Gabriel S. Gusmão

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Gabriel Sabença Gusmão is a scientist, chemical engineer, and an enthusiast of scientific machine learning (SciML) and its applications to the chemical and healthcare industries. Gabriel currently serves as Chief Machine Learning Officer (CMLO) and interim Chief Technology Officer (CTO) at GlucoSense Inc., where he leads the development of real-time hybrid models for glucose metabolism and diabetes management using neural-ODEs and advanced analytics deployed over scalable cloud infrastructure.

He designed and implemented GlucoScore™, a proprietary real-time glucose stability metric, and architects the company's retrieval-augmented generation (RAG) pipeline, leveraging semantic search and curated knowledge bases to deliver personalized, actionable health insights powered by LLMs.

Gabriel holds a Ph.D. in Chemical and Biomolecular Engineering from the School of Chemical and Biomolecular Engineering (ChBE) at Georgia Institute of Technology. He was a graduate research assistant in the Medford Group under Dr. Andrew J. Medford's advisement. During his Ph.D., Gabriel developed a framework for solving high-dimensional inverse problems in transient kinetics using physics-informed neural networks (PINNs), and introduced maximum-likelihood estimators and dimensionality reduction strategies for mean-field microkinetic models. His work earned him the prestigious IBM PhD Fellowship in 2021.

Gabriel's research bridges scientific computing and chemical kinetics, enabling direct uncertainty quantification via algebraic decomposition and Fisher Information analysis. At GlucoSense, he applies these techniques to build interpretable machine learning models deployed in real-time healthcare environments.

Gabriel has over 5 years of experience in the chemical industry with Braskem (Renewables Techonologies Department), where he worked on process modeling, catalyst screening, KPIs, and advanced control using plant data from ODBC/IP21 historians. He is known for his versatility, adaptability, and the ability to translate domain knowledge into scalable ML systems.

He received his undergraduate degree in Chemical Engineering from Unicamp and completed a visiting research year at the University of California, Riverside with Prof. Phillip Christopher, working on heterogeneous catalysis using experimental and computational techniques.

Gabriel is passionate about the intersection of physics-based modeling and modern machine learning. His current challenges involve deploying PINNs at scale, addressing stiffness in dynamic systems, and ensuring interpretability in hybrid modeling frameworks.

News

• 2025-04 Co-authored Unifying thermochemistry concepts in computational heterogeneous catalysis in Chemical Society Reviews.
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• 2025-03 Co-authored Reactive Capture and Conversion of Carbon Dioxide to Methanol with ZnZrO₂ and Alkali-Promoted Mg₃AlOₓ Mixed Oxide Catalytic Sorbents in ACS Sustainable Chemistry & Engineering.
• 2024-06 Co-authored Micro-kinetic modeling of TAP data using KINNs in Digital Discovery.
• 2024-03 Co-authored Model-based design of TAP reactors in Industrial & Engineering Chemistry Research.
• 2024-01 Appointed Chief Machine Learning Officer and interim CTO at GlucoSense Inc., leading real-time ML systems for healthcare analytics.
• 2023-12 Published Maximum-likelihood estimators in PINNs in Computers & Chemical Engineering.
• 2023-11 Presented poster Using Neural Networks to Interpret Transient Kinetic Data at the 2023 AIChE Annual Meeting.
• 2023-03 Co-authored CO₂ hydrogenation over ZnZrOx/ZSM-5 in The Journal of Physical Chemistry C.
• 2022-11 Presented poster Dimensionality Reduction of Chemical Kinetics Based on Extent-of-Reaction in a Physics-Inspired Machine Learning Framework at the 2022 AIChE Annual Meeting.
• 2022-09 Co-authored Training stiff dynamic process models via neural ODEs in Computer Aided Chemical Engineering.
• 2022-08 Co-authored Impact of TAP initial state uncertainties on kinetics in AIChE Journal.
• 2022-08 Completed research internship at IBM Research, San Jose CA, applying graph neural networks to catalysis datasets.
• 2022-04 Published Kinetics-Informed Neural Networks in Catalysis Today.
• 2022-01 Awarded the IBM PhD Fellowship for work on ML-based inverse problems in catalysis.
• 2022-01 Contributed to Online Certificate in Data Science for the Chemical Industry in Chemical Engineering Education.
• 2021-11 Presented poster PINNs for Kinetic Parameter Estimation and Uncertainty Quantification at the 2021 AIChE Annual Meeting.
• 2021-05 Received Outstanding Ph.D. Proposal Award from Georgia Tech School of Chemical & Biomolecular Engineering.
• 2020-12 Received Shell Outstanding Teaching Assistant Award at Georgia Tech.
• 2019-08 Awarded Best Poster Presentation at the SUNCAT Summer Institute, Stanford University.
• 2019-05 Recognized for Outstanding Performance on the Qualifying Exam at Georgia Tech.
• 2018-08 Started Ph.D. in Chemical Engineering at Georgia Tech.
• 2018-07 Co-authored Process modeling for green ethylene production in Industrial & Engineering Chemistry Research.
• 2016-03 Co-authored Mechanism of CO₂ reduction on Ru(0001) in Journal of Catalysis.
• 2015-06 Published A general and robust approach to microkinetic systems in AIChE Journal.