cv

PhD Chemical Engineering

West Virginia University, USA

• Strategies for Process Systems Mapping and Control Based on Operability Analysis, under Dr. Fernando V. Lima's supervision.
• GPA: 3.80/4.00
• • Development of emerging techniques for process operability calculations, involving mainly supervised machine-learning, constrained nonlinear programming (NLP) and automatic differentiation (AD) for efficient algorithm development.
  • Development of an open-source python package for process operability calculations, for ease of use and dissemination of operability algorithms in academia and industry.
• Control, Optimization and Design for Energy and Sustainability (CODES) Research Group leader.
  • CODES Research Group leader, supervising group activities, as well as organizing the group's semester schedule, workshops, weekly meetings and relevant announcements.

Master's Degree (M.Sc.), Chemical Engineering

Federal University of Campina Grande, Brazil

• Metamodel-based Numerical Techniques for Self-Optimizing Control.
• Academic Coefficient: 10.00/10.00
• • Developed a methodology capable of using Gaussian Process Regression (GPR) to aid the selection of controlled variables (CVs) in an industrial process, following the Self-Optimizing Control (SOC) methodology.

Exchange Student, Chemical Engineering (During my Bachelor's Degree)

University of Birmingham, United Kingdom

• Exchange Student, participating in the "Science Without Borders" Brazilian scientific mobility program.
• British Degree Classification: Upper Second

Bachelor of Science, Chemical Engineering

Federal University of Campina Grande, Brazil

• Academic Coefficient: 8.69/10.00

Postdoctoral Fellow

Carnegie Mellon University

• Working with Dr. Carl D. Laird, Dr. John R. Kitchin and Dr. Anne S. Robinson on hybrid scientific machine learning (SciML) models that embed first-principles physics with data-driven components through differentiable programming for process systems engineering (PSE).
• Developing optimization-based training frameworks for hybrid models, enabling rigorous constraint enforcement, uncertainty quantification, and scalable dynamic optimization, as well as conditional generative modeling approaches for feasible design and control.
• Collaborating with industrial and research partners, including GSK, NIIMBL (National Institute for Innovation in Manufacturing Biopharmaceuticals), Goodyear Tire and Rubber Company, Shell, and the National Energy Technology Laboratory (NETL, U.S. Department of Energy).
• Mentoring graduate and undergraduate students, contributing to research proposals, and coordinating the Process Systems Engineering (PSE) Seminar.

Adjunct Instructor

Carnegie Mellon University

• Instructor for the course 06-325 Numerical Methods and Machine Learning for Chemical Engineering, covering root-finding, ordinary differential equations, and optimization, as well as modern machine learning techniques and tools applied to chemical engineering problems.

Graduate Research Assistant (Ph.D.)

West Virginia University

• Worked with Dr. Fernando V. Lima on emerging techniques for process operability calculations using supervised machine learning, constrained nonlinear programming (NLP) and automatic differentiation (AD), and developed an open-source Python package for operability analysis.
• Collaborated on a WVU–NETL partnership on machine learning-based system identification and served as CODES research group leader.

Graduate Research Assistant (M.Sc.) and Software Developer

Federal University of Campina Grande

• Research and development of BRPWC for Petrobras, an automated software capable of easily selecting the most promising self-optimizing control structures in large-scale industrial processes, including the Python calculation engine and user interface mock-ups.

Process Engineering Intern

SigmaCT as a contractor to Braskem

• Experience in Vinyl Chloride Monomer (VCM) and Polyvinyl Chloride (PVC) production plants, including simulations in Aspen Plus and Aspen Plus Dynamics to investigate operating regions.

Opyrability

• Opyrability - A Python-based package for process operability analysis - is an open-source project for advanced process operability analyses. The opyrability codebase includes the main operability algorithms, supplementary analysis and visualization methods to allow for the assessment of simultaneous design and control objectives early in the conceptual phase. As of 2026, opyrability has surpassed 36,000 downloads.

Metacontrol

• Metacontrol is a Python-based software which assembles several methodologies into a single bundle so that a fast implementation of the Self-Optimizing Control (SOC) technique can be achieved.

Numerical Methods and Machine Learning for Chemical Engineering

Carnegie Mellon University

• Instructor.

Chemical Process Control

West Virginia University

• Teaching Assistant. Prepared lectures, MATLAB/Simulink tutorials, and problem sets in an active learning-based fashion for senior undergraduates.

Graduate Research Mentor

Carnegie Mellon University

• Yi Yu — neural differential-algebraic systems of equations (neural DAEs).
• Saket Prashant Dhake — plantwide control software tool in the Pyomo ecosystem.
• Amogh Amonkar — time series forecasting using Gaussian process-based nonlinear autoregressive exogenous models (GP-NARX).
• Sydney Butikofer — mechanistic models for biopharmaceutical processes and model-based design of experiments (MBDoE).

Undergraduate Research Mentor

Carnegie Mellon University

• Felix Siedel and Adi Mallik — hybrid machine learning models (e.g., neural ODEs) for bioprocessing applications.

Undergraduate Research Mentor

West Virginia University

• Savannah Sakhai and Dean Sweeney — simulation projects in hydrogen production and membrane reactor modeling.

• Sweeney D., Alves V., Sakhai S., Dinh S. and Lima F. V. Techno-economic Optimization of a Palladium Membrane Reactor for Steam Methane Reforming Industrial Process. AIChE Annual Student Conference, Phoenix, AZ. 2022.
• 3rd place at the 2022 AIChE undergraduate student poster competition award. Computing and Process Control Group II.