Curriculum Vitae

You can also see my CV as pdf here.

Education

• B.Eng. Chemical Engineering, UNAM, 2018
• M.Sc. Advanced Chemical Engineering with Process Systems Engineering, Imperial College London, 2019
• Ph.D Process Systems Engineering, MPI-Magdeburg and Otto von Guericke University, 2024 (expected)

Research experience

• Doctoral researcher — (Nov 2019 - present)
  • Max Planck Institute for Dynamics of Complex Technical System, Germany – In group of Kai Sundmacher
  • GNNs for molecular and mixture property prediction. ML for process optimization.
• Research intern — (May 2018 - Jul 2018)
  • The University of Manchester/Imperial College London, UK – In group of Antonio del Rio Chanona and Dongda Zhang
  • Coded several stochastic optimization algorithms in Python
• Research assistant — (Jun 2017 - Sep 2017)
  • McMaster University, Canada – In group of Thomas A. Adams II
  • Studied semi-continuous distillation systems using Aspen Plus
• Research assistant — (Feb 2017 - Jun 2017)
  • University of California - Riverside, USA – In group of Haizhou Liu
  • Light dispersion experiments for antiscalants analysis
• Research intern — (Jun 2016 - Aug 2016)
  • University of Maryland, USA – In group of Jeffery B. Klauda
  • Molecular dynamics using NAMD and VMD

Work experience

• Electricity Market Analyst — (Nov 2017 - Feb 2018)
  • Bravos Energia, Mexico
  • Developed a Mexican electricity tariffs model for forecasting

M.Sc. Theses supervised

• 2023 Christoph Griehl (now PhD researcher at MPI Magdeburg), Phosphine ligand selection for homogeneous catalysis applications embedding Machine Learning methods, OVGU.
• 2023 Sreekanth Kunchapu (now PhD researcher at University of Jena), Prediction of Infinite Dilution Activity Coefficients in Polymer Solutions Using Graph Neural Networks, OVGU.
• 2022 Rahul Bankala (now project manager at AKKA Technologies), Multi-objective optimization of an organic Rankine cycle system using Gaussian processes, OVGU.
• 2021 Anahita Iravanizad (now PhD researcher at MPI Magdeburg), Enriching Graph Neural Network Input via Random Walks on Graphs, TU-Chemnitz.
• 2021 Manoj Mangipudi (now PhD researcher at KIT), Gaussian processes for the prediction of molecular Environmental, Health and Safety (EHS) properties, OVGU.
• 2021 Viswa Sunkavalli (now Research assistant at BAM), Graph Neural Networks for the prediction of environmental indicators of molecules, OVGU.

Publications

Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution

Medina, E.I.S., Linke, S., Stoll, M. and Sundmacher, K., 2023. Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution. Digital Discovery.

Graph neural networks for the prediction of infinite dilution activity coefficients

Medina, E.I.S., Linke, S., Stoll, M. and Sundmacher, K., 2022. Graph neural networks for the prediction of infinite dilution activity coefficients. Digital Discovery, 1(3), pp.216-225.

RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs

Iravanizad, A., Medina, E.I.S. and Stoll, M., 2021. RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs. arXiv preprint arXiv:2109.07555.

Prediction of bioconcentration factors (BCF) using graph neural networks

Medina, E.S., Linke, S. and Sundmacher, K., 2021. Prediction of bioconcentration factors (BCF) using graph neural networks. In Computer Aided Chemical Engineering (Vol. 50, pp. 991-997). Elsevier.

Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regression

Medina, E.S., Vallejo, D.R., Chachuat, B., Sundmacher, K., Petsagkourakis, P. and del Rio-Chanona, E.A., 2021. Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regression. In Computer Aided Chemical Engineering (Vol. 50, pp. 1117-1123). Elsevier.

Hybrid semi‐parametric modeling in separation processes: a review

McBride, K., Sanchez Medina, E.I. and Sundmacher, K., 2020. Hybrid semi‐parametric modeling in separation processes: a review. Chemie Ingenieur Technik, 92(7), pp.842-855.

Impacts of antiscalants on the formation of calcium solids: Implication on scaling potential of desalination concentrate

Jain, T., Sanchez, E., Owens-Bennett, E., Trussell, R., Walker, S. and Liu, H., 2019. Impacts of antiscalants on the formation of calcium solids: Implication on scaling potential of desalination concentrate. Environmental Science: Water Research & Technology, 5(7), pp.1285-1294.

Understanding the dynamic behaviour of semicontinuous distillation

Madabhushi, P.B., Medina, E.I.S. and Adams II, T.A., 2018. Understanding the dynamic behaviour of semicontinuous distillation. In Computer Aided Chemical Engineering (Vol. 43, pp. 845-850). Elsevier.

Teaching

Machine Learning in Chemical Engineering (lectures)

Machine Learning in Chemical Engineering (tutorials)

Process Systems Engineering (tutorials)

Service and leadership

• Executive Board member — (Dec 2021 - Oct 2022)
  • RIIAA, LATAM
  • Duties: Strategic management of RIIAA and Summer school organization
• Chair — (Sep 2020 - Nov 2021)
  • RIIAA, LATAM
  • Duties: Leader of the Summer school organization team
• Organizing member — (Dec 2019 - Sep 2020)
  • RIIAA, LATAM
  • Duties: Summer school organization