JIIT

Academic Experts

Dr. Mohammad Shad

ASSISTANT PROFESSOR(GRADE I)

mohammad.shad@mail.jiit.ac.in

Biography

Dr. Mohammad Shad is an Assistant Professor in the Department of Mathematics, Jaypee Institute of Information Technology, Noida, India. He received his Ph.D. in Mathematics & Scientific Computing from NIT Hamirpur and completed his M.Sc. in Mathematics from DDU Gorakhpur University, Gorakhpur. His academic journey includes research and teaching roles at institutions such as REC Sonbhadra and Amity University Madhya Pradesh, with a total of two years of teaching and research experience. Dr. Shad's research expertise includes time series modeling and forecasting, pattern recognition. His work extends to machine learning and deep learning models for prediction, with applications in areas such as climate forecasting and renewable energy sources, including wind and solar energy prediction. He has published three SCI and two Scopus-indexed research papers in reputed journals.

Research Highlights

His ongoing research focuses on developing new hybrid predictive models that combine non-Gaussian approaches with deep learning techniques. These models are applied to real-world problems in environmental systems and socio-economic planning, especially under uncertain and dynamic conditions. He is also working on the application of various non-Gaussian models for the prediction of renewable energy sources. His previous research involved the development of non-Gaussian time series models and their application to Indian climatic data.

Areas Of Interest

Time series Modeling & Forecasting
Pattern Recognition
Statistical modeling, Decision Science

Publications

1. Shad, M., Sharma, Y. D., & Narula, P. (2024). Relative humidity prediction across the Indian
subcontinent using Kumaraswamy distribution based non-Gaussian model.
Environmental Science and Pollution Research, 31(59), 66780-66795.

2. Shad, M., Sharma, Y. D., & Narula, P. (2024). Wind speed prediction using non-Gaussian
model based on Kumaraswamy distribution. Energy Sources, Part A: Recovery,
Utilization, and Environmental Effects, 46(1), 719-735.

3. Shad, M., Sharma, Y. D., & Narula, P. (2023). Forecasting Southwest Indian monsoon
rainfall using the Beta seasonal autoregressive moving average (β SARMA) model. Pure
and Applied Geophysics, 180(1), 405-419.

4. Shad, M., Sharma, Y. D., & Singh, A. (2022). Forecasting of monthly relative humidity in
Delhi, India, using SARIMA and ANN models. Modeling earth systems and environment,
8(4), 4843-4851.

5. Shad, M., Sharma, Y., & Singh, A. (2022). A generalized time series model based on
Kumaraswamy distribution to predict double-bounded relative humidity data. Electronic
Journal of Applied Statistical Analysis, 15(1), 123-44.