Dr. Rakesh Kumar Sanodiya

OpenGovDataHack National Award (2nd Runners Up)
International IoT Grant Challenge (Won Second Prize)
Smart India Hackathon (Won First Prize)
Intel @ Higher Education Challenge (Won First Prize)
Postdoctoral:Selected at NTU, Singapore
GATE Qualified:2012, 2013, 2014, 2015, 2016, 2017
UGC-JRF Qualified:June-2015
UGC-NET Qualified:Dec-2015, June-2015, Dec-2014, June-2014
MHRD Travel Grant: to visit 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018
CSIR Travel Grant: to visit IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, June 10-13, 2019
SERB Travel Grant: to visit 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12-15, 2019

Journal articles:

O. Gilo, J. Mathew, S. Mondal, and R. K. Sandoniya, “Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation,” Pattern Analysis and Applications, vol. 27, no. 1, p. 13, 2024. doi: 10.1007/s10044-024-01232-9.
R. Lekshmi, B. R. Jose, J. Mathew, and R. K. Sanodiya, “Mnemonic: Multikernel contrastive domain adaptation for time-series classification,” Engineering Applications of Artificial Intelligence, vol. 133,p. 108 255, 2024. doi: 10.1016/j.engappai.2024.108255.
S. R. Singh, R. R. Yedla, S. R. Dubey, R. K. Sanodiya, and W.-T. Chu, “Frequency disentangled residual network,” Multimedia Systems, vol. 30, no. 1, pp. 1–13, 2024.
A. Devika, R. K. Sanodiya, B. R. Jose, and J. Mathew, “Visual domain adaptation through locality information,” Engineering Applications of Artificial Intelligence, vol. 123, p. 106 172, 2023, issn: 0952-1976. doi: 10.1016/j.engappai.2023.106172.
O. Gilo, J. Mathew, S. Mondal, and R. K. Sanodiya, “Rdaot: Robust unsupervised deep sub-domain adaptation through optimal transport for image classification,” IEEE Access, 2023.
O. Gilo, J. Mathew, S. Mondal, and R. K. Sanodiya, “Unsupervised sub-domain adaptation using optimal transport,” Journal of Visual Communication and Image Representation, p. 103 857, 2023, issn: 1047-3203. doi: 10.1016/j.jvcir.2023.103857.
R. R. P. Karn, R. K. Sanodiya, and P. Bajpai, “A unified framework for visual domain adaptation with covariance matching,” Knowledge-Based Systems, p. 110 894, 2023. doi: 10.1016/j.knosys.2023.110894.
R. K. Lakshmi Sanodiya, B. R. Jose, and J. Mathew, “Kernelized global-local discriminant information preservation for unsupervised domain adaptation,” Applied Intelligence, pp. 1–23, 2023. doi: 10.1007/s10489-023-04706-1.
S. Mishra and R. K. Sanodiya, “A novel angular based unsupervised domain adaptation framework for image classification,” IEEE Transactions on Artificial Intelligence, 2023, issn: 2691-4581. doi: 10.1109/TAI.2023.3293077.
R. S. R. Singh and R. K. Sanodiya, “Zero-shot transfer learning framework for plant leaf disease classification,” IEEE Access, 2023.
R. K. Sanodiya, S. Mishra, P. Arun, et al., “Manifold embedded joint geometrical and statistical alignment for visual domain adaptation,” Knowledge-Based Systems, vol. 257, p. 109 886, 2022, issn: 0950-7051. doi: 10.1016/j.knosys.2022.109886.
R. K. Sanodiya, J. Mathew, R. Aditya, A. Jacob, and B. Nayanar, “Kernelized unified domain adaptation on geometrical manifolds,” Expert Systems with Applications, vol. 167, p. 114 078, 2021, issn: 0957-4174. doi: 10.1016/j.eswa.2020.114078.
R. K. Sanodiya and L. Yao, “Discriminative information preservation: A general framework for unsupervised visual domain adaptation,” Knowledge-Based Systems, vol. 227, p. 107 158, 2021, issn: 0950-7051. doi: 10.1016/j.knosys.2021.107158.
R. K. Sanodiya, J. Mathew, S. Saha, and P. Tripathi, “Particle swarm optimization based parameter selection technique for unsupervised discriminant analysis in transfer learning framework,” Applied Intelligence, vol. 50, pp. 3071–3089, 2020. doi: 10.1007/s10489-020-01710-7.
R. K. Sanodiya, S. Saha, and J. Mathew, “Semi-supervised orthogonal discriminant analysis with relative distance: Integration with a moo approach,” Soft Computing, vol. 24, pp. 1599–1618, 2020, issn: 1432-7643. doi: 10.1007/s00500-019-03990-9.
R. K. Sanodiya, M. Tiwari, J. Mathew, S. Saha, and S. Saha, “A particle swarm optimization-based feature selection for unsupervised transfer learning,” Soft Computing, vol. 24, pp. 18 713–18 731, 2020, issn: 1432-7643. doi: 10.1007/s00500-020-05105-1.
R. K. Sanodiya and L. Yao, “A subspace based transfer joint matching with laplacian regularization for visual domain adaptation,” Sensors, vol. 20, no. 16, p. 4367, 2020, issn: 1424-8220. doi: 10.3390/s20164367.
R. K. Sanodiya and L. Yao, “Linear discriminant analysis via pseudo labels: A unified framework for visual domain adaptation,” IEEE Access, vol. 8, pp. 200 073–200 090, 2020, issn: 2169-3536. doi: 10.1109/ACCESS.2020.3035422.
R. K. Sanodiya and L. Yao, “Unsupervised transfer learning via relative distance comparisons,” IEEE Access, vol. 8, pp. 110 290–110 305, 2020, issn: 2169-3536. doi: 10.1109/ACCESS.2020.3002666.
R. K. Sanodiya and J. Mathew, “A framework for semi-supervised metric transfer learning on manifolds,” Knowledge-Based Systems, vol. 176, pp. 1–14, 2019, issn: 0950-7051. doi: 10.1016/j.knosys.2019.03.021.
R. K. Sanodiya and J. Mathew, “A novel unsupervised globality-locality preserving projections in transfer learning,” Image and Vision Computing, vol. 90, p. 103 802, 2019, issn: 0262-8856. doi: 10.1016/j.imavis.2019.08.006.
R. K. Sanodiya, J. Mathew, B. Paul, and B. A. Jose, “A kernelized unified framework for domain adaptation,” IEEE Access, vol. 7, pp. 181 381–181 395, 2019, issn: 2169-3536. doi: 10.1109/ACCESS.2019.2958736.
R. K. Sanodiya, J. Mathew, S. Saha, and M. D. Thalakottur, “A new transfer learning algorithm in semi-supervised setting,” IEEE Access, vol. 7, pp. 42 956–42 967, 2019, issn: 2169-3536. doi: 10.1109/ACCESS.2019.2907571.
R. K. Sanodiya, S. Saha, and J. Mathew, “A kernel semi-supervised distance metric learning with relative distance: Integration with a moo approach,” Expert Systems with Applications, vol. 125,pp. 233–248, 2019, issn: 0957-4174. doi: 10.1016/j.eswa.2018.12.051.

Conference Proceeding:

S. Jangala and R. K. Sanodiya, “A novel framework for multi-source domain adaptation with discriminative feature learning,” in 202/ International Joint Conference on Neural Networks (ĲCNN), IEEE, 2023, pp. 1–7. doi: 10.1109/IJCNN54540.2023.10191410.
A. S. Namboodiri, R. K. Sanodiya, and P. Arun, “Remote sensing cloud removal using a combination of spatial attention and edge detection,” in 202/ 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149875.
N. R. Nandyala and R. K. Sanodiya, “Underwater object detection using synthetic data,” in 202/ 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149870.
J. Prakash, M. Ghorai, and R. Sanodiya, “Transfer learning: Kernel-based domain adaptation with distance-based penalization,” in International Conference on Pattern Recognition and Machine Intelligence, Springer, 2023, pp. 189–198.
B. Y. Reddy, S. R. Dubey, R. K. Sanodiya, and R. R. P. Karn, “Context unaware knowledge distillation for image retrieval,” in Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, Springer, 2023, pp. 65–77. doi: 10.1007/978-981-19-7867-8_6.
R. Sidibomma and R. K. Sanodiya, “Learning semantic representations and discriminative features in unsupervised domain adaptation,” in 202/ 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149872.
S. Suryavardan, V. Pulabaigari, and R. K. Sanodiya, “Unsupervised domain adaptation supplemented with generated images,” in Neural Information Processing: 2yth International Conference, ICONIP 2022, Virtual Event, November 22–2G, 2022, Proceedings, Part IV, Springer, 2023, pp. 659–670. doi: 10.1007/978-981-99-1639-9_55.
P. Bajpai and R. K. Sanodiya, “A unified framework for covariance adaptation with multiple source domains,” in 2022 IEEE yth Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, 2022, pp. 1–6. doi: 10.1109/UPCON56432.2022.9986432.
R. R. P. Karn, R. K. Sanodiya, E. S. Chandaluri, S. Suryavardan, L. R. Reddy, and L. Yao, “Virtual try-on using style transfer,” in Responsible Data Science: Select Proceedings of ICDSE 2021, Springer, 2022,pp. 131–139. doi: 10.1007/978-981-19-4453-6_9.
R. R. P. Karn, R. K. Sanodiya, T. Sharma, et al., “A feature and parameter selection approach for visual domain adaptation using particle swarm optimization,” in 2022 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2022, pp. 1–7. doi: 10.1109/CEC55065.2022.9870263.
R. Lekshmi, R. K. Sanodiya, B. R. Jose, and J. Mathew, “Joint cross-domain preserving and distribution adaptation for heterogeneous domain adaptation,” in 2022 IEEE 1yth India Council International Conference (INDICON), IEEE, 2022, pp. 1–6. doi: 10.1109/INDICON56171.2022.10039779.
S. Mishra and R. K. Sanodiya, “Scatter matrix normalization for unsupervised domain adaptation,” in 2022 IEEE yth Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, 2022, pp. 1–6. doi: 10.1109/UPCON56432.2022.9986396.
R. Satya Rajendra Singh, R. K. Sanodiya, and P. Arun, “Joint geometrical and statistical alignment using triplet loss for deep domain adaptation,” in Responsible Data Science: Select Proceedings of ICDSE 2021, Springer, 2022, pp. 119–130. doi: 10.1007/978-981-19-4453-6_8.
R. Lekshmi, R. K. Sanodiya, R. Linda, B. R. Jose, and J. Mathew, “Kernelized transfer feature learning on manifolds,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part II 28, Springer, 2021, pp. 297–308. doi:10.1007/978-3-030-92270-2_26.
R. K. Sanodiya, C. Sharma, S. Satwik, A. Challa, S. Rao, and L. Yao, “A novel metric learning framework for semi-supervised domain adaptation,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part I 28, Springer, 2021, pp. 165–176. doi: 10.1007/978-3-030-92185-9_14.
M. Tiwari, R. K. Sanodiya, J. Mathew, and S. Saha, “A particle swarm optimization based feature selection approach for multi-source visual domain adaptation,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V 28, Springer, 2021, pp. 701–709. doi: 10.1007/978-3-030-92307-5_82.
M. Tiwari, R. K. Sanodiya, J. Mathew, and S. Saha, “Multi-source based approach for visual domain adaptation,” in 2021 International Joint Conference on Neural Networks (ĲCNN), IEEE, 2021, pp. 1–7. doi: 10.1109/IJCNN52387.2021.9534305.
L. Yao, S. Prasad, R. K. Sanodiya, and D. Paul, “Statistical and geometrical alignment for unsupervised deep domain adaptation,” in Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020, Springer, 2021, pp. 433–444. doi: 10.1007/978-981-33-4087-9_37.
R. K. Sanodiya, P. Kumar, M. Tiwari, L. Yao, and J. Mathew, “A modified joint geometrical and statistical alignment approach for low-resolution face recognition,” in Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 2/–27, 2020, Proceedings, Part I 27, Springer, 2020, pp. 88–100. doi: 10.1007/978-3-030-63830-6_8.
R. K. Sanodiya, A. Mathew, J. Mathew, and M. Khushi, “Statistical and geometrical alignment using metric learning in domain adaptation,” in 2020 International Joint Conference on Neural Networks (ĲCNN), IEEE, 2020, pp. 1–8. doi: 10.1109/IJCNN48605.2020.9206877.
R. K. Sanodiya, D. Paul, L. Yao, J. Mathew, and A. Juhi, “A feature selection approach to visual domain adaptation in classification,” in Neural Information Processing: 27th International Conference, ICONIP2020, Bangkok, Thailand, November 2/–27, 2020, Proceedings, Part II 27, Springer, 2020, pp. 77–89. Doi: 10.1007/978-3-030-63833-7_7.
R. K. Sanodiya, S. Saha, J. Mathew, M. D. Thalakottur, and U. Aadya, “Multi-objective approach for semi-supervised discriminant analysis with relative distance,” in 201y IEEE Congress on Evolutionary Computation (CEC), IEEE, 2019, pp. 2808–2815. doi: 10.1109/CEC.2019.8790027.
R. K. Sanodiya, C. Sharma, and J. Mathew, “Unified framework for visual domain adaptation using globality-locality preserving projections,” in Neural Information Processing: 2Gth International Conference, ICONIP 201y, Sydney, NSW, Australia, December 12–15, 201y, Proceedings, Part I 2G, Springer, 2019, pp. 340–351. doi: 10.1007/978-3-030-36708-4_28.
R. K. Sanodiya, M. D. Thalakottur, J. Mathew, and M. Khushi, “Semi-supervised regularized coplanar discriminant analysis,” in Neural Information Processing: 2Gth International Conference, ICONIP 201y, Sydney, NSW, Australia, December 12–15, 201y, Proceedings, Part V 2G, Springer, 2019, pp. 198–205. doi: 10.1007/978-3-030-36802-9_22.
R. K. Sanodiya, S. Saha, and J. Mathew, “A multi-kernel semi-supervised metric learning using multi-objective optimization approach,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 1/–1G, 2018, Proceedings, Part II 25, Springer, 2018 pp. 530–541. doi: 10.1007/978-3-030-04179-3_47.
R. K. Sanodiya, S. Saha, J. Mathew, and P. Bangwal, “Semi-supervised transfer metric learning with relative constraints,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 1/–1G, 2018, Proceedings, Part III 25, Springer, 2018, pp. 230–241. doi: 10.1007/978-3-030-04182-3_21.
R. K. Sanodiya, S. Saha, J. Mathew, and A. Raj, “Supervised and semi-supervised multi-task binary classification,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 1/-1G, 2018, Proceedings, Part IV 25, Springer, 2018, pp. 380–391. doi: 10.1007/978-3-030-04212-7_33.

PhD Student

1. Satya Rajendra Singh:Completed[2020-2024]

Thesis Topic: Exploration in Convolutional Neural Network Models with Applications to Image Classifi- cation and Retrieval.

2. Ravi Ranjan Karn:On going[2021-]

Thesis Topic: Exploration of Domain Adaptation Approaches for Image Classification.

MTech Student

1.Midhun V:Completed [2022]

Thesis Topic: Domain Adaptation for Semi-supervised Semantic Segmentation. Company: Mercedes Benz.

BTech with Honors (Research) Student

Batch [2023-2025]

1.Animesh Shree-H25RKS02

Project Topic: Exploring Deep Domain Adaptation Approaches for Image Classification.

2.Aarav Nigam -H25RKS01

Project Topic: Navigation using Reinforcement Learning.

3.Ashraf Ali-H25RKS03

Project Topic: Development of Deep Learning Techniques for activity recognition.

4.Subhangi-H25RKS04

Project Topic: Development of Shallow Domain Adaptation Approaches for Image Classification.

Batch [2022-2024]

1.Rushendra Sidibomma-H24RKS01

Project Topic: Exploring Deep Learning Approaches for Unsupervised Domain Adaptation.
Publication: Conf. Pub. 11th-IEEE ESDC-202/.

2. Amal S Namboodiri-H24RKS02

Project Topic: Exploring Deep Learning Approaches for Remote Sensing.
Publication: Conf. Pub. 11th IEEE-ESDC-202/.

3. Sampreeth Jangala-H24RKS03

Project Topic: Exploring Deep Learning Approaches for Image Classification.
Publication: Conf. Pub. IEEE-ĲCNN-202/.

4. Nitish Reddy-H24RKS04

Project Topic: Exploration Deep Learning Approaches for Underwater Object Recognition.
Publication: Conf. Pub. 11th- IEEE ESDC-202/.

Batch [2021-2023]

1.Shreyash Mishra

Project Topic: Exploration of Shallow Domain Adaptation Approaches for Image Classification.
Publication: Trans. Pub. IEEE TAI-202/, Joural Pub. KBS-2022, Conf. Pub. yth IEEE UPCON-2022.

2.Priyam Bajpai

Project Topic: Exploring Shallow Unsupervised Domain Adaptation Approaches for Image Classification.
Publication: Conf. Pub. yth- IEEE UPCON-2022.

Batch [2020-2022]

1. B Y Reddy

Project Topic: Context Unaware Knowledge Distillation for Image Retrieval.
Publication: Conf. Pub. CVMI-2022.

BTech with Project Student

Batch [2023]

1. Group B23RKS01: Lanka Sai Ramya, Kolusu Manasa, and Sneha H S

Project Topic: Exploring Generative Models for Precision Agriculture.

2. Group B23RKS02: Golla Lalith, Sai Srikar, and Manohar Shashank

Project Topic: Exploring Metric Learning Approaches for Image Classification.

3. Group B23RKS03: Aritro Ghosh, G. Yashswi, and V. Nithin

Project Topic: Exploring Metric Learning Approaches for Image Classification.

Batch [2022]

1. Group B22RKS01: R. Tholuchuru, V. Sathvik, and K. Sumanth

Project Topic: Semi-supervised Domain Adaptation.

2. Group B22RKS02:S. Kokanti, M. Shashank, and C. Anand

Project Topic: Metric Learning.

3. Group B22RKS03: A. Reddy, G. Chetan, and C. Teja

Project Topic: Unsupervised Domain Adaptation.

4. Group B22RKS04:H. Chowdary, M. Sheetal, and P. Vignesh

Project Topic: Virtual Try-on.

Batch [2021]

1. Group B21RKS01: Y. Akhilesh, K. Hrudai, and L. Praneeth

Project Topic: Object Recognition.

2. Group B21RKS03: C. Nikhilesh, N. Siva, Krishna, and N. Praneeth

Project Topic: Pose Estimation.

3. Group B21RKS03: C. Eswara, L. Reddy, and M. Sai

Project Topic: Exploring Generative Model.

4. Group B21RKS04: G. Vishnu, K. Lakshmi, and D. Pravnav

Project Topic: Deep Domain Adaptation.

5. Group B21RKS05: V. Hanseesha, R. Anusri, and D. Neeharika

Project Topic: Person Re-Identification.

6. Group B21RKS06: E. Suma, V. Amrutha, and B. Sairam

Project Topic: Object Detection and Localization.

AY:[2023-2024]

Spring-24:

Deep Learning
Robotics Intelligence

Monsoon-23:

Advanced data structure and Algorithm, Syllabus, PPT
Full Stack Development-2
Overview of Computer Workshop

AY:[2022-2023]

Spring-23:

Deep Learning
Robotics Intelligence

Monsoon-22:

Advanced data structure and Algorithm
Full Stack Development-2

AY:[2021-2022]

Spring-22:

Robotics Intelligence

Monsoon-21:

Database Management System
Advanced data structure and Algorithm Computer Programming

AY:[2020-2021]

Spring-21:

Web Application Development
Enterprise Application Development
Data Structure and Algorithm
Probability and Statistical Theory

Academic Qualifications

Education:

Research Areas of Interest

Machine Learning

Deep Learning

Robotics Intelligence

AI/ML Applications

Awards / Honours

Projects

Publications