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DTSTAMP:20260401T103514
DTSTART;TZID=America/Detroit:20250604T120000
DTEND;TZID=America/Detroit:20250604T130000
SUMMARY:Well-being:Learn to Meditate in 3 days
DESCRIPTION:Make meditation part of your goal to strengthen your mental well-being. Discover three core practices—meditation\, rejuvenation\, and inner connect in just three session.\n\nMeditation is a mindful journey for regulating your mind. It’s like a mental workout\, training the mind to focus on a single thought amid the 60\,000 that pass through daily. With 3 core practices it cultivates effortless concentration\, heightened awareness\, and presence in the moment\, allowing a shift from thinking to feeling. Meditation also leads to a deeper state of relaxation\, regulating the stress response and promoting numerous health benefits.\n\nThe session will be guided by a trainer via Zoom meeting for all 3 days from noon to 1 p.m. All U-M students\, faculty\, and staff are welcome to join at no cost. No prior experience with meditation is required.\n\nEvent Details\n*When: Every month for 3 days (attending all 3 sessions is recommended)*\n\nThe session is Remote over Zoom and upon registration you will have the Zoom MeetingId and Passcode\nSee Related Links for registration\n\nThis wellness program is coordinated by Information Technology and Services (ITS) Teaching & Learning\, and is provided at no cost by heartfulness.org.\n\nJoin the MCommunity group for email updates – Meditation for wellness
UID:128708-21865140@events.umich.edu
URL:https://events.umich.edu/event/128708
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Free,Virtual,Well-being
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250522T135802
DTSTART;TZID=America/Detroit:20250604T130000
DTEND;TZID=America/Detroit:20250604T150000
SUMMARY:Presentation:José R. Hernández-Meléndez - Dissertation Defense
DESCRIPTION:Please join José Hernández-Meléndez for their dissertation defense titled \"Iron metalloenzymes for biocatalytic C–C bond formation\".\n\n*Date:* Wednesday\, June 4th\, 2025\n*Time:* 1:00 p.m.\n*Where:* Room 1706\, Chemistry Building
UID:135801-21877276@events.umich.edu
URL:https://events.umich.edu/event/135801
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Chemistry
LOCATION:Chemistry Dow Lab - 1706
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250528T172055
DTSTART;TZID=America/Detroit:20250604T130000
DTEND;TZID=America/Detroit:20250604T150000
SUMMARY:Lecture / Discussion:Statistical Inference for Spatial Transcriptomics in the Age of Deep Learning
DESCRIPTION:Single-cell spatial transcriptomics (ST) enables the measurement of gene expression of individual cells while simultaneously capturing the spatial positions of these cells within a tissue sample. To utilize these spatial positions effectively\, careful model selection is required to ensure conclusions reflect spatial dependencies in the underlying biology. In this dissertation\, we contribute three novel methodologies that merge deep learning with statistical inference for ST data.\n\nFirst\, we attempt to better predict gene expression by leveraging the spatial context included in spatial transcriptomics data. Comparing predictions from a spatial model to those from a baseline regressor without cell neighborhood information offers insights into how expression changes because of cell-cell communication (CCC) signals. However\, to trust conclusions reached from such a paired modeling framework\, the baseline version of a model needs to be a valid non-spatial reference point. To this end\, we develop a graph convolutional network (GCN) that uses graphs defined by cellular positions to predict gene expression and compare against a counterpart model without spatial context. \n\nSecond\, we study a clustering task for ST data through a Bayesian framework. A central challenge in spatial transcriptomics is to identify distinct cell communities that not only reflect transcriptional heterogeneity but also preserve spatial coherence across tissue. These clusters often represent biological components such as cortical layers\, tissue microenvironments\, or pathological regions\, whose spatial organization is critical for interpreting tissue structure and function. Existing exact Bayesian methods often rely on hard assignments\, limiting flexibility. To address this limitation\, we introduce a stochastic variational inference (SVI) method designed to learn posterior spot cluster distributions that are both spatially coherent and biologically interpretable. This approach is more computationally efficient than methods that rely on posterior sampling techniques\, such as Markov Chain Monte Carlo (MCMC)\, which can be expensive to retrain. \n\nThird\, we leverage normalizing flows as the approximate posterior distributions for variational inference on ST data. Normalizing flows transform simple base distributions into more expressive ones by stacking invertible transformations based on the change-of-variables formula. This allows us to model flexible\, multi-modal posteriors over soft cluster assignments beyond the capacity of standard variational families.
UID:135878-21877364@events.umich.edu
URL:https://events.umich.edu/event/135878
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250102T120705
DTSTART;TZID=America/Detroit:20250604T133000
DTEND;TZID=America/Detroit:20250604T150000
SUMMARY:Workshop / Seminar:CoderSpaces - Wednesday
DESCRIPTION:Are you grappling with a piece of code\, trying to compute on a cluster\, or just getting started with a new method such as machine learning? Then we might have just the right space for you.\n\nAll members of the U-M community are invited to join our weekly virtual CoderSpaces to get research support and connect with others.\n\nTuesdays\, 9:30-11 a.m. ET\, via Zoom (Meeting ID:94181215786)\nWednesdays\, 1:30-3 p.m. ET\, via Zoom (Meeting ID: 98659357324)
UID:117252-21865886@events.umich.edu
URL:https://events.umich.edu/event/117252
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Data Management,Data Science,Information and Technology,Machine Learning,Data Linkage,Data Curation,Data Collection,Data Analysis,Data
LOCATION:Off Campus Location
CONTACT:
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