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Academic Experts
Dr. Anupama Padha

Biography

Beginning from a corporate career in SAP, Dr. Anupama’s transition to academia was driven by a passion for education and a commitment to inspire the next generation of professionals. She possesses a total work experience of around 15 years with 12 years in academics and 3 years in industry. Her major areas of interest are Quantum Computing, Deep Learning and Machine Learning. She has authored many research papers for various national and international journals/conferences. She has completed her Ph.D. in Quantum Enhanced Machine Learning in May 2025 and holds M. Tech and B.E. degrees in Computer Science & Engineering. She has taught a range of courses in computer science such as Data Structures, DevOps, Operating Systems, Object Oriented Programming, Compiler Design etc. She has authored around 10 research papers for various national and international journals/conferences.

Research Highlights

Her research focuses on quantum machine learning, time series analysis, and affective computing, with a strong emphasis on mental health monitoring. Notable contributions include the development of QCLR, a quantum-enhanced LSTM framework with contrastive learning for continuous monitoring, and MAQML, a meta-learning approach designed to handle sample variability in quantum models. She has also proposed S2QR2, a self-supervised quantum relational reasoning framework for uncovering temporal patterns in mental health data. Her work on quantum deep neural networks and ensemble quantum LSTM architectures for real-time stress detection has been featured in reputed journals and conferences, including Expert Systems with Applications, Quantum Machine Intelligence, Quantum Information Processing, Springer, IEEE, and ACM proceedings.

Areas of Interest
  • Quantum Machine Learning
  • Deep Learning
  • Time Series Data Analytics
  • Self-supervised Quantum AI
Publications
  1. A. Padha and A. Sahoo, "QCLR: Quantum-LSTM Contrastive Learning Framework for Continuous Mental Health Monitoring," Expert Systems with Applications, vol. 232, Sep. 2023. DOI: https://doi.org/10.1016/j.eswa.2023.121921 (SCIE, Scopus indexed, Impact Factor: 8.5)
  2. A. Padha and A. Sahoo, "MAQML: A Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring," Quantum Machine Intelligence, vol. 5, no. 17, 2023. DOI: https://doi.org/10.1007/s42484-023-00108-1 (ESCI, Scopus indexed, Impact Factor: 4.8)
  3. A. Padha and A. Sahoo, "Quantum Deep Neural Networks for Time Series Analysis," Quantum Information Processing, 2024. DOI: https://doi.org/10.1007/s11128-024-04404-y
    (SCIE, Scopus indexed, Impact Factor: 2.5)
  4. A. Padha and A. Sahoo, "Self-supervised quantum relational reasoning (S2QR2) of time series data for mental health monitoring," International Journal of Information Technology, pp. 1–19, 2025. DOI: https://doi.org/10.1007/s41870-025-02525-w (Scopus indexed)
  5. S. Gupta, M. Mittal, and A. Padha, "Predictive analytics of sensor data based on supervised machine learning algorithms," in Proc. 2017 Int. Conf. Next Generation Computing and Information Systems (ICNGCIS), Jammu, India, Dec. 2017, pp. 171–176. DOI: https://ieeexplore.ieee.org/document/8322425
  6. 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). (Springer Nature, Scopus indexed)
  7. 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 Xplore, Scopus indexed)
  8. 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) https://doi.org/10.1145/3549206.3549288 (ACM Digital Library, Scopus indexed)
  9. Padha, A., & Sharma, M. Fault Tolerance in Distributed Mobile Computing-A Review.
  10. Padha, A., & Sharma, M. To Improve Fault Tolerance in Mobile Distributed System.