MINED Experimental Group
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Education

  • Ph.D., Massachusetts Institute of Technology, 1992
  • M.S., Case Western Reserve University, 1987
  • B. Tech., Indian Institute of Technology, Madras, 1985

Teaching Interests

Professor Kalidindi's teaching focuses on fundamental and advanced topics in materials science and engineering, including materials design, microstructure-property relationships, and computational materials science. His courses serve both undergraduate and graduate students, emphasizing rigorous understanding of mechanical behavior of materials and data-driven approaches. Professor Kalidindi integrates research insights into the curriculum to prepare students for interdisciplinary challenges in mechanics of materials and encourages active student participation in learning.

Research Interests

Professor Kalidindi’s research centers on data science and computational approaches to materials design and discovery. He develops methods for extracting meaningful knowledge from complex microstructural data to predict materials properties and behavior. His work spans materials informatics, integration of imaging and characterization techniques, and modeling of material response, aiming to accelerate the deployment of engineered materials with optimized performance across multiple length scales.

Recent Publications

  • MAS Mahmoud, D Renner, A Khosravani, SR Kalidindi, Sequential Bayesian Inference of the GTN damage model using multimodal experimental data, Acta Materialia, 121902, 2026
  • J Hur, D Hoover, K Ballard, V Varshney, CP Przybyla, SR Kalidindi, Computational Protocols for the Study of Damage Initiation in Unidirectional Fiber‑Reinforced Polymer Matrix Composites, Integrating Materials and Manufacturing Innovation, 10.1007/s40192-025-00429-y, 2025
  • VSK Adapa, SR Kalidindi, CJ Saldana, A combinatorial AM–ML framework for high-throughput exploration of CPSP linkages in gamma prime-tailored nickel-based superalloys, Journal of Alloys and Compounds 1047, 2025
  • VSK Adapa, A Burl, K Saleeby, SR Kalidindi, CJ Saldana, An interpretable machine learning framework for the prediction of cracking in additively manufactured high gamma-prime Nickel-based superalloys, 2025
  • P Ray, H Cavalli, KD Tynes, G Bizana, AR Castillo, S Vyas, RL Siefert, ..., Unraveling the PFAS helix: A statistical approach, 2025