The Department of Neuroscience Welcomes Dr. Keri Martinowich.

Senior Investigator and Director, Translational Neuroscience, Lieber Institute for Brain Development

Professor, Departments of Psychiatry and Neuroscience, Johns Hopkins University School of Medicine

Spatially-resolved molecular approaches for understanding structure-function relationships in the human brain

This talk will focus on projects that aim to generate data and develop methods for spatially-resolved molecular omics approaches in the context of complex brain disorders. While single cell sequencing approaches have rapidly advanced generation of molecular profiles for various cell types in the brain, a disadvantage of these techniques is the lack of spatial context. Here, I will first describe how we used a combination of data-driven approaches to identify spatial domains within the dorsolateral prefrontal cortex and hippocampus of the human brain, map cell-cell and circuit interactions across these domains, and map enrichment of cell types and disease-associated profiles to discrete spatial domains.

Brief bio

Dr. Martinowich received a B.A. in International Relations from the George Washington University and a Ph.D. in Neuroscience from the University of California, Los Angeles. Following graduate work, she conducted translational research in neuropsychiatry as a postdoctoral fellow at the National Institute of Mental Health. She joined the faculty at Johns Hopkins and Lieber Institute for Brain Development where she oversees a research group that takes a cross-species approach to study how programs of gene expression in defined populations of cells contribute to circuit function that is relevant for neuropsychiatric disorders. The lab uses genetic manipulation in combination with molecular, cellular and systems-level techniques in animal models, and integrate these data with cell- and circuit-specific transcriptomic studies in the postmortem human brain and hiPSC-derived culture models

Writing with AI: Capturing Its Influence, Designing Its Future.

Professor Mina Lee of The University of Chicago will discuss the CoAuthor platform, a systemic review of AI writing assistants, and the evolving societal norms and expectations around AI in writing.

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The Simpson Querrey Institute for Epigenetics presents:

Andrew Pospisilik, PhD

Full Professor and Chair, Department of Epigenetics
Van Andel Research Institute, Grand Rapids, MI

"Variegating Epigenetic Mechanisms as Complex Disease Switches"

Abstract:

Our goal is to elucidate mechanisms underpinning complex disease susceptibility and presentation. Focusing on non-genetic, non-environmental origins of disease susceptibility, we previously identified the epigenetic silencer, Trim28, and the imprinted gene Nnat, as critical regulators of developmental robustness. Loss-of-function of either gene, intriguingly, triggers a unique developmental phenomenon known as ‘polyphenism’, in which animals can take on one of two distinct developmental phenotypic forms (and disease risk states) despite being genetically identical and environmentally controlled. Profiling human cohorts we find signatures of the same processes being active in approximately 50% of metabolically diseased patients. Our models represent the first formal demonstrations of mammalian polyphenisms and carry profound implications for our understanding of the origins of disease risk. I will share data that (i) characterize the distinctions between these triggerable disease in cancer, obesity and food-addiction; (ii) dissect the mechanism underpinning the underlying developmental bifurcation; (iii) provide evidence for alternate developmental trajectories in humans; and (iv) show one machine learning approach we are using to begin to tackle this problem in the genetically and environmentally heterogeneous human population. Collectively, our data highlight an underappreciated mechanistic layer heterogeneity (or sub-types) across the disease landscape.

I

Machine Learning Enabled Parametric Multiscale Modeling of Metals & Composites: From Fatigue Crack Nucleation to Damage Sensing

Professor Somnath Ghosh

Civil & Systems Engineering, Mechanical Engineering, and Materials Science & Engineering

Johns Hopkins University

The rapid surge of machine learning (ML) tools in developing efficient surrogate models for solving challenging problems has drawn significant attention from the Mechanics of Materials community. However, ML techniques rely on extensive training datasets and often lack physical interpretability. Also, exclusively data-driven models can result in ill-posed problems or non-physical solutions. Alternatively, the notion of ML-enhanced parametric upscaling has been introduced for multi-scale analysis of fatigue failure in metallics materials, damage and failure of unidirectional and woven composites, and damage sensing in multifunctional composites. The Parametrically Upscaled Constitutive Model (PUCM) for metallic materials like Ti alloys and the Parametrically Upscaled Continuum Damage Mechanics Model (PUCDM) for composites are thermodynamically-consistent constitutive models that bridge multiple spatial scales through the explicit representation of representative aggregated microstructural parameters (RAMPs), representing statistical distributions of morphological and crystallographic descriptors of the microstructure. ML tools, viz. genetic programming-based symbolic regression (GPSR) and artificial neural networks (ANN) are implemented for generating PUCM/PUCDM coefficients as functions of lower-scale RAMPs, using data sets of homogenized micromechanical response variables. For damage sensing in piezocomposite structures, the Parametrically Upscaled Coupled Constitutive Damage Model (PUCCDM) is developed coupling mechanical, damage, and electrical fields. An advanced machine learning model (ConvLSTM) based on the combination of a convolutional neural network and a recurrent neural network is developed to predict microstructural damage mechanisms from macroscopic electric signal and RAMPs. The computational tool chain outputs the highly efficient PUCM/PUCDM/PUCCDM, which are invaluable tools for multiscale analysis with implications in location-specific design.

Winter exams begin

Spring Break Begins

The NU-MRSEC will partner with M3S for this event.

Breakfast & Lunch will be provided.
* Registration is required to receive complimentary lunch and breakfast.

Spring Break Ends

Title: Nonlinear tomographic reconstruction via nonsmooth optimization

Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research.

Vasilis Charisopoulos is a postdoctoral scholar in the Willett Group at the University of Chicago. He is broadly interested in developing numerical optimization methods for machine learning, signal processing and scientific computing. He holds a PhD in Operations Research & Information Engineering from Cornell University. Vasilis was recognized as a Rising Star in Computational and Data Sciences by the UT Austin Oden Institute in 2023.

Learn more about Vasilis Charisopoulos' research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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Xuesong (Simon) Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment, Arizona State University (ASU), Tempe, Arizona. Dr. Zhou's research focuses on developing methodological advancements in multimodal transportation planning applications, including dynamic traffic assignment, traffic estimation and prediction, large-scale routing, and rail scheduling. Dr. Zhou has served as an Associate Editor of Transportation Research Part C, is currently the Executive Editor-in-Chief of Urban Rail Transit, and an Editorial Board Member of Transportation Research Part B. He has also chaired the INFORMS Rail Application Section (2016 and 2025) and currently serves as a subcommittee chair of the TRB Committee on Transportation Network Modeling (AEP40).

Dr. Zhou is the Director of the ASU Transportation+AI Lab, where he is the principal architect and programmer for several open-source packages, including DTALite, NEXTA, and OSM2GMNS, which have collectively received over 100,000 downloads and many system deployments at various metropolitan planning agencies and state DOTs. He has published over 100 papers in Transportation Research Part B, Transportation Research Part C, and other leading transportation journals, with an H-index of 60 and a total of 11,000 citations in Google Scholar.

In addition to his academic achievements, Dr. Zhou is passionate about connecting practitioners, researchers, academics, students, and others involved in transportation planning and travel modeling. He serves as the conference chair for the TRB Innovations in Travel Analysis and Planning Conference in 2023, and a board member of Zephyr Foundation, a non-profit organization dedicated to advancing transportation research and education.

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