In addition to the broad scope of papers below, check out our AI/ML Research Spotlight page for selected high-impact publications.
Alkasimi A., Pham A-V., Gardner C., Funsten B. (2023). “Human Activity Recognition Based on 4-Domain Radar Deep Transfer Learning.” Proceedings of the IEEE Radar Conference.
Antoniuk E.R., Cheon G., Wang G., et al. (2023). “Predicting the Synthesizability of Crystalline Inorganic Materials from the Data of Known Material Compositions.” npj Computational Materials.
Bertin N., Zhou F. (2023). “Accelerating Discrete Dislocation Dynamics Simulations with Graph Neural Networks.” Journal of Computational Physics.
Broberg D., Bystrom K., Srivastava S., et al. (2023). “High-Throughput Calculations of Charged Point Defect Properties with Semi-Local Density Functional Theory—Performance Benchmarks for Materials Screening Applications.” npj Computational Materials.
Brown N., Echols B., Zarins J., Grosser T. (2023). “Exploring the Suitability of the Cerebras Wafer Scale Engine for Stencil-Based Computation Codes.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Bui V-H., Chang F., Su W., et al. (2023). “Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters.” 2023 International Conference on Computing, Networking and Communications, ICNC 2023.
Chapline G. (2023). “Quantum Mechanics and Bayesian Machines.” Quantum Mechanics and Bayesian Machines.
Chen Z., Kailkhura B., Zhou Y. (2023). “An Accelerated Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-Level Optimization.” Machine Learning.
Cheung S.W., Choi Y., Copeland D.M., Huynh K. (2023). “Local Lagrangian Reduced-Order Modeling for the Rayleigh-Taylor Instability by Solution Manifold Decomposition.” Journal of Computational Physics.
da Silva F.L., Goncalves A., Nguyen S., et al. (2023). “Language Model-Accelerated Deep Symbolic Optimization.” Neural Computing and Applications.
Glatt R., da Silva F.L., da Costa Bianchi R.A., Costa A.H.R. (2023). “A Study on Efficient Reinforcement Learning Through Knowledge Transfer.” Adaptation, Learning, and Optimization.
Gokhale T., Anirudh R., Thiagarajan J.J., et al. (2023). “Improving Diversity with Adversarially Learned Transformations for Domain Generalization.” Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023.
He X., Choi Y., Fries W.D., et al. (2023). “gLaSDI: Parametric Physics-Informed Greedy Latent Space Dynamics Identification.” Journal of Computational Physics.
Hoang D., Bhatia H., Lindstrom P., Pascucci V. (2023). “Progressive Tree-Based Compression of Large-Scale Particle Data.” IEEE Transactions on Visualization and Computer Graphics.
Huhn Q.A., Tano M.E., Ragusa J.C., Choi Y. (2023). “Parametric Dynamic Mode Decomposition for Reduced Order Modeling.” Journal of Computational Physics.
Ingólfsson H.I., Bhatia H., Aydin F., et al. (2023). “Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.” Journal of Chemical Theory and Computation.
Kadeethum T., Jakeman J.D., et al. (2023). “Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems.” IEEE Access.
Karargyris A., Umeton R., Sheller M.J., et al. (2023). “Federated Benchmarking of Medical Artificial Intelligence with MedPerf.” Nature Machine Intelligence.
Kesavan S.P., Bhatia H., Bhatele A., et al. (2023). “Scalable Comparative Visualization of Ensembles of Call Graphs.” IEEE Transactions on Visualization and Computer Graphics.
Klauber C., Burroughs H., Zhou A. (2023). “Collaborative and Autonomous Black Start: Theory and Implementation.” 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023.
Kuťák D., Vázquez P-P., Isenberg T., et al. (2023). “State of the Art of Molecular Visualization in Immersive Virtual Environments.” Computer Graphics Forum.
Leventhal S., Gyulassy A., Heimann M., Pascucci V. (2023). “Exploring Classification of Topological Priors with Machine Learning for Feature Extraction.” IEEE Transactions on Visualization and Computer Graphics.
Li S., Lindstrom P., Clyne J. (2023). “Lossy Scientific Data Compression with SPERR.” Proceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023.
Li Y., Zhao P., Lin X., et al. (2023). “Less is More: Data Pruning for Faster Adversarial Training.” CEUR Workshop Proceedings.
Liu Y., Ponce C., Brunton S.L., Kutz J.N. (2023). “Multiresolution Convolutional Autoencoders.” Journal of Computational Physics.
Paz Soldan Palma J., Gong R., Bocklund B.J., et al. (2023). “Thermodynamic Modeling with Uncertainty Quantification using the Modified Quasichemical Model in Quadruplet Approximation: Implementation into PyCalphad and ESPEI.” Calphad: Computer Coupling of Phase Diagrams and Thermochemistry.
Pearce O., Brink S. (2023). “Finding the Forest in the Trees: Enabling Performance Optimization on Heterogeneous Architectures through Data Science Analysis of Ensemble Performance Data.” International Journal of High Performance Computing Applications.
Subramanyam R., Heimann M., Jayram T.S., et al. (2023). “Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification.” Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023.
Sun H., Shang S-L., Gong R., et al. (2023). “Thermodynamic Modeling of the Nb-Ni System with Uncertainty Quantification using PyCalphad and ESPEI.” Calphad: Computer Coupling of Phase Diagrams and Thermochemistry.
Thopalli K., Anirudh R., Turaga P., Thiagarajan J.J. (2023). “The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation.” IEEE Access.
Trümper L., Ben-Nun T., Schaad P., et al. (2023). “Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization.” Proceedings of the International Conference on Supercomputing.
Tsiokanos I., Tompazi S., Georgakoudis G., et al. (2023). “ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis.” IEEE Transactions on Computers.
Vita J.A., Schwalbe-Koda D. (2023). “Data Efficiency and Extrapolation Trends in Neural Network Interatomic Potentials.” Machine Learning: Science and Technology.
Witman M.D., Goyal A., Ogitsu T., et al. (2023). “Defect Graph Neural Networks for Materials Discovery in High-Temperature Clean-Energy Applications.” Nature Computational Science.
Yang J., Dzanic T., Petersen B., et al. (2023). “Reinforcement Learning for Adaptive Mesh Refinement.” Proceedings of Machine Learning Research.
Yang J., Mittal K., Dzanic T., et al. (2023). “Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.” Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS.
Barrera J.L., Geiss M.J., and Maute K. (2022). “Minimum Feature Size Control in Level Set Topology Optimization via Density Fields.” Structural and Multidisciplinary Optimization.
Chakraborty I., Kelley B.M., and Gallagher B. (2022). “Device Classification for Industrial Control Systems using Predicted Traffic Features.” Frontiers in Computer Science.
Chapman J., Goldman N., and Wood B.C. (2022). “Efficient and Universal Characterization of Atomic Structures through a Topological Graph Order Parameter.” npj Computational Materials.
Chen E., Tamm A., Wang T., et al. (2022). “Modeling Antiphase Boundary Energies of Ni3Al-Based Alloys using Automated Density Functional Theory and Machine Learning.” npj Computational Materials.
Clement M.D., Logan N.C., and Boyer M.D. (2022). “Neoclassical Toroidal Viscosity Torque Prediction via Deep Learning.” Nuclear Fusion.
Copeland D.M., Cheung S.W., Huynh K., and Choi Y. (2022). “Reduced Order Models for Lagrangian Hydrodynamics.” Computer Methods in Applied Mechanics and Engineering.
da Silva F.L., MacAlpine P., Rădulescu R., et al. (2022). “Special Issue on Adaptive and Learning Agents.” Neural Computing and Applications.
Desai S., Reeve S.T., and Belak J.F. (2022). “Implementing a Neural Network Interatomic Model with Performance Portability for Emerging Exascale Architectures.” Computer Physics Communications.
Garcia-Cardona C., Fernández-Godino M.G., O’Malley D., and Bhattacharya T. (2022). “Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture.” Computational Materials Science.
Iftekharuddin K., Preza C., Awwal A.A.S., and Zelinski M.E. (2022). “Artificial Intelligence and Machine Learning in Optical Information Processing: Introduction to the Feature Issue.” Applied Optics.
Ingolfsson H.I., Neale C., Carpenter T.S., et al. (2022). “Machine Learning–Driven Multiscale Modeling Reveals Lipid-Dependent Dynamics of RAS Signaling Proteins.” Proceedings of the National Academy of Sciences.
Kalvakala K.C., Pal P., Gonzalez J.P., et al. (2022). “Numerical Analysis of Soot Emissions from Gasoline-Ethanol and Gasoline-Butanol Blends under Gasoline Compression Ignition Conditions.” Fuel.
Kaplan A.D., Cheng Q., Mohan K.A., et al. (2022). “Mixture Model Framework for Traumatic Brain Injury Prognosis using Heterogeneous Clinical and Outcome Data.” IEEE Journal of Biomedical and Health Informatics.
Kim Y., Choi Y., Widemann D., and Zohdi T. (2022). “A Fast and Accurate Physics-Informed Neural Network Reduced Order Model with Shallow Masked Autoencoder.” Journal of Computational Physics.
Kustowski B., Gaffney J.A., Spears B.K., et al. (2022). “Suppressing Simulation Bias in Multi-Modal Data using Transfer Learning.” Machine Learning: Science and Technology.
Lapointe S., Guss G., Reese Z., et al. (2022). “Photodiode-Based Machine Learning for Optimization of Laser Powder Bed Fusion Parameters in Complex Geometries.” Additive Manufacturing.
Li S., Ding T., Jia W., et al. (2022). “A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems.” IEEE Transactions on Power Systems.
Lindsey R.K., Huy Pham C., Goldman N., et al (2022). “Machine-Learning a Solution for Reactive Atomistic Simulations of Energetic Materials.” Propellants, Explosives, Pyrotechnics.
Martin W., Sheynkman G., Lightstone F.C., et al. (2022). “Interpretable Artificial Intelligence and Exascale Molecular Dynamics Simulations to Reveal Kinetics: Applications to Alzheimer’s Disease.” Current Opinion in Structural Biology.
Mehra A., Kailkhura B., Chen P-Y., and Hamm J. (2022). “Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning.” 35th Conference on Neural Information Processing Systems.
Peterson J.L., Bay B., Koning J., et al. (2022). “Enabling Machine Learning-Ready HPC Ensembles with Merlin.” Future Generation Computer Systems.
Ritt C.L., Liu M., Pham T.A., et al. (2022). “Machine Learning Reveals Key Ion Selectivity Mechanisms in Polymeric Membranes with Subnanometer Pores.” Science Advances.
Sirunyan A.M., Tumasyan A., Adam W., et al. (2022). “A New Calibration Method for Charm Jet Identification Validated with Proton-Proton Collision Events at √s = 13TeV.” Journal of Instrumentation.
Taylor J.A., Larraondo P., and de Supinski B.R. (2022). “Data-Driven Global Weather Predictions at High Resolutions.” International Journal of High Performance Computing Applications.
Turner J.A., Belak J., Barton N., et al. (2022). “ExaAM: Metal Additive Manufacturing Simulation at the Fidelity of the Microstructure.” International Journal of High Performance Computing Applications.
Venkat A., Gyulassy A., Kosiba G., et al. (2022). “Towards Replacing Physical Testing of Granular Materials with a Topology-Based Model.” IEEE Transactions on Visualization and Computer Graphics.
Venkatakrishnan S.V., Mohan K.A., Ziabari A.K., and Bouman C.A. (2022). “Algorithm-Driven Advances for Scientific CT Instruments: From Model-Based to Deep Learning-Based Approaches.” IEEE Signal Processing Magazine.
Xia F., Allen J., Balaprakash P., et al. (2022). “A Cross-Study Analysis of Drug Response Prediction in Cancer Cell Lines.” Briefings in Bioinformatics.
Adams J., Morzfeld M., Joyce K., et al. (2021). “A Blocking Scheme for Dimension-Robust Gibbs Sampling in Large-Scale Image Deblurring.” Inverse Problems in Science and Engineering.
Alexander F.J., Ang J., Bilbrey J.A., et al. (2021). “Co-Design Center for Exascale Machine Learning Technologies (ExaLearn).” International Journal of High Performance Computing Applications.
Aligholian A., Shahsavari A., Stewart E.M., et al. (2021). “Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements.” IEEE Transactions on Smart Grid.
Anirudh R., Lohit S., and Turaga P. (2021). “Generative Patch Priors for Practical Compressive Image Recovery.” Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision.
Anirudh R., Thiagarajan J.J., Sridhar R., and Bremer P-T. (2021). “MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis.” Frontiers in Big Data.
Bernstein J. (2021). “Probabilistic Data Association for Orbital-Element Estimation using Multistage Expectation–Maximization.” Journal of Aerospace Information Systems.
Bhatia H., Carpenter T.S., Ingólfsson H.I., et al. (2021). “Machine-Learning-Based Dynamic-Importance Sampling for Adaptive Multiscale Simulations.” Nature Machine Intelligence.
Bhatia H., Kirby R.M., Pascucci V., and Bremer P-T. (2021). “Vector Field Decompositions using Multiscale Poisson Kernel.” IEEE Transactions on Visualization and Computer Graphics.
Bhatia H., Natale F.D., Moon J.Y., et al. (2021). “Generalizable Coordination of Large Multiscale Workflows: Challenges and Learnings at Scale.” International Conference for High Performance Computing, Networking, Storage and Analysis, SC.
Bhatia H., Petruzza S.N., Anirudh R., et al. (2021). “Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Cadena J., Ray P., Chen H., et al. (2021). “Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks.” IEEE Transactions on Signal Processing.
Chen C., Kailkhura B., Goldhahn R., and Zhou Y. (2021). “Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing.” Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems.
Choi Y., Brown P., Arrighi W., et al. (2021). “Space–Time Reduced Order Model for Large-Scale Linear Dynamical Systems with Application to Boltzmann Transport Problems.” Journal of Computational Physics.
Djordjević B.Z., Kemp A.J., Kim J., et al. (2021). “Modeling Laser-Driven Ion Acceleration with Deep Learning.” Physics of Plasmas.
Fiore S., Nassisi P., Nuzzo A., et al. (2021). “A Climate Change Community Gateway for Data Usage & Data Archive Metrics across the Earth System Grid Federation.” CEUR Workshop Proceedings.
Glatt R., Silva F.L.D., Soper B., et al. (2021). “Collaborative Energy Demand Response with Decentralized Actor and Centralized Critic.” BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments.
Greenberg H.J., Watson J-P., and Woodruff D.L. (2021). “Parametric Stochastic Programming with One Chance Constraint: Gaining Insights from Response Space Analysis.” International Series in Operations Research and Management Science.
Guo L., Li D., and Laguna I. (2021). “PARIS: Predicting Application Resilience using Machine Learning.” Journal of Parallel and Distributed Computing.
Hatfield P.W., Gaffney J.A., Anderson G.J., et al. (2021). “The Data-Driven Future of High-Energy-Density Physics.” Nature.
Heimann M., Murić G., Ferrara E. (2021). “Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales.” Studies in Computational Intelligence.
Hickmann K., Shutt D., Robinson A.K., and Lind J. (2021). “Data-Driven Learning of Impactor Strength Properties from Shock Experiments with Additively Manufactured Materials.” Proceedings of SPIE - The International Society for Optical Engineering.
Hoang D., Bhatia H., Lindstrom P., and Pascucci V. (2021). “High-Quality and Low-Memory-Footprint Progressive Decoding of Large-Scale Particle Data.” Proceedings - 2021 IEEE 11th Symposium on Large Data Analysis and Visualization.
Hoang D., Summa B., Bhatia H., et al. (2021). “Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution.” IEEE Transactions on Visualization and Computer Graphics.
Hong W., Chakraborty I., Wang H., and Tao G. (2021). “Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System using Future Speed Prediction.” IEEE Transactions on Intelligent Vehicles.
Huang X., Klacansky P., Petruzza S., et al. (2021). “Distributed Merge Forest: A New Fast and Scalable Approach for Topological Analysis at Scale.” Proceedings of the International Conference on Supercomputing.
Islam T.Z., Liang P.W., Sweeney F., et al. (2021). “College Life Is Hard! - Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis.” Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021.
Jacobs S.A., Moon T., McLoughlin K., et al. (2021). “Enabling Rapid COVID-19 Small Molecule Drug Design through Scalable Deep Learning of Generative Models.” The International Journal of High Performance Computing Applications.
Jain A., Moon T., Benson T., et al. (2021). “SUPER: SUb-graph parallelism for TransformERs.” Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium.
Jayatilaka T., Ueno H., Georgakoudis G., et al. (2021). “Towards Compile-Time-Reducing Compiler Optimization Selection via Machine Learning.” ACM International Conference Proceeding Series.
Jia R., Wu F., Sun X., et al. (2021). “Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Jones D., Kim H., Zhang X., et al. (2021). “Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.” Journal of Chemical Information and Modeling.
Kailkhura B., Chen P-Y., Lin X., and Li B. (2021). “Editorial: Safe and Trustworthy Machine Learning.” Frontiers in Big Data.
Kaplan A.D., Cheng Q., Mohan K.A., et al. (2021). “Mixture Model Framework for Traumatic Brain Injury Prognosis using Heterogeneous Clinical and Outcome Data.” IEEE Journal of Biomedical and Health Informatics.
Kesavan S., Bhatia H., Bhatele A., et al. (2021). “Scalable Comparative Visualization of Ensembles of Call Graphs.” IEEE Transactions on Visualization and Computer Graphics.
Li L., Weber M., Xu X., et al. (2021). “TSS: Transformation-Specific Smoothing for Robustness Certification.” Proceedings of the ACM Conference on Computer and Communications Security.
Liao C., Lin P-H., Verma G., et al. (2021). “HPC Ontology: Towards a Unified Ontology for Managing Training Datasets and AI Models for High-Performance Computing.” Proceedings of MLHPC 2021: Workshop on Machine Learning in High Performance Computing Environments. Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis.
Lohit S., Anirudh R., and Turaga P. (2021). “Recovering Trajectories of Unmarked Joints in 3D Human Actions using Latent Space Optimization.” Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision.
Mackay S., Ponce C., Osborn S., and McGarry M. (2021). “Finding Diverse Ways to Improve Algebraic Connectivity through Multi-Start Optimization.” Journal of Complex Networks.
Mallick A., Dwivedi C., Kailkhura B., et al. (2021). “Deep Kernels with Probabilistic Embeddings for Small-Data Learning.” 37th Conference on Uncertainty in Artificial Intelligence.
McBane S. and Choi Y. (2021). “Component-Wise Reduced Order Model Lattice-Type Structure Design.” Computer Methods in Applied Mechanics and Engineering.
McDonald T., Shrestha R., Yi X., et al. (2021). “Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion.” Computer Graphics Forum.
McLoughlin K.S., Jeong C.G., Sweitzer T.D., et al. (2021). “Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump.” Journal of Chemical Information and Modeling.
Mehra A., Kailkhura B., Chen P-Y., and Hamm J. (2021). “How Robust are Randomized Smoothing Based Defenses to Data Poisoning?” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Meng R., Soper B., Lee H.K.H., et al. (2021). “Nonstationary Multivariate Gaussian Processes for Electronic Health Records.” Journal of Biomedical Informatics.
Mohan K.A. and Kaplan A.D. (2021). “AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning.” IEEE Journal of Biomedical and Health Informatics.
Muniraju G., Kailkhura B., Thiagarajan J.J., et al. (2021). “Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization.” IEEE Transactions on Neural Networks and Learning Systems.
Nan Z., Guan H., Shen X., and Liao C. (2021). “Deep NLP-Based Co-Evolvement for Synthesizing Code Analysis from Natural Language.” CC 2021 - Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction.
Narayanaswamy V.S., Thiagarajan J.J., and Spanias A. (2021). “On the Design of Deep Priors for Unsupervised Audio Restoration.” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH.
Narayanaswamy V.S., Thiagarajan J.J., and Spanias A. (2021). “Using Deep Image Priors to Generate Counterfactual Explanations.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.
Nguyen H.T., Bhatele A., Jain N., et al. (2021). “Visualizing Hierarchical Performance Profiles of Parallel Codes using CallFlow.” IEEE Transactions on Visualization and Computer Graphics.
Nguyen N.T.T., Maskaly G.R., Liao A.S., et al. (2021). “Predicting Shockwaves in Radiograph Images using Different Deep Learning Models.” Proceedings of SPIE - The International Society for Optical Engineering.
Nguyen P., Loveland D., Kim J.T., et al. (2021). “Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning.” Journal of Chemical Information and Modeling.
Oyama Y., Maruyama N., Dryden N., et al. (2021). “The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism.” IEEE Transactions on Parallel and Distributed Systems.
Pineda Flores S.D. (2021). “Chembot: A Machine Learning Approach to Selective Configuration Interaction.” Journal of Chemical Theory and Computation.
Ramadan T., Islam T.Z., Phelps C., et al. (2021). “Comparative Code Structure Analysis using Deep Learning for Performance Prediction.” Proceedings - 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021.
Rao S., Narayanaswamy V., Esposito M., et al. (2021). “COVID-19 Detection using Cough Sound Analysis and Deep Learning Algorithms.” Intelligent Decision Technologies.
Rao S., Narayanaswamy V., Esposito M., et al. (2021). “Deep Learning with Hyper-Parameter Tuning for COVID-19 Cough Detection.” IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications.
Rivera D., Bernstein J., Schmidt K., et al. (2021). “Bayesian Calibration of Strength Model Parameters from Taylor Impact Data.” Computational Materials Science.
Sohn C., Choi H., Kim K., et al. (2021). “Line Chart Understanding with Convolutional Neural Network.” Electronics (Switzerland).
Song H., Thiagarajan J.J., and Kailkhura B. (2021). “Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications.” Frontiers in Artificial Intelligence.
Steil T., Reza T., Iwabuchi K., et al. (2021). “Tripoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata.” International Conference for High Performance Computing, Networking, Storage and Analysis, SC.
Stevenson G.A., Jones D., Kim H., et al. (2021). “High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models.” International Conference for High Performance Computing, Networking, Storage and Analysis, SC.
Sun D., He S., Bennett W.F.D., et al. (2021). “Atomistic Characterization of Gramicidin Channel Formation.” Journal of Chemical Theory and Computation.
Taylor J.A., Larraondo P., and de Supinski B.R. (2021). “Data-Driven Global Weather Predictions at High Resolutions.” International Journal of High Performance Computing Applications.
Thomas D., Meyers J., and Kahn S.M. (2021). “Improving Astronomy Image Quality through Real-Time Wavefront Estimation.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
Verma G., Emani M., Liao C., et al. (2021). “HPCFAIR: Enabling FAIR AI for HPC Applications.” Proceedings of MLHPC 2021: Workshop on Machine Learning in High Performance Computing Environments. Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis.
Woo S., Kim K., Noh J., et al. (2021). “Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs.” Electronics (Switzerland).
Wood C., Georgakoudis G., Beckingsale D., et al. (2021). “Artemis: Automatic Runtime Tuning of Parallel Execution Parameters using Machine Learning.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Wyatt M.R., Yamamoto V., Tosi Z., et al. (2021). “Is Disaggregation Possible for HPC Cognitive Simulation?” Proceedings of MLHPC 2021: Workshop on Machine Learning in High Performance Computing Environments. Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis.
Xu Y., Korkali M., Mili L., et al. (2021). “An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty.” IEEE Transactions on Smart Grid.
Youssef K., Iwabuchi K., Feng W-C., and Pearce R. (2021). “Privateer: Multi-Versioned Memory-Mapped Data Stores for High-Performance Data Science.” 2021 IEEE High Performance Extreme Computing Conference.
Zeno G., La Fond T., and Neville J. (2021). “DYMOND: Dynamic Motif-Nodes Network Generative Model.” The Web Conference 2021 - Proceedings of the World Wide Web Conference.
Zhang Z., Kailkhura B., and Han T.Y-J. (2021). “Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows.” ACS Omega.
Zhong X., Gallagher B., Eves K., et al. (2021). “A Study of Real-World Micrograph Data Quality and Machine Learning Model Robustness.” NPJ Computational Materials.
Zhu J., Lu X., Heimann M., and Koutra D. (2021). “Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding.” SIAM International Conference on Data Mining.
Anirudh R., Thiagarajan J.J., Bremer P-T., et al. (2020). “Accurate Calibration of Agent-Based Epidemiological Models with Neural Network Surrogates.” Preprint.
Anirudh R., Thiagarajan J.J., Bremer P-T., and Spears B.K. (2020). “Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies.” PNAS.
Cadena J., Sales A.P., Lam D., et al. (2020). “Modeling the Temporal Network Dynamics of Neuronal Cultures.” PLOS Computational Biology.
Choi Y., Boncoraglio G., Anderson S., et al. (2020). “Gradient-Based Constrained Optimization using a Database of Linear Reduced-Order Models.” JCP.
Choi Y., Brown P., Arrighi W., et al. (2020). “Space–Time Reduced Order Model for Large-Scale Linear Dynamical Systems with Application to Boltzmann Transport Problems.” JCP.
Choi Y., Coombs D., and Anderson R. (2020). “SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction.” SISC.
Feiger B., Gounley J., Adler D., et al. “Accelerating Massively Parallel Hemodynamic Models of Coarctation of the Aorta using Neural Networks.” Scientific Reports.
Hoang C., Choi Y., and Carlberg K. (2020). “Domain-Decomposition Least-Squares Petrov-Galerkin (DD-LSPG) Nonlinear Model Reduction.” CMAME.
Kim Y., Choi Y., Widemann D., and Zohdi T. (2020). “A Fast and Accurate Physics-Informed Neural Network Reduced Order Model with Shallow Masked Autoencoder.” JCP.
Kim H., Han J., and Han T.Y-J. (2020). “Machine Vision-Driven Automatic Recognition of Particle Size and Morphology in SEM Images.” Nanoscale.
Lee X.Y., Sahab S.K., Sarkar S., and Giera B. (2020). “Two Photon Lithography Additive Manufacturing: Video Dataset of Parameter Sweep of Light Dosages, Photo-Curable Resins, and Structures.” Data in Brief.
Liu S., Anirudh R., Thiagarajan J.J., and Bremer P-T. (2020). “Uncovering Interpretable Relationships in High-Dimensional Scientific Data Through Function Preserving Projections.” Mach. Learn.: Sci. Technol.
Maitia A. Venkat A., Kosiba G.D., et al. (2020). “Topological Analysis of X-Ray CT Data for the Recognition and Trending of Subtle Changes in Microstructure under Material Aging.” Computational Materials Science.
Narayanaswamy V., Thiagarajan J.J., Anirudh R., and Spanias A. (2020). “Unsupervised Audio Source Separation using Generative Priors.” Preprint.
Pallotta G. and Santer B.(2020). “Multi-Frequency Analysis of Simulated Versus Observed Variability in Tropospheric Temperature.” Journal of Climate.
Shanthamallu U., Thiagarajan J.J., and Spanias A. (2020). “Regularized Attention Mechanism for Graph Attention Networks.” IEEE ICASSP.
Soper B.C., Nygård M., Abdulla G., et al. (2020). “A Hidden Markov Model for Population‐Level Cervical Cancer Screening Data.” Statistics in Medicine.
Thiagarajan J.J., Bremer P-T., Anirudh R., Get al. (2020). “Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models.” Preprint.
Thiagarajan J.J., Rajan D., Katoch S., and Spanias A. (2020).“DD xNet: A Deep Learning Model for Automatic Interpretation of Electronic Health Records, Electrocardiograms and Electroencephalograms.” Scientific Reports.
Thiagarajan J.J., Venkatesh B., Anirudh R., et al. (2020). “Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models.” Nat Commun 11.
Thiagarajan J.J., Venkatesh B., and Rajan D. (2020). “Learn-by-Calibrating: Using Calibration as a Training Objective.” IEEE ICASSP.
Thiagarajan J.J., Venkatesh B., Sattigeri P., and Bremer P-T. (2020). “Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors.” 34th AAAI Conference on Artificial Intelligence.
Tipnis U., Abbas K., Tran E., et al. (2020). “Functional Connectome Fingerprint Gradients in Young Adults.” Preprint.
Zhang J., Kailkhura B., and Han T.Y-J. (2020). “Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning.” ICML.
Alom, Z., Taha, T., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, S., Hasan, M., Van Essen, B., Awwal, A., and Asari, V. (2019). “A State-of-the-Art Survey on Deep Learning Theory and Architectures.” Electronics.
Anirudh, R., Kim, H., Thiagarajan, J.J., Mohan, A.K., and Champley, K. (2020). “Improving Limited Angle CT Reconstruction with a Robust GAN Prior.” Neurips 2019: Solving Inverse Problems with Deep Learning Workshop.
Anirudh, R. and Thiagarajan, J.J. (2019). “Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Anirudh, R., Thiagarajan, J.J., Liu, S., Bremer, P-T., and Spears, B. (2020). “Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion.” Neurips 2019: Machine Learning and the Physical Sciences Workshop.
Chu, A., Nguyen, D., Talathi, S.S., (…), Stolaroff, J.K., and Giera, B. (2019). “Automated Detection and Sorting of Microencapsulation: via Machine Learning.” Lab on a Chip.
Cong, G., Domeniconi, G., Shapiro, J., Zhou, F., and Chen, B. (2019). “Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs.” Proceedings of the 30th International Symposium on Computer Architecture and High Performance Computing.
Cong, G., Domeniconi, G., Yang, C.-C., Shapiro, J., and Chen, B. (2019). “Video Action Recognition with an Additional End-To-End Trained Temporal Stream.” 2019 IEEE Winter Conference on Applications of Computer Vision.
Deelman, E., Mandal, A., Jiang, M., and Sakellariou, R. (2019). “The Role of Machine Learning in Scientific Workflows.” International Journal of High Performance Computing Applications.
Druzgalski, C., Lapointe, S., Whitesides, R., and McNenly, M. (2020). “Predicting Octane Number from Microscale Flame Dynamics.” Combustion and Flame.
Dryden, N., Maruyama, N., Benson, T., Moon, T., Snir, M., and Van Essen, B. (2019). “Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism.” International Parallel and Distributed Processing Symposium.
Dryden, N., Maruyama, N., Moon, T., (…), Snir, M., and Van Essen, B. (2019). “Aluminum: An Asynchronous, GPU-Aware Communication Library Optimized for Large-Scale Training of Deep Neural Networks on HPC Systems.” Proceedings of Machine Learning in HPC Environments and the International Conference for High Performance Computing, Networking, Storage and Analysis.
Endrei, M., Jin, C., Dinh, M.N., (…), DeRose, L., and de Supinski, B.R. (2019). “Statistical and Machine Learning Models for Optimizing Energy in Parallel Applications.” International Journal of High Performance Computing Applications.
Fan, Y.J. (2019). “Autoencoder Node Saliency: Selecting Relevant Latent Representations.” Pattern Recognition.
Humbird, K.D., Peterson, J.L., and Mcclarren, R.G. (2019). “Deep Neural Network Initialization with Decision Trees.” IEEE Transactions on Neural Networks and Learning Systems.
Kafle, S., Gupta, V., Kailkhura, B., Wimalajeewa, T., and Varshney, P.K. (2019). “Joint Sparsity Pattern Recovery with 1-b Compressive Sensing in Distributed Sensor Networks.” IEEE Transactions on Signal and Information Processing over Networks.
Kailkhura, B., Gallagher, B., Kim, S., Hiszpanski, A., and Han, T.Y-J. (2019). “Reliable and Explainable Machine-Learning Methods for Accelerated Material Discovery.” npj Computational Materials.
Kamath, C. and Fan, Y.J. (2020). “Compressing Unstructured Mesh Data using Spline Fits, Compressed Sensing, and Regression Methods.” 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings 8646678, pp. 316-320.
Kim, S., Kim, H., Yoon, S., Lee, J., Kahou, S., Kashinath, K., and Prabhat, M. (2019). “Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events using ConvLSTM.” 2019 IEEE Winter Conference on Applications of Computer Vision.
Leach, W., Henrikson, J., Hatarik, R., (…), Palmer, N., and Rever, M. (2019). “Using Convolutional Neural Networks to Classify Static X-ray Imager Diagnostic Data at the National Ignition Facility.” Proceedings of the International Society for Optical Engineering.
Maiti, A. (2019). “Second-Order Statistical Bootstrap for the Uncertainty Quantification of Time-temperature-superposition Analysis.” Rheologica Acta.
Narayanaswamy, V., Thiagarajan, J.J., Anirudh, R., Forouzanfar, F., Bremer, P-T., and Wu, X-H. (2020). “Designing Deep Inverse Models for History Matching in Reservoir Simulations.” Neurips 2019: Machine Learning and the Physical Sciences Workshop.
Narayanaswamy, V.S., Thiagarajan, J.J., Song, H., and Spanias, A. (2019). “Designing an Effective Metric Learning Pipeline for Speaker Diarization.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Nathan, E., Sanders, G., and Henson, V.E. (2019). “Personalized Ranking in Dynamic Graphs using Nonbacktracking Walks.” Lecture Notes in Computer Science, including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
Patki, T., Thiagarajan, J.J., Ayala, A., and Islam, T. (2020). “Performance Optimality or Reproducibility: That Is the Question.” Supercomputing 2019.
Petersen, B.K., Yang, J., Grathwohl, W.S., (…), An, G., and Faissol, D.M. (2019). “Deep Reinforcement Learning and Simulation as a Path toward Precision Medicine.” Journal of Computational Biology.
Reza, T., Ripeanu, M. Tripoul, N., Sanders, G., and Pearce, R. (2019). “PruneJuice: Pruning Trillion-edge Graphs to a Precise Pattern-matching Solution.” Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis.
Roberts, R.S., Goforth, J.W., Weinert, G.F., (…), Stinson, B.J., and Duncan, A.M. (2019). “Automated Annotation of Satellite Imagery using Model-based Projections.” Proceedings of the Applied Imagery Pattern Recognition Workshop.
Shanthamallu, U., Li, Q., Thiagarajan, J.J., Anirudh, R., Kaplan, A., and Bremer, P-T. (2020). “Modeling Human Brain Connectomes using Structured Neural Networks.” Neurips 2019: Graph Representation Learning Workshop.
Shanthamallu, U., Thiagarajan, J.J., Song, H., and Spanias, A. (2020). “GrAMME: Semi-Supervised Learning using Multi-Layered Graph Attention Models.” IEEE Transactions on Neural Networks and Learning Systems.
Shukla, R., Lipasti, M., Van Essen, B., Moody, A., and Maruyama, N. (2019). “Remodel: Rethinking Deep CNN Models to Detect and Count on a Neurosynaptic System.” Frontiers in Neuroscience.
Song, H., and Thiagarajan, J.J. (2020). “Improved Deep Embeddings for Inferencing with Multi-Layered Graphs.” Deep Graph Learning: Methodologies and Applications, IEEE Big Data 2019.
Thiagarajan, J.J., Anirudh, R., Sridhar, R., and Bremer, P-T. (2019). “Unsupervised Dimension Selection using a Blue Noise Graph Spectrum.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Thiagarajan, J.J., Kashyap, S., and Karagyris, A. (2020). “Distill-to-Label: Weakly Supervised Instance Labeling using Knowledge Distillation.” IEEE International Conference on Machine Learning and Applications 2019.
Thiagarajan, J.J., Kim, I., Anirudh, R., and Bremer, P-T. (2019). “Understanding Deep Neural Networks through Input Uncertainties.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Thiagarajan, J.J., Rajan, D., and Sattigeri, P. (2019). “Understanding Behavior of Clinical Models under Domain Shifts.” 2019 KDD Workshop on Applied Data Science for Healthcare.
Thopalli, K., Anirudh, R., Thiagarajan, J.J., and Turaga, P. (2019). “Multiple Subspace Alignment Improves Domain Adaptation.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Tran, K., Panahi, A., Adiga, A., Sakla, W., and Krim, H. (2019). “Nonlinear Multi-Scale Super-resolution using Deep Learning.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Tripoul, N., Halawa, H., Reza, T., (…), Pearce, R., and Ripeanu, M. (2019). “There Are Trillions of Little Forks in the Road. Choose Wisely! Estimating the Cost and Likelihood of Success of Constrained Walks to Optimize a Graph Pruning Pipeline.” Proceedings of IA3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms, and the International Conference for High Performance Computing, Networking, Storage and Analysis.
Veldt, N., Klymko, C., and Gleich, D.F. (2019). “Flow-Based Local Graph Clustering with Better Seed Set Inclusion.” SIAM International Conference on Data Mining.
White, D.A., Arrighi, W.J., Kudo, J., and Watts, S.E. (2019). “Multiscale Topology Optimization using Neural Network Surrogate Models.” Computer Methods in Applied Mechanics and Engineering.
Yuan, B., Giera, B., Guss, G., Matthews, M., and McMains, S. (2019). “Semi-Supervised Convolutional Neural Networks for in-situ Video Monitoring of Selective Laser Melting.” IEEE Winter Conference on Applications of Computer Vision.
Anirudh R., Kim H., Thiagarajan J.J., et al. (2018). “Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion.” Conference on Computer Vision and Pattern Recognition.
Kamath C. and Fan Y.J. (2018). “Compressing Unstructured Mesh Data using Spline Fits, Compressed Sensing, and Regression Methods.” IEEE Global Conference on Signal and Information Processing.
Kamath C. and Fan Y.J. (2018). "Regression with small data sets: A case study using code surrogates in additive manufacturing." Knowledge and Information Systems: An International Journal.
Lin Y., Wang S., Thiagarajan J.J., et al. (2018). "Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm." Geophysical Journal International.
Liu S., Bremer P-T., Thiagarajan J.J., et al. (2018). "Visual Exploration of Semantic Relationships in Neural Word Embeddings." IEEE Transactions on Visualization and Computer Graphics.
Mundhenk T.N., Ho D., and Chen B.Y. (2018). "Improvements to context based self-supervised learning." Conference on Computer Vision and Pattern Recognition.
Rajan D. and Thiagarajan J.J. (2018). “A Generative Modeling Approach to Limited Channel ECG Classification.” Conference proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Song H., Rajan D., Thiagarajan J.J., and Spanias A. (2018). "Attend and Diagnose: Clinical Time Series Analysis using Attention Models." AAAI Conference.
Song H., Thiagarajan J.J., Sattigeri P., and Spanias A. (2018). "Optimizing Kernel Machines using Deep Learning." IEEE Transactions on Neural Networks and Learning Systems.
Song H., Willi M., Thiagarajan J.J., et al. (2018). “Triplet Network with Attention for Speaker Diarization.” Proceedings of the Annual Conference of the International Speech Communication Association.
Thiagarajan J.J., Anirudh R., Kailkhura B., et al. (2018). "PADDLE: Performance Analysis using a Data-driven Learning Environment." IEEE International Parallel and Distributed Processing Symposium.
Thiagarajan J.J., Jain N., Anirudh R., et al. (2018). “Bootstrapping Parameter Space Exploration for Fast Tuning.” Association for Computing Machinery.
Thiagarajan J.J., Liu S., Ramamurthy K., and Bremer P-T. (2018). "Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections." Conference on Visualization.
Zheng P., Aravkin A.Y., Ramamurthy K., and Thiagarajan J.J. (2018). "Visual Exploration of Semantic Relationships in Neural Word Embeddings." IEEE International Conference on Computer Vision Workshops.
Anirudh R., Kailkhura B., Thiagarajan J.J., and Bremer P-T. (2017). "Poisson Disk Sampling on the Grassmannian: Applications in Subspace Optimization." Conference on Computer Vision and Pattern Recognition.
Kim S., Ames, S., Lee J., et al. (2017). "Massive Scale Deep Learning for Detecting Extreme Climate Events." International Workshop on Climate Informatics.
Kim S., Ames, S., Lee J., et al. (2017). "Resolution Reconstruction of Climate Data with Pixel Recursive Model." IEEE International Conference on Data Mining.
Lennox K.P., Rosenfield P., Blair B., et al. (2017). "Assessing and Minimizing Contamination in Time of Flight Based Validation Data." Nuclear Instruments and Methods in Physics Research.
Li Q., Kailkhura B., Thiagarajan J.J., and Varshney P.K. (2017). "Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models." Conference on Neural Information Processing Systems.
Lin Y., Wang S., Thiagarajan J.J., et al. (2017). "Towards Real-Time Geologic Feature Detection from Seismic Measurements using a Randomized Machine-Learning Algorithm." SEG Annual Conference.
Marathe A., Anirudh R., Jain N., et al. (2017). "Performance Modeling Under Resource Constraints using Deep Transfer Learning." Supercomputing Conference.
Mudigonda M., Kim S., Mahesh A., et al. (2017). "Segmenting and Tracking Extreme Climate Events using Neural Networks." Conference on Neural Information Processing Systems.
Mundhenk N.T., Kegelmeyer L.M., and Trummer S.K. (2017). "Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility." Thirteenth International Conference on Quality Control by Artificial Vision.
Pallotta G., Konjevod G., Cadena J., and Nguyen P. (2017). "Context-aided Analysis of Community Evolution in Networks." Statistical Analysis and Data Mining: The ASA Data Science Journal.
Sakla W., Konjevod G., and Mundhenk N.T. (2017). "Deep Multi-modal Vehicle Detection in Aerial ISR Imagery." IEEE Winter Conference on Applications of Computer Vision.
Song H., Thiagarajan J.J., Sattigeri P., and Spanias A. (2017). "A Deep Learning Approach to Multiple Kernel Learning." IEEE International Conference on Acoustics, Speech and Signal Processing.
Zheng P., Aravkin A.Y., Ramamurthy K., and Thiagarajan J.J. (2017). "Learning Robust Representations for Computer Vision." IEEE International Conference on Computer Vision Workshops.
