Dr. Achyut Mani Tripathi

Assistant Professor, Department of Computer Science & Engineering (CSE)
Indian Institute of Technology Dharwad (IIT Dharwad), Dharwad-580007, Karnataka, India
Room No: Academic Block A1-129, Intercom Number: 1084
Email: t.achyut [at] iitdh [dot] ac [dot] in

About Me

नमस्कार! I am अच्युत मणि त्रिपाठी (Achyut Mani Tripathi) , originally from Bilaspur, Chhattisgarh, India. I am an Assistant Professor (सहायक प्राध्यापक) in the Computer Science & Engineering Department (संगणक विज्ञान एवं अभियांत्रिकी विभाग) at the Indian Institute of Technology Dharwad (IIT-Dh) (भारतीय प्रौद्योगिकी संस्थान धारवाड़) in Karnataka, India. I obtained my Ph.D. from the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Guwahati, Assam, India.

Research Interests

Designing Deep Architectures (Audio, Image, Time Series, Video, 3D Point Clouds), Adversarial Machine Learning (Adversarial and Backdoor Attacks), Machine Unlearning (Unimodal and Multimodal), Multivariate Time Series (Forecasting, Anomaly Detection and Classification), MultiModal Learning (AQA, AVQA, AVS, AVEL), Design and Development of Fast Parameter-Efficient Fine-Tuning (PEFT) Algorithms for Deep Neural Architectures (Unimodal Diffusion Models, Pre-trained Models, and Multimodal Models), Deep Model Compression (Knowledge Distillation, Dataset Distillation, PEFT Knowledge Distillation), Applied Deep Learning for Computer Networks, Computer Architecture and Power Electronics

Announcements

  1. I will not be offering any BTP or R&D opportunities to B.Tech students for the next one year (Aug 2026-Aug 2027), including those who have previously worked with me. Kindly do not contact me regarding BTP or R&D positions during this period. Thanks!. New Update
  2. Congratulations to Abhishek on the acceptance of his M.S. research work as a paper titled "WaveletMamba: Harnessing Learnable Multi-Scale State Space Dynamics for Time Series Forecasting Learning Systems" in the prestigious Neural Networks journal (Q1-Ranked). New Publication
  3. I am seeking highly motivated B.Tech students with exceptional coding skills and a strong mathematical foundation to join my research group. If you have a CGPA of 9.0 or higher and are eager to contribute to BTP/R&D projects in this area, please send me your detailed CV. Just so you know, BTP will not be offered until a student has completed R&D work under my supervision. BTP will be considered if the student’s R&D performance meets expectations. I am particularly interested in students who are committed to pursuing higher studies and are willing to invest significant time in publishing research articles in top venues. Irrespective of the academic program (B.Tech, M.Tech, M.S., or Ph.D.), any student interested in joining the group must first complete a programming assignment test. Based on their performance on the test, a position in the group may be offered. If the results are not satisfactory, the student is advised to seek guidance from another faculty member for their R&D, BTP, MTP, M.S., or Ph.D. work. Additionally, one should only contact me for BTP/R&D if they are confident in their coding skills; let's save and respect each other's time. I am not interested in offering any Short-Term Internships, BTP, R&D, MTP, MS, Ph.D., Online Internships, or Mini-Projects for the next few years.
  4. Please do not request a Letter of Recommendation (LOR) solely based on attending my courses. An LOR will only be provided if you have participated in R&D or BTP under my supervision and demonstrated exceptional performance in the project.

Publications

Patents

  1. "A System and Method for Activity Identification and Problem Prediction During Oil and Gas Well Drilling". [From Ph.D. Thesis]

Refereed Journal Articles

  1. "WaveletMamba: Harnessing Learnable Multi-Scale State Space Dynamics for Time Series Forecasting Learning Systems," Neural Networks, Elsevier, 2026. (Accepted)
  2. "FAOSPA: Frequency-Adaptive Orthogonal Spatio-Temporal Perturbation Attack for Exploiting Vulnerabilities in Video Anomaly Detection," IEEE Transactions on Artificial Intelligence (IEEE-TAI), 2026. (Accepted)
  3. " MalaNet: A Small World Inspiblack Neural Network for Automated Malaria Diagnosis," IEEE Transactions on Artificial Intelligence (IEEE-TAI), 2025. (Accepted)
  4. "Anchor-Based Void Detouring Routing Protocol In Three Dimensional IoT networks," Computer Networks, Elsevier, 2023
  5. " Divide and Distill: New Outlooks On Knowledge Distillation for Environmental Sound Classification, IEEE/ACM Transactions on Audio, Speech, and Language Processing, (IEEE TASLP), Vol.31, Page.1100-1113, 2023
  6. "When Sub-Band Feature Meets Attention Mechanism While Knowledge Distillation for Sound Classification," Applied Acoustics, Elsevier, Vol.195, Page.108813, 2022.
  7. "Data Augmentation Guided Knowledge Distillation for Environmental Sound Classification", Neurocomputing, Elsevier, Vol.489, Page 59-77, 2022.
  8. "(Adv-ESC): Adversarial Attack Datasets for An Environmental Sound Classification", Applied Acoustics, Elsevier, 108437, Vol.185, 2021.
  9. "Environment Sound Classification Using An Attention-Based Residual Neural Network," Neurocomputing, Elsevier, Vol.460, Page 409-423, 2021
  10. "Self-Supervised Learning for Environmental Sound Classification," Applied Acoustics, Elsevier, 108183, Vol.182, 2021.
  11. "Oil Well Drilling Activities Recognition Using a Hierarchical Classifier", Journal of Petroleum Science & Engineering, 107883, Vol.196, 2021.

Refereed Conference

  1. "MaHaWave-Net: A Lightweight Multi-Scale Model for Fine-Grained Medical Image Segmentation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 4-8, Barcelona, Spain, 2026 [Core Rank=A*]
  2. "Semi-Supervised Knowledge Distillation Framework Towards Lightweight Large Language Model for Spoken Language Translation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 6-11, Hyderabad, India, 2025 [Core Rank=A*]
  3. "When Visual State Space Model Meets Backdoor Attack ", IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 28 Feb-4 March, 2025, Tucson, Arizona. [Core Rank=A*]
  4. "Bandit-Based Attention Mechanism in Vision Transformer," IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 28 Feb-4 March, 2025, Tucson, Arizona. [Core Rank=A*]
  5. "TM-PATHVQA: 90000+ Textless Multilingual Questions for Medical Visual Question Answering ", Conference of the International Speech Communication Association (INTERSPEECH), Sept 1-5, 2024, Kos Island, Greece. [Core Rank=A*]
  6. "Towards Multi-Lingual Audio Question Answering," Conference of the International Speech Communication Association (INTERSPEECH), Aug 20-24, 2023, Dublin, Ireland. [Core Rank=A*]
  7. "Sub-band Contrastive Learning-based Knowledge Distillation for Sound Classification," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 4-10, Rhodes Island, Greece, 2023 [Core Rank=A*]
  8. "Temporal Self-Attention-Based Residual Network for an Environmental Sound Classification," Conference of the International Speech Communication Association (INTERSPEECH), Sept 18-22, 2022, Incheon, Korea. [Core Rank=A*]
  9. "Revamped Knowledge Distillation for Environmental Sound Classification," IEEE International Joint Conference on Neural Networks (IJCNN), July 18-23, 2022, Padua, Italy.[Core Rank=A]
  10. "Reverse Adversarial Attack To Enhance Environmental Sound Classification," IEEE International Joint Conference on Neural Networks (IJCNN),July 18-23, 2022, Padua, Italy. [Core Rank=A]
  11. "Defensive Bit Planes: Defense Against Adversarial Attacks," IEEE International Joint Conference on Neural Networks (IJCNN),July 18-23, 2022, Padua, Italy. [Core Rank=A]
  12. Investigation of Performance of Visual Attention Mechanisms for Environmental Sound Classification: A Comparative Study", IEEE International Joint Conference on Neural Networks (IJCNN),July 18-23, 2022, Padua, Italy.[Core Rank=A]
  13. "Adv-IFD: Adversarial Attack Datasets for An Intelligent Fault Diagnosis", IEEE International Joint Conference on Neural Networks (IJCNN),July 18-23, 2022, Padua, Italy. [Core Rank=A]
  14. "Enhancing Multivariate Time Series Classification Using Long Short Term Memory and Evidence Feed Forward HMM, IEEE International Joint Conference on Neural Networks (IJCNN), July 19-24, 2020, Glasgow, UK, [Core Rank=A]
  15. "Multivariate Time Series Classification With An Attention-Based Multivariate Convolutional Neural Network", IEEE International Joint Conference on Neural Networks (IJCNN), July 19-24, 2020, Glasgow, UK, [Core Rank=A]
  16. "Acoustic Event Detection Using Oriented Fuzzy Local Binary Pattern and Ensemble of Convolutional Neural Network", IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July 19-24, 2020, Glasgow, UK, [Core Rank=A]
  17. "Contextual Anomaly Detection in Time Series Using Dynamic Bayesian Network", Asian Conference on Intelligent Information and Database Systems, Phuket, Thailand, March 26-29, 2020,[Core Rank=B]
  18. "Anomaly Detection in Multivariate Time Series Using Fuzzy AdaBoost and Dynamic Naive Bayesian Classifier", in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Bari, Oct 6-9, 2019, Italy., [Core Rank=B]
  19. "Incremental Cauchy Non-Negative Matrix Factorization and Fuzzy Rule-based Classifier for Acoustic Source Separation", in Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), June 23-27, 2019, New Orleans, USA, [Core Rank=A]
  20. "Acoustic Event Classification Using Cauchy Non-Negative Matrix Factorization and Fuzzy Rule-Based Classifier", in Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July 9-12, 2017, Naples, Italy, pp.1-6, [Core Rank=A]
  21. "Anomaly Detection in Data Streams Based on Graph Coloring Density Coefficients", in Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI), Dec 6-9, 2016, Athens, Greece, pp.1-7, [Core Rank=C]
  22. "Acoustic Event Classification Using an Ensemble of One-Class Classifiers for Monitoring Application", in Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI), Dec 7-10, 2015, Cape Town, South Africa, pp.1681-1686, [Core Rank=C]
  23. "Acoustic Sensor-Based Activity Recognition Using Ensemble of One-Class Classifiers", in Proceedings of IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) , Dec 1-3, 2015, Duoai, France, pp.1-7, [Core Rank=C]
  24. "Ultrasonic Sensor-based Human Detector Using One-Class Classifiers", in Proceedings of IEEE Conference on Evolving and Adaptive Intelligent Systems Systems (EAIS), Dec 1-3 ,2015, Duoai, France, pp.1-6., [Core Rank=C]

Teaching

  1. Database Management & Information Systems (Theory) (CS 205) (Aug 2026- Nov 2026)
  2. Database Management & Information Systems (Lab) (CS 202) (Aug 2026- Nov 2026)
  3. Compilers (CS 323) (Jan-May 2026)
  4. Compilers Lab (CS 316) (Jan-May 2026)
  5. Introduction To Programming (Theory+Lab) (CS 101) (Aug 2025- Nov 2025)
  6. Compilers (CS 323) (Jan-May 2025)
  7. Compilers Lab (CS 316) (Jan-May 2025)
  8. Database Management & Information Systems (Theory) (CS 303) (Aug 2024- Nov 2024)
  9. Database Management & Information Systems (Lab) (CS 313) (Aug 2024- Nov 2024)
  10. Compilers (CS 323) (Jan-May 2024)
  11. Compilers Lab (CS 316) (Jan-May 2024)

Students

I have only two BTP and two R&D positions available in my group. Please refrain from applying if the positions are already filled, as I will not be able to offer a position in that case.

MS-Research

  1. Abhishek Sharma (MS-R) (CSE) (Aug 2024-July 2026) [Topic= Lightweight Deep Models for Multivariate Time Series Forecasting] [Open Seminar Completed][Joint Supervision With Dr.Kedar Khandeparkar]
  2. Sachin Maurya (MS-R) (CSE) (Aug 2024-July 2026) [Topic= Compressed Deep Models for Image Segmentation] [Open Seminar Completed][Joint Supervision With Dr.Kedar Khandeparkar]

BTP

  1. Om Suhas Deshmukh (B.Tech) (CSE) (2025) [Topic= Architectural Backdoor Attacks] [Completed]

R&D

  1. Simarjeet Singh (B.Tech-II Year) (Physics) (2029) [Topic= Bias-Aware Machine Unlearning and PEFT Techniques] [Ongoing]
  2. Sai Sarvesh Thatoi (B.Tech-II Year) (CSE) (2029) [Topic= PEFT Techniques and Multimodal Learning] [Ongoing]
  3. Darisi Surya Teja (B.Tech-III Year) (CSE) (2027) [Topic= Computationally Efficient Machine Unlearning] [Completed]
  4. Abhinandan Jain (B.Tech-III Year) (CSE) (2027) [Topic= Backdoor Attacks and Efficient 3D Point Cloud Representation Learning ] [Completed]
  5. Samartha (B.Tech-III Year) (CSE) (2027) [Topic= RL-Based Parameter-Efficient Fine-Tuning and Machine Unlearning] [Completed]
  6. Prachet Vikas Rane (B.Tech-III Year) (CSE) (2027) [Topic= Compressed Deep Model for 3D Medical Image Segmentation] [Completed]
  7. Sankalp Nagaonkar (B.Tech) (EECS) (2025) [Topic= Adaptive Knowledge Fusion Ratio-Based Knowledge Distillation and Backdoor Attacks] [Completed]
  8. Tanisq Trivedi (B.Tech) (CSE) (2025) [Topic= Adversarial Audio Classification: An Empirical Study] [Completed]

Projects

1. Automatic Diarization of Multiple Speakers in Audio Recordings Using AI/ML Algorithms for Mandarin Language (Funding Agency: DRDO Young Scientist Laboratory (DYSL-CT)) (Principal Investigator)

2. AI-Enhanced Non-Invasive Wearable Patch for Early Heart Attack Prediction via Real-Time Cardiac Troponin-I Monitoring Using a Pseudoknot-Engineered Aptamer (Funding Agency: ANRF) (Co-Principal Investigator)