Optimizing consistency checking for memory-intensive transactions. - Justin Emile Gottschlich; Daniel A. Connors (PODC, 2008)

An efficient lock-aware transactional memory implementation. - Justin Emile Gottschlich; Jeremy G. Siek; Manish Vachharajani; Dwight Y. Winkler; Daniel A. Connors (ICOOOLPS@ECOOP, 2009)

An efficient software transactional memory using commit-time invalidation. - Justin Emile Gottschlich; Manish Vachharajani; Jeremy G. Siek (CGO, 2010)

Optimizing the Concurrent Execution of Locks and Transactions. - Justin Emile Gottschlich; JaeWoong Chung (LCPC, 2011)

CoreRacer: a practical memory race recorder for multicore x86 TSO processors. - Gilles Pokam; Cristiano Pereira; Shiliang Hu; Ali-Reza Adl-Tabatabai; Justin Emile Gottschlich; Jungwoo Ha; Youfeng Wu (MICRO, 2011)

Visualizing transactional memory. - Justin Emile Gottschlich; Maurice Herlihy; Gilles Pokam; Jeremy G. Siek (PACT, 2012)

Concurrent predicates: A debugging technique for every parallel programmer. - Justin Emile Gottschlich; Gilles Pokam; Cristiano Pereira; Youfeng Wu (PACT, 2013)

Using Elimination and Delegation to Implement a Scalable NUMA-Friendly Stack. - Irina Calciu; Justin Emile Gottschlich; Maurice Herlihy (HotPar, 2013)

But How Do We Really Debug Transactional Memory Programs? - Justin Emile Gottschlich; Rob C. Knauerhase; Gilles Pokam (HotPar, 2013)

QuickRec: prototyping an intel architecture extension for record and replay of multithreaded programs. - Gilles Pokam; Klaus Danne; Cristiano Pereira; Rolf Kassa; Tim Kranich; Shiliang Hu; Justin Emile Gottschlich; Nima Honarmand; Nathan Dautenhahn; Samuel T. King; Josep Torrellas (ISCA, 2013)

Invyswell: a hybrid transactional memory for haswell's restricted transactional memory. - Irina Calciu; Justin Gottschlich; Tatiana Shpeisman; Gilles Pokam; Maurice Herlihy (PACT, 2014)

Towards Transactional Memory for OpenMP. - Michael Wong; Eduard Ayguadé; Justin Gottschlich; Victor Luchangco; Bronis R. de Supinski; Barna L. Bihari (IWOMP, 2014)

TSXProf: Profiling Hardware Transactions. - Yujie Liu 003; Justin Gottschlich; Gilles Pokam; Michael F. Spear (PACT, 2015)

AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder. - Mohammad Mejbah Ul Alam; Justin Gottschlich; Md. Mostofa Ali Patwary; Saeef Ahmad; Abdullah Muzahid (CoRR, 2017)

Precision and Recall for Time Series. - Nesime Tatbul; Tae Jun Lee; Stan Zdonik; Mejbah Alam; Justin Gottschlich (NeurIPS, 2018)

The three pillars of machine programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (MAPL@PLDI, 2018)

Paranom: A Parallel Anomaly Dataset Generator. - Justin Gottschlich (CoRR, 2018)

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection. - Tae-Jun Lee; Justin Gottschlich; Nesime Tatbul; Eric Metcalf; Stan Zdonik (CoRR, 2018)

Precision and Recall for Range-Based Anomaly Detection. - Tae Jun Lee; Justin Gottschlich; Nesime Tatbul; Eric Metcalf; Stan Zdonik (CoRR, 2018)

Toward Scalable Verification for Safety-Critical Deep Networks. - Lindsey Kuper; Guy Katz; Justin Gottschlich; Kyle Julian; Clark W. Barrett; Mykel J. Kochenderfer (CoRR, 2018)

A New Model for Evaluating Range-Based Anomaly Detection Algorithms. - Nesime Tatbul; Tae Jun Lee; Stan Zdonik; Justin Gottschlich (CoRR, 2018)

The Three Pillars of Machine-Based Programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (CoRR, 2018)

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. - Mejbah Alam; Justin Gottschlich; Nesime Tatbul; Javier S. Turek; Tim Mattson; Abdullah Muzahid (NeurIPS, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

NetSyn: Neural Evolutionary Technique to Synthesize Programs. - Shantanu Mandal; Todd A. Anderson 001; Mejbah Alam; Justin Gottschlich; Abdullah Muzahid (CoRR, 2019)

An Abstraction-Based Framework for Neural Network Verification. - Yizhak Yisrael Elboher; Justin Gottschlich; Guy Katz (CAV (1), 2020)

Learned garbage collection. - Lujing Cen; Ryan Marcus; Hongzi Mao; Justin Gottschlich; Mohammad Alizadeh; Tim Kraska (MAPL@PLDI, 2020)

Context-Aware Parse Trees. - Fangke Ye; Shengtian Zhou; Anand Venkat; Ryan Marcus; Paul Petersen; Jesmin Jahan Tithi; Tim Mattson; Tim Kraska; Pradeep Dubey; Vivek Sarkar; Justin Gottschlich (CoRR, 2020)

Software Language Comprehension using a Program-Derived Semantic Graph. - Roshni G. Iyer; Yizhou Sun; Wei Wang 010; Justin Gottschlich (CoRR, 2020)

MISIM: An End-to-End Neural Code Similarity System. - Fangke Ye; Shengtian Zhou; Anand Venkat; Ryan Marcus; Nesime Tatbul; Jesmin Jahan Tithi; Paul Petersen; Timothy G. Mattson; Tim Kraska; Pradeep Dubey; Vivek Sarkar; Justin Gottschlich (CoRR, 2020)

Class-Weighted Evaluation Metrics for Imbalanced Data Classification. - Akhilesh Gupta; Nesime Tatbul; Ryan Marcus; Shengtian Zhou; Insup Lee 001; Justin Gottschlich (CoRR, 2020)

AI programmer: autonomously creating software programs using genetic algorithms. - Kory Becker; Justin Gottschlich (GECCO Companion, 2021)

Learning Fitness Functions for Machine Programming. - Shantanu Mandal; Todd A. Anderson 001; Javier Turek; Justin Gottschlich; Shengtian Zhou; Abdullah Muzahid (MLSys, 2021)

ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures. - Niranjan Hasabnis; Justin Gottschlich (MAPS@PLDI, 2021)

Predictive data locality optimization for higher-order tensor computations. - Tharindu Rusira Patabandi; Anand Venkat; Abhishek Kulkarni; Pushkar Ratnalikar; Mary W. Hall; Justin Gottschlich (MAPS@PLDI, 2021)

Toward Code Generation: A Survey and Lessons from Semantic Parsing. - Celine Lee; Justin Gottschlich; Dan Roth 001 (CoRR, 2021)

Simulation Intelligence: Towards a New Generation of Scientific Methods. - Alexander Lavin; Hector Zenil; Brooks Paige; David Krakauer; Justin Gottschlich; Tim Mattson; Anima Anandkumar; Sanjay Choudry; Kamil Rocki; Atilim Günes Baydin; Carina Prunkl; Olexandr Isayev; et al. (CoRR, 2021)

MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision. - Urs C. Muff; Celine Lee; Paul Gottschlich; Justin Gottschlich (CoRR, 2022)

Synthesizing Programs with Continuous Optimization. - Shantanu Mandal; Todd A. Anderson 001; Javier Turek; Justin Gottschlich; Abdullah Muzahid (CoRR, 2022)

Machine Programming: Turning Data into Programmer Productivity. - Abdul Wasay; Nesime Tatbul; Justin Gottschlich (Proc. VLDB Endow., 2022)