FOPAM Conference Overview

FOPAM 2023, the CACHE conference on "Foundations of Process Analytics and Machine learning," will be held on July 30-August 3, 2023, at the University of California, Davis. Building on the successful FOPAM 2019, FOPAM aims to be the premier forum for researchers from industry and academia to discuss current status and future directions of data analytics and machine learning in the process industries.

Conference Format

The format of the conference is 3-1/2 days of presentations and discussions, preceded by an optional 1-1/2-day workshop. The conference begins on Monday evening, July 31, with an opening keynote by Cenk Ündey, VP and Global Head of PTD Data & Digital at Roche/Genentech. In the next three days, morning and evening sessions will follow a format of two talks, followed by audience Q&A and serious technical discussions with the two speakers and an invited discussor. Continental breakfast and lunch will be provided Tuesday-Friday. Two of the afternoons will be used for poster sessions, small-group discussions, and other unstructured activities. On the last evening, a banquet and brief rapporteur analyses will conclude the conference.

 

Schedule

Day Morning Afternoon Evening
Sun, Jul 30, 2023 Arrival Optional workshop by Leo H. Chiang, Dow; R. Bhushan Gopaluni, Univ British Columbia; and Ali Mesbah, Univ California Berkeley Hospitality
Mon, Jul 31, 2023 Optional workshop (continued) Optional workshop (concluded)

Welcoming Reception

Conference introduction/overview

Keynote speaker: Cenk Ündey, VP and Global Head of PTD Data & Digital, Roche / Genentech: "Unlocking Full Potential of Data and Digital: Enabling Speed, Efficiency and Culture in Pharmaceutical Technical Development"

Followed by Hospitality

Tue, Aug 1, 2023

Emerging Methods in Machine Learning and Data Science

Victor M. Zavala, Baldovin-DaPra Professor, University of Wisconsin Madison: "Topological Data Analysis: Concepts, Tools, and Applications"

Ioannis Kevrekidis, Bloomberg Distinguished Professor, The Johns Hopkins University: "Physics-informed machine learning for equation-free and variable-free modeling"

Poster Session 1, small-group discussions and other unstructured activities 

Industrial Data-Science Applications I

Elif Ozkirimli, Head of Computational Science Products, Roche: "Natural language processing of biochemical languages - toward more generalizable protein-compound interaction prediction models"

Detlef Hohl, Chief Scientist - Computation and Data Science, Shell: "What Artificial Intelligence really Is, and how it will change the chemical process industry"

Followed by Hospitality

Wed, Aug 2, 2023

Machine Learning for Process and Product Chemistry

Zachary Ulissi, Associate Professor, Carnegie Mellon: "Large Open Datasets and Graph Neural Networks for Generalizable Models in Catalysis"

Kristen Severson, Senior Researcher, Microsoft: "Data-driven design of concrete with amortized Gaussian processes and multi-objective optimization"

Poster Session 2, small-group discussions and other unstructured activities

Industrial Data-Science Applications II

Salvador García Muñoz, Executive Director - Engineering, Eli Lilly: "Process analytics in pharma"

Bea Braun, Sr. Research Scientist - Data Science and Hybrid Modeling, Dow: "Machine Learning in the Chemical Industry – Success Stories and What We Need to Move to Scale"

Followed by Hospitality

Thu, Aug 3, 2023

Data Science for Processes and Control

Rolf Findeisen, Prof. Dr.-Ing., Technische Universität Darmstadt: "Machine learning for cyber-physical systems"

Xiaonan Wang, Associate Professor, Tsinghua University / National University of Singapore: "Smart systems engineering contributing to material and process development for a carbon-neutral future"

Past and Future of Process/Product Analytics & Machine Learning, including Education and Workforce Development

Venkat VenkatasubramanianSamuel Ruben-Peter G. Viele Professor of Engineering, Columbia University: "Combining Symbolic and Numeric AI: Challenges and Opportunities in Research and Education"

John D. Hedengren, Professor, Brigham Young University: "Data-Driven Engineering Education with Hands-On Learning"

Conference Banquet with after-dinner rapporteurs

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