Our research group in Mechanical Engineering at The University of Texas at San Antonio work on the analysis of very large and very small datasets including complex experimental and observational data, multi-stream signals, and image data, for predictive analytics and decision making. Our work is strongly data-driven, where mathematical models are paired with data and statistical/empirical methods to drive meaningful change in complex systems. Our developed methodologies have been considerably funded and widely utilized in a variety of applications ranging from healthcare to defense to manufacturing.





  • R Meka, A Alaeddini, K Bhaganagar, A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables, Energy 221, 119759

  • J. Nielson, K. Bhaganagar, R. Meka, A. Alaeddini, Using Atmospheric Inputs for Artificial Neural Networks to Improve Wind Turbine Power Prediction, Energy, In Press. (5-Year Impact Factor: 5.747)

  • S.H.A. Faruqui, Y. Du, R. Meka, A. Alaeddini, C. Li, S. Shirinkam, J. Wang, Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial JMIR mHealth and uHealth 7, no. 11 (2019): e14452. (Impact Factor: 4.301)

  • A. Alaeddini, R. Meka, S. Martinez, E. Kraft, Sequential Laplacian Regularized V-Optimal Design of Experiments for Response Surface Modeling of Expensive Tests: An Application in Wind Tunnel Testing, IIE Transactions. 51.5 (2019): 559-576. DOI: 10.1080/24725854.2018.1508928



  • ​June 2021, Publication in IEEE ACCESS, Publication in Robotics, Multi-Armed Bandit Regularized ExpectedImprovement for Efficient Global Optimization of Expensive Computer Experiments with Low Noise.

  • March 2021, Publication in Energy, A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables, 

  • Febuary 2021, Publication in Robotica, Control policies for a large region of attraction for dynamically balancing legged robots: a sampling-based approach

  • January 2021, Publication in Fatigue & Fracture of Engineering Materials & Structures, Characterization of residual stresses from cold expansion using spatial statistics


  • A Graduate Research Assistant (GRA) position is available for students with a solid math (statistical) background and good programming (MATLAB/Python) skills