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DTSTART;TZID=America/Detroit:20250131T120000
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DTSTAMP:20250127T193042Z
CREATED:20250127T173918Z
LAST-MODIFIED:20250127T193042Z
UID:10000799-1738324800-1738328400@micde.umich.edu
SUMMARY:FSML Lecture Series - Panos Stinis: When big neural networks are not enough: physics\, multi-fidelity and kernels
DESCRIPTION:Zoom link \nBio: Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems\, multiscale modeling\, uncertainty quantification\, and machine learning. He studied aeronautical engineering at the Technical University of Athens\, Greece. He earned his PhD in applied mathematics in 2003\, from Columbia University in New York and began his career as a postdoctoral fellow at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research. In 2008\, he became a faculty member at the Mathematics Department at the University of Minnesota. He moved to the Pacific Northwest National Laboratory in 2014\, where he is currently leading the Computational Mathematics group. \nWhen big neural networks are not enough: physics\, multi-fidelity and kernels\nAbstract: Modern machine learning has shown remarkable promise in multiple applications. However\, brute force use of neural networks\, even when they have huge numbers of trainable parameters\, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics\, exploiting multi-fidelity knowledge\, and the kernel representation of neural networks can lead to a significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas. \n 
URL:https://micde.umich.edu/event/fsml-lecture-series-panos-stinis-when-big-neural-networks-are-not-enough-physics-multi-fidelity-and-kernels/
LOCATION:2004 Lay Auto Lab
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/Panos-Stinis.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241206T120000
DTEND;TZID=America/Detroit:20241206T130000
DTSTAMP:20241204T144214Z
CREATED:20241011T181202Z
LAST-MODIFIED:20241204T144214Z
UID:10000779-1733486400-1733490000@micde.umich.edu
SUMMARY:FSML Lecture Series - Anoushka Bhutani: Foundation Model for Molecular Design
DESCRIPTION:Zoom link \nBio: Anoushka is a third-year PhD student in Prof. Venkat Viswanathan’s group at the University of Michigan. Her research interests include machine learning for materials design and electrochemical battery modeling. \nFoundation Model for Molecular Design\nAbstract: The paradigm of molecular machine learning for material screening has accelerated material development cycles\, improved efficiency\, and reduced costs. However\, current state-of-the-art molecular property prediction models still require labeled training data generated using wet-lab experiments or Density Functional Theory (DFT) calculations. Their utility is limited by the scarcity and heterogeneity of labeled materials datasets. Foundation models (FMs) offer a solution to this: these models use self-supervised pre-training strategies to leverage unlabeled datasets and learn representations of data that can be applied to downstream tasks. Large unlabeled datasets of billions of synthesizable molecules are readily available. Prior attempts to train FMs for molecular property prediction demonstrate promise; however\, equivariant geometric models trained using supervised learning are still more accurate. This can be attributed to the fact that foundation models are extremely expensive to train and can be difficult to interpret; they require huge computing budgets\, complex distributed computing techniques\, and extensive hyperparameter searches. Our work addresses these challenges on three fronts: (1) we have prototyped a scalable workflow for distributed training of molecular foundation models (2) we have trained large foundation models using this workflow which demonstrates state-of-the-art molecular property prediction capabilities across several benchmarks\, and (3) we have applied model interpretability strategies such as the attention visualization to shed insight on molecular structure relationships learn by the transformer. \n 
URL:https://micde.umich.edu/event/workshop-seminaranoushka-bhutani-foundation-model-for-molecular-design/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Computational Science,Engineering,FSML,Graduate School,Graduate Students,Michigan Engineering,Rackham,Research,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Copy-of-MICDE-2022-2023-Fellowship-Portraits.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241115T120000
DTEND;TZID=America/Detroit:20241115T130000
DTSTAMP:20260522T154425Z
CREATED:20241012T182154Z
LAST-MODIFIED:20260522T154425Z
UID:10000785-1731672000-1731675600@micde.umich.edu
SUMMARY:FSML Lecture Series - Hongfan Chen: Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process
DESCRIPTION:Zoom link \nBio: Hongfan Chen is a third-year PhD student in the Department of Mechanical Engineering at the University of Michigan. His research interests include data assimilation\, uncertainty quantification\, and machine learning applications in space weather. \nGlobal Geomagnetic Perturbation Forecasting with Quantified Uncertainty Using Deep Gaussian Process\nAbstract: Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH)\, as a proxy for Geomagnetically Induced Currents (GICs)\, is crucial for estimating the impact of geomagnetic storms and remains a topic under active investigation. The current state-of-the-practice Geospace model is computationally expensive for fine-grid global simulations while existing machine learning methods consistently tend to underestimate dBH. Additionally\, these models either lack uncertainty quantification (UQ) or provide UQ that lacks calibration. In this work\, as part of the NextGen SWMF project funded by NSF\, we develop a data-driven\, grid-free global model using deep Gaussian process (DGP)\, a Bayesian non-parametric approach that forecasts the dBH for the full surface of Earth with calibrated uncertainty. The model uses solar wind measurements and the Dst index as input\, and it is trained based on ground magnetometer station data provided by SuperMAG over the period 1995-2022. The model’s predictions are evaluated based on the Heidke skill score (HSS) for a total of 22 geomagnetic storms in 2015. We further test the model on the 2024 May 10-12 storm. The results demonstrate that our model outperforms the state-of-the-art model\, with predictions exhibiting high accuracy in mid-latitudes and high-latitude regions in the northern hemisphere. \n 
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-8/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2024/10/Hongfan_Chen.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241101T120000
DTEND;TZID=America/Detroit:20241101T130000
DTSTAMP:20241106T181820Z
CREATED:20241012T182153Z
LAST-MODIFIED:20241106T181820Z
UID:10000784-1730462400-1730466000@micde.umich.edu
SUMMARY:FSML Lecture Series - Nicholas Galioto: Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models
DESCRIPTION:Zoom link \nBio: Nick Galioto is a second-year postdoctoral research fellow in the Department of Computational Medicine and Bioinformatics at the University of Michigan (UM). He received his PhD at UM in aerospace engineering in 2023 under the advising of Alex Gorodetsky and remained in the lab for an additional year as a postdoc. In the Gorodetsky lab\, Nick researched how to use stochastic models of dynamical systems to improve system identification. Now\, Nick works in the Rajapakse lab researching how to create data-driven models of the dynamics of cell reprogramming. \n  \nDiscovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models\n  \nAbstract: Cell reprogramming\, the transformation of a cell from one cell type to another through the introduction of exogenous transcription factors (TFs)\, is a rapidly developing research area that could lead to groundbreaking therapeutic technologies in areas such as tissue regeneration\, disease modeling\, and personalized medicine. However\, many challenges still exist that obstruct its practical viability. Discovering which TFs induce reprogramming requires a combinatorial search\, and testing a single candidate set of TFs experimentally can cost tens of thousands of dollars and take multiple months. Moreover\, even when an effective set of TFs is known\, cell conversion efficiency lies only around 5%. Faced with these challenges\, researchers have developed computational surrogate models to rapidly explore the TF search space at a fraction of the cost of wet lab experimentation. Unfortunately\, these models have seen limited success in practice due to the difficulty of capturing the complex gene-gene interactions within the cell\, most of which are still not well understood. With the recent high-profile rise of transformer-based foundation models for natural language\, researchers are now turning to the transformer to push past\, current performance limitations in a wide range of digital biology tasks\, including cell reprogramming. Of particular interest in these models is the attention mechanism\, which is potentially well-suited for capturing long-range gene-gene interactions at a higher fidelity than previously possible. In this talk\, I will describe how the transformer architecture has been adapted for cellular biology and analyze the utility of one such model\, Geneformer\, in identifying TFs for cell reprogramming. Specifically\, I will present the results of an in silico perturbation experiment for reprogramming fibroblast cells to hematopoietic stem cells and compare the outcomes to experimental results found in the literature. I will conclude the talk with a discussion of the drawbacks and limitations of the Geneformer model and provide an assessment of what will be needed in the future for digital biology to fully reap the benefits of large-scale foundation models.
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-7/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:Engineering,FSML,Science
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241018T120000
DTEND;TZID=America/Detroit:20241018T130000
DTSTAMP:20241104T135643Z
CREATED:20241012T182153Z
LAST-MODIFIED:20241104T135643Z
UID:10000783-1729252800-1729256400@micde.umich.edu
SUMMARY:FSML Lecture Series: Domain decomposition and coupling data-driven models of fluid flows by Christopher Wentland\, Sandia National Labs
DESCRIPTION:Zoom link \n \nAbstract: Simulating complex physical systems often requires joining non-uniform subsystems\, which may be characterized by different geometries or mesh topologies. Coupling these separate subsystems often relies on time-intensive meshing workflows or empirical coupling models\, which may not generalize well across all operational regimes. The Schwarz alternating method proposes to overcome these issues\, establishing an effective domain decomposition framework that allows for the coupling of arbitrary geometries. This talk presents a brief history of Schwarz-based coupling work at Sandia National Laboratories\, along with recent work on combining the Schwarz alternating method with data-driven modeling approaches\, namely projection-based reduced order models (PROMs) and operator inference. This approach can generate surrogates that are capable of simulating advection-dominated fluid flows with higher accuracy and lower cost than comparable monolithic models\, aiding analysis in many-query applications such as uncertainty quantification and engineering design. Several nuances of the Schwarz algorithm and their impacts on model performance are explored\, specifically non-overlapping decompositions and PROM hyper-reduction under domain decomposition. A look into ongoing Schwarz coupling work at Sandia discusses existing challenges and efforts to apply this approach to relevant engineering problems.
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-6/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241004T120000
DTEND;TZID=America/Detroit:20241004T130000
DTSTAMP:20241104T140255Z
CREATED:20241002T220654Z
LAST-MODIFIED:20241104T140255Z
UID:10000774-1728043200-1728046800@micde.umich.edu
SUMMARY:FSML Lecture Series: Tokenization for Chemistry by Alex Wadell\, University of Michigan
DESCRIPTION:Alex Wadell is a PhD candidate at the University of Michigan Department of Mechanical Engineering. \nTokenization for Chemistry\nMolecular Foundation Models are emerging as a powerful tool for molecular design\, material science\, and cheminformatics. By leveraging the transformer architecture\, these models attempt to learn the language of chemistry and discover robust molecular embeddings. However\, current models are constrained by tokenizers that fail to capture the full breadth of chemical space or even the periodic table of elements. In his talk\, Alex will introduce smirk\, a new tokenizer for molecular foundation models that can represent the entirety of the OpenSMILES specification. We’ll also discuss performance metrics for tokenizers and the results of Alex’s systematic evaluation of thirteen chemistry-specific tokenizers using N-gram language models as a low-cost proxy for transformer models. \nIf you are unable to attend in person but are interested\, please feel free to join virtually. \nJoin Zoom Meeting\nhttps://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1\nMeeting ID: 978 2352 7756\nPasscode: 2024
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:FSML,Micde,Sciml
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/SciML-Lectures-e1727980667262.png
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