Winner. Scientist of the Year 2024

Niket Sharma

Academic title, degree: Ph.D. in Chemical Engineering, M.Eng. in Computer Science
Fields of science: Chemical Engineering, Artificial Intelligence
Research interest: Machine Learning, Process Systems Engineering
Career Development: Experienced
Organization: Aspen Technology
Position: Senior Engineer
City: Burlington, Massachusetts
Country: USA

Dr. Niket Sharma is a seasoned Chemical Engineer and researcher with over a decade of industrial experience. He is renowned for his ability to bridge Chemical Engineering, Data Science, and Machine Learning to push industry boundaries. As a Senior Engineer at Aspen Technology, he leads advanced R&D in AI applications to optimize complex chemical processes, enhancing operational efficiency across various chemical and process engineering challenges.

Dr. Sharma holds a Ph.D. in Chemical Engineering and an M.Eng. in Computer Science with a specialization in Machine Learning from Virginia Tech (2021) and an M.Eng. in Chemical Engineering from the Indian Institute of Science, India’s premier research university. His Ph.D. research, one of the first to integrate process modeling, data analytics, and machine learning, paved the way for creating sustainable polyolefin processes with reduced costs, utility usage, and waste, maximizing recycling and reuse. Published by ACS, this groundbreaking work earned him the prestigious AIChE Process Development Award after global competition and rigorous peer review. (https://doi.org/10.1021/acs.iecr.9b02277)

In another significant project, Dr. Sharma explored hybrid process modeling, creating a science-guided machine learning approach that blends scientific knowledge with data-driven insights to achieve more precise, consistent predictions. This impactful research was published in the AIChE Journal, where it became one of the journal’s top-downloaded articles. (https://doi.org/10.1002/aic.17609)

Dr. Sharma also collaborated with Novonesis, a bio-solutions company, to address bioprocess foaming issues that impact process efficiency. By using multiway partial least squares analysis, he developed predictive models to identify foaming causes, significantly reducing product loss and enhancing process control while promoting sustainability in fermentation industries. (https://doi.org/10.1002/aic.18250)

In his industry roles, Dr. Sharma served as a Research Engineer at SABIC, a subsidiary of Saudi ARAMCO, the world’s largest oil company, where he focused on process development, scale-up, and modeling, along with reaction kinetics and polymerization. He played a critical role in successfully upgrading and launching a U.S. polyester process to create specialty polymers for healthcare and electronics.

Additionally, he led the R&D of a patented technology that enhanced PPE powder quality and reduced solvent usage through optimized bulk density control, contributing to sustainability.
(https://patents.google.com/patent/US11802184B2/en)

Earlier in his career, Dr. Sharma also worked as a Production Engineer at Indian Oil Corporation, India’s largest oil company, where he focused on maintaining process quality and closely monitored plant capacity and critical parameters for safe and efficient operations.

Dr. Sharma’s career showcases his commitment to advancing Chemical Engineering and AI for modern manufacturing processes.

Dr. Niket Sharma has made impactful contributions to Chemical Engineering, notably through his co-authorship of two textbooks published by Wiley Inc., which explore modeling, control, data analytics, and machine learning in chemical and polymer processes. Endorsed by academic and industry experts, these books cover topics from steady-state and dynamic modeling to advanced process control and data analytics. Designed for students, faculty, and professionals, the books offer hands-on workshops with real plant data, providing practical methods for optimizing polyolefin manufacturing.

At Aspen Technology, Dr. Sharma leads AI application development and was instrumental in creating the AI Model Builder, a hybrid tool combining first-principal models with machine learning to enhance process modeling and efficiency. Recognized by Hydrocarbon Processing magazine as a top modeling technology, this software is widely adopted across chemical and energy sectors, driving cost reductions, efficiency improvements, and sustainability solutions, such as carbon capture and plastic recycling.

Additionally, Dr. Sharma is a peer reviewer for leading journals, including AIChE Journal and Computer and Chemical Engineering, highlighting his field expertise. He is a senior member of AIChE and is regularly invited as a keynote speaker at top conferences, underscoring his reputation in academia and industry.