Laboratory of Statistical Artificial Intelligence and Machine Learning

Journal Articles

1. A S Sambyal, U Niyaz, N C Krishnan, and D R Bathula, Understanding Calibration of Deep Neural Networks for Medical Image Analysis, Computer Methods and Programs in Biomedicine, 2023.

2. D A. O’Brien, S Deb, S Sidheekh, N C Krishnan, P S Dutta, C F Clements, EWSmethods: an R package to forecast tipping points at the community level using early warning signals and machine learning models, Ecography, 2023.

3. A Aimen, S Sidheekh, H Ahuja, B Ladrecha and N C Krishnan, Adaptation: Blessing or Curse for Higher-way Meta-learning, IEEE Transactions on Artificial Intelligence, 2023.

4. V Kamakshi and N C Krishnan, Explainable Image Classification: The Journey so far and the Road Ahead, Artificial Intelligence special issue on Interpretable and Explainable AI Applications, 2023.

5. A Aimen, S Sidheekh, B Ladrecha, and N C Krishnan, Not All Tasks are Equal - Task Attended Meta-learning for Few-shot Learning, Springer Nature Computer Science, 2023.

6. S Gupta, G Ghalme, N C. Krishnan, and S Jain, Efficient Algorithms for Fair Clustering with a New Fairness Notion, Data Mining and Knowledge Discovery, 2023.

7. K Sharma, Abhishek Ghosh, N C Krishnan, S Kathirvel, D Basu, A Kumar, and B B. George, Digital screening and brief intervention for illicit drug misuse in college students: A mixed methods, pilot, cluster, randomized trial from India, Asian Journal of Psychiatry, 2023.

8. T Rohilla, N Singh, N C Krishnan, and D K Mahajan, Designing sulfonated polyimide-based fuel cell polymer electrolyte membranes using machine learning approaches, Computational Materials Science, 2023.

9. R Bhatt, A Rai, N C Krishnan, and S Chanda, Pho(SC)-CTCt: A Hybrid Approach Towards Zero-shot Word Image Recognition, International Journal on Document Analysis and Research, 2022.

10. S Deb, S Sidheekh, C F Clements, N C Krishnan, and P S Dutta, Machine Learning Methods can Anticipate Critical Transitions in Complex Systems, Royal Society Open Science 2022.

11. A Kumar, K Sehgal, P Garg, V Kamakshi, and N C Krishnan, MACE – A Model Agnostic Concept Extractor for Explaining Image Classification Networks, accepted, IEEE Transactions on Artificial Intelligence, 2021.

12. A Ghosh, F Roub, N C Krishnan, S Choudhury, A Basu, Can Google Trend Search Inform Us About the Population Response and Public Health of Alcohol Policy? - A Case Study from India During the Covid-19 Pandemic, International Journal of Drug Policy, 2020.

13. S Sukhija, and N C Krishnan, Supervised Heterogeneous Feature Transfer via Random Forests, Artificial Intelligence, 268, 30-53, 2019.

14. B Das, D Cook, N C Krishnan, and M Schmitter-Edgecombe, One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes, IEEE Journal of Special Topics in Signal Processing, 10(5), 914-923, 2016.

15. B Das, N C Krishnan, D Cook, WRACOG: A Wrapper Approach to Oversampling for Learning from Imbalanced Class Datasets, IEEE Transactions on Knowledge and Data Engineering, 27(1), 222-234, 2015.

16. D Cook, N C Krishnan, Mining Smart Home data, Journal of Intelligent Information Systems, Journal of Intelligent Information Systems, 43(3):503-519, 2014.

17. N C Krishnan, D Cook, Activity Recognition on Streaming Sensor Data, Journal of Pervasive and Mobile Computing, 10(B), 138-154, 2014.

18. N C Krishnan, D Cook, Z Wellminger, Learning a Taxonomy of Predefined and Discovered Activity Patterns, Journal of Ambient Intelligent and Smart Environments, 5(6), 621-637, 2013.

19. R I Dogan, Y Gil, H Hirsh, N C Krishnan, M Lewis, C Meriçli, P Rashidi, V Raskin, S Swarup, W Sun, J M. Taylor, L Yeganova: Reports on the 2012 AAAI Fall Symposium Series. AI Magazine 34(1): 93-100, 2013.

20. D Cook, K D Feuz, N C Krishnan, Transfer Learning for Activity Recognition: A Survey, Springer International Journal on Knowledge and Information Systems, 36(3), 537-556, 2013.

21. D Cook, A S Crandall, B L Thomas, N C Krishnan, CASAS: A Smart Home in a Box, IEEE Computers, 46(7), 62-69, 2013.

22. D Cook, N C Krishnan, P Rashidi, Activity Discovery and Activity Recognition: A New Partnership, IEEE Transactions on systems, man and cybernetics, 43(3), 820-828, 2013.

23. N C Krishnan, C Juillard, D Colbry, S Panchanathan, Recognition of hand movements using wearable accelerometers, in the Journal of Ambient Intelligent and Smart Environments, Special Issue on Wearable Computing, Vol. 1 (2), pp. 143 – 156, 2009.

Books

1. Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data, D Cook and N C Krishnan, John Wiley & Sons Inc., 2015.

Book Chapters

1. S Gupta, S Jain, G Ghalme, N C Krishnan, N Hemachandra, Group and Individual Fairness in Clustering Algorithms, Studies in Computational Intelligence, 2023.

2. S Sukhija, and N C Krishnan, Shallow Domain Adaptation - A survey, Springer book on Domain Adaptation in Computer Vision with Deep Learning, Eds: H Venkateswara, and S Panchanathan, 2020.

3. B Das, N C. Krishnan, D J. Cook, Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset, Springer book on Data Mining for Service,119-219, 2014.

4. N C Krishnan, S Panchanathan, Body Sensor Networks for Activity and Gesture Recognition, Springer Book on Wireless Sensor Networks, 2013.

5. B Das, N C Krishnan, D Cook, Automated Activity Interventions to Assist with Activities of Daily Living, IOS Press book on Agent-Based Approaches to Ambient Intelligence, 137-158, 2012.

6. P Rashidi, N C Krishnan, D Cook, Discovering and tracking patterns of interest in security sensor streams, In Securing Cyber-Physical Infrastructures, Eds: S Das, K Kant and N Zhang, Chapter 19, 2012.

Conference Articles (**Premier conferences, *good conferences)

1. V Kamakshi and N C Krishnan, SCE: Shared Concept Extractor to Explain a CNN’s Classification Dynamics, ACM International Joint Conference on Data Science and Management of Data (CODS-COMAD), 2024.

2. A Gaurav, J Jensen, N C Krishnan, and S Chanda, ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image Recognition, Iberian Conference on Pattern Recognition and Image Analysis (IbPRAI), 2023.

3. ** S Gupta, G Ghalme, N C Krishnan, and S Jain, Group Fair Clustering Revisited – Notions and Efficient Algorithms, International Conference on Autonomous Agents and Multi-Agent systems (AAMAS), 2023

4. M Singh, S S Kancheti, S Gupta, G Ghalme, S Jain, N C Krishnan, Algorithmic Recourse based on User’s Feature-order Preference, accepted ACM International Joint Conference on Data Science and Management of Data, Young Researcher’s Symposium, 2023.

5. **A Aimen, B Ladrecha, and N C Krishnan, Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2022.

6. *V Kamakshi and N C Krishnan, Explainable Supervised Domain Adaptation, IEEE International Joint Conference on Neural Networks, 2022

7. R Sharma, N. Reddy, V Kamakshi, N C Krishnan, and S Jain, MAIRE- A Model Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers, Cross Domain Machine Learning and Knowledge Extraction (CD-MAKE), 2021.

8. **S Sidheekh, A Aimen, and N C Krishnan, On Characterizing GAN Convergence Through Proximal Duality Gap, International Conference on Machine Learning (ICML), 2021.

9. *A Rai, N C Krishnan, and S Chanda, Pho(SC)Net: An Approach Towards Zero-shot Word Image Recognition in Historical Documents, International Conference on Document Analysis and Research (ICDAR), 2021.

10. *V Kamakshi, U Gupta, and N C Krishnan, PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs, IEEE International Joint Conference on Neural Networks (IJCNN), 2021.

11. *S Sidheekh, A Aimen, V Madan, and N C Krishnan, On Duality Gap as a Measure for Monitoring GAN Training, IEEE International Joint Conference on Neural Networks (IJCNN), 2021.

12. *P Munjal, A Paul, and N C Krishnan, Implicit Discriminator in Variational Autoencoder, IEEE International Joint Conference on Neural Networks (IJCNN), 2020.

13. *S Sukhija, S Varadarajan, N C Krishnan, and S Rai, Multi-Partition Feature Alignment Network for Unsupervised Domain Adaptation, IEEE International Joint Conference on Neural Networks (IJCNN), 2020.

14. **A Paul, P Munjal, and N C Krishnan, Semantically Aligned Bias Reducing Zero-shot Learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7056-7065, 2019.

15. **J Garg, S V Peri, H Tolani, and N C Krishnan, Deep Cross modal learning for Caricature Verification and Identification (CaVINet), ACM Conference on Multimedia (ACM MM), 1101-1109, 2018.

16. **S Sukhija and N C Krishnan, Web-Induced Heterogeneous Transfer Learning with Sample Selection, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 777-793, 2018.

17. S M Pandey, T Agarwal, and N C Krishnan, Multi-task Deep Learning for Predicting Poverty from Satellite Images, AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI), 7793-7798, 2018.

18. S Sukhija, N C Krishnan, and D Kumar, Supervised Heterogeneous Transfer Learning using Random Forests, to ACM International Joint Conference on Data Science and Management of Data (CODS-COMAD), 2018.

19. A Sikka, G Mittal, D B Reddy, and N C Krishnan, Supervised Deep Segmentation Network for Brain Extraction, International Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2016.

20. **G Mittal, K B Yagnik, M Garg, and N C Krishnan, Spot Garbage: Smartphone App to Detect Garbage Using Deep Learning, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 940-945, 2016.

21. **S Sukhija, N C Krishnan, and G Singh, Supervised Heterogeneous Domain Adaptation via Random Forests, International Joint Conference on Artificial Intelligence (IJCAI), 2039-2045, 2016.

22. *R Kumar, I Qamar, J S Virdi and N C Krishnan, Multi-label Learning for Activity Recognition, International Conference on Intelligent Environments (IE), 152-155, 2015. (Nominated for best paper award in Work in Progress category)

23. **B Das, N C Krishnan, D Cook, wRACOG: A Gibbs Sampling-Based Oversampling Technique, IEEE International Conference on Data Mining (ICDM), 111-120, 2013.

24. N Darnall, N C Krishnan, J D Carlson, D R Greely, J Mark, M Schmitter-Edgecombe, D C Lin, Identifying the presence of Dyskenisa in patients with Parkinson’s disease from accelerometer data, ASME Summer Bioengineering Conference 2013.

25. *S Dernbach, B Das, N C Krishnan, B L Thomas, D Cook, Activity Recognition on Smart Phones, IEEE International Conference on Intelligent Environments (IE), 214-221, 2012.

26. *R Chattopadhyay, N C Krishnan, S Panchanathan, Hierarchical domain adaptation for SEMG signal classification across multiple subjects, 33rd IEEE Conference on Engineering in Medicine and Biology (EMBC), 2011.

27. *N C Krishnan, L Prasanth, S Panchanathan, Activity gesture spotting using a threshold model based on Adaptive Boosting, International Conference on Multimedia and Expo (ICME), 2010

28. S Krishna, N C Krishnan, S Panchanathan, Detecting Stereotype body rocking behavior through embodied Motion sensors, Rehabilitation Engineering and Assistive Technology Society of North America Annual Conference, 2009.

29. *N C Krishnan, S Panchanathan, Analysis of low-resolution accelerometer data for human activity recognition, International Conference on Acoustic Speech and Signal Processing (ICASSP), 2008.

30. S Krishna, V Balasubramanian, N C Krishnan, C Juillard, T Hedgpeth, S Panchanathan, A wearable wireless RFID system for accessible shopping environments, International Conference on Body Area Networks (BodyNets), 2008.

31. S Krishna, V Balasubramanian, N C Krishnan, T Hedgpeth, The iCARE Ambient Interactive Shopping Environment, California State University, Northridge, Center on Disabilities’ 23rd Annual International Technology and Persons with Disabilities Conference, 2008.

32. N C Krishnan, B Li, S Panchanathan, Detecting and classifying frontal, back and profile views of humans, International Conference on Vision theory and applications (VISAPP), 2007.

33. J A Black, S B Braiman, N C Krishnan, S Panchanathan, The role of eye movement signals in dorsal and ventral processing, SPIE Conference on Human Vision and Electronic Imaging (HVEI), 2007

34. **K Kahol, N C Krishnan, V Balasubramanian, S Panchanathan, M Smith, J Ferrara, Measuring movement expertise in surgical tasks, ACM Conference on Multimedia (ACM MM), 2006.

35. *B S Raghavendra, N C Krishnan, G Sita, A G Ramakrishnan, M Sriganesh, Prototype learning methods for online handwriting recognition, International Conference on Document Analysis and Recognition (ICDAR) 2005.

36. P Saravanan, N C Krishnan, P V S S Prakash, G V P Rao, Techniques for video mosaicing, World Enformatika Conference, 2005.

37. N C Krishnan, M C Prakash, G V P Rao, High-level feature extraction in JPEG compressed domain, SPIE International symposium on optical science and engineering, 2004.

Workshop Articles

1. A Aimen, B Ladrecha, and N C Krishnan, Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning, AutoML Conference, Late Breaking Results Workshop, 2022.

2. A Aimen, H Ahuja, S Sidheekh and N C Krishnan, Deciphering Meta Initialized and Optimized Neural Networks, RBCDSAI and FCA Conference on Deployable AI, 2022.

3. S Gupta, G Ghalme, N C. Krishnan, and S Jain, Efficient Algorithms for Fair Clustering with a New Fairness Notion, RBCDSAI and FCA Conference on Deployable AI, 2022. (Best paper award)

4. A Aimen, S Sidheekh, B Ladrecha, and N C Krishnan, Task Attended Meta-Learning for Few-shot Learning, NeuRIPS Workshop on Meta-Leanring, 2021.

5. S Z S Sunder, V Kamakshi, N Lodhi, and N C Krishnan, Evaluation of Salience-based Explainability Methods, ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2021.

6. A Aimen, S Sidheekh, V Madan, and N C Krishnan, Stress Testing of Meta-Learning Approaches for Few-Shot Learning, AAAI Meta-Learning and Challenge Workshop, 2021.

7. A Kumar, D Khurana, S Pattanaik, M Kumar, M Modi, S Kaur, S Ghai, N C Krishnan, and M Nagi. Development, Feasibility and Validation of “Stroke Home Care” Application in a Resource Limited Setting: A Proof of Concept Study, International Stroke Conference, 2020

8. A Kumar, D Khurana, S Kaur, S Ghai, M Modi, S. Pattanaik, P Sharma, M Kumar A Dhaliwal, D Kumar, M Nagi, and N C Krishnan, Post-Stroke Complications And Caregivers’ Stress Among Stroke Patients In A Developing Country, European Stroke Conference, 2019.

9. S Sukhija, and N C Krishnan, Supervised Heterogeneous Domain Adaptation via Random Forests, Indian Workshop on Machine Learning, 2016.

10. B Das, N C Krishnan, D Cook, Handling Class Overlap and Imbalance to Detect Prompt Situations in Smart Homes, IEEE International Conference on Data Mining Workshop on Data Mining in Bioinformatics and Healthcare, 2013.

11. A Crandall, L Zulas, K Feuz, N C Krishnan, D Cook, Visualizing Your Ward: Bringing Smart Home Data to Caregivers, ACM CHI workshop on Emerging Technologies for Healthcare and Aging, 2012.

12. Yasamin Sahaf, N C Krishnan, D Cook, Defining Activity Complexity, AAAI workshop on Activity and Context Representation, 2011.

13. N C Krishnan Scalable Activity Recognition, NSF workshop on Pervasive Computing at Scale, Lead on Machine Learning, Behavior Modeling and Data mining, USA, 2011.

14. R Chattopadhyay, N C Krishnan, S Panchanathan, Topology preserving domain adaptation for addressing subject based variability in SEMG signal, AAAI spring symposium on Computational Physiology, Palo Alto, USA, 2011

15. Prasanth Lade, N C Krishnan, S Panchanathan, Task prediction in cooking activities using hierarchical state space Markov chain and object-based task grouping, IEEE International symposium on Multimedia workshop on multimedia for cooking and eating activities, Taichung, Taiwan, 2010.

16. Ashok Venkatesan, N C Krishnan, S Panchanathan, Cost sensitive boosting for concept Drift, accepted to European Conference on Machine learning workshop on handling concept drift in Adaptive Information systems, Barcelona, Spain, 2010.

17. N C Krishnan, G N Pradhan, S Panchanathan, Recognizing short duration hand movements from accelerometer data, ICME workshop on Multimedia Aspects in Pervasive Health Care 2009.

18. S Panchanathan, N C Krishnan, S Krishna, T McDaniel, V Balasubramanian, Enriched human-centered multimedia computing through inspirations from disabilities and deficit centered computing solutions, ACM MM 3rd Workshop on Human Centered Computing, Vancouver, Canada, 2008.

19. N C Krishnan, D Colbry, C Juillard, S Panchanathan, Real time human activity recognition using tri-axial accelerometers, Sensors Signals and Information Processing Workshop, Sedona, USA, 2008.

Other

1. A Aimen, M Tapaswi, S Dhavala, and N C Krishnan, A Concise and Unified Taxonomy of Transfer Learning, under review, 2023.

2. S Sidheekh, A Aimen, V Madan, and N C Krishnan, Perturbed Duality Gap as a Measure for Monitoring GAN Training, 2021.

3. S Varshney, J Kumar, R Singh, A Tiwari, V Gunturi, N C Krishnan, Deep Geospatial Interpolation Networks, 2021.

4. S Sharma, S Rai, and N C Krishnan, Wheat Crop Yield Prediction Using Deep LSTM Model, 2020

5. N C Krishnan, Study on audio correlates of stress and team performance during code blue in ICU, Work conducted in collaboration with Dr. Bhavesh Patel from Mayo Clinic, Phoenix, 2010

6. N C Krishnan, A Venkatesan, R Chattopadhyay, S Panchanathan, Accelerometric feature analysis for movement pattern recognition, Technical Report, TR_CUBIC2010.

7. N C Krishnan, G. Pradhan, S. Panchanathan, Cognitive Orthotic for Prompting Tasks in Complex ADL, Arizona Alzheimer’s Research Consortium, Annual Conference, 2009.

8. N C Krishnan, D Colbry, C Juillard, S Panchanathan Real time human activity recognition using tri-axial accelerometers Poster in symposium on Co-Adaptive Learning : Adaptive Technology for the Aging, Arizona State University, 2009

9. N C Krishnan, Low resolution accelerometer data for human activity recognition, Research in Engineering & Applied Sciences (REAS) Symposium, Arizona State University, 2007

10. N C Krishnan, S Balasubramaniam, G V P Rao, Image processing in the JPEG compressed domain, symposium on emerging trends in Computer Science Technology, Rajiv Gandhi Memorial Institute of Technology, India, 2004