Todd Mytkowicz

Registered user since Wed 25 Mar 2015

Name: Todd Mytkowicz

Country: United States

Affiliation: Microsoft Research

Personal website: https://www.microsoft.com/en-us/research/people/toddm/

Contributions

PLDI 2021 Committee Member in Student Research Competition (SRC) within the SRC-track
PPoPP 2021 Committee Member in Program Committee
Session Chair of Session 7. Posters 2 (part of Main Conference)
Author of Synthesizing Optimal Collective Algorithms within the Main Conference-track
ECOOP 2021 PC Member in Program Committee within the ECOOP Research Papers-track
PPoPP 2020 Committee Member in Program Committee
PLMW @ PLDI 2019 Author of A week in the life of an MSR Researcher within the PLMW @ PLDI 2019-track
VEE 2019 Committee Member in Steering Committee
SPLASH 2019 Committee Member in Review Committee within the OOPSLA-track
PPoPP 2019 PC Member in Program Committee
PLDI 2019 Author of CHET: An Optimizing Compiler for Fully-Homomorphic Neural-Network Inferencing within the PLDI Research Papers-track
PPoPP 2018 Committee Member in Program Committee
MAPL 2017 Author of Debugging Probabilistic Programs within the MAPL 2017-track
PLDI 2018 Committee Member in Program Committee
PPS 2017 Author of Reasoning about inference in probabilistic programs within the PPS-track
VEE 2017 General Chair in Organizing Committee
SPLASH 2017 Author of Static Stages for Heterogeneous Programming within the OOPSLA-track
PLDI 2017 Author of Fusing Effectful Comprehensions within the PLDI Research Papers-track
* ICSE 2018 * Author of Cross-Language Optimizations in Big Data Systems: A Case Study of SCOPE within the SEIP - Software Engineering in Practice-track
PLDI 2016 Committee Member in Program Committee
PLDI 2015 Committee Member in External Review Committee within the Research Papers-track
SPLASH 2014 Committee Member in Program Committee within the OOPSLA-track
SPLASH 2013 Committee Member in Artifacts within the OOPSLA Artifacts-track
Author of The Latency, Accuracy, and Battery (LAB) Abstraction: Programmer Productivity and Energy Efficiency for Continuous Mobile Context Sensing within the OOPSLA-track