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Dr. T. Sreenivasulu

Assistant Professor (Level 10), Mathematics, School of Engineering, Amaravati

Qualification: Ph.D
t_sreenivasulu@av.amrita.edu
ORCID ID
Google Scholar Profile
Scopus Author ID
Research Interest: Predictive Analytics, Uncertainty Quantification, Machine Learning, Environmental Modelling

Bio

Dr. T. Sreenivasulu is a Statistician and Data Science researcher with a Ph.D. in Statistics from the School of Advanced Sciences, VIT University, Vellore, India. His research interests include statistical modelling, time-series analysis, machine learning, predictive analytics, uncertainty quantification, and data-driven modelling of complex real-world systems.

He has experience working with large-scale and heterogeneous datasets using Python and R, with applications in environmental systems, public health, and epidemiological data analysis. He has published research articles in peer-reviewed international journals and has experience in interdisciplinary research, academic teaching, and scientific peer review.

Publications

Journal Article

Year : 2025

Air pollution forecasting using advanced machine learning techniques and ensemble stacking in Delhi

Cite this Research Publication : T. Sreenivasulu, G. Mokesh Rayalu, Air pollution forecasting using advanced machine learning techniques and ensemble stacking in Delhi, Environmental Health Engineering and Management Journal, Maad Rayan Publishing Company, 2025, https://doi.org/10.34172/EHEM.1370

Publisher : Maad Rayan Publishing Company

Year : 2025

Accurate hourly AQI prediction using temporal CNN-LSTM-MHA+GRU: A case study of seasonal variations and pollution extremes in Visakhapatnam, India

Cite this Research Publication : Sreenivasulu T, Mokesh Rayalu G, Accurate hourly AQI prediction using temporal CNN-LSTM-MHA+GRU: A case study of seasonal variations and pollution extremes in Visakhapatnam, India, Results in Engineering, Elsevier BV, 2025, https://doi.org/10.1016/j.rineng.2025.106303

Publisher : Elsevier BV

Year : 2024

Enhanced PM2.5 prediction in Delhi using a novel optimized STL-CNN-BILSTM-AM hybrid model

Cite this Research Publication : T. Sreenivasulu, G. Mokesh Rayalu, Enhanced PM2.5 prediction in Delhi using a novel optimized STL-CNN-BILSTM-AM hybrid model, Asian Journal of Atmospheric Environment, Springer Science and Business Media LLC, 2024, https://doi.org/10.1007/s44273-024-00048-7

Publisher : Springer Science and Business Media LLC

Awards
  • Raman Research Award (Institution-level research recognition)
FDP / Workshops
  • Two-Day Workshop on LaTeX for Technical Writing —Jawaharlal Nehru University (JNU), New Delhi.
  • One-Day National Webinar on “Essential Skills and Sampling: Key to Success in Statistics” — Organized under IQAC, Karnataka State Statistics Association and Alumni Association.
  • One-Day Training Programme on “Foundation of Mathematics” — Central University of Tamil Nadu, India.
  • Fifteen-Day Faculty Development Programme on Python Training — Teaching Learning Centre of Excellence, VIT, Vellore.
  • International Workshop on “Integrated Approaches of Stochastic Modelling and Data Science for Sustainable Development” — Sri Venkateswara University, Tirupati.
  • Faculty Development Programme on “Essentials of Data Science” — Vellore Institute of Technology, Chennai.
  • Faculty Development Programme on “Overcoming Common Mistakes in Writing High-Quality Research Articles” — Vellore Institute of Technology, Vellore.
Conferences
  • “Hybrid Machine Learning Model for Accurate Prediction of PM2.5 Concentration in Delhi, India” Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India.
  • “Comparative Analysis of Machine Learning Algorithms for Air Quality Index Prediction” Sacred Heart College, Tirupattur, Tamil Nadu, India.
  • “Time-Series Forecasting of PM10 and PM2.5 Concentrations Using LSTM and GRU Networks in Kolkata” Allahabad University, Prayagraj, Uttar Pradesh, India.
  • “A Hybrid LSTM–LightGBM Model with Uncertainty Estimation for Multi-Horizon PM2.5 Forecasting in Delhi” International Conference on Recent Advances and Innovative Statistics with Enhancing Data Science (IC-RAISE DS), jointly organized by the Indian Society for Probability and Statistics (ISPS) and VIT-AP University, Amaravati, Andhra Pradesh, India, December 22–24, 2025.
  • “Accurate Hourly AQI Prediction Using Temporal CNN–LSTM Model: A Case Study of Seasonal Variations and Pollution Extremes in Visakhapatnam, India” International Conference on Computational Mathematics & Artificial Intelligence (ICCMAI–2026), organized by Auxilium College, Vellore, Tamil Nadu, India, February 13, 2026.
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