Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (IEEE BigData, 2016)

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. - Evan Racah; Seyoon Ko; Peter J. Sadowski; Wahid Bhimji; Craig Tull; Sang-Yun Oh; Pierre Baldi; Prabhat (ICMLA, 2016)

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. - Alex Gittens; Jey Kottalam; Jiyan Yang; Michael F. Ringenburg; Jatin Chhugani; Evan Racah; Mohitdeep Singh; Yushu Yao; Curt Fischer; Oliver Rübel; Benjamin P. Bowen; Norman G. Lewis; et al. (IPDPS Workshops, 2016)

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. - Md. Mostofa Ali Patwary; Nadathur Rajagopalan Satish; Narayanan Sundaram; Jialin Liu 002; Peter J. Sadowski; Evan Racah; Surendra Byna; Craig Tull; Wahid Bhimji; Prabhat; Pradeep Dubey (IPDPS, 2016)

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (CoRR, 2016)

Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. - Yunjie Liu; Evan Racah; Prabhat; Joaquin Correa; Amir Khosrowshahi; David Lavers; Kenneth Kunkel; Michael F. Wehner; William D. Collins (CoRR, 2016)

Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. - Evan Racah; Christopher Beckham; Tegan Maharaj; Prabhat; Christopher J. Pal (CoRR, 2016)

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. - Evan Racah; Christopher Beckham; Tegan Maharaj; Samira Ebrahimi Kahou; Prabhat; Chris Pal (NIPS, 2017)

Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. - Wahid Bhimji; Steven Andrew Farrell; Thorsten Kurth; Michela Paganini; Prabhat; Evan Racah (CoRR, 2017)

Unsupervised State Representation Learning in Atari. - Ankesh Anand; Evan Racah; Sherjil Ozair; Yoshua Bengio; Marc-Alexandre Côté; R. Devon Hjelm (NeurIPS, 2019)

Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments. - Evan Racah; Christopher J. Pal (CoRR, 2019)

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. - Harm van Seijen; Hadi Nekoei; Evan Racah; Sarath Chandar (NeurIPS, 2020)

Slot Contrastive Networks: A Contrastive Approach for Representing Objects. - Evan Racah; Sarath Chandar (CoRR, 2020)

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. - Eli Bronstein; Mark Palatucci; Dominik Notz; Brandyn White; Alex Kuefler; Yiren Lu 001; Supratik Paul; Payam Nikdel; Paul Mougin; Hongge Chen; Justin Fu; Austin Abrams; et al. (IROS, 2022)