Eric Atkinson MIT CSAIL Programming and Reasoning with Partial Observability OOPSLA 2020 Link
7/8/2021 Ajay Brahmakshatriya MIT CSAIL A Unified Graph Compiler Framework for Novel Architectures CGO 2021
ISCA 2021
Link
7/1/2021 John (Jack) Feser MIT CSAIL Deduction Optimization of Relational Data Storage OOPSLA 2020 Link
6/24/2021 Tej Chajed MIT CSAIL Combining automated and interactive proofs to verify the DaisyNFS concurrent and crash-safe NFS server OSDI 2021 Link
5/6/2021 Shashank Srikant MIT CSAIL ML models for programming tasks -- Do these models learn good representations of programs? Can cognitive science help learn better representations? ICLR 2021 eLife 2020 N/A
4/29/2021 Yishen (Tom) Chen MIT CSAIL Vegen: A Vectorizer Generator for SIMD and Beyond ASPLOS 2021 N/A
4/22/2021 Jeevana Inala MIT CSAIL Neurosymbolic Learning for Robust and Reliable Intelligent Systems ICLR 2020 NeurIPS 2020 Link
4/15/2021 Anitha B. Gollamudi Harvard Secure-by-Construction Applications using Trusted Execution Environments OOPSLA 2016
CSF 2019
Link
4/8/2021 Thomas Bourgéat and Clément Pit-Claudel MIT CSAIL The essence of Bluespec: a core language for rule-based hardware design PLDI 2020 Link
4/1/2021 Shivam Handa MIT CSAIL Inductive Program Synthesis over Noisy Data ESEC/FSE 2020 Link
3/25/2021 Alex Renda MIT CSAIL DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates ICML 2019 MICRO 2020 N/A
 
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