Chemical Engineering Program
JUNE 7th
- Session Topic- Surface Science & Catalysis
- 8:30-9:00- Andrew Medford (Georgia Institute of Technology) – “Catalysis Informatics: Utilizing machine-learning and data science to extract knowledge from catalytic data”
- 9:00-9:30- Zack Ulissi (Carnegie Mellon) – “Practical Applications of Machine Learning to Catalyst Design and Discovery”
- 9:30-10:00- Richard West (Northeastern University) – “Unsupervised Machine Learning for Data-Driven Representation of Reactions”
- 10:00-10:30- Hongliang Xin (Virginia Polytechnic Institute) – “Machine Learning for Understanding Nonadiabatic Surface Chemistry and Accelerating Catalyst Discovery”
- break
- Dion Vlachos (University of Delaware) – “Predictive Modeling of Complex Chemical Reactions: Correlated Data, Uncertainty Quantification, and Machine Learning”
- 11:00-11:30- Andreas Heyden (University of South Carolina) – “Identifying the active site of the water-gas shift reaction over platinum-based catalysts”
- 11:30-12:00- Bryan Goldsmith (University of Michigan) – “Finding descriptors in materials data using subgroup discovery and compressed sensing”
- 12:00-12:30- Srinivas Rangarajan (Lehigh University) – “Harnessing systems and informatics approaches in mechanistic analysis of catalytic chemistries”
- 12:30-1:30- lunch/posters
- 1:30-2:30- plenary
- Session Topic - Systems Engineering
- 2:30-3:00- Fani Boukouvala (Georgia Institute of Technology) – “Best surrogate approximations for data-driven optimization”
- 3:00-3:30- Bhusan Golupani (University of British Columbia) – “Deep Neural Networks for Supervised and Unsupervised Learning of Process Faults”
- 3:30-4:00- Lorenz Biegler (Carnegie Mellon University) – “Data-driven optimization with Truth Models”
- break-
- Venkat Venkatasubramanian (Columbia University) – “Machine Learning in Process Systems Engineering: Opportunities and Challenges”
- 4:30-5:00- Luke Achenie (Virginia Polytechnic Institute) – “ODEs as Machine Learners?”
- 5:00-5:30- Victor Zavala (University of Wisconsin-Madison) – “Machine Learning Algorithms for Liquid Crystals-Based Sensors”
- 5:30-6:00- Heather Mayes (University of Michigan) – “Providing the Foundation for Chemical Engineers to Become Data Scientists”
JUNE 8th
- 9:00-9:30
- 9:30-10:00-
- 10:00-10:30
- break
- 11:00-11:30
- 11:30-12:00
- 12:00-12:30
- 12:30-1:30- lunch/posters
- 1:30-2:30- plenary
- session topic
- 2:30-3:00
- 3:00-3:30
- 3:30-4:00
- break
- 4:30-5:00
- 5:00-5:30
- 5:30-6:00
Posters
Hemanth Pillai (Virginia Polytechnic Institute) – “A Machine Learning Model for Accelerating Biomimetic Electrocatalyst Discovery”
Jiamin Wang (Virginia Polytechnic Institute) – “Machine Learning Molecular Dynamics for Understanding Nonadiabatic Surface Reactions”
Jinchao Feng (University of Massachusetts Amherst) – “Model-Form Uncertainty Quantification in Fuel Cell Design”
Aini Palizhati (Carnegie Mellon University) – “Using Data Science to Reduce Large Reaction Networks in Catalysis”
Jiazhou Zhu (Clemson University) – “Expanding Methods from Computationally-Driven Design of Catalysts to Designing Advanced Materials”
Junwoong Yoon (Carnegie Mellon University) – “Surfactant Design with Molecular Simulations and Machine Learning”
Dilip Krishnamurthy (Carnegie Mellon University) – “Machine Learning Generalized Geometric Descriptors for Oxygen Reduction Activity on Transition Metal Sulfides”
Kevin Tran (Carnegie Mellon University) – “Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution”
Ray Lei (Georgia Institute of Technology) – “Data-driven Exchange-Correlation Functional Design and Visualization of Electronic Environments”