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X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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DTSTART;TZID=America/Los_Angeles:20250411T140000
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SUMMARY:
DESCRIPTION:Seminar Information\n\nSeminar Date\nApril 11\, 2025 – 2:00 PM\n\n\n\nLocation\nThe FUNG Auditorium – PFBH\n\n  \n\n\n\n\n\n\n\n\n\n  \n\nAbstract\n\nPersonal and population health applications built on top of large-scale mobile sensor data and computing platforms have a great potential to impact the way we diagnose diseases\, track\, and manage our health. However\, the existing sensing mechanisms often fail to accurately capture and infer syndromic signatures that are indicative of anomalies in internal physiological and behavioral processes at an earlier stage. A mobile sensing system that can harness early syndromic signals at an individual or a community level can pave the way to effective just-in-moment intervention\, early screening\, and prevention. \nIn this talk\, I will present our recent and ongoing research to demonstrate how physiological time series data harnessed from on-body wearable systems can be used for modeling opioid use/administration\, affective states including craving\, pain\, stress and euphoria\, and opioid misuse.  I will talk about different approaches (attention based approach and Large Language Model based approach) to fuse multimodal physiological biomarker data\, behavioral data\, clinical health record data\, demographic data as well as symptom data. I will highlight how integration of pharmacological knowledge such as Pharmacokinetics of a specific substance can help neural networks to better generalize and learn opioid related physiological events better than a purely data driven approach. \nChronic opioid use induces neuroplastic changes in brain circuits\, causing predictable changes in different states including heightened stress\, increased pain\, and intense cravings. The fluctuations or changes in stress\, pain and craving states carries telltale signal for opioid misuse risk. In the last part of the talk\, I will present how the heart rate variability data from a wearable wristband can be used to predict momentary pain\, stress and craving state trajectories with a personalized hierarchical deep learning model\, obviating the need for obtrusive ecological momentary assessments throughout the day. We adopt a nonlinear dynamical systems approach with different features including persistence entropy to extract subtle trends from the moment-by-moment fluctuations or changes in pain\, stress and craving states. Our analysis reveals a hidden counter-intuitive association between high entropy or lack of predictability (i.e.\, chaos) in the momentary pain\, stress and craving states with the decrease in opioid misuse risk. Leveraging Chaos Theory\, the entropy-based nonlinear dynamical features can be used to train a deep learning based approach for accurate opioid misuse risk assessment. \n\n\n\nSpeaker Bio\n\nTauhidur Rahman is an Assistant Professor in the Halıcıoğlu Data Science Institute and Computer Science and Engineering at the University of California San Diego where he directs the Mobile Sensing and Ubiquitous Computing Laboratory (MOSAIC Lab). His current research focuses on building novel ubiquitous and mobile health sensing technologies that capture observable low-level physical signals in the form of an acoustic and electromagnetic wave from our bodies and surrounding environments and map them to relevant biological and behavioral measurements. Some of his notable accomplishments include a Google Research Scholar Award 2023\, a Google Ph.D. fellowship in 2016 in mobile computing\, a finalist position in Qualcomm innovation fellowship in 2015\, Outstanding Teaching Award 2015 from Cornell University\, one best paper award in ACM Digital Health 2016\, one best paper honorable mention award in ACM Ubicomp 2015 and a distinguished paper award from ACM IMWUT in 2021. Tauhidur received his B.S. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology\, his M.S. in Electrical Engineering from the University of Texas at Dallas and PhD in Information Science from Cornell University. He has a long track-record working with large-scale multi-modal and multi-rate sensor data\, especially in the application areas of digital epidemiology\, substance use disorder\, mental health and sleep. His work has been featured in several US-based and International media outlets including Wall Street Journal\, MIT Technology Review\, NewScientist\, Public Television for Western New England\, Daily Mail (UK) and Hindustan Times (India). His laboratory has been funded by NSF\, NIH\, DARPA and industry grants. \n 
URL:https://datascience.ucsd.edu/event/34697/
LOCATION:Powell-Focht Bioengineering Hall (PFBH)\, FUNG Auditorium
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20240222T140000
DTEND;TZID=America/Los_Angeles:20240222T150000
DTSTAMP:20240202T225717Z
CREATED:20240126T183316Z
LAST-MODIFIED:20240202T225717Z
UID:10000431-1708610400-1708614000@datascience.ucsd.edu
SUMMARY:The continuum of gene regulation at single cell resolution\, from Drosophila development to human complex traits | Diego Calderon
DESCRIPTION:Single-cell technologies have emerged as powerful tools for studying development\, enabling comprehensive surveys of cellular diversity at profiled timepoints. They shed light on the dynamics of regulatory element activity and gene expression changes during the emergence of each cell type. Despite their potential\, nearly all atlases of embryogenesis are constrained by sampling density\, i.e.\, the number of discrete time points at which individual embryos are harvested. This limitation affects the resolution at which regulatory transitions can be characterized. In this talk\, I present a novel cell collection approach capable of constructing a continuous representation of dynamic regulatory processes. I applied this approach to generate a continuous\, single-cell atlas of chromatin accessibility and gene expression spanning Drosophila embryogenesis. Additionally\, I will discuss my past and future research\, applying new genomic technologies to characterize gene regulation important for human diseases.
URL:https://datascience.ucsd.edu/event/special-seminar-diego-calderon/
LOCATION:Powell-Focht Bioengineering Hall (PFBH)\, FUNG Auditorium
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20240205T110000
DTEND;TZID=America/Los_Angeles:20240205T123000
DTSTAMP:20240130T224530Z
CREATED:20240126T182051Z
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SUMMARY:Lessons from the deep: engineering biosensors\, workflows\, and visualizations for communication and collaboration in comparative medicine and climate science | Jessica Kendall-Bar
DESCRIPTION:Abstract: Effective conservation and management relies on an in-depth understanding of the health of marine ecosystems. Dr. Kendall-Bar’s interdisciplinary approach combines engineering\, visualization\, and computation to study ocean resilience in terms of the extreme physiology and behavior of marine animals\, establishing eco-physiological baselines to track over time in the face of climate change. This seminar and chalk talk will review her work to create innovative tools to detect\, visualize\, and analyze the physiology and behavior of animals in extreme environments that showcase their biological resilience to oxygen and sleep deprivation. From individuals to ecosystems\, Kendall-Bar conducts multidisciplinary physiological studies that combine basic and applied science with potential to advance conservation and comparative medicine. This seminar reviews Kendall-Bar’s dissertation research on sleep in seals and presents some current and ongoing projects to combine high-performance computing\, automation\, and visualization to assess diving physiology in human freedivers\, epilepsy in sea lions\, and cardiac performance in some of the largest (blue whales) and smallest (emperor penguins) divers. Kendall-Bar’s newest projects involve novel data visualizations and science communication to inform research as well as international policy in domains ranging from marine mammal conservation to traditional ecological knowledge and coral reef restoration.\n \nBio: Dr. Jessica Kendall-Bar is a Schmidt AI in Science Postdoctoral Fellow at Scripps Institution of Oceanography\, UC San Diego. Her research combines engineering\, data science\, ecology\, and visualization to measure behavior and physiology of marine animals amidst a changing climate. For her dissertation\, she developed a non-invasive system to record and visualize the first recordings of marine mammal sleep at sea published in Science. She is an award-winning scientist\, artist\, and science communicator who designs data visualization courses\, large-scale exhibits\, immersive analytical tools\, and decision support tools. Her data visualizations\, published in local news outlets\, The New York Times and The Atlantic\, have informed international policy in domains ranging from marine mammal conservation to coral reef restoration.\n 
URL:https://datascience.ucsd.edu/event/special-seminar-jessica-kendall-bar/
LOCATION:Powell-Focht Bioengineering Hall (PFBH)\, FUNG Auditorium
CATEGORIES:Seminar
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