He heads a research and innovation program covering various aspects of computational engineering across products, materials and manufacturing processes and development of enabling digital platforms. Besides science driven modeling and simulation, his current interests extend to utilization of state-of-the-art machine learning and knowledge engineering frameworks for engineering decision making. Gautham holds 8 granted patents, has published one book and over 75 papers in journals & conference proceedings

He has over 35 years of experience in applied research in areas such as language processing, model driven engineering, modelling tools, databases and knowledge engineering. He had played an active role in the standardization efforts of the Object Management Group (OMG). He has also played a key role in the development of TCS PREMAP, a platform for computational materials engineering. The platform brings together domain knowledge, modelling and simulation and machine learning to support decision making processes in materials engineering. He has served on the program committees of ISEC, APSEC, MODELS and ICSE. He has around 50 publications and 20 granted patents.

His research has contributed to large-scale data-driven discoveries in various scientific and engineering disciplines, such as materials science, healthcare, social media, and bioinformatics. He has co-authored 150+ peer-reviewed publications, co-developed and released 15+ software, been on program committees of 40+ conferences/workshops, and served as a PI/Co-PI on 15+ sponsored projects funded by various US federal agencies (e.g., NSF, DOE, AFOSR, NIST, DARPA, DLA). He is one of the few computer scientists who are actively introducing AI and advanced data science techniques in the field of materials science and has successfully led several large-scale materials informatics projects.

His research is focussed on utilizing machine learning for problems in materials engineering, especially for characterization of material microstructure images and building process-structure-property correlation models. He holds one granted patent, 8 publications, including in top materials science journals and premier machine learning conferences.