Jun 2023–present: Senior Research Specialist, Machine Learning, Optimization & Statistics, The Dow Chemical Company
Chiang, L., Christiansen, D., Malloure, M., Briceno-Mena, L. A., & Kim, S. H. (Under review). Artificial Intelligence at Scale in the Chemical Industry: From Legacy to Leadership. Current Opinion in Chemical Engineering.
Briceño Mena, L., & Durán Herrera, E. (2020). Heterogeneous Catalytic Ozonation of Phenol over Iron-based Catalysts in a Trickle Bed Reactor. Revista Ingeniería, 30(2), 1-13.
Briceno-Mena, L. A., Venugopalan, G., Romagnoli, J. A., & Arges, C. G. (2021). Machine learning for guiding high-temperature PEM fuel cells with greater power density. Patterns, 2(2), 100187.
Briceno-Mena, L. A., Nnadili, M., Benton, M. G., & Romagnoli, J. A. (2022). Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques. Data-Centric Engineering, 3.
\Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2022). Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells. In 32nd European Symposium on Computer Aided Process Engineering (ESCAPE32), Toulouse, France.
Briceno-Mena, L. A., Romagnoli, J. A., & Arges, C. G. (2022). PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems. Industrial & Engineering Chemistry Research, 61(9), 3350-3357.
Gokul Venugopalan, Deepra Bhattacharya, Evan Andrews, Luis Briceno-Mena, José Romagnoli, John Flake, and Christopher G. Arges ACS Energy Lett. 2022, 7, XXX, 1322–1329 DOI: 10.1021/acsenergylett.1c02853.
Venugopalan, G., Bhattacharya, D., Andrews, E., Briceno-Mena, L., Romagnoli, J., Flake, J., & Arges, C. G. (2022). Electrochemical Pumping for Challenging Hydrogen Separations. ACS Energy Letters, 7(4), 1322-1329.
Briceno-Mena, L., Nnadili, M., Benton, M., & Romagnoli, J. (2022). Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques. Data-Centric Engineering, 3, E18. doi:10.1017/dce.2022.21
Webb, Z. T., Nnadili, M., Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A. (2022). Optimization of multi-mode classification for process monitoring. Frontiers in Chemical Engineering, 4.
Arunagiri, K., Wong, A. J., \textbfBriceno-Mena, L. Energy \& Environmental Science 2023 16, 5916-5932
Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2023). Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells. Computers & Chemical Engineering, 171, 108159.
Gallage Dona, H. K., Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., Kumar, R., & Romagnoli, J. A. (2023). Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations. Industrial & Engineering Chemistry Research, 62(24), 9533-9548.
Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A. (2023). Unsupervised learning: Local and global structure preservation in industrial data. Computers & Chemical Engineering, 178, 108378.
Schmidt, A., Briceno-Mena, L., Rajagopalan, S., Ma, K., Reiner, B., & Braun, B. (2024). Introduction to Formulation Optimization. In A. Schmidt & K. Wallace (Eds.), The Digital Transformation of Product Formulation. CRC Press.
Rahnama, A., Vaithiyanathan, M., Briceno-Mena, L., Dugas, T. M., Yates, K. L., Romagnoli, J. A., & Melvin, A. T. (2024). A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability. Analyst, 149, 4256-4266.
Romagnoli, J. A., Briceno-Mena, L., & Manee, V. (2024). AI in Chemical Engineering - Unlocking the Power Within Data. CRC Press.
Olayiwola, T., Briceno-Mena, L. A., Arges, C. G., & Romagnoli, J. A. (2024). Synergizing data-driven and knowledge-based hybrid models for ionic separations. ACS ES&T Engineering, 4.
Arense-Feffin, E., Sagar, N., Briceno-Mena, L. A., Braun, B., Castillo, I., Rizzo, C., Bui, L., Xu, J., Chiang, L., & Braatz, R. (2025). The Incorporation of Qualitative Knowledge in Hybrid Modeling. Computers & Chemical Engineering, 109484.
Conference presentation at 22nd International Ozone Association World Congress, Barcelona, Spain
Conference presentation at 23rd International Ozone Association World Congress, Washington, D.C., United States
Conference presentation at 23rd International Ozone Association World Congress, Washington, D.C., United States
Workshop talk at Facultad de Ciencias Naturales y Matematicas, ESPOL, Ecuador
Conference abstract at ECS Meeting Abstracts,
Workshop talk at Facultad de Ciencias Naturales y Matematicas, ESPOL, Ecuador
Conference talk at PROCESA 3rd AIChE Colombia Student Regional Conference, Colombia
Workshop talk at Telluride Science Research Center Workshop on Machine Learning and Informatics for Chemistry and Materials, Telluride, Colorado, United States
Conference talk at APS March Meeting Abstracts,
Conference presentation at 2022 AIChE Annual Meeting, Phoenix, Arizona, United States
Conference presentation at 33rd European Symposium on Computer Aided Process Engineering (ESCAPE33), Athens, Greece
Conference talk at Foundations of Process-product Analytics and Machine Learning, UC Davis, California, United States
Conference talk at AIChE Spring Meeting, New Orleans, Louisiana, United States
Conference talk at 2024 AIChE Annual Meeting, Orlando, Florida, United States
Guest lecture at NRT: Computing and Data Science Training for Materials Innovation, Discovery, and Analytics, University of Delaware, Delaware, United States
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