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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:20240221T140000
DTEND;TZID=America/Los_Angeles:20240221T153000
DTSTAMP:20260603T110740
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SUMMARY:The Synergy between Machine Learning and the Natural Sciences | Max Welling
DESCRIPTION:Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC\, Belief Propagation\, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed\, with new tools developed in the ML community impacting physics\, chemistry and biology. Examples include faster DFT\, Force-Field accelerated MD simulations\, PDE Neural Surrogate models\, generating druglike molecules\, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields\, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks. \nBio: Prof. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies\, professor at UC Irvine\, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton\, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. \nMax Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015\, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).
URL:https://datascience.ucsd.edu/event/distinguished-colloquium-max-welling/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
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DTSTART;TZID=America/Los_Angeles:20240214T140000
DTEND;TZID=America/Los_Angeles:20240214T153000
DTSTAMP:20260603T110740
CREATED:20240201T193829Z
LAST-MODIFIED:20240220T172238Z
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SUMMARY:Enabling Performant and Trustworthy Learning-enabled CPS-IoT Systems | Mani Srivastava
DESCRIPTION:Abstract: “The previously discrete technologies of IoT and AI have now entered a tight virtuous embrace. IoT allows sensing and actuation in our physical\, social\, and urban spaces with unimaginable ubiquity. AI allows sophisticated inferences and decisions to be made algorithmically using deep neural networks\, even from unstructured and high- dimensional data\, with uncanny performance. Together they seek to perform sophisticated perception-cognition-communication-action loops in diverse applications. However\, designers of learning-enabled IoT systems face the challenge of extremely resource-constrained edge platforms operating in uncertain environments while assuring performance and trustworthiness. Moreover\, in many applications\, the systems go beyond taking actions based on rich inferences about the world state to perform long-term reasoning about complex events and obey the underlying physics\, rules\, and constraints. Based on our experience in designing such systems in applications including mHealth\, ocean animal health\, agriculture robotics\, and military\, This talk explores meeting these challenges through a combination of (i) neurosymbolic architectures that allow the incorporation of physics awareness and human knowledge while enhancing user trust\, (ii) automatic platform-aware architecture search and code generation\, and (iii) techniques to efficiently adapt to the deployment environment.”            \n \nBio: “Mani Srivastava is Distinguished Professor and Vice Chair at UCLA’s ECE Department with a joint appointment in the CS Department. His research is broadly in human-cyber-physical and IoT systems that are learning-enabled\, resource- constrained\, and trustworthy. It spans problems across the entire spectrum of applications\, architectures\, algorithms\, and technologies in the context of systems and applications for mHealth\, sustainable buildings\, smart environments\, etc. He is a Fellow of the ACM and the IEEE.”    
URL:https://datascience.ucsd.edu/event/special-seminar-mani-srivastava/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20240213T023000
DTEND;TZID=America/Los_Angeles:20240213T160000
DTSTAMP:20260603T110740
CREATED:20240207T223735Z
LAST-MODIFIED:20240220T172208Z
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SUMMARY:Principled Approaches for Trustworthy Algorithms\, Statistics\, and Machine Learning | Gautam Kamath
DESCRIPTION:Abstract: Despite impressive recent advances\, machine learning models exhibit a number of critical deficiencies. They are prone to leaking sensitive information about their training data. They remain alarmingly brittle to attacks by malicious parties. Troublingly\, these issues stem from more fundamental statistical vulnerabilities\, which remain unresolved even decades later\, highlighting significant gaps in our understanding of how to deal with these important considerations. As long as these problems remain\, our models will not be appropriate for use beyond deployment in toy settings. In this talk\, I will discuss recent advances on a number of these problems\, which give key new algorithmic insights into how to address these considerations\, and enable real-world deployments that were previously thought infeasible. In a first vignette\, we will explore how to guarantee individual privacy in machine learning models\, with a particular focus on large language models and the important role played by public data in the training pipeline. In a second vignette\, we focus on how to robustly perform mean estimation\, giving the first efficient and accurate algorithms for multivariate settings. We will go on to discuss connections to robustness against data poisoning attacks\, robust exploratory data analysis\, and surprising conceptual and technical connections with privacy. \nBio: Gautam Kamath is an Assistant Professor at the University of Waterloo\, and a Faculty Member and Canada CIFAR AI Chair at the Vector Institute for Artificial Intelligence. His research interests are in trustworthy algorithms\, statistics\, and machine learning\, particularly focusing on considerations like data privacy and robustness. He has a B.S. from Cornell University and a Ph.D. from MIT. He is the recipient of the 2023 Golden Jubilee Research Excellence Award\, recognizing him as the most outstanding junior researcher in the University of Waterloo’s Faculty of Math. Beyond research\, he is celebrated for his teaching. His course on differential privacy is the most popular resource for learning the topic\, with his lecture videos having over 100\,000 views. He has also given invited tutorials on the topic in multiple different countries. He is further well known for his passion and commitment to service and improving the community. Besides organizing and chairing several workshops and conferences\, he is an Editor-in-Chief of Transactions on Machine Learning Research\, and on the Executive Committee of the Learning Theory Alliance.
URL:https://datascience.ucsd.edu/event/principled-approaches-for-trustworthy-algorithms-statistics-and-machine-learning-gautam-kamath/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20240212T140000
DTEND;TZID=America/Los_Angeles:20240212T153000
DTSTAMP:20260603T110740
CREATED:20240126T182854Z
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SUMMARY:Integrating Longitudinal Multimodal Data To Realize Precision Medicine | Samantha Piekos
DESCRIPTION:Abstract: The interplay of biology\, environment\, and lifestyle direct the development and progression of complex diseases and other health outcomes. Therefore\, integration of longitudinal multimodal data is needed to understand the mechanisms underpinning major molecular transitions. Previously during my doctoral work at Stanford\, I integrated multiomics data to elucidate the epigenetic mechanism of human surface ectoderm differentiation. I also built a pipeline to investigate the role of polymorphism\, particularly non-coding genetic variants\, in complex diseases. To address the common pain point of data silos limiting the interpretation of multimodal data integration\, I formed a collaboration with Google Data Commons to build a free\, open-source biomedical knowledge graph with a common schema and API. Currently it is composed of approximately 130 million nodes and 1.7 trillion triples (node-edge-node) from 22 publicly available biomedical datasets. Knowledge graphs are a key tool for hypothesis generation\, data interpretation\, and dimensionality reduction required for systems medicine research. Upon starting my postdoctoral work at the Institute for Systems Biology\, I identified pregnancy as an excellent model system for prototyping precision medicine approaches. I used electronic healthcare records (EHR) from Providence St. Joseph Healthcare to investigate the impact of COVID-19 maternal infection and vaccination on maternal-fetal outcomes. In addition\, I integrated multiomics placental data to investigate molecular network changes (interomics and intraomics) in common obstetric disorders. In a follow-up study (enrollment complete) we have longitudinal deep-phenotyping data of 435 people throughout pregnancy 80 of which have pregnancy complications. This includes multiomics\, survey\, EHR\, and air quality data collected from first prenatal visit through delivery. My lab will use this data to define major molecular transition states throughout pregnancy. I will also investigate the disease mechanisms of common obstetric disorders including identifying for an individual the earliest possible point of deviation from a healthy trajectory. This interdisciplinary approach will identify potential drug targets\, biomarker panels\, and individualized clinical interventions. \n  \nBio: Samantha completed her PhD in Stem Cell Biology and Regenerative Medicine with a PhD minor in Biomedical Informatics at Stanford University under the advisement of Dr. Anthony Oro. Using a multiomics approach\, Samantha demonstrated how transcription factors direct keratinocyte differentiation by changing the epigenetic landscape\, including chromatin looping\, thereby effecting the cell transcriptional program. Samantha has also been collaborating with Google since June 2019 to build Biomedical Data Commons\, a knowledge graph that integrates biomedical data from a wide array of sources into a single searchable database thereby increasing data accessibility. Upon completion of her PhD in 2020\, began her postdoctoral fellowship at the Institute for Systems Biology under the advisement of Drs. Lee Hood and Nathan Price. Using electronic healthcare records (EHR)\, she has provided insight into the impact of maternal COVID-19 and vaccination on maternal-fetal outcomes. In addition to her EHR research\, Samantha is using multidimensional omics placental data to understand the molecular mechanism of common obstetric disorders. Upon transitioning to Assistant Professor\, she intends to perform multimodal data integration of longitudinal deep-phenotyping data to evaluate changes in molecular networks in complex diseases.
URL:https://datascience.ucsd.edu/event/special-seminar-samantha-piekos/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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