Shahmeer Baweja, Ph.D.  is a mechanical engineer and computational materials scientist specializing in computational mechanics, materials modeling, and scientific machine learning. His work focuses on understanding and predicting the mechanical behavior of engineering materials through physics-based simulations, finite element methods, and data-driven approaches.

Dr. Baweja earned his **Bachelor of Science in Mechanical Engineering**, with a  minor in Mathematics and membership in the Honors College, from the University of Houston. He continued at the same institution to complete a Master of Science in Mechanical & Aerospace Engineering, conducting thesis research in computational mechanics, before earning his Ph.D. in Mechanical Engineering. His doctoral research advanced crystal plasticity finite element modeling of magnesium alloys, investigating how microstructural features such as grain size, crystallographic texture, and defects influence deformation, damage evolution, and mechanical performance.

Following his Ph.D., Dr. Baweja joined Argonne National Laboratory as a Postdoctoral Appointee in the Thermal and Structural Materials Modeling and Simulations Group within the Applied Materials Division. His research focused on high-temperature structural materials for advanced energy systems, including constitutive modeling, creep deformation, stress relaxation, rupture-life prediction, uncertainty quantification, and active learning. He developed predictive computational models combining solid mechanics with machine learning, executing more than 125 high-fidelity simulations and contributing to DOE technical reports and peer-reviewed publications.

His research interests span computational mechanics, finite element analysis, crystal plasticity, continuum mechanics, fracture and damage mechanics, constitutive modeling, computational materials science, high-performance computing, scientific machine learning, and AI-driven engineering. He is particularly interested in integrating physics-based modeling with artificial intelligence to accelerate engineering design, materials discovery, and digital engineering workflows.

Born with bilateral profound sensorineural hearing loss, Dr. Baweja has navigated his academic and professional career using hearing aids, lip reading, speech reading, real-time captioning, and AI-assisted transcription technologies. His experiences have strengthened his commitment to advancing accessibility and inclusion within STEM while demonstrating that communication differences need not limit scientific achievement.

Outside research, Dr. Baweja enjoys strength training, running, reading, traveling, and exploring emerging technologies. He is passionate about applying engineering and artificial intelligence to develop safer, lighter, and more sustainable technologies that benefit society.