Ziye earned his bachelor’s in Mechanical Engineering and a minor in Data Science from China University of Geosciences (Wuhan), where he investigated energy storage materials and innovative substrates for  supercapacitors and non-enzyme glucose sensor. His graduate research focuses on application of advanced simulation techniques on the atomistic level and excellent machine learning models to augment the efficiency and accuracy of multiscale material modelling to investigate effects of stress, structural geometry, and dislocations on the phase transition of Si nanostructures.

Maria has a Bachelor of Science degree in Engineering Physics and a Master’s degree in Applied Physics from Universidad EAFIT. Maria's current research work revolves around computational modeling of molecular structures through molecular dynamics simulations, including the steric effects of fluid membranes using graphene, the influence of electrical potential on nanoscale tribology applications, and the atomistic modeling of upcycled polymer lubricant for interface engineering.

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Merve received her B.S. and M.S. degrees from the Technical University of Istanbul, in Turkey. Her current research focuses on coating technology especially the boriding process on metal and high entropy alloys technologies (e.g., dip coating, thin film coating), materials characterization (e.g., SEM, EDS, WDS, XRD, Raman Spectroscopy), metallography processing, and mechanical test (Nanoindentation, Vickers hardness), tribology test technology.​

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Seungjoo received his Masters and Bachelors in Mechanical Engineering from Korea University. His current research focuses on tribological and tribochemical studies of interfaces under electrified environment and lubricants including plastic derived lubricating oils, coatings and solid lubricants, unraveling their very unique fundamental tribological mechanisms under lubricated conditions. He has significant experience in tribological characterization techniques like 3D profilometry, SEM/EDS, and Raman spectroscopy.

Veerendra Naralasetti received his bachelor's in mechanical engineering at Jawaharlal Nehru Technological University, Kakinada in 2016 and a master’s in mechanical engineering from Louisiana State University, Baton Rouge in 2018. Veerendra's research interests include ab initio methods, molecular dynamics, ML-driven multiscale modeling, surface characterization, and interface engineering. His research focuses on developing multi-scale digital twin models to study tribological and electrochemical properties at material interfaces. He is developing ML models to extract collective variables for accelerated time-scale and length-scale simulations.​

Daniel got his Mechanical Engineering degree from Universidad EAFIT in Colombia.  He explores the atomistic, quantum and multi-scale mechanisms by which solid state structural properties occur. By coupling solid state physics, materials science and theories with AI/ML, he predicts structural and functional properties of materials. He has been contributor to extensive computational mechanics projects such as AtomDNN for ML-accelerated calculations, and Smart|DT for Small Aircraft Risk Assessment.​​​​

Henry got his Bachelors in Mechanical Engineering from Texas A&M University and is currently working towards his Masters with a specialization in practical testing of coatings used within mechanical systems. His current research focuses on analysis material coatings used in heavy-duty mechanical systems working on design of energy-efficient, long-lasting, and economic lubricants.

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Erfan obtained his Bachelors from Shahrekord University and his Masters degree from Sharif University of Technology, Iran. His research integrates computational solid mechanics, contact mechanics, fatigue, fretting fatigue, and thermal stresses to develop new models for theoretical and experimental tribology.

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Himanshu completed his bachelor’s degree in Metallurgical and Materials Engineering at the National Institute of Technology Jaipur. My research will use machine learning methods including active and reinforcement learning to predict optimal synthesis strategies for manufacturing quantum materials via different techniques such as chemical vapor transport, pulsed laser deposition and chemical vapor deposition predict previously unknown synthesis schedules for novel quantum architectures.​

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Sung is a Ph.D. student at Texas A&M University and joined CISE Lab in January 2025. He completed his bachelor’s and master’s degrees in Mechanical Engineering at the University of Alabama. His research interests include solid mechanics, surface engineering, control dynamic systems, and machine learning. This multidisciplinary background enables him to explore innovative approaches to tribological systems, focusing on wear analysis, lubrication mechanisms, and predictive modeling. His current work involves investigating friction and wear behavior in various materials and conditions, integrating computational methods to enhance tribological performance. Through his research, he aims to contribute to the advancement of high-performance and durable engineering systems.

Mingjian Tang is a Ph.D. student in the Department of Mechanical Engineering at Texas A&M University. In priority, he obtained his Bachelor's degree in Civil Engineering from Tongji University. He also pursued his master's degree in Solid Mechanics from the Institute of Mechanics, Chinese Academy of Sciences. Mingjian joined Dr. Gao's group in August, 2024. His current research involves molecular and finite element modeling for the mechanical behaviors of new materials.