Chalmers AI4Science Seminar


Advances in machine learning and AI systems are increasingly influencing how we approach the quantitative sciences, including physics, chemistry, and biology. These opportunities include having machines learn new representations of interactions between particles, how matter transforms in reactions, help us decide what experiment to conduct next or detect emerging phenomena. Sparse data situations remain a significant hurdle in many sciences or situations where common data assumptions do not hold. Consequently, it remains critical to ground our efforts in the millennia of scientific insights embodied in the literature to avoid, in the best case, having machines relearn what we already know. The Chalmers AI4Science is a monthly seminar where we invite early-career researchers to present their work at the interface of machine learning, artificial intelligence, and a scientific discipline. This seminar series aims to provide an international platform at Chalmers for discussions about these topics and strengthen interdisciplinary research involving machine learning and AI at Chalmers.


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13 October, 2022 14:00 (local Swedish time)

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Rianne van den Berg (Microsoft Research)

Abstract:

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Rianne is a Senior Researcher at Microsoft Research Amsterdam, working on the intersection of deep learning and computational chemistry and physics for molecular simulation. Her prior research includes a broad range of topics like generative modeling, variational inference, source compression, graph-structured learning and condensed matter physics. Before joining MSR van den Berg was a Research Scientist at Google Brain. Rianne received her PhD in theoretical condensed-matter physics in 2016 at the University of Amsterdam, followed by a postdoctoral stint at the Amsterdam Machine Learning Lab (AMLAB). In 2019, Rianne won the Faculty of Science Lecturer of the Year award at the University of Amsterdam for teaching a machine learning course in the master of AI.


Connect via Zoom password: ai4science




10 November, 2022 15:00 (local Swedish time)

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Pratyush Tiwary (University of Maryland, College Park)

Abstract:

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Pratyush Tiwary is an Associate Professor at the University of Maryland, College Park in the Department of Chemistry and Biochemistry and the Institute for Physical Science and Technology. His work at the interface of molecular simulations, statistical mechanics and machine learning has been recognized through many awards including Sloan Research Fellowship in Chemistry, NSF CAREER award, NIH Maximizing Investigators’ Research Award and ACS OpenEye Outstanding Junior Faculty Award. He received his undergraduate degree in Metallurgical Engineering from IIT-BHU, PhD in Materials Science from Caltech followed by postdoctoral work at ETH Zurich and Columbia University.


Connect via Zoom password: ai4science




8 December, 2022 15:30 (local Swedish time)

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Tess E. Smidt (Massachusetts Institute of Technology)

Abstract:

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Tess Smidt is an Assistant Professor of Electrical Engineering and Computer Science at MIT. Tess earned her SB in Physics from MIT in 2012 and her PhD in Physics from the University of California, Berkeley in 2018. Her research focuses on machine learning that incorporates physical and geometric constraints, with applications to materials design. Prior to joining the MIT EECS faculty, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory and a Software Engineering Intern on the Google Accelerated Sciences team where she developed Euclidean symmetry equivariant neural networks which naturally handle 3D geometry and geometric tensor data.


Connect via Zoom password: ai4science




Previous talks:


8 September, 2022 15:30 (local Swedish time)

Supervised and physics-informed learning in function spaces


Paris Perdikaris (University of Pennsylvania)

Abstract:

While the great success of modern deep learning lies in its ability to approximate maps between finite-dimensional vector spaces, many tasks in science and engineering involve continuous measurements that are functional in nature. For example, in climate modeling one might wish to predict the pressure field over the earth from measurements of the surface air temperature field. The goal is then to learn an operator, between the space of temperature functions to the space of pressure functions. In recent years, operator learning techniques have emerged as a powerful tool for supervised learning in infinite-dimensional function spaces. In this talk we will provide an introduction to this topic, present a general approximation framework for operators, and demonstrate how one can construct deep learning models that can handle functional data. We will see how such tools can help us build neural ODE and PDE solvers that can be trained even in the absence of labeled data, and enable the fast prediction of continuous spatio-temporal fields up to three orders of magnitude faster compared to conventional numerical solvers. We will also discuss key open questions related to generalization, data-efficiency and inductive bias, the resolution of which is critical for the success of AI in science and engineering.

Paris Perdikaris is an Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He received his PhD in Applied Mathematics at Brown University in 2015, and, prior to joining Penn in 2018, he was a postdoctoral researcher at the department of Mechanical Engineering at the Massachusetts Institute of Technology. His current research interests include physics-informed machine learning, uncertainty quantification, and engineering design optimization. His work and service has received several distinctions including the DOE Early Career Award (2018), the AFOSR Young Investigator Award (2019), the Ford Motor Company Award for Faculty Advising (2020), the SIAG/CSE Early Career Prize (2021), and the Scialog Fellowship (2021).


Video Recording (YouTube). Download Slides




10 March, 2022 15:30 (local Swedish time)

Multimodal Machine Learning for Protein Engineering


Kevin Yang (Microsoft Research)

Abstract:

Engineered proteins play increasingly essential roles in industries and applications spanning pharmaceuticals, agriculture, specialty chemicals, and fuel. Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein property prediction. However, protein datasets contain information in addition to sequence that can improve model performance. This talk will cover pretrained models that use both sequence and structural data, their application to predict which portions of proteins can be removed while retaining function, and a new set of protein fitness benchmarks to measure progress in pretrained models of proteins.

Kevin Yang is a senior researcher at Microsoft Research in Cambridge, MA who works on problems at the intersection of machine learning and biology. He did his PhD at Caltech with Frances Arnold on applying machine learning to protein engineering. Before joining MSR, he was a machine learning scientist at Generate Biomedicines, where he used machine learning to optimize proteins. Before graduate school, Kevin taught math and physics for three years at a high school in Inglewood, California through Teach for America.


Video Recording (YouTube). Download Slides




9 June, 2022 14:00 (local Swedish time)

De novo drug design with chemical language models


Francesca Grisoni (Eidenhoven University of Technology)

Abstract:

Artificial intelligence (AI) is fueling computer-aided drug discovery. Chemical language models (CLMs) constitute a recent addition to the medicinal chemist’s toolkit for AI-driven drug design. CLMs can be used to generate novel molecules in the form of strings (e.g., SMILES, SELFIES) without relying on human-engineered molecular assembly rules. By taking inspiration from natural language processing, CLMs have shown able to learn “syntax” rules for molecule generation, and to implicitly capture “semantic” molecular features, such as physicochemical properties, bioactivity, and chemical synthesizability. This talk will illustrate some successful applications of CLMs to design novel bioactive compounds from scratch in the context of drug discovery, at the interface between theory and wet-lab experiments. Moreover, the talk will provide a personal perspective on current limitations and future opportunities for AI in medicinal and organic chemistry, to accelerate molecule discovery and chemical space exploration.

Francesca Grisoni is a tenure-track Assistant Professor at the Eindhoven University of Technology, where she leads the Molecular Machine Learning team. After receiving her PhD in 2016 at the University of Milano-Bicocca, with a dissertation on machine learning for (eco)toxicology, Francesca worked as a data scientist and as a biostatistical consultant for the pharmaceutical industry. Later, she joined the University of Milano-Bicocca (in 2017) and the ETH Zurich (in 2019) as a postdoctoral researcher, working on machine learning for drug discovery and molecular property prediction. Her current research focuses on developing novel chemistry-centered AI methods to augment human intelligence in drug discovery, at the interface between computation and wet-lab experiments.


Video Recording (YouTube). Download Slides




12 May, 2022 14:00 (local Swedish time)

AI for Quantum Experiments


Evert van Nieuwenburg (NBI, University of Copenhagen)

Abstract:

In this talk I aim to showcase how machine learning inspired optimisations can help with current state-of-the-art experiments. In particular, I will first consider the readout of semiconductor spin qubits using simple principal component analysis. I will then highlight a specifically fabricated semiconductor device with a 3x3 ‘pixel array’, and discuss the simultaneous tuning of those 9 gate voltages to construct a quantum point contact. And finally, I will move on to larger arrays of quantum dots and the detection of transitions between charge states (i.e. finding the facets of high-dimensional coulomb diamonds).

Evert is a theoretical condensed matter physicist with a background in open systems, numerical simulations and many-body effects. He now also actively works on investigating how both condensed matter physics and machine learning can help each other.


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14 April, 2022 15:00 (local Swedish time)

Data-driven discovery of coordinates and governing equations


Bethany A Lusch (Argonne National Lab)

Abstract:

Governing equations are essential to the study of physical systems, providing models that can generalize to predict previously unseen behaviors. There are many systems of interest across disciplines where large quantities of data have been collected, but the underlying governing equations remain unknown. This work introduces an approach to discover governing models from data. The proposed method addresses a key limitation of prior approaches by simultaneously discovering coordinates that admit a parsimonious dynamical model. Developing parsimonious and interpretable governing models has the potential to transform our understanding of complex systems, including in neuroscience, biology, and climate science.

Dr. Bethany Lusch is an Assistant Computer Scientist in the data science group at the Argonne Leadership Computing Facility at Argonne National Lab. Her research expertise includes developing methods and tools to integrate AI with science, especially for dynamical systems and PDE-based simulations. Her recent work includes developing machine-learning emulators to replace expensive parts of simulations, such as computational fluid dynamics simulations of engines and climate simulations. She is also working on methods that incorporate domain knowledge in machine learning, representation learning, and using machine learning to analyze supercomputer logs. She holds a PhD and MS in applied mathematics from the University of Washington and a BS in mathematics from the University of Notre Dame.


Video Recording (YouTube). Download Slides




10 February, 2022 13:30 (local Swedish time)

Zoom and Enhance: Towards Multi-Scale Representations in the Life Sciences


Bastian Rieck (Helmholtz Pioneer Campus and Technical University of Munich)

Abstract:

With novel measurement technologies easily resulting in a deluge of data, we need to consider multiple perspectives in order to ‘see the forest for the trees.’ A single perspective or scale is often insufficient to faithfully capture the underlying patterns of complex phenomena, in particular in the life sciences. However, moving from an ‘either–or’ selection of relevant scales to a ‘both–and’ utilisation of all scales promises better insights and improved expressivity. The emerging field of topological machine learning provides us with effective tools for building multi-scale representations of complex data. This talk presents two use cases that demonstrate the power of learning such representations. The first use case involves improving antimicrobial resistance prediction—a critical problem in a world suffering from superbugs—while the second use case permits us a glimpse into how cognition changes from early childhood to adolescence.

Bastian is Principal Investigator of the AIDOS Lab at the Institute of AI for Health and the Helmholtz Pioneer Campus, focusing on machine learning methods in biomedicine. Dr. Rieck is also TUM Junior fellow and a member of ELLIS. Dr. Rieck was previously senior assistant in the Machine Learning & Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zürich and was awarded his Ph.D.  in computer science from Heidelberg University.


Video Recording (YouTube). Download Slides