Embedded Files
Lab Director
Pavan Kumar MP, Ph.D.
Assistant Professor Research
AIRES-Lab (Artificial Intelligence for Reliable Engineering Systems Laboratory)
Pavan Kumar MP, Ph.D.
Assistant Professor Research
AIRES-Lab (Artificial Intelligence for Reliable Engineering Systems Laboratory)
Affiliation: Manipal School of Information Science, Manipal, India
Affiliation: Manipal School of Information Science, Manipal, India
Address:
Lower Ground 02, Academic Block 5, MIT Campus, Eshwar Nagar, Manipal, Karnataka 576104
Tel: +91-9738584016
Email: pavankumar.mp@manipal.edu
Research Interests:
Machine Learning theory, Deep Learning, Foundation Models, Transfer Learning, Autonomous Systems, Smart Manufacturing, Model Adaptation, Reliable System Design, Scientific Learning.
Dr. Pavan Kumar MP earned his Ph.D. in Computer Science and Engineering from National Sun Yat-sen University (NSYSU), Taiwan, in 2024, under the mentorship of Prof. Kun-Chih (Jimmy) Chen. His doctoral research focused on mitigating negative transfer learning in source-free unsupervised domain adaptation, specifically for industrial applications such as rotating machinery fault diagnosis. Dr. Pavan's work has significantly contributed to the enhancement of transfer learning techniques, with an emphasis on improving predictive maintenance systems in industrial environments. His academic achievements include numerous journal articles and conference proceedings in top-tier IEEE publications, particularly in areas related to machine learning, predictive maintenance, and fault diagnosis.
Dr. Pavan Kumar MP earned his Ph.D. in Computer Science and Engineering from National Sun Yat-sen University (NSYSU), Taiwan, in 2024, under the mentorship of Prof. Kun-Chih (Jimmy) Chen. His doctoral research focused on mitigating negative transfer learning in source-free unsupervised domain adaptation, specifically for industrial applications such as rotating machinery fault diagnosis. Dr. Pavan's work has significantly contributed to the enhancement of transfer learning techniques, with an emphasis on improving predictive maintenance systems in industrial environments. His academic achievements include numerous journal articles and conference proceedings in top-tier IEEE publications, particularly in areas related to machine learning, predictive maintenance, and fault diagnosis.
Prior to his Ph.D., Dr. Pavan obtained a Master's degree in VLSI Design from BNM Institute of Technology, Bangalore, India, where he focused on the design and verification of DDR SDRAM memory controllers. He has also gained industry experience in roles such as a PCB Check-in Engineer at Sierra Circuits Ind Pvt Ltd and an SoC Design Verification Engineer Intern at Sion Semiconductors Pvt Ltd, which enriched his practical knowledge in hardware design and verification. His extensive experience in both academic research and industry provides a unique perspective on the intersection of hardware systems and advanced machine learning techniques.
Prior to his Ph.D., Dr. Pavan obtained a Master's degree in VLSI Design from BNM Institute of Technology, Bangalore, India, where he focused on the design and verification of DDR SDRAM memory controllers. He has also gained industry experience in roles such as a PCB Check-in Engineer at Sierra Circuits Ind Pvt Ltd and an SoC Design Verification Engineer Intern at Sion Semiconductors Pvt Ltd, which enriched his practical knowledge in hardware design and verification. His extensive experience in both academic research and industry provides a unique perspective on the intersection of hardware systems and advanced machine learning techniques.
Dr. Pavan is proficient in multiple programming languages, including Python, R, Verilog, and SystemVerilog, and has expertise in machine learning frameworks like TensorFlow, Keras, and PyTorch. His research interests span machine learning, transfer learning, unsupervised domain adaptation, and predictive maintenance, with applications in smart manufacturing and industrial systems. Dr. Pavan's work is characterized by a commitment to advancing AI in industrial applications, aiming to optimize system reliability and efficiency. With a solid foundation in both theoretical research and practical engineering, Dr. Pavan is poised to contribute significantly to the development of next-generation AI and machine learning technologies in industrial settings.
Dr. Pavan is proficient in multiple programming languages, including Python, R, Verilog, and SystemVerilog, and has expertise in machine learning frameworks like TensorFlow, Keras, and PyTorch. His research interests span machine learning, transfer learning, unsupervised domain adaptation, and predictive maintenance, with applications in smart manufacturing and industrial systems. Dr. Pavan's work is characterized by a commitment to advancing AI in industrial applications, aiming to optimize system reliability and efficiency. With a solid foundation in both theoretical research and practical engineering, Dr. Pavan is poised to contribute significantly to the development of next-generation AI and machine learning technologies in industrial settings.
Experience
2025/04 - 2025/08, Assistant Professor Research, Manipal School of Information Science, Manipal, India
2025/01 - 2025/03, Associate Data Scientist, Infinite Uptime India Pvt Ltd
2023/01 - 2024/05, National Center for High Performance Computing, Tainan, Taiwan, ML Research Intern
2020/10 - 2024/09, Department of Computer Science and Engineering, National Sun Yat-sen University, Research Assistant
2018/08 - 2018/11, Sion Semiconductors Pvt Ltd, Bangalore, India, SoC Design Verification Engineer Intern
2016/10 - 2020/10, Sierra Circuits Ind Pvt Ltd, Bangalore, India, PCB Check-in Engineer
Education
Ph.D. in Computer Science Engineering - National Sun Yat-Sen University, Kaohsiung, Taiwan (2020-2024)
Master of Technology in VLSI Design - Visvesvaraya Technological University, India (2017-2019)
Bachelor of Engineering in Electronics and Communication - Visvesvaraya Technological University, India (2012-2016)
Academic Service
Track Chair:
IEEE
Technical Program Committee (TPC):
IEEE COINS 2025
Session Chair：
IEEE
Reviewer:
Journal Paper
Engineering Applications of Artificial Intelligence
ACM Transactions on Design Automation of Electronic Systems (ACM TODAES)
IEEE Sensor Journal
IEEE Transactions on Mechatronics
IEEE Transactions on Signal Processing
IEEE Transactions on Very Large Scale Integrating (VLSI) Systems
IEEE Transactions on Parallel and Distributed Systems (TPDS)
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
IEEE Transactions on Industrial Informatics
IEEE Transactions on Computers
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Systems, Man and Cybernetics: Systems
IEEE Solid-State Circuits Letters
IEEE Transactions on Reliability
Conference Paper
IEEE International Symposium on Embedded Multicore/Manycore Systems-on-Chip (MCSoC)
IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
IEEE Asian Solid-State Circuits Conference (A-SSCC)
IEEE International System-on-Chip Conference (SOCC)
IEEE Int. Symp. Circuits and Systems (ISCAS)
IEEE International Conference on Very Large Scale Integration (VLSI) and System-on-Chip design (VLSI-SOC)
International VLSI Symposium on Technology, Systems and Applications (VLSI TSA)
International Conference on Omni-layer Intelligent Systems (COINS)
Page updated
Google Sites
Report abuse