Highlighted Publications
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Viet Dung Nguyen, Reynold Bailey, Gabriel J. Diaz, Chengyi Ma, Alexander Fix, and Alexander Ororbia. Deep domain adaptation: A Sim2Real neural approach for improving eye-tracking systems. In Proceedings of ACM Computer Graphics and Interactive Techniques, vol 7, issue 2, article 25, May 2024. (Best long paper award ACM ETRA 2024).
Kevin Barkevich, Reynold Bailey, and Gabriel J. Diaz. Using deep learning to increase eye-tracking robustness, accuracy, and precision in virtual reality. In Proceedings of ACM Computer Graphics and Interactive Techniques, vol 7, issue 2, article 27, May 2024.
Michael Peechatt, Cecilia O. Alm, and Reynold Bailey. 2024. MULTICOLLAB: A multimodal corpus of dialogues for analyzing collaboration and frustration in language. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, pages 11713-11722.
Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen, Reynold Bailey, Alexander G. Ororbia. A neural active inference model of perceptual-motor learning. Frontiers in Computational Neuroscience, vol. 17, February 2023.
Anisha Ashwath, Michael Peechatt, Cecilia O. Alm and Reynold Bailey. 2023. Early vs. late multimodal fusion for recognizing confusion in collaborative tasks. In Proceedings of the Affective Computing and Intelligent Interaction Conference: Late Breaking Results, Cambridge, Massachusetts, pages 1-4.
A'di Dust, Carola Gonzalez-Lebron, Shannon Connell, Saurav Singh, Reynold Bailey, Cecilia O. Alm, and Jamison Heard. 2023. Understanding differences in human-robot teaming dynamics between deaf/hard of hearing and hearing individuals. In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, pages 552–556.
Isabelle Arthur, Jordan Quinn, Rajesh Titung, Cecilia O. Alm, and Reynold Bailey. 2023. MDE – Multimodal data explorer for flexible visualization of multiple data streams. In Proceedings of the Affective Computing and Intelligent Interaction Conference: Demos, Cambridge, Massachusetts, pages 1-3.
Ammina Kothari, Andrea Orama, Rachel Miller, Matthew Peeks, Reynold Bailey, and Cecilia O. Alm. 2023. News consumption helps readers identify model-generated news. In Proceedings of the 2023 IEEE Western New York Image and Signal Processing Workshop, Rochester, New York, pages 1-10.
Cecilia O. Alm, Reynold Bailey, and Hannah Miller. 2022. Remote early research experiences for undergraduate students in computing. In Proceedings of the SIGCSE Technical Symposium 2022, pages43-49, Providence, Rhode Island.
Cecilia O. Alm and Reynold Bailey. 2022. Scientific skills, identity, and career aspiration development from early research experiences in computer science. Journal of Computational Science Education, 13(1): 2-16.
Camille Mince, Skye Rhomberg, Cecilia O. Alm, Reynold Bailey, and Alex Ororbia. 2022. Multimodal modeling of task-mediated confusion. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop (at NAACL 2022), 188-194.
Trent Rabe, Anisa Callis, Zhi Zheng, Jamison Heard, Reynold Bailey, and Cecilia O. Alm. 2022. Theory of mind assessment with human-human and human-robot interactions. n: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_41.
Angela Saquinaula, Adriel Juarez, Joe Geigel, Reynold Bailey, and Cecilia O. Alm. 2022. Emotional empathy and facial mimicry of avatar faces. In Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pages 770-771, doi: 10.1109/VRW55335.2022.00236.
Rakshit S. Kothari, Reynold J. Bailey, Christopher Kanan, Jeff B. Pelz, and Gabriel J. Diaz, 2022. EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking. In Proceedings of the ACM on Human-Computer Interaction, 6(ETRA), pp.1-17.
Olivia Greathouse, Anthony Illescas, Nalin Ranjan, Joe Geigel, Reynold Bailey, and Cecilia O. Alm. Quantifying Engagement Levels in Interaction with a Human vs. an Avatar Interlocutor. The Winthrop McNair Research Bulletin, p.9.
Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen, Reynold Bailey, and Alexander Ororbia. 2022. A Neural Active Inference Model of Perceptual-Motor Learning. arXiv preprint arXiv:2211.10419.
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Zimeng Lyu, Alexander Ororbia, Rui Li and Travis Desell. Minimally Supervised Regression using Topological Projections in Self-Organizing Maps. The International Joint Conference on Neural Networks (IJCNN 2025). Rome, Italy. July 2025. AbdElRahman ElSaid and Travis Desell. CG-CANTS-N: A Versatile Graph-Based Framework for Scalable and Adaptive Problem Solving Across Domains. The Genetic and Evolutionary Computation Conference Companion (GECCO 2025). Malaga, Spain. July 2025. Joshua Karns and Travis Desell. Evaluation Time Bias in Asynchronous Evolutionary Algorithms: A Replication Study and a Novel Mitigation Strategy. The Genetic and Evolutionary Computation Conference (GECCO 2025). Malaga, Spain. July 2025. Evan Patterson, Joshua Karns, Zimeng Lyu and Travis Desell. Visualizing the Dynamics of Neuroevolution with Genetic Distance Projections. The Genetic and Evolutionary Computation Conference (GECCO 2025). Malaga, Spain. July 2025. Best Paper Nominee. Hong Yang, Qi Yu and Travis Desell. Can We Ignore Labels in Out-of-Distribution Detection?. The Thirteenth International Conference on Learning Representations (ICLR 2025).. Singapore. April 2025. Zimeng Lyu, Devroop Kar, Matthew Simoni, Rohaan Nadeem, Avinash Bhojanapalli, Hao Zhang and Travis Desell. Evolving RNNs for Stock Forecasting: A Low Parameter Efficient Alternative to Transformers. The 28th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2025). Trieste, Italy. April 23-25, 2025. Zimeng Lyu, Pujan Thapa, and Travis Desell. Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification. The International Joint Conference on Neural Networks (IJCNN). Yokohama, Japan. June 30-July 5, 2024. Devroop Kar, Zimeng Lyu, Alexander G. Ororibia, Travis Desell, and Daniel Krutz. Enabling An Informed Contextual Multi-Armed Bandit Framework For Stock Trading With Neuroevolution. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. Jared Murphy, Devroop Kar, Joshua Karns, and Travis Desell. EXA-GP: Unifying Graph-Based Genetic Programming and Neuroevolution for Explainable Time Series Forecasting. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. Jared Murphy, Travis Desell. Minimizing the EXA-GP Graph-Based Genetic Programming Algorithm for Interpretable Time Series Forecasting. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. Aditya Shankar Thakur, Akshar Bajrang Awari, Zimeng Lyu, and Travis Desell. Efficient Neuroevolution using Island Repopulation and Simplex Hyperparameter Optimization. The 2023 IEEE Symposium Series on Computational Intelligence (SSCI 2023). Mexico City, Mexico. December 5-8, 2023. Amit Dilip Kini∗, Swaraj Sambhaji Yadav∗, Aditya Shankar Thakur, Akshar Bajrang Awari, Zimeng Lyu, and Travis Desell. Co-evolving Recurrent Neural Networks and their Hyperparameters with Simplex Hyperparameter Optimization. The Genetic and Evolutionary Computation Conference Companion (GECCO ’23 Companion). Lisbon, Portugal. July 15–19, 2023. *Indicates equal contribution. Joshua Karns and Travis Desell. Local Stochastic Differentiable Architecture Search for Memetic Neuroevolution Algorithms. The Genetic and Evolutionary Computation Conference Companion (GECCO ’23 Companion). Lisbon, Portugal. July 15–19, 2023. AbdElRahman ElSaid, Karl Ricanek, Zimeng Lyu, Alexander Ororbia and Travis Desell. Backpropagation-free 4D continuous ant-based neural topology search. Applied Soft Computing. August, 2023. Zimeng Lyu, Alexander Ororbia and Travis Desell. Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting. Applied Soft Computing. June, 2023. Javier Lenzi, Andrew Barnas, AbdElRahman ElSaid, Travis Desell, Robert Rockwell and Susan Ellis-Felege. Artificial Intelligence for Automated Detection of Large Mammals Creates Path to Upscale Drone Surveys. Scientific Reports. January, 2023. Farhad Akhbardeh, Marcos Zampieri, Cecilia O. Alm, and Travis Desell. 2022. Transfer learning methods for domain adaptation in technical logbook datasets. In Proceedings of the 13th Conference on Language Resources and Evaluation, pages 4235-4244, Marseille, France. |
Fromm, C. A., Maddox, R. K., Polonenko, M. J., Huxlin, K. R., and G. J. Diaz. "Multisensory stimuli facilitate low-level perceptual learning on a difficult global motion task in virtual reality". PLOS One, 20.3 (2025), e0319007.
Giguere, A. P., K. R. Huxlin, D. Tadin, B. R. Fajen, and G. J. Diaz. “Optic flow density modulates corner‑cutting in a virtual steering task for younger and older adults”. In: Scientific Reports 14.1 (2024), p. 27693.
N. V. Powell, X. Marshall, G. J. Diaz, and B. R. Fajen. “Coordination of gaze and action during high‑speed steering and obstacle avoidance”. In: PLOS ONE 19.3 (2024), e0289855. DOi: 10.1371/journal.pone.0289855.
Nguyen, Viet Dung, Reynold Bailey, Gabriel J. Diaz, Ma, Chengyi, Alexander Fix, and Alexander Ororbia. “Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye‑Tracking Systems”. In: Proceedings of the ACM on Computer Graphics and Interactive Techniques 7.2 (2024), pp. 1–17. DOi: 10.1145/3654703.
Barkevich, Kevin, Reynold Bailey, and Gabriel J. Diaz. “Using Deep Learning to Increase Eye‑Tracking Robustness, Accuracy, and Precision in Virtual Reality”. In: Proceedings of the ACM on Computer Graphics and Interactive Techniques 7.2 (2024), pp. 1–16. DOi: 10.1145/3654705.
J. Fooken, B. R. Baltaretu, D. A. Barany, G. Diaz, J. A. Semrau, T. Singh, and J. D. Crawford. “Perceptual‑Cognitive Integration for Goal‑Directed Action in Naturalistic Environments”. In: Journal of Neuroscience 43.45 (2023), pp. 7511–7522.
Aaron R. Seitz, Allison Sekuler, Barbara Dosher, Beverly A. Wright, Chang‑Bing Huang, C. Shawn Green, Christopher C. Pack, Dov Sagi, Dennis Levi, Duje Tadin, Elizabeth Quinlan, Fang Jiang, Gabriel J. Diaz, Geoffrey Ghose, Jozsef Fiser, Karen Banai, Kristina Visscher, Krystel Huxlin, Ladan Shams, Lorella Battelli, Marisa Carrasco, Michael Herzog, Michael Webster, Miguel Eckstein, Nicholas B. Turk‑Browne, Nitzan Censor, Peter De Weerd, Rufin Vogels, Shaul Hochstein, Takeo Watanabe, Yuka Sasaki, Uri Polat, Zhong‑Lin Lu, and Zoe Kourtzi. “Perceptual Learning: Policy Insights From Basic Research to Real‑World Applications”. In: Policy Insights from the //doi.org/10.1177/23727322231195268. URL: https://doi.org/10.1177/23727322231195268.
Yang, Z., G. J. Diaz, B. R. Fajen, R. Bailey, and A. G. Ororbia. “A neural active inference model of perceptual-motor learning”. In: Frontiers in Computational Neuroscience 17 (2023), p. 1099593. DOi: 10.3389/fncom.2023.1099593.
Rakshit S. Kothari, Reynold J. Bailey, Christopher Kanan, Jeff B. Pelz, and Gabriel J. Diaz, 2022. EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking. In Proceedings of the ACM on Human-Computer Interaction, 6(ETRA), pp.1-17.
Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen, Reynold Bailey, and Alexander Ororbia. 2022. A Neural Active Inference Model of Perceptual-Motor Learning. arXiv preprint arXiv:2211.10419.
Nathaniel Powell, Xavier Marshall, Gabriel Diaz, Brett Fajen; The visual control of gaze, steering, and obstacle avoidance in experienced quadcopter pilots. Journal of Vision 2022;22(14):4315. doi: https://doi.org/10.1167/jov.22.14.4315.
Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen, Reynold Bailey, Alexander Ororbia; An active inference model of anticipation in locomotor interception. Journal of Vision 2022;22(14):4027. doi: https://doi.org/10.1167/jov.22.14.4027.
A. K. Chaudhary, N. Nair, R. J. Bailey, J. B. Pelz, S. S. Talathi and G. J. Diaz, “: From real infrared eye-images to synthetic sequences of gaze behavior,” in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 11, pp. 3948-3958, Nov. 2022, doi: 10.1109/TVCG.2022.3203100.
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Bockrath, K., Ernst, L., Nadeem, R., Pedraza, B., and Dera, D. (2025). Trustworthy navigation with variational policy in deep reinforcement learning. Frontiers in Robotics and AI, 12, 1652050. https://doi.org/10.3389/frobt.2025.1652050.
Carannante, G., Bouaynaya, N. C., Dera, D., Fathallah-Shaykh, H. M., and Rasool, G., “SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks,” Pattern Recognition, 2025. https://doi.org/10.1016/j.patcog.2025.112503.
Flack, D., Tripathi, A.*, Waqas, A.*, Rasool, G., and Dera, D., “Robust Multimodal Fusion for Oncology.” Cancer Informatics Journal. 2025; 24. doi:10.1177/11769351251376192.
Li, B., Ding, K., Dera, D., “MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan populations.” Journal of Medical Imaging 12(2), 024007 (22 April 2025). https://doi.org/10.1117/1.JMI.12.2.024007.
Bhavsar, P., Safro, I., Bouaynaya, N., Polikar R., Dera, D., Dutta, P., and Aminul, O., Chapter 13 - Artificial intelligence in transportation data analytics, in Data Analytics for Intelligent Transportation Systems (Second Edition), Elsevier, 2025, Pages 337-382. https://doi.org/10.1016/B978-0-443-13878-2.00008-4.
Lekhak, S., Ientilucci, J. E., Dera, D., Ghosh, S., August 2025. “Uncertainty Quantification in Surface Landmines and UXO Classification using MC Dropout.” IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
Dera, D., Ahmed, S., Rasool, G., and Bouaynaya, N. C., “Trustworthy Uncertainty Propagation for Sequential Time-Series Analysis in RNNs,” in IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 2, pp. 882-896, Feb. 2024. Doi: 10.1109/TKDE.2023.3288628.
Kandel, D., Dera, D., July 2024. “Adaptive Robust Continual Learning based on Bayesian Variational Uncertainty Propagation.” IEEE 27th International Conference on Information Fusion (FUSION).
Quaye, K., Xu, P., Dera, D., Foltz, H., Tarawneh, C., Diaz A., “Feature Extraction from Vibration Signature Required from Railroad Bearing Onboard Condition Monitoring Sensor Modules,” ASME/IEEE Joint Rail Conference, Columbia, South Carolina, USA. May 13–15, 2024, 87776, V001T01A013. https://doi.org/10.1115/JRC2024-130045.
Zhou, K., and Dera, D. (2024). “Robust Denoising and DenseNet Classification Framework for Plant Disease Detection,” in the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 166-174.
Bin Karim, F. R., Dera, D., “Robust Bayesian Vision Transformer for Image Analysis and Classification,” in IEEE Western New York Image and Signal Processing Workshop (WNYISPW), Rochester, NY, USA, 2023, pp. 1-4, doi: 10.1109/WNYISPW60588.2023.10349631.
Pedraza, B., and Dera, D., “Robust Active Simultaneous Localization and Mapping Based on Bayesian Actor-Critic Reinforcement Learning,” IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, June 2023, pp. 63-66, doi: 10.1109/CAI54212.2023.00035.
Ezequiel Zubieta, Ryan Missel, Susana B Araujo Furlan, Carlos O Lousto, Federico Garcia, Santiago del Palacio, Guillermo Gancio, Jorge A Combi and Linwei Wang, Study of the 2024 major Vela glitch at the Argentine Institute of Radioastronomy, Astronomy and Astrophysics 698, A72, 2025.
Dillon J Dzikowicz, Brenda Hernandez-Romero, Nancy Wood, Beau W Abar, David H Adler, Linwei Wang, and Wojciech Zareba, Utilization of an undergraduate emergency department research associate program for the screening and recruitment of research subjects with heart failure into a clinical student, Contemporary Clinical Trials Communications (43), 2025.
Maryam Toloubidokhti, Ryan Missel, Shichang Lian, and Linwei Wang. Meta-Learning Physics-Informed Neural Networks for Personalized Cardiac Modeling. Medical Image Computing & Computer-Assisted Intervention (MICCAI), 2025.
Kailong Fan, Yubo Ye, Huafeng Liu, and Linwei Wang. IMREPET: Implicit Neural Representation for Unsupervised Dynamic PET Reconstruction. Medical Image Computing & Computer-Assisted Intervention (MICCAI), 2025.
Ryan Missel, Linwei Wang. Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How and When to Adapt. International Conference on Learning Representations (ICLR), 2025.
Pradeep Bajracharya, Rui Li, Linwei Wang, On the Interdependence between Data Selection and Architecture Optimization in Deep Active Learning, Transactions of Machine Learning Research, accepted, 2024.
Nikhil Shenoy, Maryam Toloubidokhti, Omar Gharbia, MirMilad P Khoshknab, Saman Nazarian, John L Sapp, Vivek Singh, Ankur Kapoor, Linwei Wang. A novel 3D camera-based ECG-imaging system for electrode position discovery and heart-torso registration. IEEE Journal of Biomedical and Health Informatics, 2024.
C. Meisenzahl, K Gillette, A. J. Prassl, G. Plank, J. L. Sapp, and L. Wang. BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-Mapping. Computers in Biology and Medicine 182 (November):109201, 2024.
Y. Ye, M. Toloubidokhti, S. Vadhavkar, X. Jiang, H. Liu, and L. Wang. On the Identifiability of Hybrid Physics-Neural Models: Meta-Learning as a Solution. Neural Information Processing Systems (NeurIPS), 2024.
D. Wang, S. Azadvar, J. S. Heiselman, X. Jiang, M. Miga, and L. Wang, LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, LNCS 15006: 359-368, 2024.
Maryam Toloubidokhti, Yubo Ye, Ryan Missel, Xiajun Jiang, Nilesh Kumar, Ruby Shrestha and Linwei Wang. DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics Informed Neural Networks. International Conference on Learning Representations (ICLR), 2024.
Guohan Sun, Can Qin, Huazhu Fu, Linwei Wang, and Zhiqiang Tao. Self-Training Large Language and Vision Assistant for Medical Question-Answering. EMLNP, 2024.
Y Ye, M Tolou, S Vadhavkar, X Jiang, H Liu, L Wang. 2024. On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution. Advances in Neural Information Processing Systems 37, 7714-7735.
Xiajun Jiang, Ryan Missel, Maryam Toloubidokhti, Karli Gillette, Anton J Prassl, Gernot Plank, B Milan Horáček, John L Sapp, Linwei Wang. Hybrid neural state-space modeling for supervised and unsupervised electrocardiographic imaging. IEEE Transactions on Medical Imaging 43 (8), 2733-2744, 2024.
X Jiang, R Missel, Z Li, L Wang. Sequential latent variable models for few-shot high-dimensional time-series forecasting. The Eleventh International Conference on Learning Representations. 2023.
Ezequiel Zubieta, Ryan Missel, Valentina Sosa Fiscella, Carlos O Lousto, Santiago del Palacio, Federico G López Armengol, Federico García, Jorge A Combi, Linwei Wang, Luciano Combi, Guillermo Gancio, Carolina Negrelli, Eduardo M Gutiérrez. First results of the glitching pulsar monitoring programme at the Argentine Institute of Radioastronomy. Monthly Notices of the Royal Astronomical Society 521 (3), 4504-4521, 2023.
DJ Dzikowicz, M Aktas, L Wang, W Zareba. 2023. Topic Models Using Notes From Electronic Medical Records Can Classify Non ST Elevation Myocardial Infarction Patients Based on the Presence of an Occluded Culprit Artery. Circulation 148 (Suppl_1), A15133-A15133.
P Bajracharya, AJ Prassl, K Gillette, G Plank, L Wang. Parameter Estimation for Personalized Cardiac Models via Active Learning. 2023 Computing in Cardiology (CinC) 50, 1-4, 2023.
R Missel, J Raphael, C Haggerty, D Hartzel, J Ruhl, B Fornwalt, L Wang. A Comparative Analysis of Data-Driven Modelling Techniques for 30-Day Heart Failure Readmission Prediction. 2023 Computing in Cardiology (CinC) 50, 1-4, 2023.
M Toloubidokhti, OA Gharbia, N Trayanova, J Sapp, L Wang. Feasibility of ECGI Endocardial Solutions in Localizing the VT Reentrant Circuit. 2023 Computing in Cardiology (CinC) 50, 1-4, 2023.
N Kumar, PK Gyawali, S Ghimire, L Wang. Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 255-265, 2023.
Y Ye, H Liu, X Jiang, M Toloubidokhti, L Wang. A spatial-temporally adaptive PINN framework for 3D bi-ventricular electrophysiological simulations and parameter inference. International Conference on Medical Image Computing and Computer-Assisted Intervention, 163-172, 2023.
N Shenoy, M Toloubidokhti, L Wang, V Singh, A Kapoor. A 3D Camera-Based Approach to High-Density ECG Imaging. 2023 Computing in Cardiology (CinC) 50, 1-4, 2023.
X Jiang, R Missel, Z Li, L Wang. Sequential latent variable models for few-shot high-dimensional time-series forecasting. International Conference on Learning Representations (ICLR), 2023.
Xiajun Jiang, Maryam Toloubidokhti, Jake Bergquist, Brian Zenger, Wilson W Good, Rob S MacLeod, and Linwei Wang. Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences. IEEE Transactions on Medical Imaging, 2022.
Carlos O Lousto, Ryan Missel, Harshkumar Prajapati, Valentina Sosa Fiscella, Federico G López Armengol, Prashnna Kumar Gyawali, Linwei Wang, Nathan D Cahill, Luciano Combi, Santiago del Palacio, Jorge A Combi, Guillermo Gancio, Federico García, Eduardo M Gutiérrez, Fernando Hauscarriaga. Vela pulsar: single pulses analysis with machine learning techniques. Monthly Notices of the Royal Astronomical Society, 509(4), pp.5790-5808, 2022.
Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K Gyawali, Brian Zenger, Wilson W Good, Rob S MacLeod, Linwei Wang. Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 459-468). Springer, Cham, 2022.
Xiajun Jiang, Zhiyuan Li, Ryan Missel, Md Shakil Zaman, Brian Zenger, Wilson W Good, Rob S MacLeod, John L Sapp, and Linwei Wang. Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 46-56). Springer, Cham, 2022.
N. Otoo, K. Blue, G. N. Ramirez, E. Selinger, S. Foster and B. David-John, "Visceral Notices and Privacy Mechanisms for Eye Tracking in Augmented Reality," in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2025.3616837.
Stefan Maczynski, Amlan Ganguly, Mark Indovina, Purab Sutradhar, Sai Manoj Pudukotai Dinakarrao, and Sathwika Bavikadi. 2025. BrIM: A Branching In-Memory Accelerator. In Proceedings of the Great Lakes Symposium on VLSI 2025 (GLSVLSI '25). Association for Computing Machinery, New York, NY, USA, 982–989.
Stefan Maczynski, Amlan Ganguly, and Mark Indovina. 2025. The State of Simulation Frameworks for Evaluating Emerging LLM Accelerators. In Proceedings of the Great Lakes Symposium on VLSI 2025 (GLSVLSI '25). Association for Computing Machinery, New York, NY, USA, 562–567.
Sriparvathi Shaji Bhattathiri, Anton Bogovik, Masoud Abdollahi, Clark Hochgraf, Michael E. Kuhl, Amlan Ganguly, Andres Kwasinski, Ehsan Rashedi, Unlocking human-robot synergy: The power of intent communication in warehouse robotics, Applied Ergonomics, Volume 117, 2024, 104248, ISSN 0003-6870.
Andrea Galimberti; Rohan Purkait; Nahian Islam; Amlan Ganguly; Mark Indovina; Michael Zuzak, "ML-Assisted Attack Detection on NoC-Based Many-Cores Through On-Chip Traffic Monitoring," 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, 2024, pp. 1-4, doi: 10.1109/ICECS61496.2024.10848855.
S. Bavikadi, P. R. Sutradhar, M. Indovina, A. Ganguly and S. M. P. Dinakarrao, "ReApprox-PIM: Reconfigurable Approximate Look-Up-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2024.3367822.
Arnav Solanki and Zak Griffin and Purab Ranjan Sutradhar. Karisha Pradhan, Caiden Merritt, and Amlan Ganguly and Marc D. Riedel, “Neural network execution using nicked DNA and microfluidics”, PLOS One, 18 (10).
R. Gulia, A. Ganguly, A. Kwasinski, M. E. Kuhl, E. Rashedi and C. Hochgraf, "Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders," 2024 International Symposium on Networks, Computers and Communications (ISNCC), Washington DC, DC, USA, 2024, pp. 1-6.
S. Bavikadi, P. R. Sutradhar, A. Ganguly and S. M. P. Dinakarrao, "Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications," 2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID), Kolkata, India, 2024, pp. 222-227, doi: 10.1109/VLSID60093.2024.00043.
P. R. Sutradhar, S. Bavikadi, S. M. P. Dinakarrao, M. A. Indovina and A. Ganguly, "3DL-PIM: A Look-up Table oriented Programmable Processing in Memory Architecture based on the 3-D Stacked Memory for Data-Intensive Applications," in IEEE Transactions on Emerging Topics in Computing, doi: 10.1109/TETC.2023.3293140.
Gulia R, Vashist A, Ganguly A, Hochgraf C, Kuhl ME. Evaluation of 60 GHz Wireless Connectivity for an Automated Warehouse Suitable for Industry 4.0. Information. 2023; 14(9):506. https://doi.org/10.3390/info14090506.
Mountford T, Dhavlle A, Tevebaugh A, Mansoor N, Dinakarrao SMP, Ganguly A. Address Obfuscation to Protect against Hardware Trojans in Network-on-Chips. Journal of Low Power Electronics and Applications. 2023; 13(3):50. https://doi.org/10.3390/jlpea13030050.
A Dhavlle, MM Ahmed, N Mansoor, K Basu, A Ganguly, SM PD, “Defense against On-Chip Trojans Enabling Traffic Analysis Attacks based on Machine Learning and Data Augmentation”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Accepted, In Press.
Amlan Ganguly, Salvatore Monteleone, Diana Goehringer, and Cristinel Ababei. 2023. Guest Editors Introduction: Special Issue on Network-on-Chip Architectures of the Future (NoCArc). J. Emerg. Technol. Comput. Syst. 19, 3, Article 19 (July 2023), 3 pages. https://doi.org/10.1145/3609500.
Purab Ranjan Sutradhar, Sathwika Bavikadi, Mark Indovina, Sai Manoj Pudukotai Dinakarrao, Amlan Ganguly, “FlutPIM: A Look-up Table-based Processing in Memory Architecture with Floating-point Computation Support for Deep Learning Applications”, Proceedings of the Great Lakes Symposium on VLSI 2023, 2023, pp. 207-211.
Sathwika Bavikadi, Purab Ranjan Sutradhar, Amlan Ganguly, Sai Manoj Pudukotai Dinakarrao, “Heterogeneous Multi-Functional Look-Up-Table-based Processing-in-Memory Architecture for Deep Learning Acceleration”, 2023 24th International Symposium on Quality Electronic Design (ISQED), 2023, pp. 1-8.
Sriparvathi Shaji Bhattathiri, Anton Bogovik, Masoud Abdollahi, Clark Hochgraf, Michael Kuhl, Amlan Ganguly, Andres Kwasinski, Ehsan Rashedi, “Towards Improving the Human-Robot Communication in Intelligent Materials Handling Environments”, IISE Annual Conference and Expo, 2023.
Prangon Das, Purab Ranjan Sutradhar, Mark Indovina, Sai Manoj Pudukotai Dinakarrao, Amlan Ganguly. “Implementation and Evaluation of Deep Neural Networks in Commercially Available Processing in Memory Hardware,” 2022 IEEE 35th International System-on-Chip Conference (SOCC), 2022, pp. 1-6, doi: 10.1109/SOCC56010.2022.9908126.
Sai Manoj Pudukotai Dinakarrao, Arun Joseph, Amlan Ganguly, Anand Haridass, and Vijay Janappa Reddi. 2022, June. Guest Editors’ Introduction: Special Issue on Benchmarking Machine Learning Systems and Applications, in IEEE Design & Test, vol. 39, no. 3, pp. 5-7. doi: 10.1109/MDAT.2021.3100547.
Amlan Ganguly, Sergi Abadal, Ishan Thakkar, Natalie Enright Jerger, Marc Riedel, Masoud Babaie, Rajeev Balasubramonian, Abu Sebastian, Sudeep Pasricha, and Baris Taskin. 2022, May-June. Interconnects for DNA, Quantum, In-Memory, and Optical Computing: Insights From a Panel Discussion, in IEEE Micro, vol. 42, no. 3, pp. 40-49, 1. doi: 10.1109/MM.2022.3150684.
Abhishek Vashist, Sharan Vidash Vidya Shanmugham, Amlan Ganguly, and Sai Manoj PD. 2022. DQN Based Exit Selection in Multi-Exit Deep Neural Networks for Applications Targeting Situation Awareness, 2022 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-6, doi: 10.1109/ICCE53296.2022.9730182.
Rahul Singh Glia, Sayed Ashraf Mamun, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, Andres Kwasinski, and Michael E Kuhl .2022. Evaluation of Wireless Connectivity in an Automated Warehouse at 60 GHz,” 2022 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-6. doi: 10.1109/ICCE53296.2022.9730123.
Patents:
Amlan Ganguly, Sai Manoj Pudukotai Dinakarrao, Mark Connolly, Purab Ranjan Sutradhar, Sathwika Bavikadi, and Mark Allen Indovina, Rochester Institute of Technology. 2022. Look-up table containing processor-in-memory cluster for data-intensive applications. U.S. Patent Application 17/717,947.
View more publications by Amlan >
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S. Singh and J. Heard, “A Human-Aware Decision Making System for Human-Robot Teams,” 2022 17th Annual System of Systems Engineering Conference (SOSE), 2022, pp. 268-273, doi: 10.1109/SOSE55472. 2022.9812641. |
Rahul Singh Gulia, Sayed Ashraf Mamun, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, Andres Kwasinski, and Michael E Kuhl. 2022. Evaluation of Wireless Connectivity in an Automated Warehouse at 60 GHz. 2022 IEEE International Conference on Consumer Electronics (ICCE), 2022, pp. 1-6. doi: 10.1109/ICCE53296.2022.9730123.
Caluã de Lacerda Pataca SooYeon Ahn Suhyeon Yoo JooYeong Kim Khai N. Truong Jin-Hyuk Hong Roshan L Peiris Matt Huenerfauth. 2025. CuCap: Comparative Analysis of Customized Captioning between North American and South Korean d/Deaf and Hard-of-Hearing Users. In Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '25). Association for Computing Machinery, New York, NY, USA.
Caluã de Lacerda Pataca, Saad Hassan, Lloyd May, Michelle Olson, Toni D'Aurio, Roshan Peiris, Matt Huenerfauth. 2025. Tactile Emotions: Multimodal Affective Captioning with Haptics Improves Narrative Engagement for d/Deaf and Hard-of-Hearing Viewers. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA.
Matthew Seita, Sarah Andrew, Matt Huenerfauth. 2025. Notification Designs for Influencing Hearing Speakers' Behaviors During Captioned Conversations Among Mixed DHH-Hearing. In Proceedings of the 22nd International Web for All Conference (W4A’25). Association for Computing Machinery, New York, NY, USA.
Saad Hassan, Caluã de Lacerda Pataca, Akhter Al Amin, Laleh Nourian, Diego Navarro, Sooyeon Lee, Alexis Gordon, Matthew Watkins, Garreth W. Tigwell, and Matt Huenerfauth. 2024. “Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners.” ACM Transactions on Accessible Computing 17, 3, Article 14 (September 2024), 35 pages. https://doi.org/10.1145/3690647
Elahe Vahdani, Longlong Jing, Matt Huenerfauth, and Yingli Tian. 2024. Multi-Modal Multi-Channel American Sign Language Recognition, International Journal of Artificial Intelligence and Robotics Research, Volume 1, Number 1, Page 2450001. https://doi.org/10.1142/S2972335324500017
Oliver Alonzo, Sooyeon Lee, Akhter Al Amin, Mounica Maddela, Wei Xu, and Matt Huenerfauth. 2024. "Design and Evaluation of an Automatic Text Simplification Prototype with Deaf and Hard-of-hearing Readers." In Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '24). Association for Computing Machinery, New York, NY, USA, Article 40, 1–18. https://doi.org/10.1145/3663548.3675645
Caluã de Lacerda Pataca, Saad Hassan, Nathan Tinker, Roshan Lalintha Peiris, and Matt Huenerfauth. 2024. "Caption Royale: Exploring the Design Space of Affective Captions from the Perspective of Deaf and Hard-of-Hearing Individuals." In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24). Association for Computing Machinery, New York, NY, USA, Article 899, 1–17. https://doi.org/10.1145/3613904.3642258
Conference Award: Honorable Mention for Best Paper (top 5% of submissions), CHI 2024
Caluã de Lacerda Pataca, Matthew Watkins, Roshan Peiris, Sooyeon Lee, and Matt Huenerfauth. 2023. "Visualization of Speech Prosody and Emotion in Captions: Accessibility for Deaf and Hard-of-Hearing Users." In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 831, 1–15. https://doi.org/10.1145/3544548.3581511
Akhter Al Amin, Saad Hassan, Matt Huenerfauth, and Cecilia Ovesdotter Alm. 2023. "Modeling Word Importance in Conversational Transcripts: Toward improved live captioning for Deaf and hard of hearing viewers." In Proceedings of the 20th International Web for All Conference (W4A '23). Association for Computing Machinery, New York, NY, USA, 79–83. https://doi.org/10.1145/3587281.3587290
Akhter Al Amin, Saad Hassan, Sooyeon Lee, and Matt Huenerfauth. 2023. "Understanding How Deaf and Hard of Hearing Viewers Visually Explore Captioned Live TV News." In Proceedings of the 20th International Web for All Conference (W4A '23). Association for Computing Machinery, New York, NY, USA, 54–65. https://doi.org/10.1145/3587281.3587287
Akhter Al Amin, Joseph Mendis, Raja Kushalnagar, Christian Vogler, and Matt Huenerfauth. 2023. "Who is speaking: Unpacking In-text Speaker Identification Preference of Viewers who are Deaf and Hard of Hearing while Watching Live Captioned Television Program." In Proceedings of the 20th International Web for All Conference (W4A '23). Association for Computing Machinery, New York, NY, USA, 44–53. https://doi.org/10.1145/3587281.3587286
Conference Award: Best Technical Paper Candidate, W4A 2023
Oliver Alonzo, Sooyeon Lee, Mounica Maddela, Wei Xu, Matt Huenerfauth. 2022. “A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification.” Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).
Matt Huenerfauth. 2022. “Human-Computer Interaction and Automatic Text Simplification: Understanding the Perspective of Deaf and Hard of Hearing Users.” Keynote Presentation, Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).
Joseph Bochner, Vincent Samar, Emily Prud’hommeaux, and Matt Huenerfauth. 2022. “Phoneme Categorization in Prelingually Deaf Adult Cochlear Implant Users.” Journal of Speech, Language, and Hearing Research, Volume 65, pages 4429–4453, November 2022. https://doi.org/10.1044/2022_JSLHR-22-00038.
Oliver Alonzo, Lisa Elliot, Becca Dingman, Sooyeon Lee, Akhter Al Amin, and Matt Huenerfauth. 2022. “Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals in the U.S.: Their Reading Experiences, Interests and Perceptions of Social Accessibility.” ACM Transactions on Accessible Computing, 15, 2, Article 16 (June 2022), 31 pages. https://doi.org/10.1145/3520198.
Saad Hassan, Sooyeon Lee, Dimitris Metaxas, Carol Neidle, and Matt Huenerfauth. 2022. “Understanding ASL Learners’ Preferences for a Sign Language Recording and Automatic Feedback System to Support Self-Study.” In Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ‘22). Association for Computing Machinery, New York, NY, USA, Article 85, 1–5. https://doi.org/10.1145/ 3517428.3550367.
Saad Hassan, Akhter Al Amin, Caluã de Lacerda Pataca, Diego Navarro, Alexis Gordon, Sooyeon Lee, and Matt Huenerfauth. 2022. “Support in the Moment: Benefits and use of video-span selection and search for sign-language video comprehension among ASL learners.” In Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ‘22). Association for Computing Machinery, New York, NY, USA, Article 29, 1–14. https://doi.org/10.1145/3517428.3544883.
Conference Award: Best Paper Nominee, ASSETS 2022. (Top 5% of submissions.)
Saad Hassan, Matthew Seita, Larwan Berke, Yingli Tian, Elaine Gale, Sooyeon Lee, Matt Huenerfauth. 2022. “ASL-Homework-RGBD Dataset: An annotated dataset of 45 fluent and non-fluent signers performing American Sign Language homeworks.” In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, at the Language Resources and Evaluation Conference (LREC 2022).
Zhaoyang Xia, Yuxiao Chen, Qilong Zhangli, Matt Huenerfauth, Carol Neidle, Dimitris Metaxas. 2022. “Sign Language Video Anonymization.” In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, at the Language Resources and Evaluation Conference (LREC 2022).
Akhter Al Amin, Saad Hassan, Cecilia Alm, Matt Huenerfauth. 2022. “Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users.” The Second Workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI), at the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Association for Computational Linguistics. Pages 35-40. https://aclanthology.org/2022.ltedi-1.5 DOI: 10.18653/v1/2022.ltedi-1.5.
Akhter Al Amin, Saad Hassan, Sooyeon Lee, and Matt Huenerfauth. 2022. “Watch It, Don’t Imagine It: Creating a Better Caption-Occlusion Metric by Collecting More Ecologically Valid Judgments from DHH Viewers.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 459, 1–14. https://doi.org/10.1145/3491102.3517681.
Matthew Seita, Sooyeon Lee, Sarah Andrew, Kristen Shinohara, and Matt Huenerfauth. 2022. “Remotely Co-Designing Features for Communication Applications using Automatic Captioning with Deaf and Hearing Pairs.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 460, 1–13. https://doi.org/10.1145/3491102.3501843.
Abraham Glasser, Matthew Watkins, Kira Hart, Sooyeon Lee, and Matt Huenerfauth. 2022. “Analyzing Deaf Abraham Glasser, Matthew Watkins, Kira Hart, Sooyeon Lee, and Matt Huenerfauth. 2022. “Analyzing Deaf and Hard-of-Hearing Users’ Behavior, Usage, and Interaction with a Personal Assistant Device that Understands Sign-Language Input.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 306, 1–12. https://doi.org/10.1145/3491102.3501987.
Oliver Alonzo, Jessica Trussell, Matthew Watkins, Sooyeon Lee, and Matt Huenerfauth. 2022. “Methods for Evaluating the Fluency of Automatically Simplified Texts with Deaf and Hard-of-Hearing Adults at Various Literacy Levels.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 267, 1–10. https://doi.org/10.1145/3491102.3517566.
Saad Hassan, Akhter Al Amin, Alexis Gordon, Sooyeon Lee, and Matt Huenerfauth. 2022. “Design and Evaluation of Hybrid Search for American Sign Language to English Dictionaries: Making the Most of Imperfect Sign Recognition.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 195, 1–13. https://doi.org/10.1145/3491102.3501986.
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Rahul Singh Gulia, Sayed Ashraf Mamun, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, Andres Kwasinski, Michael E Kuhl. “Evaluation of Wireless Connectivity in an Automated Warehouse at 60 GHz,” 2022 IEEE International Conference on Consumer Electronics (ICCE), 2022, pp. 1-6, doi: 10.1109/ICCE53296.2022.9730123.
Ankita Tondwalkar and Andres Kwasinski. 2022. Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation. arXiv preprint arXiv:2205.13944.
John Jenco, Omar Abdul Latif, Andres Kwasinski, and Muhieddin Amer. 2022. Network Slicing for Wireless Networks Operating in a Shared Spectrum Environment. 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2435-2440.
Andres Kwasinski and Alexis Kwasinski. 2022. Increasing Physical Resiliency of Wireless Networks through Virtual Energy Transfer. 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2541-2546. doi: 10.1109/WCNC51071.2022.9771622.
Rahul Singh Gulia, Sayed Ashraf Mamun, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, Andres Kwasinski, and Michael E Kuhl. 2022. Evaluation of Wireless Connectivity in an Automated Warehouse at 60 GHz. 2022 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-6. doi: 10.1109/ICCE53296.2022.9730123.
Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla: Understanding imbalanced data: XAI & interpretable ML framework. Machine Learning 113(6): 3751-3769 (2024)
Gabriel Aguiar, Bartosz Krawczyk, Alberto Cano: A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. Machine Learning 113(7): 4165-4243 (2024)
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz: The class imbalance problem in deep learning. Machine Learning 113(7): 4845-4901 (2024)
Mohammed Ayyat, Tamer Nadeem, Bartosz Krawczyk: ClassyNet: Class-Aware Early-Exit Neural Networks for Edge Devices. IEEE Internet of Things Journal 11(9): 15113-15127 (2024)
William C. Sleeman, Martha I. Roseberry, Preetam Ghosh, Alberto Cano, Bartosz Krawczyk: Improved KD-tree based imbalanced big data classification and oversampling for MapReduce platforms. Applied Intelligence 54(23): 12558-12575 (2024)
Lukasz Korycki, Bartosz Krawczyk: Class-Incremental Mixture of Gaussians for Deep Continual Learning. CVPR Workshops 2024: 4097-4106
Jedrzej Kozal, Jan Wasilewski, Bartosz Krawczyk, Michal Wozniak: Continual Learning with Weight Interpolation. CVPR Workshops 2024: 4187-4195
Jan Wasilewski, Michal Wozniak, Bartosz Krawczyk: Combining Active Learning and Self-Labeling for Deep Learning from Data Streams. ICDM Workshops 2024: 842-849
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla: DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Transactions on Neural Networks and Learning Systems 34(9): 6390-6404 (2023)
Lukasz Korycki, Bartosz Krawczyk: Adversarial concept drift detection under poisoning attacks for robust data stream mining. Machine Learning 112(10): 4013-4048 (2023)
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla: Efficient Augmentation for Imbalanced Deep Learning. ICDE 2023: 1433-1446
Mohammed Ayyat, Tamer Nadeem, Bartosz Krawczyk: Class-Aware Neural Networks for Efficient Intrusion Detection on Edge Devices. SECON 2023: 204-212
View more publications by Bartosz
Corey Peters, Ran Adler, Garry Goldstein, Yong-Xin Yao, Nicola Lanatà, and Gabriel Kotliar, "Efficient force calculations in strongly correlated materials within DFT+G using Portobello", Computer Physics Communications 316, 109784 (2025).
Tsung-Han Lee, Corey Melnick, Ran Adler, Xue Sun, Yong-Xin Yao, Nicola Lanatà, Gabriel Kotliar, "Charge self-consistent density functional theory plus ghost rotationally-invariant slave-boson theory for correlated materials", Phys. Rev. B 110, 115126 (2024).
Tao Jiang, John Rogers, Marius S. Frank, Ove Christiansen, Yong-Xin Yao, and Nicola Lanatà, "Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning", Phys. Rev. Research 6, 033069 (2024).
Marius S. Frank, Denis G. Artiukhin, Tsung-Han Lee, Yong-Xin Yao, Kipton Barros, Ove Christiansen, and Nicola Lanatà, "Active Learning approach to simulations of Strongly Correlated Matter with the Ghost Gutzwiller Approximation", Phys. Rev. Research 6, 013242 (2024).
Tsung-Han Lee, Corey Melnick, Ran Adler, Nicola Lanatà, and Gabriel Kotliar, "Accuracy of ghost-rotationally-invariant slave-boson theory for multiorbital Hubbard models and realistic materials", Phys. Rev. B 108, 245147 (2023).
Nicola Lanatà, "Derivation of the Ghost Gutzwiller Approximation from Quantum Embedding principles: the Ghost Density Matrix Embedding Theory", Phys. Rev. B 108, 235112 (2023).
Nicola Lanatà, "Slave Boson theories of multi-orbital correlated systems", Open-Access book, Chapter 15: Autumn School on Correlated Electrons: Orbital Physics in Correlated Matter, Forschungszentrum Jülich (2023).
Daniele Guerci, Massimo Capone, and Nicola Lanatà, "Time-dependent ghost-Gutzwiller non-equilibrium dynamics", Phys. Rev. Research 5, L032023 (2023).
Tsung-Han Lee, Nicola Lanatà, and Gabriel Kotliar, "Accuracy of ghost-rotationally-invariant slave-boson and dynamical mean field theory as a function of the impurity-model bath size", Phys. Rev. B 107, L121104 (2023).
Alla Chikina, Gargee Bhattacharyya, Davide Curcio, Charlotte E. Sanders, Marco Bianchi, Nicola Lanatà, Matthew Watson, Cephise Cacho, Martin Bremholm and Philip Hofmann, “One-dimensional electronic states in a natural misfit structure”, Phys. Rev. Materials 6, L092001 (2022).
Nicola Lanatà, “Operatorial formulation of the ghost rotationally-invariant slave-Boson theory”, Phys. Rev. B 105, 045111 (2022).
Alfred J. H. Jones, Ryan Muzzio, Sahar Pakdel, Deepnarayan Biswas, Davide Curcio, Nicola Lanatà, Philip Hofmann, Kathleen M. McCreary, Berend T. Jonker, Kenji Watanabe, Takashi Taniguchi, Simranjeet Singh, Roland J. Koch, Chris Jozwiak, Eli Rotenberg, Aaron Bostwick, Jill A. Miwa, Jyoti Katoch and Søren Ulstrup, “Visualizing band structure hybridization and superlattice effects in twisted MoS2/WS2 heterobilayers”, 2D Mater. 9, 015032 (2022).
View more publications by Nicola
Pengfei Li, Jianyi Yang, Mohammad A. Islam, and Shaolei Ren. 2025. Making AI Less ’Thirsty’. Communications of the ACM, June 2025.
Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, and Shaolei Ren. 2025. Learning-augmented online control for decarbonizing water infrastructures. In Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems (e-Energy), June 2025.
Pengfei Li, Nicolas Christianson, Jianyi Yang, Adam Wierman, and Shaolei Ren. 2025. Learning for sustainable online scheduling with competitive fairness guarantees. In Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems (e-Energy), June 2025.
Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, and Shaolei Ren. 2025. A water efficiency dataset for African data centers. In Proceedings of the 2025 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’25), pages 453–460.
Pengfei Li, Jianyi Yang, Adam Wierman, and Shaolei Ren. 2025. Learning-augmented decentralized online convex optimization in networks. In Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS), June 2025.
Runjia Zeng, James Chenhao Liang, Cheng Han, Zhiwen Cao, Jiahao Liu, Xiaojun Quan, Yingjie Victor Chen, Lifu Huang, Tong Geng, Qifan Wang, Dongfang Liu. Probabilistic Token Alignment for Large Language Model Fusion. NeurIPS. 2025.
Yijun Hu, Bing Fan, Xin Gu, Haiqing Ren, Dongfang Liu, Heng Fan, Libo Zhang. Robust Ego-Exo Correspondence with Long-Term Memory. NeurIPS. 2025.
Yiyang Liu, James Chenhao Liang, Heng Fan, Wenhao Yang, Yiming Cui, Xiaotian Han, Lifu Huang, Dongfang Liu, Qifan Wang, Cheng Han. All You Need is One: Capsule Prompt Tuning with a Single Vector. NeurIPS. 2025.
Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng, Sohail Dianat, Xiaotian Han, Raghuveer Rao, Xueling Zhang, Cheng Han, Lifu Huang, Dongfang Liu. MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper. EMNLP. 2025.
Taowen Wang, Cheng Han, James Chenhao Liang, Wenhao Yang, Dongfang Liu, Luna Xinyu Zhang, Qifan Wang, Jiebo Luo, Ruixiang Tang. Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics. ICCV. 2025.
Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Tong Geng, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu*. "Visual Agents as Fast and Slow Thinkers." ICLR. 2025.
Yiyang Liu, James Chenhao Liang, Ruixiang Tang, Yugyung Lee, Majid Rabbani, Sohail Dianat, Raghuveer Rao, Lifu Huang, Dongfang Liu*, Qifan Wang, Cheng Han. Re-Imagining Multimodal Instruction Tuning: A Representation View. ICLR. 2025.
Ruibing Song, Chuan Liu, Chunshu Wu, Ang Li, Dongfang Liu, Ying Nian Wu, Tong Geng. "DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models." ICLR. 2025.
Chuan Liu, Chunshu Wu, shihui cao, Mingkai Chen, James Chenhao Liang, Ang Li, Michael Huang, Chuang Ren, Ying Nian Wu, Dongfang Liu, Tong Geng. Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models. ICLR. 2025.
Runjia Zeng, Cheng Han, Qifan Wang, Chunshu Wu, Tong Geng, Lifu Huang, Ying Nian Wu, Dongfang Liu*. Visual Fourier Prompt Tuning. NeurIPS. 2024.
Cheng, Z., Liang, J., Choi, H., Tao, G., Cao, Z., Liu, D. and Zhang, X., 2022. Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches. ECCV 2022.
Cao, Z., Liu, D. and Chen, Y., 2022. Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian. ECCV 2022.
Yan, L., Ma, S., Wang, Q., Chen, Y., Zhang, X., Savakis, A. and Liu, D., 2022. Video Captioning Using Global-Local Representation. IEEE Transactions on Circuits and Systems for Video Technology.
Yan, L., Wang, Q., Cui, Y., Feng, F., Quan, X., Zhang, X. and Liu, D., 2022. GL-RG: Global-Local Representation Granularity for Video Captioning. IJCAI 2022.
Wang, Q., Fang, Y., Ravula, A., Feng, F., Quan, X. and Liu, D., 2022, April. WebFormer: The Web-page Transformer for Structure Information Extraction. In Proceedings of the ACM Web Conference 2022 (pp. 3124-3133).
Wang, Q., Yang, L., Quan, X., Feng, F., Liu, D., Xu, Z., Wang, S., and Ma, H. Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation . December 7-11, 2022. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 46 - 61, 2022 Association for Computational Linguistics.
Wang, W., Liang, J., and Liu, D. 2022 Learning Equivariant , Segmentation with Instance-Unique Querying. arXiv preprint arXiv:2210.00911.
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Eduardo Lima, Xumin Liu. 2025. Learning Optimal Heterogeneous Service Network Representation" Service Oriented Computing and Applications Journal.
Xumin Liu. 2025. IUSE: Engaging Non-Computing Majors in Hands-on Data Science Learning through a Web-based Learning Platform, ASEE 2025.
Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu. 2024. Balancing Feature Similarity and Label Variability for Optimal Size-Aware Subset Selection, ICML 2024.
Xumin Liu, Erik Golen, Rajendra K. Raj, Kimberly Fluet. 2023. Offering Data Science Coursework to Non-Computing Majors. DataEd@SIGMOD 2023.
Xumin Liu, Erik Golen, Rajendra K. Raj, Kimberly Fluet. 2023. A Web-Based Learning Platform for Teaching Data Science to Non-Computer Majors. FIE 2023: 1-9.
Xumin Liu and Erik Golen. 2023. Enhance Data Science Education for Non-Computing Majors through Accessible Hands-on Experiences, ASEE 2023.
Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu. 2024. Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection. ICML 2024
Xumin Liu, Erik Golen, Rajendra K Raj. 2022, March. Introducing Data Science Topics to Non-Computing Majors. SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2, p. 1201. https://doi.org/10.1145/3478432.3499156.
Xumin Liu, Erik Golen, Rajendra K Raj. 2022, March. DSLP: A Web-based Data Science Learning Platform to Support DS Education for Non-Computing Majors. SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2, p. 1181. https://doi.org/10.1145/3478432.3499255.
Moayad Alshangiti, Weishi Shi, Eduardo Lima, Xumin Liu, Qi Yu. 2022, November. Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews. Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022), pp. 558-569.
K. Ruck, J. Manico, D. Kloosterman, A. Loui, and M. Das, “System and method for predictive curation, production infrastructure,” US Patent No. 11,429,832, Aug. 30, 2022.
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Bao, M., Zhang, S., Ten Pas, C., Dollery, S.J., Bushnell, R.V., Yuqing, F.N.U., Liu, R., Lu, G., Tobin, G.J. and Du, K., 2022. Computer vision enabled funnel adapted sensing tube (FAST) for power-free and pipette-free nucleic acid detection. Lab on a Chip. |
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C. Merkel, “Enhancing Adversarial Attacks on Single-Layer NVM Crossbar-Based Neural Networks with Power Consumption Information,” IEEE 35th International System-on-Chip Conference (SOCC), pp. 1-6, 2022.
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Reem Alsuhaibani, Christian D. Newman, Michael J. Decker, Michael L. Collard, Jonathan I. Maletic, “An
Approach to Automatically Assess Method Names”, 30th International Conference on Program Comprehension,
2022, May 2022, Pages 202–213. https://doi.org/10.1145/3524610.3527780.
Peruma, Anthony and Christian D. Newman. “Understanding Digits in Identifier Names: An Exploratory
Study.” The 1st Intl. Workshop on Natural Language-based Software Engineering, NLBSE ’22, May 8, 2022,
Virtual Event, USA, arXiv:2203.00113v4 [cs.SE] 15 March 2022, to appear
View more publications by Christian >
Seongbum Seo, Sangbong Yoo, Hyelim Lee, Yun Jang, Ji Hwan Park, and Jeong-Nam Kim “A Sentence-Level Visualization of Attention in Large Language Models”, Proceedings of the Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL): Human Language Technologies (System Demonstrations), pages 313–320, 2025
Tien Tran, Hea-Na Lee, Ji Hwan Park, “Accessible Visualization for People with ADHD”, ACM conference on Human Factors in Computing Systems (CHI), 64, pp 1 – 19, 2024 (HONORABLE MENTION)
Ji Hwan Park, Vikash Prasad, Sydney Newsom, Fares Najar, and Rakhi Rajan, “idMotif: An Interactive Motif Identification in Protein Sequences”, IEEE Computer Graphics and Application, 44(3), pp 114 – 125, 2024
Braden Roper, James C. Mathews and Saad Nadeem, and Ji Hwan Park, “Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification”, IEEE Visualization and Visual Analytics (VIS), pp. 106-110, 2023
Songyuan Yao, Richard Van, Xiaoliang Pan, Ji Hwan Park, Yuezhi Mao, Jingzhi Pu, Ye Mei, and Yihan Shao, “Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations”, RSC Adv.,13, 4565-4577, 2023
Madhan Srinivasan Kumar, Veena Gujju, Ji Hwan Park, Debra Hogue, Abdul Rafeh Naqash, and Taha Al-Juhaishi, “Machine Learning can Outperform Ann Arbor Staging in Predicting Survival in Patient with Diffuse Large B-Cell Lymphoma: Analysis of a Large National Cancer Database”, Blood, vol. 142, Supplement 1, pp. 4513, 2023
View more publications by Ji
Rick Lagiewski, Victor Perotti. Customer experiences and situational vulnerability: An exploration of hotel services during a disaster. International Journal of Hospitality Management, Volume 108, 2023, 103360, ISSN 0278-4319. https://doi.org/10.1016/j.ijhm.2022.103360.
View more publications by Victor
Dhar, K., Jain, P., Amujo, O. E., & Rantanen, E. M. (2025). Machine learning in phishing detection: Comparison of models and human performance. Poster presentation at the 7th IEEE International Conference on Cognitive Machine Intelligence, Pittsburgh, PA (November 12, 2025)
Chiagoziem Onwuegbuche, F., Titung, R., Rantanen, E. M., Jurcut, A. D., Alm, C. O., & Pasquale, L. (2025). Securing the weakest link: Exploring affective states exploited in phishing emails with large language models. IEEE Access, 13, 173460–173486
Haas, T. R., & Rantanen, E. M. (2025). Usability of a Healthcare App Among Different User Groups. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 14(1), 77-81. SAGE Publications. DOI: 10.1177/2327857925141016
Alm, C., Bailey, R., Delany, S. J., Ifrim, G., Mac Namee, B., Rantanen, E. M., & Sahin, F. (2025). International Mobility for PhD Students: Key Learnings. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE TS 2025), Feb 26–Mar1, 2025, Pittsburgh, PA, USA. ACM, New York, NY, USA. DOI: 10.1145/3641555.370520460
Gebhardt, W., Ororbia, A., Heard, J., & Rantanen, E. (2025). A Neural Hypervector Model of Memory-Driven Spatial Navigation. In Proceedings of the Human Factors and Ergonomics Society 2025 Annual Meeting, 69(1). SAGE Publications. DOI: 10.1177/10711813251364803
Pulido, B., Haas, T., Reynolds, M., & Rantanen, E. (2025). The Future of Human Factors: Expanding Opportunities in Higher Education. In Proceedings of the Human Factors and Ergonomics Society 2025 Annual Meeting, 69(1). SAGE Publications. DOI: 10.1177/10711813251357894
Rantanen, E. M. (2025). Whither Human Factors in the Era of AI. In Proceedings of the Human Factors and Ergonomics Society 2025 Annual Meeting, 69(1). SAGE Publications. DOI: 10.1177/10711813251366292
Oksama, L., Kulomäki, J., Hyönä, J., & Rantanen, E. (2024). Measuring individual differences in multitasking ability. Journal of Vision, 24(10), 816
Alm, C. O., Rantanen, E., Shinohara, K., Sahin, F., BaileyShea, C., & Bailey, R. (2024). Achieving Diversity in AI-focused
Graduate Research Traineeships. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 2 (SIGCSE 2024). March 20–23, 2024, Portland, OR, USA. ACM. DOI: 10.1145/3626253.3635489
Gray, M. A., Yong, Z., Wasti, A., Rantanen, E. M., & Heard, J. R. (2023). Measuring temporal awareness for human-aware AI. In Proceedings of the Human Factors and Ergonomics Society 2023 Annual Meeting, 67(1), 1817–1823. SAGE Publications. DOI: 10.1177/21695067231192635
Lintern, G., Motavalli, A., Chua, Z., Rantanen, E.M., Peres, S.C. and Boorman, D., 2022. Rapid Development of a Hospital Checklist in a Time of COVID-19. Ergonomics in Design, 30(4), pp.5-13.
Bragg, T., Rantanen, E.M., Pelletier, J.M., and Rashedi, E. Cognitive Engineering Modeling of Phishing. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66 (1), pp. 2088-2092. doi: 10.1177/1071181322661330.
Kang, R., Rantanen, E.M., and Youngstrom, E.A. Machine Learning in Healthcare: Two Case Studies. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66 (1), pp. 774-778.
Kulomäki, J., Oksama, L., Rantanen, E. and Hyönä, J., 2022. Attention control in a demanding dynamic time-sharing environment: An eye-tracking study. Attention, Perception, & Psychophysics, 84(2), pp.352-371.
Genoese-Zerbi, Valentina and Justus Robertson. "Adversarial Strong Story Experience Management." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2025, Edmonton AB, CA. Ed. Seth Cooper and Matthew Guzdial. Washington, DC, USA: AAAI Press, 2025.
Robertson, Justus, Valentina Genoese-Zerbi, and Rogelio E. Cardona-Rivera. "State Space Visualization for Strong Story Experience Management Design." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2025, Edmonton AB, CA. Ed. Seth Cooper and Matthew Guzdial. Washington, DC, USA: AAAI Press, 2025.
Robertson, Justus, John Heiden, and Rogelio E. Cardona-Rivera. "Evolving Interactive Narrative Worlds." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2023, Salt Lake City, UT, USA. Ed. Markus Eger and Rogelio Enrique Cardona-Rivera. Washington, DC, USA: AAAI Press, 2023
Sulabh Kumra, Shirin Joshi, and Ferat Sahin. 2022. Learning Multi-step Robotic Manipulation Policies from Visual Observation of Scene and Q-value Predictions of Previous Action. arXiv preprint arXiv:2202.11280.
Celal Savur and Ferat Sahin, 2022, October. The 17th IEEE International Conference on Systems and Systems Engineering [Conference Reports], in IEEE Systems, Man, and Cybernetics Magazine, vol. 8, no. 4, pp. 57-59. doi: 10.1109/MSMC.2022.3205492.
Sulabh Kumra, Shirin Joshi, and Ferat Sahin. 2022. GR-ConvNet v2: A Real-Time Multi-Grasp Detection Network for Robotic Grasping. Sensors (Basel, Switzerland), 22(16), p.6208.
Anthony Ambrose, Celal Savur, and Ferat Sahin. 2022, June. Low Cost Real Time Location Tracking with Ultra-Wideband. In 2022 17th Annual System of Systems Engineering Conference (SOSE), pp. 445-450. IEEE.
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S Yan, Y Wang, K Zhao, P Shi, Z Zhao, Y Zhang, J Li. 2025. HeMoRa: Unsupervised Heuristic Consensus Sampling for Robust Point Cloud Registration. Proceedings of the Computer Vision and Pattern Recognition Conference, 1363-1373.
Y Yang, L Zhu, Z Yang, Y Zhu, Q Huang, P Shi, Q Lin, X Zhao, Z Hu. 2025. Periodicity constrained and block accelerated thin plate spline approach for cardiac motion estimation. Biomedical Signal Processing and Control 104, 107655.
Y Zhou, Q Zheng, Y Wang, W Yan, P Shi, J Zhu. 2024. Multi-level consistency collaborative multi-view clustering. Expert Systems with Applications 238, 121976.
Y Xie, J Zhu, S Li, N Hu, P Shi. 2024. HECPG: Hyperbolic embedding and confident patch-guided network for point cloud matching. IEEE Transactions on Geoscience and Remote Sensing 62, 1-12.
S Yan, P Shi, J Li. 2024. ML-SemReg: Boosting point cloud registration with multi-level semantic consistency. European Conference on Computer Vision, 19-37.
N Hu, H Cheng, Y Xie, P Shi, J Zhu. 2024. Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4973-4979.
J Li, P Shi, Q Hu, Y Zhang. 2023. QGORE: Quadratic-time guaranteed outlier removal for point cloud registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (9), 11136-11151.
Y Xie, J Zhu, S Li, P Shi. 2023. Cross-modal information-guided network using contrastive learning for point cloud registration. IEEE Robotics and Automation Letters 9 (1), 103-110.
Y Chen, P Sh. 2023. Rotation-invariant completion network. Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 115-127.
E Zheng, Q Yu, R Li, P Shi, A Haake. 2023. Knowledge acquisition for human-in-the-loop image captioning. International Conference on Artificial Intelligence and Statistics, 2191-2206.
P Shi, J Li, X Liu, Y Zhang. 2023. Indoor cylinders guided LiDAR global localization and loop closure detection. Geomatics and Information Science of Wuhan University 49 (7), 1088-1099.
F Cueva, P Shi, P Cedillo. 2023.Designing Bike-Sharing Systems Supported by Data: A Systematic Literature Review. IEEE Access 12, 162731-162754.
Bondy, C., Chen, L., Grover, P, and Shi, P. 2022. Advancing Ubiquitous Collaboration for Telehealth – A Framework to Evaluate Technology-mediated Collaborative Workflow for Telehealth, Hypertension Exam Workflow Study, J Pharmacol Pharm Res, 5(1): 1–20. DOI: 10.31038/JPPR.2022513.
Zheng, E., Yu, Q., Li, R., Shi, P., and Haake, A. R.: Dual-Level Adaptive Information Filtering for Interactive Image Segmentation, AISTATS, 2022. P. 6862-6879.
View more publications by Pengcheng >
Sung, H., & Shin, G.-H. (2025). Towards robust morphosyntactic analysis of L2 Korean: Evaluating and fine-tuning a Korean language model. Transactions on Asian and Low-Resource Language Information Processing.
Sung, H., Wolf, M. K., Suhan, M., & Kyle, K. (2025). Lexical richness in young English learners’ writing: A focus on opinion and listen-write task types. Assessing Writing.
Sung, H., & Kyle, K. (2025). Usage-based analysis of L2 oral proficiency: Characteristics of argument structure construction use. Studies in Second Language Acquisition.
Sung, H., & Kyle, K. (2025). ASC analyzer: A Python package for measuring argument structure construction usage in English texts. In Proceedings of the 2nd Workshop on Construction Grammars and NLP (CxGs+NLP).
Sung, H., Csuros, K., & Sung, M.-C. (2025). Comparing human and LLM proofreading in L2 writing: Impact on lexical and syntactic features. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications.
Sung, H., Shin, G.-H., Lee, C., Sung, Y.-K., & Jung, B.-K. (2025). UD-KSL Treebank v1.3: A semi-automated framework for aligning XPOS-extracted units with UPOS tags. In Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX).
Sung, H., & Shin, G.-H. (2025). Second language Korean Universal Dependency treebank v1.2: Focus on data augmentation and annotation scheme refinement. In Proceedings of the Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025).
Book Chapters
M. Di Somma, G. Graditi, B. Yan, “Handbook of Smart Energy Systems,” Chapter 2. Cost-Sustainability Trade-Off Solutions for the Optimal Planning of Local Integrated Energy Systems from Nanogrids to Communities, Springer, 2022.
N. Raghunathan, M. A. Bragin, B. Yan, and P. Luh, “Exploiting Soft Constraints within Decomposition and Coordination Methods for Sub-hourly Unit Commitment,” International Journal of Electrical Power and Energy Systems, Vol. 139, 2022.
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Journal Articles
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Dingrong Wang, Krishna Prasad Neupane, Ervine Zheng, Qi Yu, Looking into User’s Long-term Interests through the Lens of Conservative Evidential Learning, ICLR 2025. Hong Yang, Qi Yu, Travis Desell, Can We Ignore Labels in Out of Distribution Detection? ICLR 2025. Ervine Zheng, Qi Yu, Hierarchical Multi-Source Uncertainty Aggregation for Interactive Video Captioning, AAAI 2025 (Oral). Mahsa Mozaffari, Hitesh Sapkota, Qi Yu, GLEN: Generalized Focal Loss Ensemble of Low-Rank Networks for Calibrated Visual Question Answering, AAAI 2025. Dingrong Wang, Muhammad Tayyab Asif, Zhenyu Zhang, Jie Zhou, Melody Xuan, Xinyang Shen, Qi Yu, Inferring online retailing product seasonality via LLMs for cold-start cases, WWW 2025. Dingrong Wang, Hitesh Sapkota, Qi Yu: Adaptive Important Region Selection with Reinforced Hierarchical Search for Dense Object Detection, NeurIPS 2024. Dayou Yu, Minghao Li, Weishi Shi, Qi Yu: Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity, NeurIPS 2024. Krishna Prasad Neupane, Ervine Zheng, Qi Yu: Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation, NeurIPS 2024. Deep Shankar Pandey, Spandan Pyakurel, Qi Yu: Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Foundation Models, NeurIPS 2024. Dingrong Wang, Hitesh Sapkota, Zhiqiang Tao, Qi Yu: Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness, KDD 2024. Xiaofan Que and Qi Yu: Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data, ECCV 2024. Spandan Pyakurel and Qi Yu: Hierarchical Novelty Detection via Fine-Grained Evidence Allocation, ICML 2024. Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu: Balancing Feature Similarity and Label Variability for Optimal Size-Aware Subset Selection, ICML 2024. Hitesh Sapkota, Krishna Prasad Neupane, Qi Yu: Meta Evidential Transformer for Few-Shot Open-Set Recognition, ICML 2024. Xiaofan Que and Qi Yu: Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks. AAAI 2024. Weishi Shi, Heather Moses, Qi Yu, Samuel A. Malachowsky, Daniel E. Krutz: ALL: Supporting Experiential Accessibility Education and Inclusive Software Development. ACM Trans. Softw. Eng. Methodol. 33(2): 39:1-39:30 (2024). J Hinz, Dayou Yu, Deep Shankar Pandey, Hitesh Sapkota, Qi Yu, DI Mihaylov, VV Karasiev, SX Hu: The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning. APL Machine Learning. 2(2) (2024). Dayou Yu, Deep Shankar Pandey, Joshua Hinz, Deyan I. Mihaylov, Valentin V. Karasiev, S. X. Hu, Qi Yu:Deep energy-pressure regression for a thermodynamically consistent EOS model. Mach. Learn. Sci. Technol. 5(1): 15031 (2024) Dayou Yu, Weishi Shi, Qi Yu: Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice. NeurIPS 2023. Hitesh Sapkota, Dingrong Wang, Zhiqiang Tao, Qi Yu: Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training. NeurIPS 2023. Dayou Yu, Weishi Shi, Qi Yu: Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. ICML 2023. Ervine Zheng, Qi Yu: Evidential Interactive Learning for Medical Image Captioning. ICML 2023. Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu: Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. ICML 2023. Deep Shankar Pandey, Qi Yu: Learn to Accumulate Evidence from All Training Samples: Theory and Practice. ICML 2023. Hitesh Sapkota, Qi Yu: Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data. ICLR 2023. Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng, "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks" AAAI 2023 (oral). Ervine Zheng, Qi Yu, and Zhi Zheng: Sparse Maximum Margin Learning From Multimodal Human Behavioral Patterns, AAAI 2023 (oral). Dayou Yu, Weishi Shi, and Qi Yu: STARS: Spatial-Temporal Active Re-Sampling for Label-Efficient Learning from Noisy Annotations. AAAI 2023. Deep Pandey and Qi Yu: Evidential Conditional Neural Processes. AAAI 2023 (oral). Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Knowledge Acquisition for Human-In-The-Loop Image Captioning. AISTATS 2023. Krishna Prasad Neupane, Ervine Zheng, Yu Kong, and Qi Yu. 2022. A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations. AAAI Conference on Artificial Intelligence (AAAI). https://ui.adsabs.harvard.edu/abs/2022arXiv220400970P. |
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Khan, Monoshiz Mahbub, and Zhe Yu. Approaching code search for python as a translation retrieval problem with dual encoders. Empirical Software Engineering 30, no. 1 (2025): 12. Yu, Zhe, Joymallya Chakraborty, and Tim Menzies. Fairbalance: How to achieve equalized odds with data pre-processing. IEEE Transactions on Software Engineering (2024). E.A. AlOmar, J. Liu, K. Addo, M.W. Mkaouer, C. Newman, A. Ouni, and Z. Yu. 2022. On the documentation of refactoring types. Automated Software Engineering 29 (1), 1-40. |
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Trent Rabe, Anisa Callis, Zhi Zheng, Jamison Heard, Reynold Bailey, and Cecilia O. Alm. 2022. Theory of mind assessment with human-human and human-robot interactions. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_41.