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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250502T113000
DTEND;TZID=America/Los_Angeles:20250502T123000
DTSTAMP:20260428T211113
CREATED:20250423T111527Z
LAST-MODIFIED:20250616T164658Z
UID:25882-1746185400-1746189000@idre.ucla.edu
SUMMARY:Extracting low-order representations of vortex dominated flows using deep neural networks
DESCRIPTION:Speaker: Barbara Lopez-Doriga\, Ph.D.\nIDRE Postdoctoral Fellow\nMechanical and Aerospace Engineering\nUniversity of California Los Angeles \nPlace: Virtual (Register here for the zoom link) \n  \n\n\nAbstract: There is significant practical interest in understanding and modeling fluid systems\, not only to gain insights into the complex dynamics that govern them\, but also to enable their control for different purposes. These systems are often characterized by a vast number of degrees of freedom and typically demand large amounts of high-fidelity data and computational resources for accurate simulation. In this talk\, I will briefly review several fluid dynamics problems currently being tackled with the aid of large-scale computational tools\, before focusing on the specific challenge addressed by my research. \nUnsteady aerodynamic effects are prevalent in the atmospheric boundary layer and can negatively impact the performance and stability of small- to medium-scale air vehicles. This research aims to identify the parameters and physical factors that can help mitigate these unsteady effects (modeled here as vortex gust encounters) on the aerodynamic loads experienced by such vehicles. To achieve this\, we compile a dataset of gust interactions\, varying in strength and size\, with fixed airfoils of different geometries and angles of attack. We analyze the trends that emerge across these different scenarios and examine how these dynamics are captured and encoded into a low-dimensional latent space via an observable autoencoder. This framework not only enhances our understanding of gust-induced aerodynamic phenomena but also lays the groundwork for future shape optimization studies aimed at identifying airfoil designs that minimize transient aerodynamic loads. \nAbout the speaker: Dr. Lopez-Doriga recently started her position as a postdoctoral scholar in Professor Kunihiko (Sam) Taira’s lab in the Department of Mechanical and Aerospace Engineering at UCLA. Barbara received an MS and a BS in Mechanical Engineering from the Polytechnic University of Madrid\, Spain (UPM). She then received her ME and PhD in Mechanical and Aerospace Engineering from Illinois Institute of Technology (Chicago\, IL). Since joining Taira’s lab\, her interest has been in developing a data-driven machine-learning-based framework to find the optimal airfoil design and control systems for gust mitigation.
URL:https://idre.ucla.edu/calendar-event/barbara-ldoriga-may-2-2025
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250404T113000
DTEND;TZID=America/Los_Angeles:20250404T123000
DTSTAMP:20260428T211113
CREATED:20250312T180604Z
LAST-MODIFIED:20250404T233956Z
UID:25785-1743766200-1743769800@idre.ucla.edu
SUMMARY:Bridging AI and Generative Models with Mean Field Control through Score Based Normalizing Flow
DESCRIPTION:Speaker: Mo Zhou\, Ph.D.\nIDRE Postdoctoral Fellow\nDepartment of Mathematics (UCLA)\nUniversity of California Los Angeles    \n\nPlace: Virtual (Link to the video recording)\n\n\n\n\n\n\nAbstract:      Generative models\, a cornerstone of modern AI\, learn to approximate complex probability distributions and generate realistic data. A key challenge in these models is efficiently computing the “score function\,” which helps guide the learning process. Interestingly\, a similar challenge arises in mean field control (MFC)\, a mathematical framework for decision-making in large-scale systems\, such as crowd dynamics and financial markets. \nIn this talk\, I will introduce a novel approach that computes MFC problems using score based neural ordinary differential equations (ODEs) and normalizing flows. We develop a system of ODEs to compute both first- and second-order score functions\, reframing MFC problems as unconstrained optimization tasks. Our method also introduces a regularization technique inspired by Hamilton–Jacobi–Bellman (HJB) equations\, ensuring better accuracy and stability. I will show applications\, including probability flow matching and Wasserstein proximal operators\, explaining how this approach enhances both theoretical understanding and practical computation in generative modeling and control. \nAbout the speaker:  Dr. Zhou is an Assistant Adjunct Professor in the Department of Mathematics at UCLA\, under Professor Stanley Osher’s guidance. He earned his bachelor’s degree in mathematics from Tsinghua University in 2018 and Ph.D. in mathematics from Duke University in 2023\, under Professor Jianfeng Lu. During his Ph.D. studies\, Dr. Zhou developed advanced deep learning algorithms to overcome the curse of dimensionality and addressed traditional scientific computing challenges\, including eigenvalue problems and optimal control problems. Currently\, his research focuses on mean-field control and games.
URL:https://idre.ucla.edu/calendar-event/bridging-ai-and-generative-models-with-mean-field-control-through-score-based-normalizing-flow
CATEGORIES:Conferences and Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240628T113000
DTEND;TZID=America/Los_Angeles:20240628T123000
DTSTAMP:20260428T211113
CREATED:20240620T183709Z
LAST-MODIFIED:20240701T220736Z
UID:25039-1719574200-1719577800@idre.ucla.edu
SUMMARY:TimeAutoDiff : Combining auto-encoder and diffusion model for time series tabular synthesizing
DESCRIPTION:  \n  \n\n\n\nSpeaker: Najoon Suh\, Ph.D \nIDRE Fellow \nDepartment of Statistics and Data Science \nUniversity of California Los Angeles \n  \nLocation: Zoom (Recording link)\n\n\n\n\n  \nAbstract: In the work to be presented\, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations\, the heterogeneous nature of the feature in the table has been one of the main obstacles in time series tabular data modeling. We tackle this problem by combining the ideas of the variational auto-encoder (VAE) and the denoising diffusion probabilistic model (DDPM). Our model\, named “TimeAutoDiff\,” has several key advantages\, including (1) Generality\, the ability to handle the broad spectrum of time series tabular data from single to multi-sequence datasets; (2) Good fidelity and utility guarantees: numerical experiments on six publicly available datasets demonstrating significant improvements over state-of-the-art models in generating time series tabular data\, across four metrics measuring fidelity and utility; (3) Fast sampling speed: entire time series data generation as opposed to the sequential data sampling schemes implemented in the existing diffusion-based models\, eventually leading to significant improvements in sampling speed\, (4) Entity conditional generation: the first implementation of conditional generation of multi-sequence time series tabular data with heterogenous features in the literature\, enabling scenario exploration across multiple scientific and engineering domains.\n\n\nAbout the speaker: Dr. Namjoon Suh is a UCLA adjunct assistant professor. He is an IDRE fellow and is associated with Prof. Dr. Guang Cheng’s lab in the UCLA Statistics and Data Science Department. He obtained his Machine Learning Ph.D. degree at Stewart School of Industrial & Systems Engineering\, Georgia Tech\, in December 2022 and earned an M.Sc. in Statistics at Georgia Tech in 2018. Before Georgia Tech\, he received a B.Sc. degree from Korea University in 2015\, majoring in Industrial Engineering.
URL:https://idre.ucla.edu/calendar-event/ecr-june-28-2024
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240531T113000
DTEND;TZID=America/Los_Angeles:20240531T123000
DTSTAMP:20260428T211113
CREATED:20240508T030807Z
LAST-MODIFIED:20240531T214334Z
UID:24988-1717155000-1717158600@idre.ucla.edu
SUMMARY:Challenges to mitigating climate change drivers and associated risks of surpassing lower emission targets
DESCRIPTION:  \n\n\n\nSpeaker: Robert Fofrich\, Ph.D.\nIDRE and UC President’s Postdoctoral Fellow\nInstitute of the Environment and Sustainability\nUniversity of California Los Angeles    \n\nPlace: Virtual (Link to the video recording)\n\n\n\n\n\n\nAbstract: Lower climate change mitigation pathways require large and swift reductions in anthropogenic CO2 emissions worldwide\, a substantial portion arising from fossil energy sources utilized in electricity generation. Thus\, stabilizing global mean temperatures at or below 2 degrees necessitates retiring fossil-burning infrastructure well before their operational lifespans have elapsed\, resulting in stranded assets worldwide. However\, sizable investments in fossil energy infrastructure have continued to rise globally\, posing a threat to international climate change mitigation\, food security\, and financial objectives. Thus\, we will discuss challenges associated with attaining lower climate warming targets and the potential repercussions for global agriculture and human well-being if these targets are exceeded. \nAbout the speaker: Dr. Fofrich was born in East Los Angeles and is an alumnus of West Los Angeles College. Currently\, he is a UC President’s Postdoctoral Fellow at the Institute of the Environment and Sustainability and works under the supervision of Dr. Elsa Ordway and Dr. Thomas Smith within the Department of Ecology and Evolutionary Biology. Before joining UCLA\, Dr. Fofrich joined the Climate Impact lab and was briefly a postdoctoral scholar in the Department of Earth and Planetary Sciences at Rutgers University. Dr. Fofrich received his Ph.D. in 2022 from the Department of Earth System Science at the University of California\, Irvine\, where his research focused on energy and agricultural systems as they relate to climate change mitigation and adaptation. He has also served as a researcher at NASA-JPL and the Center for Environmental Biology in Orange County\, California. His passion for the natural environment and a profound commitment to underserved communities steered his decision to study ways to mitigate anthropogenic environmental damages and protect vulnerable populations from these changes.
URL:https://idre.ucla.edu/calendar-event/robert-fofrich-may-31-2024
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240328T113000
DTEND;TZID=America/Los_Angeles:20240328T123000
DTSTAMP:20260428T211113
CREATED:20240308T010052Z
LAST-MODIFIED:20240401T194223Z
UID:24846-1711625400-1711629000@idre.ucla.edu
SUMMARY:Interacting Dynamical System Modeling for Science: Construction\, Generalization\, and Applications
DESCRIPTION:Speaker: Xiao Luo\, Ph.D.\nIDRE Fellow\nDepartment of Computer Science\nUniversity of California Los Angeles   Time: 11:30 AM – 12:30 PM (PST)\nDate: March 28\, 2024 \nLink to the recording: https://youtu.be/0_WwPe-kV-Q\n\n\n\n\nAbstract: Many real-world systems such as disease transmission\, molecular dynamics\, and spring systems can be considered as multi-agent dynamical systems\, where multiple objects interact with each other and exhibit complex behavior along the time. In this talk\, I will discuss my current research on interacting dynamics system modeling for scientific problems\, especially focusing on model construction and model generalization. I will begin by discussing my work on graph ODEs for efficiently capturing continuous high-order correlations. Then\, I will discuss different types of distribution shifts in dynamical system modeling and how to address them to improve the generalization ability. Finally\, I will introduce future research directions in the field of dynamical system modeling. \nAbout the speaker: Dr. Xiao Luo is a postdoctoral researcher at UCLA’s Department of Computer Science. Previously\, he received a B.S. degree in Mathematics from Nanjing University\, Nanjing\, China\, in 2017 and a Ph.D. in the School of Mathematical Sciences from Peking University\, Beijing\, China in 2022. His research interests include machine learning on graphs\, dynamical systems\, statistical models\, and AI for Science.
URL:https://idre.ucla.edu/calendar-event/ecr-march-28-2024
CATEGORIES:Conferences and Seminars,Education and Training,Meetings,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240308T110000
DTEND;TZID=America/Los_Angeles:20240308T143000
DTSTAMP:20260428T211113
CREATED:20240216T212407Z
LAST-MODIFIED:20240401T195759Z
UID:24733-1709895600-1709908200@idre.ucla.edu
SUMMARY:Using Topological Data Analysis to characterize fluctuations in brain activity patterns in healthy and patient populations
DESCRIPTION:Link to the recording:  https://youtu.be/CxpGAIjidvQ \n  \n  \nSpeaker: Prof. Manish Sagger\nTashia and John Morgridge Endowed Faculty Scholar in Pediatric Translational Medicine\, Stanford Maternal & Child Health Research Institute\nAssistant Professor\, Department of Psychiatry & Behavioral Sciences\nPrincipal Investigator\, Brain Dynamics Lab\nStanford University School of Medicine  \n  \nAbstract: Understanding the neurobiological underpinnings of psychiatric disorders has long been a challenge. This talk addresses this issue by exploring how noninvasive neuroimaging\, despite its inherent limitations\, can be leveraged to anchor psychiatric disorders into neurobiology. Two main challenges in this endeavor are identified: (a) the inherent noise in noninvasive neuroimaging devices and (b) the limited utilization of biophysical models. To tackle the first challenge\, we propose the application of Topological Data Analysis (TDA)\, specifically Mapper\, as a novel approach. I present some promising results on how Mapper can capture evoked transitions during tasks\, intrinsic transitions during resting states\, changes in the landscape or shape associated with psychiatric disorders\, and various pharmacological interventions and neuromodulation techniques. I will highlight a few methodological advances for Mapper that could enhance its applicability in noninvasive neuroimaging studies. Finally\, the talk concludes by posing open questions to understand the neurobiological basis of psychiatric disorders better and pave the way for innovative therapeutic strategies. \nDemo talk title: A short tutorial on Topological Data Analysis based Mapper approach \nAbstract: In this tutorial\, I will introduce and provide a high-level overview of Topological Data Analysis\, mainly the Mapper approach. The hands-on portion of this tutorial will include a brief introduction to the DyNeuSR package from my lab (more information here – https://braindynamicslab.github.io/dyneusr/). DyNeuSR is a Python visualization library for topological representations of neuroimaging data. Developed with neuroimaging data analysis in mind\, DyNeuSR connects existing implementations of Mapper (e.g. KeplerMapper) with network analysis tools (e.g. NetworkX) and other neuroimaging data visualization libraries (e.g. Nilearn) and provides a high-level interface for interacting with and manipulating shape graph representations of neuroimaging data and relating these representations to neurophysiology. \nAbout the presenter: Manish Saggar is an assistant professor in the Psychiatry & Behavioral Sciences department at Stanford University and currently directs the Brain Dynamics Lab. His lab aims to develop computational methods for anchoring psychiatric diagnosis into biological features (e.g.\, neural circuits and spatiotemporal neurodynamics). Manish received his Ph.D. in Computer Science from the University of Texas at Austin and later received postdoctoral training in Psychiatry from Stanford University.
URL:https://idre.ucla.edu/calendar-event/topological-data-analysis-march-8-2024
CATEGORIES:Classes and Workshops,Conferences and Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240301T113000
DTEND;TZID=America/Los_Angeles:20240301T123000
DTSTAMP:20260428T211113
CREATED:20240209T203023Z
LAST-MODIFIED:20240302T002639Z
UID:24712-1709292600-1709296200@idre.ucla.edu
SUMMARY:Computational approaches in clinical epigenomics
DESCRIPTION:Speaker: Fei-Man Hsu\, Ph.D.\nIDRE Fellow\nDepartment of Molecular\, Cell\, and Developmental Biology\nUniversity of California Los Angeles \n  \n  \n  \nTime: 11:30 AM – 12:30 PM (PST)\nDate: March 1\, 2024\nView recording: https://youtu.be/RVfoXBN66HU\n\n\n\n\nAbstract: DNA methylation signatures have high predictive value and could be used to predict health outcomes. Challenges remained in clinical studies such as the large population variations and the biopsy with mixed cell types which all contribute to DNA methylation dynamics. In this presentation I will introduce the computational approaches of clinical epigenomics with our recent research that applied targeted bisulfite sequencing (TBS-seq) to peripheral blood mononuclear cells (PBMCs) from 156 individuals before lung or kidney transplant in two medical centers to study the impact of cytomegalovirus (CMV) to the host epigenome. Cell type composition contributes most DNA methylation changes in PBMCs\, and we resolved the mixed cell type issue with a reference-based cell type deconvolution method. Lastly\, I will direct to a reference-free source-of-origin cell type classifier under development. \nAbout the speaker: Dr. Fei-Man Hsu is a postdoctoral fellow at the Pellegrini lab in the Department of Molecular\, Cell\, and Developmental Biology at UCLA. With a Ph.D. from the University of Tokyo\, Dr. Hsu specializes in bioinformatics. She holds a M.S. degree in Molecular and Cellular Biology\, and a B.S. degree in Life Science from National Taiwan University.
URL:https://idre.ucla.edu/calendar-event/idre-ecr-march-1-2024
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240126T113000
DTEND;TZID=America/Los_Angeles:20240126T123000
DTSTAMP:20260428T211113
CREATED:20240109T204343Z
LAST-MODIFIED:20240213T051136Z
UID:24634-1706268600-1706272200@idre.ucla.edu
SUMMARY:Data-driven prediction of vortex dynamics with hierarchical graph neural networks
DESCRIPTION:Alec Linot \n  \nSpeaker: Alec Linot\, Ph.D.\nIDRE Fellow\nMechanical and Aerospace Engineering\nUniversity of California Los Angeles \n  \n  \nLocation: Zoom (Registration required)\n \nTime: 11:30 AM – 12:30 PM (PST)\nVideo Link: https://youtu.be/E8f2lrHMOa8 \n  \nAbstract: Forecasting the dynamics of fluid flows plays a crucial role in our understanding of processes such as the swimming of fish\, turbulence on a plane\, and hurricane formation. Unfortunately\, simulating these systems can be prohibitively expensive even though we often know the equations of motion. Due to this high computational cost\, major effort has gone toward developing reduced-order models (ROMs) of fluid flows both from first principles and in a data-driven manner. Various ROMs using Galerkin methods and neural networks\, for example\, have been shown to accurately predict the dynamics of fluid systems with far fewer degrees of freedom than needed in high-resolution simulations. However\, these ROMs typically apply to very specific systems with a fixed state size (e.g. grid size or latent space size). In this work\, we present a data-driven ROM method for discovering vortex dynamics that overcomes the challenge of a fixed state size by using a hierarchy of graph neural networks (GNNs). This method allows us to consider a fluid flow as a graph of the vortices within a flow. Then\, by grouping clusters of vortices\, we construct a hierarchy of graphs with which we train GNNs to predict vortex dynamics. Notably\, this hierarchal approach mirrors our intuition on how groups of vortices often cluster to act as a cohesive unit. We show that this hierarchical method is both more accurate and faster than constructing a fully connected GNN\, and we show that this approach allows us to predict vortex dynamics with state sizes (i.e. the number of vortices) outside of our training data. \nAbout the speaker: Dr. Alec Linot is a postdoctoral researcher with Prof. Kunihiko (Sam) Taira in the Mechanical and Aerospace Engineering Department at UCLA. He received a BS in Chemical Engineering from Kansas State University. His Ph.D is in Chemical and Biological Engineering from the University of Wisconsin – Madison. In his Ph.D.\, he developed machine learning techniques for modeling and controlling turbulent flows. His current research is in modeling\, control\, and stability of chaotic dynamical systems. Chaotic dynamical systems are deterministic systems where small perturbations to the system result in dramatically different dynamics over time.
URL:https://idre.ucla.edu/calendar-event/idre-fellow-jan26-2024
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231201T113000
DTEND;TZID=America/Los_Angeles:20231201T123000
DTSTAMP:20260428T211113
CREATED:20231120T194651Z
LAST-MODIFIED:20231202T062938Z
UID:24445-1701430200-1701433800@idre.ucla.edu
SUMMARY:Panel Discussion on Harnessing High-performance Computing across STEM Disciplines
DESCRIPTION:Venue: Virtual via zoom\nRecording is available at: https://youtu.be/Nq8CkX20xjM \nThe IDRE Early Career Researchers group is pleased to announce a discussion on harnessing high-performance computing across STEM disciplines. The panel will be led by the following new cohort of IDRE fellows (2023-2024) from various disciplines across the UCLA campus. \n\nRobert Fofrich – Institute of the Environment and Sustainability\nFei-man Hsu – Department of Molecular\, Cell\, and Development Biology\nAlec Linot – Mechanical and Aerospace Engineering\nXiao Luo – Department of Computer Science\nNamjoon Suh – Department of Statistics and Data Science\n\nJoin us for the discussion on how high-performance computing (HPC) is used across STEM disciplines. We will explore how HPC revolutionizes research methodologies and allows for the analysis of very large datasets\, fueling innovation in fields ranging from geoscience to engineering. The panel will also be discussing applications across STEM disciplines and how to harness the power of HPC in your research.
URL:https://idre.ucla.edu/calendar-event/ecr-panel-discussion-dec-1-23
LOCATION:Virtual
CATEGORIES:Conferences and Seminars,Meetings
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231005T113000
DTEND;TZID=America/Los_Angeles:20231005T133000
DTSTAMP:20260428T211113
CREATED:20230919T224332Z
LAST-MODIFIED:20231005T235108Z
UID:24313-1696505400-1696512600@idre.ucla.edu
SUMMARY:Intro to Julia: A fast dynamic language for statistical computing and data science
DESCRIPTION:  \n \n  \n  \nSpeaker: Seyoon Ko\, Ph.D.\nIDRE Fellow and Assistant Adjunct Professor\,\nMathematics Department\,\nUniversity of California Los Angeles \n  \n  \nLocation: Virtual\nLink to the recording: https://youtu.be/lmtHtuyl-Q0  \nAbstract: Julia (http://julialang.org) is a modern open-source programming language for technical computing. Its design offers much greater speed and productivity compared to R or Python\, as high-performance code does not need to be wrapped in a low-level language like C or Fortran. After almost a decade of active development\, Julia reached its first major release\, v1.0\, in 2018\, and is quickly gaining popularity in scientific computing and data science communities. In this workshop\, I will present the basic concepts of Julia and show a little comparison between Julia and other languages\, such as R\, C\, and Python. \nAbout the speaker: Dr. Ko is an Assistant Adjunct Professor at UCLA Mathematics. Previously\, he was a Postdoctoral Scholar working with Dr. Ken Lange and Dr. Hua Zhou in the Department of Computational Medicine. Dr. Ko’s research interests include large-scale computational methods in biostatistics and bioinformatics using parallel and distributed computing. He earned a Ph.D. degree in Statistics from Seoul National University in South Korea\, as well as a M.S. degree in Computational Sciences and a B.S. degree in Physics\, Mathematical Sciences\, and Computational Science.
URL:https://idre.ucla.edu/calendar-event/julia-by-seyoon-ko-ecr
CATEGORIES:Conferences and Seminars,Education and Training,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230918T110000
DTEND;TZID=America/Los_Angeles:20230918T143000
DTSTAMP:20260428T211113
CREATED:20230828T212729Z
LAST-MODIFIED:20230923T045719Z
UID:24166-1695034800-1695047400@idre.ucla.edu
SUMMARY:On Functional Brain Network and Dynamic Multivariate task fMRI Analysis
DESCRIPTION:Speaker: Prof. Nathan Spreng\nJames McGill Professor of Neurology and Neurosurgery\, McGill University\nDirector\, Laboratory of Brain and Cognition\, Montreal Neurological Institute. \nRSVP: Link to the recording of the second session. \nThe impact of loneliness on functional brain network organization across the lifespan (11:00 AM – 12:00 PM): Loneliness emerges when one’s need for interpersonal connection is unmet. Loneliness is a modifiable risk factor associated with poor mental and brain health across the lifespan. Over a series of studies examining the impact of loneliness on brain function\, measured with resting-state functional connectivity\, and analyzed using partial least squares analysis (PLS)\, we have demonstrated that associations between self-reported loneliness and functional network organization changes over the adult life course. In early adulthood\, higher levels of loneliness are associated with greater integration of visual regions with higher order association networks. From late middle-age and into older adulthood\, this pattern shifts\, with greater integration observed among higher order association networks and a relative isolation of the visual system. We hypothesize that these age-differences in network organization in the context of loneliness may reflect a shift from externally-oriented processing (e.g.\, perceiving negative social cues) in young\, to more internally-oriented processing (e.g.\, reminiscing or mentalizing about social experiences) in the later decades of life. These findings raise the intriguing possibility that the phenomena of loneliness may be a qualitatively different experience depending upon age. I will conclude with new directions of research into the impact of loneliness on older adults at risk for Alzheimer’s disease \nDynamic multivariate task fMRI analysis using Partial Least Squares in Matlab (1:00 PM – 2:30 PM): Whole brain imaging provides extraordinary opportunities to identify coherent patterns in the spatial structure and spatiotemporal functioning of cortical and subcortical brain regions. This has led to an explosion of network neuroscience research over the past two decades. Initially\, network studies adopted a general linear modelling (GLM) approach\, following the early structural and functional activation studies. However\, fMRI data is more amenable to multivariate approaches that consider dynamic aspects of brain function given its high dimensionality\, temporal complexity\, and the issue of multiple statistical comparisons. In this workshop\, I will review a dynamic multivariate approach for task based fMRI data\, Partial Least Squares (PLS). In this workshop\, I will review practical aspects of PLS statistical modelling and analyses\, introduce the PLS GUI interface in Matlab\, and include key elements of analysis implementation and results interpretation. \nAbout Speaker: Dr. Nathan Spreng is the James McGill Professor of Neurology and Neurosurgery at McGill University\, and director of the Laboratory of Brain and Cognition at the Montreal Neurological Institute. His research examines large-scale brain network dynamics and their role in cognition. Currently\, he is investigating the links between memory\, attention\, cognitive control\, and social cognition and the interacting brain networks that support them. He is also actively involved in the development and implementation of novel multivariate statistical approaches to assess activity and interactivity of large scale brain networks. His work adopts a network neuroscience approach to investigating complex cognitive processes as they change across the lifespan\, both in normal aging and brain disease.
URL:https://idre.ucla.edu/calendar-event/jason-nomi-9-18-2023
CATEGORIES:Conferences and Seminars,Education and Training,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230630T113000
DTEND;TZID=America/Los_Angeles:20230630T123000
DTSTAMP:20260428T211113
CREATED:20230614T224625Z
LAST-MODIFIED:20230724T234540Z
UID:24118-1688124600-1688128200@idre.ucla.edu
SUMMARY:Leveraging big data in ecology and atmospheric science to study impacts of wildfire smoke on birds
DESCRIPTION:  \nSpeaker: Olivia Sanderfoot\, Ph.D.\nIDRE Fellow\nEcology & Evolutionary Biology\nUniversity of California Los Angeles \n  \n  \nLocation: Link to the recording \nTime: 11:30 AM – 12:30 PM (PST)\nRegistration Link: https://youtu.be/Xked2hZBq44\n \n  \nAbstract: Global wildfire activity is increasing globally\, and people and wildlife are increasingly exposed to hazardous smoke. Despite the well-established risks wildfire smoke poses to public health\, few studies have investigated how smoke impacts non-human animals. Birds are especially vulnerable to smoke due to their heightened sensitivity to air pollution. In this talk\, I will share my vision for linking big data from ecology and atmospheric science to learn more about the effects of smoke on the health and behavior of birds and discuss several case studies that demonstrate the potential of interdisciplinary\, cross-campus collaborations to address critical knowledge gaps and inform conservation. \nAbout the speaker: Dr. Olivia Sanderfoot studies the impacts of wildfire smoke on birds and other wildlife. As the 2023 La Kretz Center for California Conservation Science Postdoctoral Fellow\, Olivia is exploring how wildfire smoke influences bird behavior and shapes species distributions in California. Additionally\, she is partnering with the Natural History Museum of Los Angeles County to launch a new community science project in southern California to learn more about how smoke impacts local birds. \nBefore moving to Los Angeles\, Olivia conducted her doctoral research in the School of Environmental and Forest Sciences at the University of Washington in Seattle. Her dissertation explored how wildfire smoke and urban air pollution impacted the detection of birds in Washington state. \nOlivia has been interviewed about her research by National Geographic\, TIME\, Discover\, and Audubon magazines\, Popular Science\, The Seattle Times\, The Washington Post\, and several local radio and TV stations. \nOlivia was born and raised in Madison\, Wisconsin\, and is a proud alumna of the University of Wisconsin – Madison; she received her B.S. in biology and Spanish in 2015 and her M.S. in environmental science in 2017. Born and raised in Wisconsin\, Olivia is driven by her passion for environmental policy and conservation\, her love for birds\, and her strong belief in the Wisconsin Idea\, the philosophy that a university’s research should be applied to solve problems and improve the health\, well-being\, and environment of the community it serves.
URL:https://idre.ucla.edu/calendar-event/olivia-sanderfoot-06-30-2023
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230525T113000
DTEND;TZID=America/Los_Angeles:20230525T123000
DTSTAMP:20260428T211113
CREATED:20230412T212045Z
LAST-MODIFIED:20230601T230618Z
UID:23905-1685014200-1685017800@idre.ucla.edu
SUMMARY:When will Quantum Computing be ready for Scientific Computing?
DESCRIPTION:Speaker: Jens Palsberg\, Ph.D.\, MBA\nProfessor of Computer Science\nSamueli School of Engineering \nUniversity of California Los Angeles \n  \nRecording link: https://youtu.be/NCcigLa_c7E \n\n  \n\nDescription: Quantum computing is rapidly getting better but still has some way to go before it can make a difference in science and business. I will introduce the field\, give the status of current hardware and simulators\, and point to resources for learning more about the technology and algorithms. Along the way\, I will mention some of the many quantum researchers at UCLA\, and I will highlight UCLA’s new quantum masters degree.\n  \nAbout the speaker: Jens Palsberg is a Professor and former Department Chair of Computer Science at University of California\, Los Angeles (UCLA). His research interests span the areas of programming languages\, software engineering\, and quantum computing. He is the director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence\, an associate editor of ACM Transactions on Quantum Computing\, and a member of the ACM Executive Committee. In 2012 he received the ACM SIGPLAN Distinguished Service Award\, and in 2023 he received the Eon Instrumentation Inc. Excellence in Teaching Award at UCLA.\n\n 
URL:https://idre.ucla.edu/calendar-event/quantum-computing-readiness
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230518T160000
DTEND;TZID=America/Los_Angeles:20230518T170000
DTSTAMP:20260428T211113
CREATED:20230317T020820Z
LAST-MODIFIED:20230317T224303Z
UID:23820-1684425600-1684429200@idre.ucla.edu
SUMMARY:Research and Creative Proposal Workshop
DESCRIPTION:Learn how to write a research or creative proposal for scholarship program applications!\nApplications for the URSP and Keck scholarship programs are due by June 20. Find out how you can receive $4\,500-$10\,000 in funding for doing research or creative inquiry in URSP or Keck: https://hass.ugresearch.ucla.edu/. \nRegister here: https://bit.ly/urcworkshop
URL:https://idre.ucla.edu/calendar-event/research-and-creative-proposal-workshop
CATEGORIES:Conferences and Seminars,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230517T160000
DTEND;TZID=America/Los_Angeles:20230517T170000
DTSTAMP:20260428T211113
CREATED:20230317T020819Z
LAST-MODIFIED:20230317T224221Z
UID:23819-1684339200-1684342800@idre.ucla.edu
SUMMARY:Virtual Presentation Workshop
DESCRIPTION:Learn how to craft a standout presentation for the Undergraduate Research & Creativity Showcase and other Undergraduate Research Week events and conferences! \nRegister here: https://bit.ly/urcworkshop
URL:https://idre.ucla.edu/calendar-event/virtual-presentation-workshop-4
CATEGORIES:Conferences and Seminars,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230428T113000
DTEND;TZID=America/Los_Angeles:20230428T123000
DTSTAMP:20260428T211113
CREATED:20230417T200200Z
LAST-MODIFIED:20230430T034600Z
UID:23981-1682681400-1682685000@idre.ucla.edu
SUMMARY:Stability and Resolvent Analysis of Fluid Flows - Methods and Challenges
DESCRIPTION:Speaker: Victoria Rolandi\, Ph.D.\nIDRE Fellow\nMechanical and Aerospace Department\nUniversity of California Los Angeles \n  \n\n\n\n\n\n\nLocation: Virtual via Zoom\n \nRecording available at: https://youtu.be/m9MTRk0ggrk \n\n\n\n\n\n\n\n\nAbstract: Understanding the transition of fluid flows has been and still is a crucial focus in fluid dynamics. Stability theory has greatly helped on this side and has opened the door to other branches in fluid dynamics\, such as flow control. By leveraging insights on flow transition\, flow control technology can help mitigate the human impact of environmental and noise pollution caused by fluid-based systems such as aircraft\, automobiles\, and wind turbines\, all while improving their overall performance. \nFrom linear stability analysis to resolvent analysis\, this talk will cover some of the methods that enable such investigations and the limitations\, in terms of computational resources\, on applying them to turbulent flows. \nAbout Speaker: Dr. Rolandi obtained a BSc in Mathematical Engineering from Politecnico di Torino and an MSc in Computational Science and Engineering from Politecnico di Milano. She later completed a Ph.D. in Fluid Dynamics at the Institute Supérieure de l’Aéronautique et de l’Espace (ISAE-Supaero) before joining the Mechanical and Aerospace department at UCLA as a postdoctoral researcher at Professor Taira’s Lab. Her research at UCLA focuses on developing and implementing algorithms helpful in characterizing\, modeling\, and controlling turbulent flows.
URL:https://idre.ucla.edu/calendar-event/victoria-4-28-2023
CATEGORIES:Conferences and Seminars,Education and Training,Meetings,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T160000
DTEND;TZID=America/Los_Angeles:20230406T165000
DTSTAMP:20260428T211113
CREATED:20230317T020806Z
LAST-MODIFIED:20230317T213924Z
UID:23797-1680796800-1680799800@idre.ucla.edu
SUMMARY:Cornerstone 1: Getting Started With Research Workshop
DESCRIPTION:Learn how to get started with research and creative inquiry at UCLA including how to find opportunities\, earn course credit for doing research or creative practice\, and how to find a faculty mentor!\nThis workshop is offered as part of the Cornerstone Research Workshop series\, a six part series of foundational research topics and skills created by URC-HASS\, the UCLA Library\, and the Undergraduate Writing Center. \nRegister here: https://bit.ly/urcworkshop
URL:https://idre.ucla.edu/calendar-event/cornerstone-1-getting-started-with-research-workshop-3
LOCATION:Hybrid: Charles E. Young & Zoom
CATEGORIES:Conferences and Seminars,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230405T160000
DTEND;TZID=America/Los_Angeles:20230405T170000
DTSTAMP:20260428T211113
CREATED:20230317T020805Z
LAST-MODIFIED:20230317T213634Z
UID:23796-1680710400-1680714000@idre.ucla.edu
SUMMARY:Undergraduate Research Week Info Session
DESCRIPTION:Learn all about Undergraduate Research Week (May 22-26\, 2023)\, UCLA’s annual weeklong celebration of undergraduate research and creative inquiry\, and how you can present your research or creative project at the Undergraduate Research & Creativity Showcase event on May 23. Plus\, learn how to apply for the Dean’s Prize and nominate your mentor for a Faculty Mentor Award!\nAll students are invited to participate in Undergraduate Research Week! Each year\, over 1\,000 students share their research and creative projects with UCLA’s global community throughout the week. Find out more: https://urweek.ugresearch.ucla.edu/. \nRegister here: https://bit.ly/urcworkshop
URL:https://idre.ucla.edu/calendar-event/undergraduate-research-week-info-session
CATEGORIES:Conferences and Seminars,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230324T113000
DTEND;TZID=America/Los_Angeles:20230324T123000
DTSTAMP:20260428T211113
CREATED:20230308T173309Z
LAST-MODIFIED:20230422T065444Z
UID:23719-1679657400-1679661000@idre.ucla.edu
SUMMARY:Urban biodiversity: the importance of scale
DESCRIPTION:Speaker: Nannan Gao\, Ph.D.\nIDRE Fellow\nDepartment of Ecology and Evolutionary Biology\nUniversity of California Los Angeles \n\n\n\n\n\n\n  \n\n\n\n\n\n\nLocation: Virtual via zoom\n \nRegistration Link: https://ucla.zoom.us/meeting/register/tJ0sdeGgqj0pHNzapieckX5w-dQAFTDr3d5D \n\n\n\n\n\n\n\n\nAbstract: While much is known about the scaling of biodiversity\, less is known about specifically how biodiversity scales in urban areas. This is an important question because over two-thirds of humans live in urban areas. Understanding how\, precisely\, biodiversity scales in urban areas will inform management. Linear relationships would imply that similar interventions should work across the range of city sizes (from small towns to the largest mega-cities) whereas non-linear relationships would imply that biodiversity strategies must be tailored to the size of the city. We focused on avian biodiversity because more than half of the species are found in urban areas (6120 species out of 11\,162 species)\, including at least 350 threatened ones. We calculated species richness in 2\,568 cities and used eBird\, a community science platform\, to estimate species richness. After controlling for a variety of variables that might explain variation in avian biodiversity\, we found a non-linear relationship in cities and contrasted this to a well-established power law found in natural areas. After controlling for other key variables that might explain variation in urban biodiversity\, the log-log relationship between city area and avian biodiversity had a slope of 0.42 until cities got bigger than 331 km2\, beyond which it decreased to 0.15. This suggests that unique processes affect urban biodiversity in smaller and larger cities. When we focused on the subset of threatened species\, we found a linear relationship with a slope of 0.20. Urbanization not only contributes to a global extinction\, but urban areas may provide important habitat for threatened species. \nAbout Speaker: Dr. Nannan Gao is a Postdoctoral associate with Daniel T. Blumstein in the Department of Ecology and Evolutionary Biology at UCLA. Her research mainly focuses on studying the relationship between urban biodiversity and city size by creating global urban biodiversity datasets that include small towns to megacities involving spatiotemporal advanced computing\, statistical computing and data science. Dr. Gao received her PhD in Chinese Academy of Science\, also studied human geography and urban planning in Peking University. She seeks to balance humans and animals in urban areas.
URL:https://idre.ucla.edu/calendar-event/urban-biodiversity
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230224T113000
DTEND;TZID=America/Los_Angeles:20230224T123000
DTSTAMP:20260428T211113
CREATED:20230211T011752Z
LAST-MODIFIED:20230224T222937Z
UID:23661-1677238200-1677241800@idre.ucla.edu
SUMMARY:High-throughput survey of brain cell diversity and organization using dimensionality-reduced spatial transcriptomics
DESCRIPTION:Speaker: Fangming Xie\, Ph.D.\nIDRE Fellow\nInstitute for Quantitative & Computational Biosciences\nUniversity of California Los Angeles \n\n\n\n\n\n\n  \n\n\n\n  \n  \n\n\n\nLocation: Virtual \nRecording of the presentation \n  \nAbstract: Biology is undergoing a revolution of information explosion. In genomics\, a single experiment measures the expression levels of thousands of genes in hundreds of thousands of cells. Meanwhile\, scalable computational tools were used to distill understanding from complex data. The combination of high-throughput experiments and analyses has transformed research paradigms and advanced our understanding in many areas\, including cell type taxonomy\, developmental trajectories\, and disease biomarkers. Yet\, we are still far from gaining the throughput to measure and analyze transcriptomes (cells’ full gene expression profiles) at whole-organ scale in organisms that are much smaller than humans. Existing methods\, including single-cell RNA-seq and spatial transcriptomics\, will take years to measure all cells (n=~100 million) in a mouse brain\, before any computational analyses to reveal cell types and their spatial organizations. \nRather than performing experiments first and analyzing data second\, we propose an integrated approach where computational dimensionality reduction methods are used to design smarter experiments that maximize the information content of spatial transcriptomics experiments. Using a variety of methods\, from principal component analysis\, non-negative matrix factorization\, to neural network\, we extracted from transcriptome data (num. dimension > 10\,000) low-dimensional (n=24) latent features\, with each latent feature being a linear combination of many genes. We designed spatial transcriptomic experiments to directly measure those latent features in situ in the mouse brain. Preliminary data from one full coronal section of the mouse brain suggests the empirically measured latent features reveal rich spatial organization that matches known brain anatomy and cell types. To demonstrate its throughput\, we are working on realizing its potential of enabling whole-organ scale spatial transcriptomics. \n\n\n\n\n\n\nAbout the speaker: Dr. Xie is a postdoc in the Department of Chemistry and Biochemistry at UCLA. Prior to joining UCLA\, Fangming received a BS degree in Physics at the University of Science and Technology of China. He visited UCLA as an undergraduate student for a summer\, working with Dr. Robijn Bruinsma and Dr. William Klug to model the self-assembly of viral capsids. He then pursued a PhD at UCSD where his research focused on integrative analyses of single-cell transcriptomes and epigenomes of brain cells. Fangming loves neuroscience\, genomics\, and physics. He believes many parts of these disciplines can be brought together to sharpen our tools and advance our understanding of the brain. \n\n\n\n\n\n\n 
URL:https://idre.ucla.edu/calendar-event/23661
CATEGORIES:Conferences and Seminars,Meetings
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230127T113000
DTEND;TZID=America/Los_Angeles:20230127T123000
DTSTAMP:20260428T211113
CREATED:20230112T212001Z
LAST-MODIFIED:20230128T073141Z
UID:23636-1674819000-1674822600@idre.ucla.edu
SUMMARY:Panel discussion on Advanced Research Computing (ARC) applications
DESCRIPTION:Venue: Virtual (via zoom) \nLink to video recording: https://youtu.be/LgLiY00bvHU \nThe Early Career Researchers group at the Institute for Digital Research and Education is pleased to announce a discussion on current applications and requirements for Advanced Research Computing (ARC). The panel will be led by the following IDRE fellows who are also early career researchers from various disciplines across the UCLA campus. \n\n  Nannan Gao – Ecology and Evolutionary Biology\n  Alp Karakoc – Civil and Environmental Engineering\n  Seyoon Ko – Computational Medicine\n  Victoria Rolandi – Mechanical and Aerospace Engineering\n  Olivia Sanderfoot – Ecology and Evolutionary Biology\n  Fangming Xie – Chemistry and Biochemistry\n\nAbout: Not long ago\, it was hard to imagine the scale at which Advanced Research Computing (ARC) at any university would drive innovation and enhance its research capabilities. ARC is increasingly important in research involving data\, computation\, communication\, and information sharing in most disciplines\, and infrastructure planning requires inputs from various segments across the university. However\, due to the fast pace of ever-changing technology\, the requirements of the ARC are constantly changing. It is a daunting exercise to keep track of the latest trends in research computing. This panel discussion on “Advanced Research Computing (ARC) applications” is to get feedback from the diverse community of early career researchers at UCLA\, who are at the forefront of technology in research.
URL:https://idre.ucla.edu/calendar-event/ecr-panel-discussion-on-advanced-research-computing
CATEGORIES:Conferences and Seminars,Meetings
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230106T113000
DTEND;TZID=America/Los_Angeles:20230106T123000
DTSTAMP:20260428T211113
CREATED:20221213T011535Z
LAST-MODIFIED:20230109T215851Z
UID:23508-1673004600-1673008200@idre.ucla.edu
SUMMARY:Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets
DESCRIPTION:Speaker: Seyoon Ko\, Ph.D.\nIDRE Fellow\nComputational Medicine\nUniversity of California Los Angeles \n\n\n\n\n\n\n  \n  \nLocation: Virtual \nRecording of the presentation: https://youtu.be/8YAyHAp9Pfc \nAbstract: Admixture estimation is crucial in ancestry inference and genomewide association studies (GWAS). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However\, these programs can be overwhelmed by the computational burdens imposed by the 10^5 to 10^6 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately\, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. This talk presents an unsupervised\, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. The simulated and real data examples show that this approach is scalable to modern biobank data sets. \n\n\n\n\n\n\nAbout the speaker: Dr. Ko is a Postdoctoral Scholar working with Dr. Ken Lange and Dr. Hua Zhou in the Department of Computational Medicine. Dr. Ko’s research interests include large-scale computational methods in biostatistics and bioinformatics using parallel and distributed computing. He earned a Ph.D. degree in Statistics from Seoul National University in South Korea\, as well as a M.S. degree in Computational Sciences and a B.S. degree in Physics\, Mathematical Sciences\, and Computational Science.\n \n\n\n\n\n\n\n 
URL:https://idre.ucla.edu/calendar-event/scale-data-set-01-06-2023
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221130T140000
DTEND;TZID=America/Los_Angeles:20221130T150000
DTSTAMP:20260428T211113
CREATED:20221114T213135Z
LAST-MODIFIED:20221201T211153Z
UID:23445-1669816800-1669820400@idre.ucla.edu
SUMMARY:Understanding scientific fields via network analysis and topic modeling
DESCRIPTION:  \nSpeaker: Harlin Lee\, Ph.D.\nIDRE Fellow\nMathematics Department\nUniversity of California Los Angeles \n  \nLocation: Virtual \nRecording: https://youtu.be/EkY_4gre9yU\n \n  \nAbstract: As scientific disciplines get larger and more complex\, it becomes impossible for an individual researcher to be familiar with the entire body of literature. This forces them to specialize in a sub-field\, and unfortunately\, such insulation can hinder the birth of ideas that arise from new connections\, eventually slowing down scientific progress. As such\, discovering fruitful interdisciplinary connections by analyzing scientific publications is an important problem in the science of science. This talk will present several past and ongoing projects towards answering that question using tools from network analysis and topic modeling: 1) a dynamic-embedding-based method for link prediction in a machine learning/AI semantic network\, 2) finding communities in cognitive science that study similar topics but do not cite each other or publish in the same venues\, and 3) developing theoretically grounded hypergraph embedding methods to capture surprising collaborations or missed opportunities. \nAbout the speaker: \nDr. Harlin Lee is a Hedrick Assistant Adjunct Professor at UCLA Mathematics. She received her Ph.D. in Electrical and Computer Engineering at Carnegie Mellon University in 2021. She also has an MS in Machine Learning from Carnegie Mellon University\, and a BS + MEng in Electrical Engineering and Computer Science from MIT. Her research is on learning from high-dimensional data supported on structures such as graphs (networks)\, low-dimensional subspace\, or sparsity\, motivated by applications in healthcare and social science. Dr. Lee’s lifelong vision is to use data theory to help everyone live physically\, mentally\, and socially healthier.
URL:https://idre.ucla.edu/calendar-event/harlin-lee-nov-30-2022
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220826T113000
DTEND;TZID=America/Los_Angeles:20220826T123000
DTSTAMP:20260428T211113
CREATED:20220808T235653Z
LAST-MODIFIED:20220901T235608Z
UID:23112-1661513400-1661517000@idre.ucla.edu
SUMMARY:Machine Learning of the Ocean Overturning Circulation
DESCRIPTION:  \nSpeaker: Aviv Solodoch\, Ph.D.\nIDRE Scholar\,\nAtmospheric and Oceanic Sciences\,\nUniversity of California Los Angeles \nLocation: Virtual (Click here for the recording)  \n  \nAbstract: The meridional overturning circulation (MOC) in the oceans is a fundamental circulation pattern whereby surface water cool and densify in polar regions\, and subsequently sink to great depths. These dense waters then spread horizontally at depth to cover virtually all deep ocean basins globally. The MOC has critical roles in the climate system\, including influencing global circulation patterns and heat fluxes\, and regulating the amount of anthropogenic heat and CO2 that is absorbed into the deep ocean\, buffering the advance of climate change. Therefore\, monitoring MOC variability and its interaction with climate change are of fundamental importance. In-situ monitoring of the MOC presents significant technological and logistical challenges due to the global extent of this circulation pattern. However\, some aspects of ocean circulation are now regularly measured via satellite remote sensing\, e.g.\, sea surface elevation and ocean bottom pressure. Therefore\, we develop a methodology to monitor MOC variability based on machine learning of satellite-measured ocean properties. We test this methodology within a data-constrained numerical simulation of the oceans\, i.e.\, using its output “satellite-observable’’ variables and MOC strength series as the ocean “truth’’. \nWe find that\, using a simple 1-layer feed-forward Neural Network (NN) with Bayesian regularization\, the MOC time-variability across most latitudes can be reconstructed with high skill. The reconstruction skill is higher than that of previously published dynamically based methods. To gain insight into the relations learned by the NN we use machine-learning interpretability techniques\, showing for example that most of the Southern Ocean MOC reconstruction skill is due to data from just a few key locations (mainly large seabed ridges)\, qualitatively consistent with fundamental physical theory. We further examine which satellite observables hold the most potential for MOC reconstruction. Finally\, we evaluate the robustness of the methodology and discuss a roadmap for implementing the method with real satellite data. \nAbout speaker: Aviv Solodoch obtained a BSc in Math and Physics from Tel Aviv University\, and a MSc in Physics from the Weizmann Institute of Science in Israel. He later completed a PhD in Atmospheric and Oceanic Sciences at UCLA\, where he is currently a postdoctoral researcher. During his MSc\, Aviv investigated air-sea interaction and heat exchange. During his PhD\, Aviv investigated processes causing instability\, offshore material exchange\, and vortex formation in oceanic currents\, using both numerical simulations and theory\, with a focus on currents which form part of the overturning circulation in the North Atlantic. Aviv also conducted observational research with UCLA Marine Operations\, studying coastal circulation dynamics in the Gulf of Mexico. He is presently studying the overturning circulation in the Southern Ocean\, as well as the dynamics of transport of material between the coastal and deep ocean regions.
URL:https://idre.ucla.edu/calendar-event/aviv-solodoc-idre-scholar
CATEGORIES:Conferences and Seminars,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220616T090000
DTEND;TZID=America/Los_Angeles:20220619T130000
DTSTAMP:20260428T211113
CREATED:20220615T155943Z
LAST-MODIFIED:20220615T160130Z
UID:23040-1655370000-1655643600@idre.ucla.edu
SUMMARY:Tigrinya Language Digital Initiatives Symposium
DESCRIPTION: June 16 – 19\, 2022\n \nTime: 9:00 am – 1:00 pm (PST)   \nPlease visit the Symposium Website for conference information.  \nOpening Keynote Address: Dr. Aida Habtezion\, Chief Medical Officer and Head of Worldwide Medical and Safety at Pfizer \nClosing Keynote Speaker: Ariam Weldeab\, Author\, Movie Producer and Director   \n  \nregister here.  \nor \nhttps://ucla.zoom.us/webinar/register/WN_3Xq_9Iz6StWR78RuHnC1NA \nConference will be virtual via Zoom webinar \nTigrinya Language Digital Initiatives Symposium highlight: \nThe mission of the Tigrinya Language Digital Initiatives Symposium is to bring Language\, Technology\, and Organizational talents together to create a professional and inclusive platform that increases Tigrinya’s footprint in the digital world. The symposium intends to enable Tigrinya speakers to be creators and beneficiaries of Language and Linguistics Technologies to empower and transform their communities through education\, research\, and development in their own Language. The 2022 symposium is the first step of many that will help us discover talents\, identify relevant projects\, and inspire collaboration among the experts towards a more significant\, inclusive\, equitable\, and more impactful undertaking to enable technological creativity and collaboration for the greater good of the Tigrinya speaking communities. \nFor any questions or further information\, please use the Contact Us link on the Symposium website. \nThe Tigrinya Language Digital Initiatives Symposium is sponsored by the UCLA IDRE (Institute for Digital Research and Education)\, ASC (African Studies Center) and\, the Stanford University African and Middle Eastern Program . \nInformation forwarded by the UCLA African Studies Center \nwww.international.ucla.edu/africa \n 
URL:https://idre.ucla.edu/calendar-event/tigrinya-language-digital-initiatives-symposium
CATEGORIES:Conferences and Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220527T120000
DTEND;TZID=America/Los_Angeles:20220527T130000
DTSTAMP:20260428T211113
CREATED:20220513T003636Z
LAST-MODIFIED:20220528T071335Z
UID:22965-1653652800-1653656400@idre.ucla.edu
SUMMARY:What is Causal Inference and Where is Data Science Going?
DESCRIPTION:  \n  \nSpeaker: Judea Pearl\nProfessor\nUCLA Computer Science Department\nUniversity of California Los Angeles \nDate and Time:May 27\, 2022 @12:00 PM (PST) \nPresentation slides: idre-may2022.pdf \nVideo recording: https://youtu.be/MNyI1Xkapxg \nAbstract: The availability of massive amounts of data coupled with an impressive performance of machine learning algorithms has turned data science into one of the most active research areas in academia. UCLA is no exception. The past few years\, however\, have uncovered basic limitations in the model-free direction that data science has taken. An increasing number of researchers have come to realize that statistical methodologies and the “black-box” data-fitting strategies used in machine learning are too opaque and brittle and must be enriched by a Causal Inference component to achieve their stated goal: Extract knowledge from data. Interest in Causal Inference has picked up momentum\, and it is now one of the hottest topics in data science*. \nThe purpose of this talk is to tell my colleagues at UCLA\, especially IDRE-minded researchers and students\, what Causal Inference is all about\, how it can be harnessed to solve practical data-scientific problems that cannot be solved by traditional methods\, and why it holds the key to the future of data science. \nAfter summarizing some glaring deficiencies of “data fitting” methods\, I will contrast them with “model-based” approaches and demonstrate how the latter can achieve a state of knowledge we can call “Deep Understanding”\, that is\, the capacity to answer questions of three types: predictions\, interventions\, and counterfactuals. \nI will further describe a computational model that facilitates reasoning at these three levels and demonstrate how features normally associated with “understanding” follow from this model. These include generating explanations\, generalizing across domains\, integrating data from several sources\, assigning credit and blame\, recovering from missing data\, and more. I will conclude by describing future research directions\, including automated scientific explorations and personalized decision-making. \n  \nBio sketch: Judea Pearl is Chancellor professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA\, where he conducts research in artificial intelligence\, human reasoning\, and the philosophy of science. He is the author of Heuristics (1983) Probabilistic Reasoning (1988) and Causality (2000\,2009) and a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science\, The Allen Newell Award from the Association for Computing Machinery\, the Benjamin Franklin Medal\, the Rumelhart Prize from the Cognitive Science Society\, the ACM Turing Award\, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect which brings Causal Inference to a general audience. \n  \n*Background material: \n\nhttps://ucla.in/3d2c2Fi\nhttps://ucla.in/3iEDRVo\nhttps://ucla.in/2HI2yyx\n\n  \n 
URL:https://idre.ucla.edu/calendar-event/causal-inference-and-data-science
CATEGORIES:Conferences and Seminars,Education and Training,Meetings,Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220225T113000
DTEND;TZID=America/Los_Angeles:20220225T123000
DTSTAMP:20260428T211113
CREATED:20220210T193439Z
LAST-MODIFIED:20220226T092741Z
UID:22725-1645788600-1645792200@idre.ucla.edu
SUMMARY:Creating a Comprehensive Lexical Resource for English Using Bayesian Deep Learning and Missing Data Methodology
DESCRIPTION:  \n \n  \nSpeaker: Bryor Snefjella\, Ph.D.\nIDRE Scholar\,\nPsychology Department\,\nUniversity of California Los Angeles \nVideo Recording: https://youtu.be/RTpW1-FtBGs \n  \nAbstract: Inquiry in the language sciences makes extensive use of open-source data sets. For example\, data sets of hand-annotations of words for properties such as their connotation and familiarity. Other common types of open-source resources include behavioural or neuroimageing recordings of responses to linguistic stimuli in controlled experiments\, or measurements taken from massive respositories of digitized natural language use. A challenge in the language sciences is extensive missing data in extant open-source data sets. Most data sets contain information on orders of magnitude fewer words than an average speaker knows\, and the words they do contain are non-randomly sampled and non-overlapping. A commonly proposed remedy to this missing data is to replace hand-annotation with machine learning. This is the approach taken by the English Lexicon Imputation Project\, the first comprehensive resource of word-level annotations created in cognitive science. In this talk I present the resource\, the Bayesian deep neural network used to create it\, and how missing data methodology was key to overcoming the limitations of prior literature on computational linguistic resource generation. The talk should be of interest to computational social scientists\, language scientists\, and those interested in deep-learning and missing data methods. \nAbout speaker: Bryor Snefjella is a postdoctoral researcher in the Psychology Department\, Cognitive Area\, mentored by Idan Blank\, Keith Holyoak\, and Hongjing Lu. Before moving to UCLA\, Bryor received a PhD in Cognitive Science of Language in McMaster University in Canada. His research on language use patterns in social media has received international media attention. Check him out on his personal website\, Twitter\, Linkedin\, and Research Gate.
URL:https://idre.ucla.edu/calendar-event/bryor-snefjella-feb25-22
CATEGORIES:Conferences and Seminars,Meetings
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220107T100000
DTEND;TZID=America/Los_Angeles:20220107T150000
DTSTAMP:20260428T211113
CREATED:20211211T002301Z
LAST-MODIFIED:20220208T020643Z
UID:22481-1641549600-1641567600@idre.ucla.edu
SUMMARY:Machine learning for Oceanic & Atmospheric Sciences
DESCRIPTION:IDRE ECR Group is excited to announce Machine learning for oceanic & atmospheric sciences workshop with the following details: \nTitle: Machine Learning for Oceanic & Atmospheric Sciences \nDate and Time: Friday\, January 7\, 2022 @10 AM (PST) \nRegistration: https://ucla.zoom.us/meeting/register/tJclde2opjsiGdU0sq31tMP5YUXd8FbvCbCm \nAbstract: Machine learning (ML) denotes a host of computational methods for inferring meaningful patterns in data. Advances over recent decades in ML methodologies\, computational power\, and dataset sizes have facilitated the rapid adaptation of these methods in practically every field of science and technology. ML allows learning directly from data to utilize hidden patterns in data where scientific theory is not fully developed or is too computationally intensive. ML has proved to be a highly useful tool in climate science\, where traditional pattern recognition is confounded by high system complexity\, multiscale interactions\, chaos\, and prohibitive datasets sizes. \nThe UCLA Machine Learning for Climate Workshop will host presentations from climate and ML scientists covering ML subjects of broad interest outside of climate as well as aspects that are especially of interest in climate science. Broad interest subjects will include discussions on choosing the right ML tools for the right task and tricks of the trade for common ML methodologies. Climate-related ML topics will consist of ML learning of spatio-temporal climate patterns\, ML constrained by physical principles\, and physical interpretability of learned patterns\, as well as short research presentations. \nAgenda: Friday\, January 7\, 2021\n \n\n\n\nTime (Pacific)\nPresentation title and speaker\n\n\n10:00-10:05\nWelcome and Introduction\n\n\n10:05-10:40\nInferring physics through self-supervised learning of climate data. Kaushik Srinivasan\, UCLA\n\n\n10:40-11:15\nExplainable AI for Climate Science: Applications and Techniques. Kirsten Mayer\, Colorado State University.\n\n\n11:15-11:30\n15-minute break\n\n\n11:30-12:05\nTips for Successful Training of Deep Neural Networks. Bryor Snefjella\, UCLA.\n\n\n12:05-12:40\nMachine learning for space weather prediction. Jacob Bortnik\, UCLA. \n\n\n12:40-13:40\nLunch break\n\n\n13:40-14:10\nLearning Stochastic Closures Using Ensemble Kalman Inversion. Jinlong Wu\, Caltech.\n\n\n14:10-14:45\nGenerative Modeling for Climate Science. Aditya Grover\, UCLA.\n\n\n14:45-15:00\nQ&A with speakers\n\n\n\n 
URL:https://idre.ucla.edu/calendar-event/machine-learning-for-aos
CATEGORIES:Classes and Workshops,Conferences and Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211119T113000
DTEND;TZID=America/Los_Angeles:20211119T123000
DTSTAMP:20260428T211113
CREATED:20220208T230110Z
LAST-MODIFIED:20220210T194322Z
UID:22719-1637321400-1637325000@idre.ucla.edu
SUMMARY:Building a patient motion model for Radiotherapy
DESCRIPTION:  \n  \nSpeaker: Ricky Savjani\, Ph.D.\nIDRE Scholar\,\nDepartment of Radiation Oncology\,\nUniversity of California Los Angeles \n  \n  \nAbstract: Several technological advances in radiotherapy have enabled the use of focused radiation to treat solid tumors within the thoracic cavity. Stereotactic Body Radiation Therapy (SBRT) offers a way to treat patients with high doses of radiation with just a few (three to five) treatments entirely non-invasively\, providing excellent tumor control for both early stage non-small cell lung cancer and metastatic disease. However\, respiratory motion causes the tumor and surrounding organs at risk to move. This movement is particularly concerning for thoracic SBRT\, as radiation pneumonitis stems largely from an inability to visualize the tumor and lungs during treatment and thus requires larger margins. Respiratory motion has been characterized as irregular (can differ from breath to breath and minute to minute) and can induce motion of 5 cm or more\, particularly at the diaphragm. Overall\, there is a pressing need to measure and monitor thoracic motion while cancer patients are being treated with radiotherapy. \nOur group at UCLA has previously created a 5DCT model to better represent a patient’s tumor motion. Patients are allowed to breathe freely while they undergo 25 fast\, low-dose helical CT scans during simulation prior to radiation therapy. An over-determined linear model is fit to find the position of any voxel based on the initial position and the current tidal volume and airflow. The corresponding imaged motion of the tumor and thoracic cavity can more accurately be measured with 5DCT compared to traditional 4DCT models. \nWe now have a large cohort of data (n = 91 patients) to begin fitting an inter-patient motion model. Working with Varian\, A Siemens Healthineers Company\, we have applied auto segmentation models to each of the 25 scans for each patient. We have trained a Conditional Variational Autoencoder (cVAE) model to generate deformations between any two pairs CT volumes. The embedded space can be visualized in 3D\, and we are now working on ways to drive the amount of inhalation/exhalation using an external surrogate (a belt the patient wears while being treated). In this way\, we envision generating 3D volumetric representations at the treatment console while patients are being treated with radiotherapy in real-time. \nAbout the speaker: Ricky Savjani is a resident physician in the Department of Radiation Oncology at UCLA. As part of his training\, he is conducting research through the American Board of Radiology Holman Research Pathway\, in addition to seeing patients clinically to become a radiation oncologist. Prior to joining UCLA\, Ricky received BS degrees in Electrical Engineering/Computer Science and Brain and Cognitive Sciences at MIT. He then pursued an MD/Ph.D. at Texas A&M College of Medicine\, where his research focused on structural and functional imaging of the human brain. Ricky loves medical imaging and hopes to continue to use advanced imaging approaches to deliver safer and better radiation to patients. \nLocation: Zoom (RSVP here for the link)
URL:https://idre.ucla.edu/calendar-event/ricky-savjani-idre-scholar
LOCATION:Zoom
CATEGORIES:Conferences and Seminars,Education and Training
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211029T113000
DTEND;TZID=America/Los_Angeles:20211029T123000
DTSTAMP:20260428T211113
CREATED:20220208T215939Z
LAST-MODIFIED:20220210T195657Z
UID:22717-1635507000-1635510600@idre.ucla.edu
SUMMARY:Panel discussion on Interdisciplinary Research and Collaboration
DESCRIPTION:Video Link: https://youtu.be/RVdWM1SbB4s \nThe IDRE Early Career Researchers group is excited to restart its monthly meetings. This first meeting will introduce five IDRE scholars selected from a large pool of applicants\, followed by a panel discussion on interdisciplinary research and collaboration. The following eminent UCLA researchers will be the panelists: \n\nKaren McKinnon\, Institute of the Environment and Sustainability\, Department of Statistics\nJacob Foster\, Department of Sociology\nMiriam Marlier\, Environmental Health Sciences\nJim McWilliams\, Department of Atmospheric and Oceanic Sciences\n\nThe panel will explore the benefits and roles of interdisciplinary research and collaborations in academia. The panelists will discuss the barriers that may discourage researchers from pursuing multidisciplinary research opportunities and dive into whether the current academic training sufficiently prepares us for multidisciplinary collaboration and how to rise to the challenges of such partnerships. Each panelist has vast experience working on collaborative projects and creating multidisciplinary teams. The audience will also have a chance to ask questions. \nPlease plan to join the event and benefit from their insights.* \n*The event was virtual\, and you can view using the link: https://youtu.be/RVdWM1SbB4s
URL:https://idre.ucla.edu/calendar-event/ecr-panel-oct29-2021
LOCATION:Zoom
CATEGORIES:Conferences and Seminars,Meetings
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