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        "time_start":"12:00:00",
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        "event_title":"DCMB Tools and Technology Seminar by Jingying Wang (Computer Science & Engineering)",
        "occurrence_title":"",
        "combined_title":"DCMB Tools and Technology Seminar by Jingying Wang (Computer Science & Engineering): \u201cHuman-AI Systems for Externalizing Surgical Expertise\u201d",
        "event_subtitle":"\u201cHuman-AI Systems for Externalizing Surgical Expertise\u201d",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Join us for the DCMB Tools & Technology Seminar Series featuring a presentation by Jingying Wang from the Department of Computer Science and Engineering. Enjoy a complimentary pizza lunch while learning about innovative research and cutting-edge tools.\n\n\u201cHuman-AI Systems for Externalizing Surgical Expertise\u201d\n\nAssociated Link:  https:\/\/wjymonica.github.io\/\n\nAbstract\nSurgical expertise is largely tacit: what separates experts from novices often lies in perceptual and decision-making skills, including where to look, when it is safe to proceed, and how to perform each action. This is where human-AI systems can help. By capturing multimodal signals such as gaze, surgical video, narration, and instrument motion, they can help replay and visualize expert performance, convert surgical recordings into learning resources, and summarize experts\u2019 mental models. In this talk, I will present three examples: SurgGaze, which captures surgeons\u2019 attention intraoperatively; Surgment, which creates perceptual and cognitive exercises from surgical recordings; and eXplainMR, an ultrasound practice platform that provides explanations for action guidance. Together, these examples show how human-AI systems can make surgical expertise more transferable, accessible, and scalable for medical students.",
        "occurrence_notes":"MS1 B-Wing",
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        "tags":["Computational Science","Biosciences","biomedical research","Ai In Science And Engineering","Artificial Intelligence","Bioinformatics","Biomedical Engineering"],
        "website":"https:\/\/umich.zoom.us\/j\/94417089006",
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        "date_end":"2026-10-14",
        "time_start":"16:00:00",
        "time_end":"17:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"CCMB\/DCMB Weekly Bioinformatics Seminar Series featuring Luis Zaman, PhD (Assistant Professor of Complex Systems and  EEB, U-M)",
        "occurrence_title":"",
        "combined_title":"CCMB\/DCMB Weekly Bioinformatics Seminar Series featuring Luis Zaman, PhD (Assistant Professor of Complex Systems and  EEB, U-M)",
        "event_subtitle":"",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Dr. Luis Zaman\u2019s research spans several disciplines to answer fundamental questions about how evolution works. While he often approaches questions from an experimentalist\u2019s perspective, work in his lab relies on a mixture of computational, mathematical, and microbial systems. He is predominantly drawn to understanding host-parasite coevolution because the constant push-and-pull of antagonistic interactions emphasizes the dynamic and interdependent nature of biological evolution. These antagonistic interactions have been implicated in large-scale evolutionary patterns, such as diversification, sexual reproduction, and increasing organismal complexity. How coevolution is further influenced by such large-scale patterns presents problems that orient the lab\u2019s long-term goals.",
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        "tags":["Basic Science","Evolutionary Biology","Biology","Bioinformatics"],
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        "date_end":"2026-10-15",
        "time_start":"12:00:00",
        "time_end":"13:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"DCMB Tools and Technology Seminar by Ricky Han, Runbo Mao, Yiqun Wang, and Zhiyuan Liu (Computational Medicine & Bioinformatics)",
        "occurrence_title":"",
        "combined_title":"DCMB Tools and Technology Seminar by Ricky Han, Runbo Mao, Yiqun Wang, and Zhiyuan Liu (Computational Medicine & Bioinformatics): \u201cFrom GLKB to PanKgraph: Knowledge Infrastructure in Action for Biomedical Discovery\u201d",
        "event_subtitle":"\u201cFrom GLKB to PanKgraph: Knowledge Infrastructure in Action for Biomedical Discovery\u201d",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Join us for the DCMB Tools & Technology Seminar Series featuring a presentation by Ricky Han, Runbo Mao, Yiqun Wang, and Zhiyuan Liu from the Department of Computational Medicine & Bioinformatics. Enjoy a complimentary pizza lunch while learning about innovative research and cutting-edge tools.\n\n\u201cFrom GLKB to PanKgraph: Knowledge Infrastructure in Action for Biomedical Discovery\u201d\n\n-Tool Link:  https:\/\/glkb.org\/\n-Tool Link:  https:\/\/pankgraph.org\/",
        "occurrence_notes":"MS1 B-Wing",
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        "tags":["Ai In Science And Engineering","Biosciences","biomedical research","biomedical","Bioinformatics","Basic Science","Artificial Intelligence"],
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    {
        "datetime_modified":"20261001T133949",
        "datetime_start":"20261020T150000",
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        "date_start":"2026-10-20",
        "date_end":"2026-10-20",
        "time_start":"15:00:00",
        "time_end":"16:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"CCMB Tuesday Popup: Biomedical Gen AI Learning Community presented by Einar Olafsson, PhD (Microbiology & Immunity)I)",
        "occurrence_title":"",
        "combined_title":"CCMB Tuesday Popup: Biomedical Gen AI Learning Community presented by Einar Olafsson, PhD (Microbiology & Immunity)I): \"Should I have Claude rewrite my code base? An adventure with the SpaCR spatial morphology analysis platform\"",
        "event_subtitle":"\"Should I have Claude rewrite my code base? An adventure with the SpaCR spatial morphology analysis platform\"",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"AI tools for research are advancing at a pace that often outstrips traditional academic training. These tools are powerful, but using them effectively to produce high quality research is a skill in itself. So how do we use AI to move faster and accelerate biological discovery without sacrificing scientific rigor and our sense of engagement with our work?\n\nWe hope to build a community dedicated to keeping up with new tools, sharing ai-augmented research workflows what work well, and staying human in an AI world.  \n\nWe are launching the Biomedical Gen AI Learning Community!\n\nOpen to: Anyone in the UM bioinformatics and biomedical community - researchers, students, and staff. \n\nThe Biomedical Gen AI Learning Community is sponsored by the Center for Computational Medicine and Bioinformatics (CCMB)",
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        "campus_maps_id":"104",
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        "tags":["Spatial Biology","Life Science","Gen Ai","Computational Science","Biosciences","Biomedical Engineering","Bioinformatics","Artificial Intelligence","Ai In Science And Engineering"],
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        "datetime_modified":"20260924T105357",
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        "time_zone":"America\/Detroit",
        "event_title":"DCMB Tools and Technology Seminar by Abhirath Anand (Computational Medicine & Bioinformatics)",
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        "combined_title":"DCMB Tools and Technology Seminar by Abhirath Anand (Computational Medicine & Bioinformatics): \"hospitraceR: An R Package for WGS-Based Genomic Epidemiological Analysis of Healthcare-Associated Infections\u201d",
        "event_subtitle":"\"hospitraceR: An R Package for WGS-Based Genomic Epidemiological Analysis of Healthcare-Associated Infections\u201d",
        "event_type":"Workshop \/ Seminar",
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        "description":"Join us for the DCMB Tools & Technology Seminar Series featuring a presentation by Abhirath Anand from the Department of Computational Medicine & Bioinformatics. Enjoy a complimentary pizza lunch while learning about innovative research and cutting-edge tools.\n\n\"hospitraceR: An R Package for WGS-Based Genomic Epidemiological Analysis of Healthcare-Associated Infections\u201d\n\n- Tool Link:  https:\/\/github.com\/theabhirath\/hospitraceR\n\nAbstract\n\nGenomic epidemiology, which combines whole-genome sequencing (WGS) with patient data is becoming increasingly common to detect the transmission of hospital-acquired infections, especially multi-drug resistant organisms. As high-throughput WGS becomes more affordable, software that facilitates the analysis of this genomic data along with patient metadata becomes crucial to generate timely insights. Prior studies have shown that there can be many different approaches to detect transmission clusters with genomic epidemiology data, and oftentimes these approaches will have tradeoffs more or less suitable for a certain dataset. We present hospitraceR, an R package designed to facilitate such analyses. hospitraceR includes functions for detecting transmission clusters with two different methods, the standard SNV threshold approach and a threshold-free approach. It also includes functions that can use patient movement information through the facility to suggest potential transmission explanations for the clusters. hospitraceR seeks to enable users to use multiple methods of transmission cluster detection and compare them easily using cluster comparison metrics. Metrics like the adjusted Rand Index (ARI) and adjusted mutual information (AMI) have been included for this purpose, as has fraction of converts with shared source (FSS), a custom metric designed specifically for comparing transmission clusters. A companion package, hospitraceRvisualize, also includes opinionated plotting functions for certain commonplace visualizations that aid in epidemiological interpretations. We apply hospitraceR to a dataset consisting of 4 long-term acute care hospitals (LTACHs) from Chicago sampled for different periods of time as part of an intervention study and evaluate the two different approaches for cluster detection. We find distinct patterns exhibited by the transmission clusters in different locations and also compare the different clusters obtained with different SNV thresholds and linkage criterion for hierarchical clustering. The hospitraceR package proves to be helpful in facilitating such comparisons and makes it easy to use WGS data to aid epidemiological investigation.",
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        "tags":["Precision Health","Bioinformatics","Biology","Biosciences","Genome","Life Science"],
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        "date_end":"2026-10-29",
        "time_start":"12:00:00",
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        "time_zone":"America\/Detroit",
        "event_title":"DCMB Tools and Technology Seminar by Xiao-Su (Frank) Hu (MIDAS)",
        "occurrence_title":"",
        "combined_title":"DCMB Tools and Technology Seminar by Xiao-Su (Frank) Hu (MIDAS): \u201cAutoGluon: A Practical Guide for Researchers\u201d",
        "event_subtitle":"\u201cAutoGluon: A Practical Guide for Researchers\u201d",
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        "website":"https:\/\/umich.zoom.us\/j\/94417089006",
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        "event_title":"DCMB Tools and Technology Seminar by Sophia Luo (Biostatistics)",
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        "combined_title":"DCMB Tools and Technology Seminar by Sophia Luo (Biostatistics): \u201cMethylModes: Intuitive Detection of Multimodal Distributions in DNA Methylation Data\u201d",
        "event_subtitle":"\u201cMethylModes: Intuitive Detection of Multimodal Distributions in DNA Methylation Data\u201d",
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        "event_title":"DCMB Tools and Technology Seminar by Akhil Kondepudi (Computational Medicine & Bioinformatics)",
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        "combined_title":"DCMB Tools and Technology Seminar by Akhil Kondepudi (Computational Medicine & Bioinformatics): \"Health system learning enables generalist neuroimaging models\u201d",
        "event_subtitle":"\"Health system learning enables generalist neuroimaging models\u201d",
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        "description":"Join us for the DCMB Tools & Technology Seminar Series featuring a presentation by Akhil Kondepudi from the Department of Computational Medicine & Bioinformatics. Enjoy a complimentary pizza lunch while learning about innovative research and cutting-edge tools.\n\nAssociated Article:  https:\/\/www.nature.com\/articles\/s41591-026-04497-1\n\nAbstract\n\nFrontier artificial intelligence (AI) models have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging is underrepresented in the public domain due to identifiable facial features within magnetic resonance imaging (MRI) and computed tomography (CT) scans, restricting model performance in clinical medicine. Here we show that frontier models underperform on neuroimaging tasks and that learning directly from uncurated data generated during routine clinical care at health systems, a paradigm we call \u2018health system learning\u2019, yields high-performance, generalist neuroimaging models. We introduce NeuroVFM, a visual foundation model trained on 5.24 million clinical MRI and CT volumes using a scalable volumetric predictive architecture. NeuroVFM learns comprehensive representations of brain anatomy and pathology, achieving state-of-the-art performance across multiple clinical tasks, including radiologic diagnosis and report generation. The model embeds MRI and CT scans into a shared neuroanatomic latent space and grounds diagnostic findings. When paired with open-source language models, NeuroVFM generates radiology reports that surpass frontier models in accuracy, clinical triage and expert preference. NeuroVFM reduces hallucinated findings and critical errors, offering safer clinical decision support. These results establish health system learning as a paradigm for building generalist medical AI and provide a scalable framework for clinical foundation models.",
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