Johannes Hachmann, University of Buffalo Assistant Professor of Chemical Engineering presents Making Machine Learning Work in Chemistry

The use of modern machine learning, informatics and data mining approaches is a relatively new development in the chemical and materials domain. These techniques have been exceedingly successful in other application fields, and since there is no fundamental reason why they should not have a similarly transformative impact on chemical and materials research, there is now a concerted effort by the community to introduce data science in this new context. However, adapting techniques from other application domains for the study of chemical and materials systems requires a substantial rethinking and redevelopment of the existing methods.

In this presentation, we will discuss our work on designing advanced, physics-infused neural network architectures, the fusion of unsupervised clustering with supervised regression for local ensemble models, active and transfer learning techniques, bootstrapping approaches to minimize our training data footprint, methods to increase the applicability domain of data-derived models and automated hyperparameter optimization.

Biosketch: Johannes Hachmann is an Assistant Professor of Chemical Engineering at the University at Buffalo (UB), the Director of the Engineering Science in Data Science graduate program, a Core Member of the UB Computational and Data-Enabled Science and Engineering graduate program, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern data science (i.e., the use of database technology, machine learning and informatics) to advance a data-driven discovery and rational design paradigm in the chemical and materials disciplines. One of the centerpieces of the group's efforts is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. This work was recognized with a 2018 NSF CAREER Award.
I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home
Mind Brain Lecture: Constructing the World of Taste in Your Head You fork the morsel into your mouth and say yum...chocolate cake. The appreciation of your dessert's taste seems to follow directly, quickly and simply from the placement of the food on your tongue. The truth, however, is far more interesting and complex: your brain actually begins determining whether you will enjoy a bite of food even before the fork approaches your mouth and continues to work the problem well after. Information about your food's color, smell, texture and taste activates multiple parts of your brain, where that information collides with your pre-mouthful beliefs about how it should taste. The coming-together and shuffling of that information around the brain takes time, as networks of neurons work together to help you decide whether the morsel in your mouth is worth swallowing. Referring to work from psychology, biology and computational neuroscience, Professor Katz will de-mystify and reveal the beauty of these complexities of the neuroscience of taste. Donald Katz, Professor of Psychology, Departments of Neuroscience, Psychology, and the Volen National Center for Complex Systems, Brandeis University Free presentation intended for a general audience. Reception to follow. https://www.stonybrook.edu/commcms/mind/
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.