Upcoming Seminars

Speaker

Dr. Tian Tian, Amii Fellow and Assistant Professor, Department of Chemical and Materials Engineering, University of Alberta

Title

Multiscale Modeling of Material Interfaces: From Quantum Descriptors to Machine Learning

Abstract
Material interfaces govern the performance of many technologies, from electronic devices and catalysts to soft and biological materials. They are also where simulation becomes most difficult. Interfaces involve many possible configurations, long-range interactions, and processes that span multiple length and time scales. My research has moved across several material systems, from quantum-mechanical modeling of two-dimensional materials to more interdisciplinary problems, and this trajectory has repeatedly highlighted why interfaces often require modeling approaches beyond conventional simulations.

Many interface problems do have dominant physical quantities that can be identified and computed using physics-based methods. These quantities often capture the essential mechanisms at play. However, once configurational variability is introduced, the complexity increases rapidly. Small changes in structure or local environment can lead to a large number of relevant configurations, making exhaustive physical simulation impractical. Machine learning offers a possible way to handle this complexity, but applying it to interface problems is not straightforward. Interface-specific studies rarely generate the large, standardized datasets that many machine-learning methods rely on, and this limits the direct use of purely data-driven models.

In this talk, I will discuss how hybrid physics–ML approaches can help bridge this gap. Drawing on recent work, I will discuss data-efficient strategies for interface simulations, along with practical challenges related to workflow design, model behavior, and integration with existing simulation tools. The goal is to show how machine learning can be used as a supporting tool to extend physically grounded simulations, rather than replacing them, in the study of complex material interfaces.

Presenter Bio
Dr. Tian Tian obtained his B.Sc. and M.Sc. in Chemistry from Tsinghua University. He completed his Ph.D. in Chemical Engineering at ETH Zürich under the supervision of Prof. Chih-Jen Shih. His doctoral research focused on the multiscale simulation and engineering of the interfacial properties of two-dimensional materials. From 2021 to 2023, he received the Swiss National Science Foundation (SNSF) Postdoc Mobility Fellowship to conduct postdoctoral research at Carnegie Mellon University with Prof. Zachary W. Ulissi. He worked on machine-learning-assisted material simulations, particularly the fine-tuning of pretrained graph neural network models for computational catalysis and developing machine-learning-assisted computational workflows. Before joining UofA, he briefly held a postdoctoral position at Georgia Institute of Technology under the supervision of Prof. Phanish Suryanarayana and Prof. Andrew J. Medford, developing software communication layers for the machine-learning-enabled density functional theory (DFT) package.  Dr. Tian’s research group develops machine learning–accelerated simulation methods for the design of interfacial materials. The group explores applications in two-dimensional materials, energy storage systems, light-emitting polymers, and colloidal soft matter, addressing the challenge of vast configurational spaces that govern interfacial behavior. His work combines physics-based modeling and data-driven learning to accelerate multiscale simulations and enable predictive materials design. In parallel, the group advances open-source computational tools and machine-learning frameworks that bridge computation and experiment for optimizing material properties and synthesis processes.

Timing & Location

Amii HQ 2nd floor event space (10065 Jasper Ave)

pizza from 11:30, seminar from noon to 1

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NO SEMINAR - Reading Week

Speaker

Dr. John D. Martin, Adjunct Professor of Computing Science at the University of Alberta and Research Fellow at OpenMind Research Institute

Title

On Externalizing Memory in Reinforcement Learning  

Abstract
Natural agents respond competently to problems that require resources beyond their cognitive abilities, in part because they leverage their environment for additional support. While natural agents are enabled by environmental resources, artificial agents are ostensibly bound by their individual system resources. In reinforcement learning (RL), an agent's system resources are established at design-time, and computational supply is commonly assumed to remain fixed throughout operation. In this paper, we show that RL agents can exploit environment dynamics as a form of additional memory, in situ. Specifically, we show that when RL agents can observe spatial paths, the amount of memory required to learn a performant policy is reduced. Although prior work from philosophy and artificial intelligence has theorized about such effects, we provide what we believe to be the first empirical report showing that computational RL agents externalize memory. Interestingly, this effect is experienced unintentionally and entirely through the agent's sensory stream. 

Presenter Bio
John Martin is a Research Fellow at the Openmind Research Institute and an Adjunct Professor of Computing Science at the University of Alberta. John studies core topics in artificial intelligence with a focus on agentic phenomena and reinforcement learning. John was a Research Scientist at Intel Labs until 2024; he completed a post-doc at the University of Alberta in 2022, and he earned his PhD from Stevens Institute of Technology in 2021. During his studies, John spent time at Columbia University, Google Brain, and DeepMind. Prior to his graduate studies, John designed autonomous flight control systems for experimental helicopters at Sikorsky Aircraft

Website

John D. Martin

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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Speaker

Dr. Blair Attard-Frost, Amii Fellow and Assistant Professor in the Department of Political Science at the University of Alberta

Title

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Timing & Location

Amii HQ 2nd floor event space (10065 Jasper Ave)

pizza from 11:30, seminar from noon to 1

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Speaker

Rohan Saha, PhD student, University of Alberta, supervised by Dr. Alona Fyshe

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Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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