Multi-core GE: automatic evolution of CPU based multi-core parallel programs. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (GECCO (Companion), 2014)

Predict the performance of GE with an ACO based machine learning algorithm. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (GECCO (Companion), 2014)

Predict the success or failure of an evolutionary algorithm run. - Gopinath Chennupati; Conor Ryan; R. Muhammad Atif Azad (GECCO (Companion), 2014)

On the efficiency of Multi-core Grammatical Evolution (MCGE) evolving multi-core parallel programs. - Gopinath Chennupati; Jeannie Fitzgerald; Conor Ryan (NaBIC, 2014)

eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification. - Gopinath Chennupati (CoRR, 2014)

Automatic Evolution of Parallel Recursive Programs. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (EuroGP, 2015)

Automatic Evolution of Parallel Sorting Programs on Multi-cores. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (EvoApplications, 2015)

Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (GECCO, 2015)

Synthesis of Parallel Iterative Sorts with Multi-Core Grammatical Evolution. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (GECCO (Companion), 2015)

On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (GECCO (Companion), 2015)

Automatic lock-free parallel programming on multi-core processors. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan (CEC, 2016)

AMM: Scalable Memory Reuse Model to Predict the Performance of Physics Codes. - Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz; Sunil Thulasidasan (CLUSTER, 2017)

A Probabilistic Monte Carlo Framework for Branch Prediction. - Bhargava Kalla; Nandakishore Santhi; Abdel-Hameed A. Badawy; Gopinath Chennupati; Stephan J. Eidenbenz (CLUSTER, 2017)

Probabilistic Monte Carlo simulations for static branch prediction. - Bhargava Kalla; Nandakishore Santhi; Abdel-Hameed A. Badawy; Gopinath Chennupati; Stephan J. Eidenbenz (IPCCC, 2017)

A Scalable Analytical Memory Model for CPU Performance Prediction. - Gopinath Chennupati; Nandakishore Santhi; Robert F. Bird; Sunil Thulasidasan; Abdel-Hameed A. Badawy; Satyajayant Misra; Stephan J. Eidenbenz (PMBS@SC, 2017)

An analytical memory hierarchy model for performance prediction. - Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz; Sunil Thulasidasan (WSC, 2017)

Synthesis of Parallel Programs on Multi-Cores. - Gopinath Chennupati; R. Muhammad Atif Azad; Conor Ryan; Stephan J. Eidenbenz; Nandakishore Santhi (Handbook of Grammatical Evolution, 2018)

Improved Decoy Selection via Machine Learning and Ranking. - Nasrin Akhter; Gopinath Chennupati; Hristo N. Djidjev; Amarda Shehu (ICCABS, 2018)

PPT-GPU: performance prediction toolkit for GPUs identifying the impact of caches: extended abstract. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (MEMSYS, 2018)

Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations. - Mohammad Abu Obaida; Jason Liu 001; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (SIGSIM-PADS, 2018)

Imcsim: Parameterized Performance Prediction for Implicit Monte Carlo codes. - Gopinath Chennupati; Stephan J. Eidenbenz; Alex Long; Olena Tkachenko; Joseph Zerr; Jason Liu 001 (WSC, 2018)

Quantum Algorithm Implementations for Beginners. - Patrick J. Coles; Stephan J. Eidenbenz; Scott Pakin; Adetokunbo Adedoyin; John Ambrosiano; Petr M. Anisimov; William Casper; Gopinath Chennupati; Carleton Coffrin; Hristo N. Djidjev; David Gunter; Satish Karra; et al. (CoRR, 2018)

POSTER: GPUs Pipeline Latency Analysis. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (ASAP, 2019)

Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures. - Nasrin Akhter; Raviteja Vangara; Gopinath Chennupati; Boian S. Alexandrov; Hristo N. Djidjev; Amarda Shehu (BIBM, 2019)

Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (HPEC, 2019)

Combating Label Noise in Deep Learning using Abstention. - Sunil Thulasidasan; Tanmoy Bhattacharya 001; Jeff A. Bilmes; Gopinath Chennupati; Jamal Mohd-Yusof (ICML, 2019)

GPUs Cache Performance Estimation using Reuse Distance Analysis. - Yehia Arafa; Gopinath Chennupati; Atanu Barai; Abdel-Hameed A. Badawy; Nandakishore Santhi; Stephan J. Eidenbenz (IPCCC, 2019)

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. - Sunil Thulasidasan; Gopinath Chennupati; Jeff A. Bilmes; Tanmoy Bhattacharya 001; Sarah Michalak (NeurIPS, 2019)

Scalable Performance Prediction of Codes with Memory Hierarchy and Pipelines. - Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (SIGSIM-PADS, 2019)

PPT-GPU: Scalable GPU Performance Modeling. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (IEEE Comput. Archit. Lett., 2019)

Instructions' Latencies Characterization for NVIDIA GPGPUs. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (CoRR, 2019)

Modeling Shared Cache Performance of OpenMP Programs using Reuse Distance. - Atanu Barai; Gopinath Chennupati; Nandakishore Santhi; Abdel-Hameed A. Badawy; Stephan J. Eidenbenz (CoRR, 2019)

Decoy Selection in Protein Structure Determination via Symmetric Non-negative Matrix Factorization. - Kazi Lutful Kabir; Gopinath Chennupati; Raviteja Vangara; Hristo N. Djidjev; Boian S. Alexandrov; Amarda Shehu (BIBM, 2020)

Verified instruction-level energy consumption measurement for NVIDIA GPUs. - Yehia Arafa; Ammar ElWazir; Abdelrahman Elkanishy; Youssef Aly; Ayatelrahman Elsayed; Abdel-Hameed A. Badawy; Gopinath Chennupati; Stephan J. Eidenbenz; Nandakishore Santhi (CF, 2020)

Distributed Non-Negative Tensor Train Decomposition. - Manish Bhattarai; Gopinath Chennupati; Erik Skau; Raviteja Vangara; Hristo N. Djidjev; Boian S. Alexandrov (HPEC, 2020)

Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling. - Raviteja Vangara; Erik Skau; Gopinath Chennupati; Hristo N. Djidjev; Thomas Tierney; James P. Smith; Manish Bhattarai; Valentin G. Stanev; Boian S. Alexandrov (ICMLA, 2020)

Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles. - Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Atanu Barai; Nandakishore Santhi; Stephan J. Eidenbenz (ICS, 2020)

NVIDIA GPGPUs Instructions Energy Consumption. - Yehia Arafa; Ammar ElWazir; Abdelrahman Elkanishy; Youssef Aly; Ayatelrahman Elsayed; Abdel-Hameed A. Badawy; Gopinath Chennupati; Stephan J. Eidenbenz; Nandakishore Santhi (ISPASS, 2020)

PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace. - Atanu Barai; Gopinath Chennupati; Nandakishore Santhi; Abdel-Hameed A. Badawy; Yehia Arafa; Stephan J. Eidenbenz (MEMSYS, 2020)

An Out of Memory tSVD for Big-Data Factorization. - Hector Carrillo-Cabada; Erik Skau; Gopinath Chennupati; Boian S. Alexandrov; Hristo N. Djidjev (IEEE Access, 2020)

Code Characterization With Graph Convolutions and Capsule Networks. - Poornima Haridas; Gopinath Chennupati; Nandakishore Santhi; Phillip Romero; Stephan J. Eidenbenz (IEEE Access, 2020)

Decoy selection for protein structure prediction via extreme gradient boosting and ranking. - Nasrin Akhter; Gopinath Chennupati; Hristo N. Djidjev; Amarda Shehu (BMC Bioinform., 2020)

Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports. - Sayera Dhaubhadel; Jamaludin Mohd-Yusof; Kumkum Ganguly; Gopinath Chennupati; Sunil Thulasidasan; Nicolas W. Hengartner; Brent J. Mumphrey; Eric B. Durbin; Jennifer A. Doherty; Mireille Lemieux; Noah Schaefferkoetter; Georgia D. Tourassi; et al. (CoRR, 2020)

Distributed non-negative matrix factorization with determination of the number of latent features. - Gopinath Chennupati; Raviteja Vangara; Erik Skau; Hristo N. Djidjev; Boian S. Alexandrov (J. Supercomput., 2020)

An Effective Baseline for Robustness to Distributional Shift. - Sunil Thulasidasan; Sushil Thapa; Sayera Dhaubhadel; Gopinath Chennupati; Tanmoy Bhattacharya 001; Jeff A. Bilmes (ICMLA, 2021)

Hybrid, scalable, trace-driven performance modeling of GPGPUs. - Yehia Arafa; Abdel-Hameed A. Badawy; Ammar ElWazir; Atanu Barai; Ali Eker; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (SC, 2021)

Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization. - Raviteja Vangara; Manish Bhattarai; Erik Skau; Gopinath Chennupati; Hristo N. Djidjev; Thomas Tierney; James P. Smith; Valentin G. Stanev; Boian S. Alexandrov (IEEE Access, 2021)

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes. - Gopinath Chennupati; Nandakishore Santhi; Phillip Romero; Stephan J. Eidenbenz (ACM Trans. Model. Comput. Simul., 2021)

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. - Gopinath Chennupati; Milind Rao; Gurpreet Chadha; Aaron Eakin; Anirudh Raju; Gautam Tiwari; Anit Kumar Sahu; Ariya Rastrow; Jasha Droppo; Andy Oberlin; Buddha Nandanoor; Prahalad Venkataramanan; et al. (KDD, 2022)

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques. - Shamminuj Aktar; Hamdy Abdelkhalik; Nazmul Haque Turja; Yehia Arafa; Atanu Barai; Nishant Panda; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz; Abdel-Hameed A. Badawy (CoRR, 2022)

Can Calibration Improve Sample Prioritization? - Ganesh Tata; Gautham Krishna Gudur; Gopinath Chennupati; Mohammad Emtiyaz Khan (CoRR, 2022)

Improved Protein Decoy Selection via Non-Negative Matrix Factorization. - Nasrin Akhter; Kazi Lutful Kabir; Gopinath Chennupati; Raviteja Vangara; Boian S. Alexandrov; Hristo N. Djidjev; Amarda Shehu (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

PPT-Multicore: performance prediction of OpenMP applications using reuse profiles and analytical modeling. - Atanu Barai; Yehia Arafa; Abdel-Hameed A. Badawy; Gopinath Chennupati; Nandakishore Santhi; Stephan J. Eidenbenz (J. Supercomput., 2022)

Federated Self-Learning with Weak Supervision for Speech Recognition. - Milind Rao; Gopinath Chennupati; Gautam Tiwari; Anit Kumar Sahu; Anirudh Raju; Ariya Rastrow; Jasha Droppo (ICASSP, 2023)

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques. - Hamdy Abdelkhalik; Shamminuj Aktar; Yehia Arafa; Atanu Barai; Gopinath Chennupati; Nandakishore Santhi; Nishant Panda; Nirmal Prajapati; Nazmul Haque Turja; Stephan J. Eidenbenz; Abdel-Hameed A. Badawy (ICPADS, 2023)

Learning When to Trust Which Teacher for Weakly Supervised ASR. - Aakriti Agrawal; Milind Rao; Anit Kumar Sahu; Gopinath Chennupati; Andreas Stolcke (INTERSPEECH, 2023)

Federated Representation Learning for Automatic Speech Recognition. - Guruprasad V. Ramesh; Gopinath Chennupati; Milind Rao; Anit Kumar Sahu; Ariya Rastrow; Jasha Droppo (CoRR, 2023)

General-purpose Unsupervised Cyber Anomaly Detection via Non-negative Tensor Factorization. - Maksim Ekin Eren; Juston S. Moore; Erik Skau; Elisabeth Moore; Manish Bhattarai; Gopinath Chennupati; Boian S. Alexandrov (DTRAP, 2023)

Significant ASR Error Detection for Conversational Voice Assistants. - John Harvill; Rinat Khaziev; Scarlett Li; Randy Cogill; Lidan Wang; Gopinath Chennupati; Hari Thadakamalla (ICASSP, 2024)