Biography
Anupama Padha is working as a Contractual Faculty at Computer Science & Information Technology Department at Jaypee Institute of Information Technology, Noida. She received her M. Tech (Computer Science) from PTU Jalandhar. She is pursuing PhD (Computer Science) from Jaypee Institute of Information Technology (JIIT). She possesses a work experience of around 7 years in academics and 3 years in industry. Her major areas of interest are Quantum Computing, Deep Learning and Machine Learning. She has authored around 10 research papers for various national and international journals/conferences.
Educational Qualifications
Pursuing Ph.D. (Computer Science and Engineering) from JIIT Noida.
M.Tech (Computer Science & Engineering) from PTU Jalandhar in 2015.
B. Tech (Computer Science & Engineering) from Jammu University in 2005.
Work Experience
Teaching Experience:
- Currently working as a Contractual Faculty in the Department of Computer Science and Information Technology at Jaypee Institute of Information Technology, Noida.
- Guest Faculty in the Department of Computer Science and Information Technology at Jaypee Institute of Information Technology, Noida from July 2023 to Dec 2023.
- Assistant Professor in the Department of Computer Science and Engineering at ABES-IT, Ghaziabad from June 2019 to June 2020.
- Assistant Professor in the Department of Computer Science and Engineering at MIET, Jammu from Aug 2016 to June 2019.
- Assistant Manager (SAP Trainer) in the CCPD department at ABES-EC, Ghaziabad from July 2014 to Jan 2016.
- Lecturer in Havard College of Education, Jammu from March 2013 to Jan 2014.
Industry Experience:
- SAP Consultant at Fujitsu Consulting India Pvt Ltd from April 2007 to May 2010.
Interest Area
Quantum Computing, Deep Learning and Machine Learning
Publications
- Padha, A., & Sahoo, A. (2024). QCLR: Quantum-LSTM contrastive learning framework for continuous mental health monitoring. Expert Systems with Applications, 238, 121921.
- Padha, A., & Sahoo, A. (2023). MAQML: a Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring. Quantum Machine Intelligence, 5(1), 17.
- Padha, A., & Sahoo, A. (2023, April). Ensemble of Parametrized Quantum LSTM Neural Networks for Multimodal Stress Monitoring. In Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications: ICAIAA 2022 (pp. 59-67). Singapore: Springer Nature Singapore.
- Padha, A., & Sahoo, A. (2022, August). Quantum Enhanced Machine Learning for Unobtrusive Stress Monitoring. In Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing (pp. 476-483).
- Padha, A., & Sahoo, A. (2022, March). A Parametrized Quantum LSTM Model for Continuous Stress Monitoring. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 261-266). IEEE.
- Gupta, S., Mittal, M., & Padha, A. (2017, December). Predictive analytics of sensor data based on supervised machine learning algorithms. In 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) (pp. 171-176). IEEE.
- Padha, A., & Sharma, M. To Improve Fault Tolerance in Mobile Distributed System.