2026
MEO: Mining Reliable Expert Signals for Offline Reinforcement Learning in Wireless Networks
Lipeng Zu, Hansong Zhou, Yu Qian, Shayok Chakraborty, Yukun Yuan, Xiaonan Zhang
ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) 2026
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
Kaixiang Zhao, Bolin Shen, Yuyang Dai, Shayok Chakraborty, Yushun Dong
Neural Information Processing Systems, Evaluations and Datasets Track (NeurIPS) 2026
Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models
Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar, Ahish Deshpande, Celso de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre
European Conference on Computer Vision (ECCV) 2026 Project Webpage
Also presented at the Geospatial AI and Applications with Foundation Models (GAIA) workshop at ECCV 2026
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Release for Offline-to-Online Reinforcement Learning
Lipeng Zu, Yu Qian, Shayok Chakraborty, Xiaonan Zhang
International Conference on Machine Learning (ICML) 2026
LLIE-CvT: A Convolutional Vision Transformer for Low Light Image Enhancement
Debanjan Goswami, Bishal Bashyal, Shayok Chakraborty
International Conference on Pattern Recognition (ICPR) 2026 Project Webpage
An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Varun Totakura, Ankita Singh, Yushun Dong, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2026
Multi-target Deep Domain Adaptation for Deepfake Detection
Md Shamim Seraj, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2026
Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults Using Source-Free Domain Adaptation: Algorithm Development and Validation
Ronast Subedi, Shayok Chakraborty, Zhe He, Yuanying Pang, Shenghao Zhang, Mia Lustria, Neil Charness, Walter Boot
JMIR Aging 2026 (Impact Factor: 4.80)
2025
MediVLM: A Vision Language Model for Radiology Report Generation from Medical Images
Debanjan Goswami, Ronast Subedi, Shayok Chakraborty
Empirical Methods in Natural Language Processing (EMNLP) Findings 2025 Project Webpage
Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision
Xiao Fang, Minhyek Jeon, Zheyang Qin, Stanislav Panev, Celso de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre
IEEE International Conference on Computer Vision (ICCV) 2025 Project Webpage
Also to be presented at the Curated Data for Efficient Learning (CDEL) and the Human-Interactive Generation and Editing (HiGen) workshops at ICCV 2025
Active Learning for Image Segmentation with Binary User Feedback
Debanjan Goswami, Shayok Chakraborty
IEEE Winter Conference on Applications of Computer Vision (WACV) 2025
LLM-Empowered Patient-Provider Communication: A Data-Centric Survey from a Clinical Perspective
Ruosi Shao, Md Shamim Seraj, Kangyi Zhao, Yingtao Luo, Lincan Li, Bolin Shen, Averi Bates, Yue Zhao, Chongle Pan, Lisa Hightow-Weidman, Shayok Chakraborty, Yushun Dong
International Joint Conference on Natural Language Processing & Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) 2025
MetaErr: Towards Predicting Error Patterns in Deep Neural Networks
Varun Totakura, Shayok Chakraborty
IEEE International Conference on Smart Multimedia (ICSM) 2025 (Special Session on Advanced Signal Processing and AI in Smart Multimedia)
Introducing the Adherence Promotion with Person-Centered Technology (APPT) Trial: Rationale, Methods, and Baseline Characteristics
Shenghao Zhang, Michael Dieciuc, Andrew Dilanchian, Mia Lustria, Dawn Carr, Antonio Terracciano, Zhe He, Shayok Chakraborty, Neil Charness, Walter Boot
Contemporary Clinical Trials 2025
2024
Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
Ronast Subedi, Lu Wei, Wenhan Gao, Shayok Chakraborty, Yi Liu
Neural Information Processing Systems (NeurIPS) 2024 Project Webpage
Knowledge Distillation in Deep Networks under a Constrained Query Budget
Ankita Singh, Shayok Chakraborty
International Conference on Pattern Recognition (ICPR) 2024
Multi-source Deep Domain Adaptation for Deepfake Detection
Md Shamim Seraj, Shayok Chakraborty
International Conference on Pattern Recognition (ICPR) 2024
ACIL: Active Class Incremental Learning for Image Classification
Aditya Bhattacharya*, Debanjan Goswami*, Shayok Chakraborty
British Machine Vision Conference (BMVC) 2024 (* = equal contribution)
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
Chutian Jiang, Hansong Zhou, Xiaonan Zhang, Shayok Chakraborty
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024
D3GU: Multi-target Active Domain Adaptation via Enhancing Domain Alignment
Lin Zhang, Linghan Xu, Saman Motamed, Shayok Chakraborty, Fernando De la Torre
IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
Active Batch Sampling for Multi-label Classification with Binary User Feedback
Debanjan Goswami, Shayok Chakraborty
IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
Semi-supervised Deep Domain Adaptation for Deepfake Detection
Md Shamim Seraj, Ankita Singh, Shayok Chakraborty
Workshop on Manipulation, Adversarial and Presentation Attacks In Biometrics (MAP-A) at IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
Predicting Adherence to Gamified Cognitive Training using Early Phase Game Performance Data: Towards a Just-in-Time Adherence Promotion Strategy
Yuanying Pang, Ankita Singh, Shayok Chakraborty, Neil Charness, Walter Boot, Zhe He
PLOS One 2024
Predicting Adherence to Computer-based Cognitive Training Programs among Older Adults: Study of Domain Adaptation and Deep Learning
Ankita Singh, Shayok Chakraborty, Zhe He, Yuanying Pang, Shenghao Zhang, Ronast Subedi, Mia Lustria, Neil Charness, Walter Boot
JMIR Aging 2024 (Impact Factor: 4.90)
Applying Multimodal Data Fusion to Track Autistic Adolescents’ Representational Flexibility Development During Virtual Reality-Based Training
Jewoong Moon, Fengfeng Ke, Zlatko Sokolikj, Shayok Chakraborty
Computers & Education: X Reality 2024
2023
Active Learning for Video Classification with Frame Level Queries
Debanjan Goswami, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2023
New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies
Zhe He, Michael Dieciuc, Dawn Carr, Shayok Chakraborty, Ankita Singh, Ibukun Fowe, Shenghao Zhang, Mia Lustria, Antonio Terracciano, Neil Charness, Walter Boot
BMC Digital Health 2023
2022
Active Sampling for Text Classification with Subinstance Level Queries
Shayok Chakraborty, Ankita Singh
AAAI Conference on Artificial Intelligence 2022 (accepted for oral presentation)
Deep Learning-based Predictions of Older Adults’ Adherence to Cognitive Training to Support Training Efficacy
Ankita Singh, Shayok Chakraborty, Zhe He, Shubo Tian, Shenghao Zhang, Mia Lustria, Neil Charness, Nelson Roque, Erin Harrell, Walter Boot
Frontiers in Psychology 2022 (Impact Factor: 4.23)
A Machine-Learning Based Approach for Predicting Older Adults’ Adherence to Technology-Based Cognitive Training
Zhe He, Shubo Tian, Ankita Singh, Shayok Chakraborty, Shenghao Zhang, Mia Lustria, Neil Charness, Nelson Roque, Erin Harrell, Walter Boot
Information Processing and Management 2022 (Impact Factor: 7.47)
Motivation to Engage in Aging Research: Are There Typologies and Predictors?
Dawn Carr, Shubo Tian, Zhe He, Shayok Chakraborty, Michael Dieciuc, Nicholas Gray, Maedeh Agharazidermani, Mia Lustria, Andrew Dilanchian, Shenghao Zhang, Neil Charness, Antonio Terracciano, Walter Boot
The Gerontologist 2022 (Impact Factor: 5.27)
2021
Deterministic Mini-batch Sequencing for Training Deep Neural Networks
Subhankar Banerjee, Shayok Chakraborty
AAAI Conference on Artificial Intelligence 2021
Deep Active Learning with Relative Label Feedback: An Application to Facial Age Estimation
Ankita Singh, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2021
High-resolution Home Location Prediction from Twitter Activities using Consensus Deep Learning
Meysam Ghaffari, Ashok Srinivasan, Xiuwen Liu, Shayok Chakraborty
Social Network Analysis and Mining (SNAM) Journal 2021
2020
A Study of Deep Learning for Predicting Freeze of Gait in Patients with Parkinson’s Disease
Alex Yuan, Shayok Chakraborty
IEEE International Conference on Machine Learning and Applications (ICMLA) 2020
Deep Active Transfer Learning for Image Recognition
Ankita Singh, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2020
Budgeted Subset Selection for Fine-tuning Deep Learning Architectures in Resource-Constrained Applications
Subhankar Banerjee, Shayok Chakraborty
IEEE International Joint Conference on Neural Networks (IJCNN) 2020
Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles
Shayok Chakraborty
AAAI Conference on Artificial Intelligence 2020
Long-Term Resting Metabolic Rate Analysis -Towards Prediction Models for Resting Metabolic Rate Changes
Shayok Chakraborty, Teresa Wu, Erica Forzani, Corrie Whisner, David Jackemeyer
International Journal of Prognostics and Health Management (IJPHM) 2020
Deep Hashing Network for Unsupervised Domain Adaptation
Raghavendran Ramakrishnan, Bhadrinath Nagabandi, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan, Hemanth Venkateswara
Domain Adaptation in Computer Vision With Deep Learning, Springer 2020
2019
When to Pull Starting Pitchers in Major League Baseball? A Data Mining Approach
Michael Woodham, Jason Hawkins, Ankita Singh, Shayok Chakraborty
IEEE International Conference on Machine Learning and Applications (ICMLA) 2019
A Generic Active Learning Framework for Class Imbalance Applications
Aditya Bhattacharya, Ji Liu, Shayok Chakraborty
British Machine Vision Conference (BMVC) 2019
Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery
Christopher Mills, Javier Escobar-Avila, Aditya Bhattacharya, Grigoriy Kondyukov, Shayok Chakraborty, Sonia Haiduc
IEEE International Conference on Software Maintenance and Evolution (ICSME) 2019
DeepSub: A Novel Subset Selection Framework for Training Deep Learning Architectures
Subhankar Banerjee, Shayok Chakraborty
IEEE International Conference on Image Processing (ICIP) 2019
Active Learning with n-ary Queries for Image Recognition
Aditya Bhattacharya, Shayok Chakraborty
IEEE Winter Conference on Applications of Computer Vision (WACV) 2019
Deep Active Learning for Image Regression
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
Deep Learning Applications, Springer 2019
Deep Domain Adaptation for Regression
Ankita Singh, Shayok Chakraborty
Development and Analysis of Deep Learning Architectures, Springer 2019
2018
Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson’s Disease
Vishwas Torvi, Aditya Bhattacharya, Shayok Chakraborty
IEEE International Conference on Machine Learning and Applications (ICMLA) 2018
Multi-Label Deep Active Learning with Label Correlation
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
IEEE International Conference on Image Processing (ICIP) 2018
Distributed Active Learning for Image Recognition
Shayok Chakraborty
IEEE Winter Conference on Applications of Computer Vision (WACV) 2018
Computer Vision for Augmentative and Alternative Communication
Sethuraman Panchanathan, Meredith Moore, Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel
Computer Vision for Assistive Healthcare, Elsevier 2018
2017
Hierarchical Learning for Automated Malware Classification
Shayok Chakraborty, Jay Stokes, Lin Xiao, Dengyong Zhou, Mady Marinescu, Anil Thomas
IEEE Military Communications Conference (MILCOM) 2017
Deep Active Learning for Image Classification
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
IEEE International Conference on Image Processing (ICIP) 2017
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 Project Webpage
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel, Sethuraman Panchanathan
Workshop on Knowledge-based Techniques for Problem Solving and Reasoning (KnowPros) at the Association for the Advancement of Artificial Intelligence (AAAI) 2017
Deep Learning Systems for Domain Adaptation in Computer Vision
Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
IEEE Signal Processing Magazine (SPM), Special Issue on Deep Learning for Visual Understanding 2017
Enriching the Fan Experience in a Smart Stadium Using Internet of Things Technologies
Sethuraman Panchanathan, Shayok Chakraborty, Troy McDaniel, Ramin Tadayon, Bijan Fakhri, Noel O’Connor, Mark Marsden, Suzanne Little, Kevin McGuinness, David Monaghan
International Journal of Semantic Computing (IJSC) 2017 (Special Issue on Best of IEEE ISM 2016)
2016 and Before
S. Panchanathan, S. Chakraborty, T. McDaniel et al. “Smart Stadium for Smart Living: Enriching the Fan Experience”, IEEE International Symposium on Multimedia (ISM) 2016 (Invited Paper)
H. Venkateswara, S. Chakraborty, S. Panchanathan, “Nonlinear Embedding Transform for Unsupervised Domain Adaptation”, Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV) at the European Conference on Computer Vision (ECCV) 2016
H. Ranganathan, S. Chakraborty, S. Panchanathan, “Transfer of Multi-modal Emotion Features ion Deep Belief Networks”, Asilomar Conference on Signals, Systems and Computers 2016
H. Ranganathan, S. Chakraborty, S. Panchanathan, “Multi-modal Emotion Recognition using Deep Learning Architectures”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2016
S. Panchanathan, S. Chakraborty, T. McDaniel, R. Tadayon, “Person-Centered Multimedia Computing: A New Paradigm Inspired by Assistive and Rehabilitative Applications”, IEEE Multimedia Magazine (MM) 2016 (2017 IEEE Multimedia Best Department Article Award)
S. Panchanathan, S. Chakraborty, T. McDaniel, “Social Interaction Assistant: A Person-Centered Approach to Enrich Social Interactions for Individuals with Visual Impairments”, IEEE Journal on Selected Topics in Signal Processing (J-STSP) 2016
S. Chakraborty, O. Tickoo, R. Iyer, “Towards Distributed Video Summarization”, ACM Multimedia Conference (ACM-MM) 2015
S. Chakraborty, V. Balasubramanian, A. Ravi Sankar, S. Panchanathan, J. Ye, “BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification”, ACM Conference on Knowledge Discovery and Data Mining (KDD) 2015
S. Chakraborty, O. Tickoo, R. Iyer, “Adaptive Keyframe Selection for Video Summarization”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2015
S. Chakraborty, V. Balasubramanian, Q. Sun, S. Panchanathan, J. Ye, “Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Adaptive Batch Mode Active Learning”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2015
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Conformal Predictions for Information Fusion: A Comparative Study of P-Value Combination Methods”, Annals of Mathematics and Artificial Intelligence (AMAI) 2014
V. Balasubramanian, S. Chakraborty, S.S. Ho, H. Wechsler, S. Panchanathan, “Active Learning using Conformal Predictions”, Morgan Kaufmann Publ. (Elsevier) 2014
S. Chakraborty, J. Zhou, V. Balasubramanian, S. Panchanathan, I. Davidson, J. Ye, “Active Matrix Completion”, IEEE International Conference on Data Mining (ICDM) 2013
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Generalized Batch Mode Active Learning for Face-based Biometric Recognition”, Pattern Recognition Journal 2012
S. Chakraborty, H. Venkateswara, V. Balasubramanian, S. Panchanathan, “Active Batch Selection for Fuzzy Classification in Facial Expression Recognition”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2011
R. Chattopadhyay, S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Optimization-based Domain Adaptation towards Person-Adaptive Classification Models”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Optimal Batch Selection for Active Learning in Multi-label Classification”, ACM Multimedia Conference (ACM-MM) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Dynamic Batch Mode Active Learning”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “An Optimization Based Framework for Dynamic Batch Mode Active Learning”, Workshop on Optimization for Machine Learning at Neural Information Processing Systems (NIPS) 2010
V. Balasubramanian, S. Chakraborty, S. Krishna, S. Panchanathan, “Enhancing Social Interactions of Individuals with Visual Impairments: A Case Study for Assistive Machine Learning”, Workshop on Machine Learning for Assistive Technologies at Neural Information Processing Systems (NIPS) 2010
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Multiple Kernel Learning for Efficient Conformal Predictions”, Workshop on New Directions in Multiple Kernel Learning at Neural Information Processing Systems (NIPS) 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2010
V. Balasubramanian, J. Ye, S. Chakraborty, S. Panchanathan, “Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Learning from Summaries of Videos: Applying Batch Mode Active Learning to Face-based Biometrics”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Biometrics 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Batch Mode Active Learning for Biometric Recognition”, SPIE International Conference on Biometric Technology for Human Identification, SPIE Defense, Security & Sensing 2010
S. Marcel, C. McCool, S. Chakraborty, V. Balasubramanian, S. Panchanathan et al. “On the Results of the First Mobile Biometry (MOBIO) Face and Speaker Verification Evaluation”, Lecture Notes on Computer Science (LNCS) 2010
V. Balasubramanian, S. Chakraborty, S. Krishna, S. Panchanathan, “Human-Centered Machine Learning in a Social Interaction Assistant for Individuals with Visual Impairments”, Symposium on Assistive Machine Learning for People with Disabilities at Neural Information Processing Systems (NIPS) 2009
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Online Active Learning using Conformal Predictions”, Workshop on Analysis and Design of Algorithms for Interactive Machine Learning at Neural Information Processing Systems (NIPS) 2009
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Generalized Query by Transduction for Online Active Learning”, IEEE International Conference on Computer Vision (ICCV), Workshop on Online Learning for Computer Vision 2009
V. Balasubramanian, S. Chakraborty, S Panchanathan, “Multiple Cue Integration in Transductive Confidence Machines for Head Pose Classification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Online Learning for Classification 2008
G. Gupta, S.K. Saha, S. Chakraborty, C. Mazumdar, “Document Frauds: Identification and Linking Fake Documents to Scanners and Printers”, IEEE International Conference on Computing, Theory and Applications (ICCTA) 2007
TECHNICAL REPORTS
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Confidence Estimation in Pattern Classification: An Analysis with Head Pose Estimation”, Technical Report TR-09-12, School of Computing, Informatics and Decision Systems Engineering, Arizona State University 2009