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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:20260303T140000
DTEND;TZID=America/Los_Angeles:20260303T150000
DTSTAMP:20260601T155126
CREATED:20260206T211743Z
LAST-MODIFIED:20260226T195702Z
UID:10000542-1772546400-1772550000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Jessilyn Dunn - The Digital Physiome: Wearables for Disease Detection and Monitoring
DESCRIPTION:Speaker: Jessilyn Dunn\nDate & Time: Monday March 2nd\, 2:00pm\nLocation: HDSI Multipurpose Room 123\nZoom Link: http://bit.ly/HDSI-Seminars\n\n\n\nTitle:  The Digital Physiome: Wearables for Disease Detection and Monitoring\n\n\nAbstract: Digital health is rapidly expanding due to surging healthcare costs\, deteriorating health outcomes\, and the growing prevalence and accessibility of mobile health and wearable technologies. Recent technological advancements make it possible to closely and continuously monitor individuals using multiple measurement modalities in real time. We are collecting and integrating such wearables data with clinical information to gain a more precise understanding of health and disease and develop actionable\, predictive health models for improving outcomes. We are simultaneously developing open source data science and machine learning tools for the digital health community\, including the Digital Biomarker Discovery Pipeline (DBDP)\, to facilitate the use of mobile device data in healthcare.\n\n\n\n\nSpeaker Bio: Jessilyn Dunn\, PhD\, is an Associate Professor of Biomedical Engineering and Biostatistics & Bioinformatics at Duke University. She directs the BIG IDEAs Lab\, which is focused on digital health innovation\, wearable sensors\, and the development and validation of AI-driven digital biomarkers. Dr. Dunn is the Principal Investigator of research initiatives funded by the NIH\, NSF\, and FDA which are developing digital biomarkers of conditions ranging from pre- and type 2 diabetes to influenza-like illness to Opioid Use Disorder. She sits on the Google Consumer Health Advisory Panel and is a recipient of the NSF CAREER Award and the IEEE EMBS Early Career Achievement Award for her leadership and innovation across engineering and medicine.
URL:https://datascience.ucsd.edu/event/jessilyn-dunn-seminar/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T140000
DTEND;TZID=America/Los_Angeles:20260302T150000
DTSTAMP:20260601T155126
CREATED:20260206T211446Z
LAST-MODIFIED:20260219T170241Z
UID:10000541-1772460000-1772463600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Emily Aiken - Empirically understanding the relative value of prediction in allocation
DESCRIPTION:Speaker: Emily Aiken\nDate & Time: Monday March 2nd\, 2:00pm\nLocation: HDSI Multipurpose Room 123\n  \nTalk Title:\nEmpirically understanding the relative value of prediction in allocation \nAbstract:\nPublic institutions increasingly use prediction to allocate scarce resources. From a design perspective\, better predictions compete with other investments\, such as expanding capacity or improving treatment quality. Here\, the big question is not how to solve a specific allocation problem\, but rather which problem to solve. In this talk\, I will introduce an empirical toolkit\nto help planners form principled answers to this question and quantify the bottom-line welfare impact of investments in prediction versus other policy levers such as expanding capacity and improving treatment quality. I will then apply the framework to two real-world case studies on predictive methods for allocating humanitarian aid in Bangladesh and Ethiopia. Related papers: this and this. \nSpeaker bio: \nEmily is an assistant professor jointly appointed in HDSI and the School of Global Policy and Strategy. Her research interests are at the intersection of data science and development economics\, with a focus on analyzing large digital traces to inform the design of social protection and humanitarian aid programs. Her work centers on the implementation and evaluation of data-driven allocation systems for aid delivery in low-income countries. \nPrior to joining UC San Diego\, Emily was a postdoctoral scholar at Carnegie Mellon University Africa. She received her PhD from the UC Berkeley School of Information\, and holds an MS in computer science from UC Berkeley and a BA in computer science from Harvard University.
URL:https://datascience.ucsd.edu/event/emily-aiken-seminar/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260113T140000
DTEND;TZID=America/Los_Angeles:20260113T150000
DTSTAMP:20260601T155126
CREATED:20260107T193320Z
LAST-MODIFIED:20260107T225249Z
UID:10000535-1768312800-1768316400@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series - Ann Kennedy - Latent state inference from neural dynamics  and behavior
DESCRIPTION:Talk Time: Mon Jan 12th\, 2026 | 2:00pm \nLocation: Data Science Building 1st Floor\, Room 123 \nTalk Abstract\nAs we interact with the world around us\, we experience a constant stream of sensory inputs\, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with our internal motivations and drives to produce behavior that is flexible and adaptive. How can we uncover evidence of this integration from observed neural activity? In this talk\, I will present our lab’s recent work inferring the structure and dynamics of latent motivational states from the observed actions and neural activity of freely behaving mice. We draw on classical normative models of animal behavior from ethology\, showing how these models can help us analyze and interpret experimental data to understand how the circuit architecture of the brain gives rise to the algorithms of survival. \nSpeaker Biography\nAnn Kennedy is a theoretical neuroscientist interested in understanding how the structure of the nervous system gives rise to its function. Originally from Virginia\, they studied Biomedical Engineering at Johns Hopkins and Neuroscience at Columbia University\, where they earned my PhD in the lab of Larry Abbott in the Center for Theoretical Neuroscience. Dr Kennedy pursued postdoctoral training in the lab of David Anderson at Caltech\, and opened their lab at Northwestern University in 2020\, moving to Scripps Research in 2024.\nThe Kennedy Lab studies the underlying neuroscience and brain structures that give rise to fundamental behaviors related to fear\, survival and social interactions. By better understanding the neural activities that guide our decision-making and behavior\, Dr. Kennedy’s work aims to reveal insights about the guiding principles of behavior\, as well as what happens in cases of dysfunctions—for example\, social dysfunction or excessive fear and anxiety.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-ann-kennedy/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250929T140000
DTEND;TZID=America/Los_Angeles:20250929T153000
DTSTAMP:20260601T155126
CREATED:20250918T200519Z
LAST-MODIFIED:20250918T205524Z
UID:10000523-1759154400-1759159800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Regina Liu - Fusion Learning: Fusing Inferences from Diverse Data Sources
DESCRIPTION:HDSI will be hosting its first Seminar Series speaker of the academic year at the end of this month. Regina Liu (Rutgers University) will be giving a talk Monday Sept 29th at 2pm in the HDSI Multipurpose room\, 1st floor Room 123. \n\nSpeaker: Regina Liu\nDate & Time: Monday Sept 29th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\nTalk Title: Fusion Learning: Fusing Inferences from Diverse Data Sources\n\nAbstract: \nAdvanced data acquisition technology has greatly increased the accessibility of complex inferences\, based on summary statistics or sample data\, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to yield a more effective overall. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study\, thus named i-Fusion?\n\nWe present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD)\, developed by combining data depth\, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions\, whose contours of level sets shrink toward the true parameter value\, and thus an all-encompassing inferential tool. The approach is efficient\, general and robust\, and readily applies to heterogeneous studies covering a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance and a zero-event studies in clinical trials with non- estimable parameters. \n\nKey words: confidence distribution\, data depth\, fusion learning\, heterogeneous studies\n\nSpeaker Bio:\nRegina Liu is Distinguished Professor\, Rutgers University. Her research areas include data depth\, resampling\, nonparametric statistics\, confidence distribution\, and fusion learning. Aside from theoretical and methodological research\, she has long collaborated with the FAA on aviation safety research projects on process control\, text mining and risk management. \nShe is an elected fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). She is the recipient of 2021 Noether Distinguished Scholar Award (ASA)\, 2024 Elizabeth Scott Award (Committee of Presidents of Statistical Societies (COPSS))\, and the IMS 2025 Neyman Award & Lecture. She has served as Co-Editor for the Journal of the American Statistical Association and as Associate Editor for several journals. She was elected President of the Institute of Mathematical Statistics (IMS)\, 2020-2021.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-reginia-liu-fusion-learning-fusing-inferences-from-diverse-data-sources/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250423T130000
DTEND;TZID=America/Los_Angeles:20250423T143000
DTSTAMP:20260601T155126
CREATED:20250404T162241Z
LAST-MODIFIED:20250404T162241Z
UID:10000517-1745413200-1745418600@datascience.ucsd.edu
SUMMARY:LIVED EXPERIENCE RESEARCH SUMMIT
DESCRIPTION:Location: HDSI MPR \nTo watch via zoom please contact: hdsiassistant@ucsd.edu \nFormerly Incarcerated professor speaking on his lived experience in research. Researchers from\nSmarr Lab and MOSAIC lab. \nNoel Vest\, PhD\, is an Assistant Professor at the Boston University School of Public Health. His research interests include mental health\, substance use disorders\, and addiction recovery. As a formerly incarcerated scholar and a person in long-term recovery\, Dr. Vest is an advocate for social justice issues and public policy concerning substance use disorder recovery and prison reentry. He completed his PhD in Experimental Psychology from Washington State University and did his postdoctoral fellowship at Stanford University. \n 
URL:https://datascience.ucsd.edu/event/lived-experience-research-summit/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2025/04/TUS_HS_HDSI_Collaboration_V4_Flyer-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250327T140000
DTEND;TZID=America/Los_Angeles:20250327T150000
DTSTAMP:20260601T155126
CREATED:20250325T192334Z
LAST-MODIFIED:20250325T192334Z
UID:10000514-1743084000-1743087600@datascience.ucsd.edu
SUMMARY:Special TILOS Seminar: Claire Boyer | Single location regression and attention-based models
DESCRIPTION:Talk Information \nSpeaker: Claire Boyer (Université Paris-Saclay) \nDate & Time: Thursday\, March 27 @ 2pm PDT \nVenue: HDSI 123 \nTitle: Single location regression and attention-based models \nAbstract: Attention-based models\, such as Transformer\, excel across various tasks but lack a comprehensive theoretical understanding\, especially regarding token-wise sparsity and internal linear representations. To address this gap\, we introduce the single-location regression task\, where only one token in a sequence determines the output\, and its position is a latent random variable\, retrievable via a linear projection of the input. To solve this task\, we propose a dedicated predictor\, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties\, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular\, despite the non-convex nature of the problem\, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
URL:https://datascience.ucsd.edu/event/special-tilos-seminar-claire-boyer-single-location-regression-and-attention-based-models/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241015T123000
DTEND;TZID=America/Los_Angeles:20241015T140000
DTSTAMP:20260601T155126
CREATED:20241008T002133Z
LAST-MODIFIED:20241008T003755Z
UID:10000500-1728995400-1729000800@datascience.ucsd.edu
SUMMARY:MathWorks Technical Seminar: AI & Machine Learning in Real-World Systems
DESCRIPTION:Beyond their use in traditional data analytics problems\, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles\, airplanes\, and even industrial production lines) are designed\, tested and fabricated. When building these systems\, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate AI models into the component and system level simulation stages of the design process\, and how they can help deploy the resulting control algorithms into the real-world systems. We will use an example where deep-learning and machine learning can be used to create a Virtual Sensor algorithm. Join us for a complementary seminar to see how this is done! Highlights include: \n\nDesigning and training machine learning components with Statistics and Machine Learning Toolbox\nDesigning and training deep learning components with Deep Learning Toolbox\nImporting trained TensorFlow models into MATLAB\nIntegrating machine learning and deep learning models into Simulink for system-level simulation\nGenerating library-free C code and performing PIL tests\nLSTM Model Compression using projection\n\nThis is the first in a 3 part seminar series in partnership with HDSI\, JSOE\, and SIO for this academic year. \nPlease note that this is open to all faculty\, staff\, and students (all grade & majors). \nRegister Here \n*Lunch is included in registration.
URL:https://datascience.ucsd.edu/event/mathworks-technical-seminar-ai-machine-learning-in-real-world-systems/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/10/10.2024-Mathworks-UCSD-Technical-Seminar-Series.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240404T080000
DTEND;TZID=America/Los_Angeles:20240405T120000
DTSTAMP:20260601T155126
CREATED:20240226T234322Z
LAST-MODIFIED:20240313T192254Z
UID:10000418-1712217600-1712318400@datascience.ucsd.edu
SUMMARY:Causality Workshop
DESCRIPTION:
URL:https://www.eventbrite.com/e/ucsd-hdsi-causality-workshop-tickets-817326594847?aff=oddtdtcreator
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Workshops
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI_Causality_Wrkshp_Eventbrite.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240402T140000
DTEND;TZID=America/Los_Angeles:20240402T153000
DTSTAMP:20260601T155126
CREATED:20240313T191528Z
LAST-MODIFIED:20240313T191528Z
UID:10000459-1712066400-1712071800@datascience.ucsd.edu
SUMMARY:Special Seminar | Xuhai Xu
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/special-seminar-xuhai-xu/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240401T140000
DTEND;TZID=America/Los_Angeles:20240401T153000
DTSTAMP:20260601T155126
CREATED:20240304T172031Z
LAST-MODIFIED:20240329T000153Z
UID:10000453-1711980000-1711985400@datascience.ucsd.edu
SUMMARY:"Instance-Optimization: Rethinking Database Design for the Next 1000X" | Jialin Ding
DESCRIPTION:Abstract: “Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However\, as a result of their generality\, databases often achieve adequate performance for the average use case but do not achieve the best performance for any individual use case. In this talk\, I will describe my work on designing databases that use machine learning and optimization techniques to automatically achieve performance much closer to the optimal for each individual use case. In particular\, I will present my work on instance-optimized database storage layouts\, in which the co-design of data structures and optimization policies improves query performance in analytic databases by orders of magnitude. I will highlight how these instance-optimized data layouts address various challenges posed by real-world database workloads and how I implemented and deployed them in production within Amazon Redshift\, a widely-used commercial database system.” \nBio: “Jialin Ding is an Applied Scientist at AWS. Before that\, he received his PhD in computer science from MIT\, advised by Tim Kraska. He works broadly on applying machine learning and optimization techniques to improve data management systems\, with a focus on building databases that automatically self-optimize to achieve high performance for any specific application. His work has appeared in top conferences such as SIGMOD\, VLDB\, and CIDR\, and has been recognized by a Meta Research PhD Fellowship. To learn more about Jialin’s work\, please visit https://jialinding.github.io/.”
URL:https://datascience.ucsd.edu/event/special-seminar-jialin-ding/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240328T140000
DTEND;TZID=America/Los_Angeles:20240328T153000
DTSTAMP:20260601T155126
CREATED:20240304T171827Z
LAST-MODIFIED:20240323T082150Z
UID:10000452-1711634400-1711639800@datascience.ucsd.edu
SUMMARY:The Emergence of Reproducibility and Generalizability in Diffusion Models | Qing Qu
DESCRIPTION:Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as “consistent model reproducibility”: given the same starting noise input and a deterministic sampler\, different diffusion models often yield remarkably similar outputs while they generate new samples. We demonstrate this phenomenon through comprehensive experiments and theoretical studies\, implying that different diffusion models consistently reach the same data distribution and scoring function regardless of frameworks\, model architectures\, or training procedures. More strikingly\, our further investigation implies that diffusion models are learning distinct distributions affected by the training data size and model capacity\, so that the model reproducibility manifests in two distinct training regimes with phase transition: (i) “memorization regime”\, where the diffusion model overfits to the training data distribution\, and (ii) “generalization regime”\, where the model learns the underlying data distribution and generate new samples with finite training data. Finally\, our results have strong practical implications regarding training efficiency\, model privacy\, and controllable generation of diffusion models\, and our work raises numerous intriguing theoretical questions for future investigation. \nBio: “Qing Qu is an assistant professor in EECS department at the University of Michigan. Prior to that\, he was a Moore-Sloan data science fellow at Center for Data Science\, New York University\, from 2018 to 2020. He received his Ph.D from Columbia University in Electrical Engineering in Oct. 2018. He received his B.Eng. from Tsinghua University in Jul. 2011\, and a M.Sc.from the Johns Hopkins University in Dec. 2012\, both in Electrical and Computer Engineering. His research interest lies at the intersection of foundation of data science\, machine learning\, numerical optimization\, and signal/image processing\, with focus on developing efficient nonconvex methods and global optimality guarantees for solving representation learning and nonlinear inverse problems in engineering and imaging sciences.\nHe is the recipient of Best Student Paper Award at SPARS’15\, and the recipient of Microsoft PhD Fellowship in machine learning in 2016\, and best paper awards in NeurIPS Diffusion Model Workshop in 2023. He received the NSF Career Award in 2022\, and Amazon Research Award (AWS AI) in 2023. He is the program chair of the new Conference on Parsimony & Learning.”
URL:https://datascience.ucsd.edu/event/special-seminar-qing-qu/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240327T140000
DTEND;TZID=America/Los_Angeles:20240327T153000
DTSTAMP:20260601T155126
CREATED:20240313T191359Z
LAST-MODIFIED:20240323T081955Z
UID:10000458-1711548000-1711553400@datascience.ucsd.edu
SUMMARY:Towards a Machine Capable of Learning Everything | Hao Liu
DESCRIPTION:Abstract: Large generative models such as ChatGPT have led to amazing results and revolutionized artificial intelligence. In this talk\, I will discuss my research on advancing the foundation of these models\, centered around addressing the architectural bottlenecks of learning from everything. First\, I will describe our efforts to remove context size limitations of the transformer architecture. Our new model architecture and training method allow for nearly infinitely large context sizes without approximations. Our proposed technique has been used for building state-of-the-art open-source and proprietary models. I will then discuss the applications of large context in world model learning and in reinforcement learning\, including Large World Model\, the world’s first multimodal model of million-length scale\, and the required training methodologies. Next\, I will introduce my research on unsupervised exploration that pioneered learning beyond existing knowledge\, allowing unsupervised pretrained models to outperform human experts in gameplay and paving the road for learning beyond imitating existing knowledge. Finally\, I will envision the modeling and training paradigms for the next generation of large generative models we should build\, focusing on advances in neural net architecture\, efficient scaling\, large context reasoning\, and discovery.” \nBio: Hao Liu is a final-year Ph.D. candidate in the Department of Electrical Engineering and Computer Sciences at UC Berkeley\, where he is advised by Pieter Abbeel. During his PhD\, he has also spent two years part-time at Google Brain and DeepMind. His research interests focus on the foundations of generative models\, including machine learning and neural networks\, with the goal of developing computationally scalable solutions for generalization. He recently developed Large World Model (LWM) and architectural advances (BlockwiseTransformers\, and RingAttention) for scaling transformers. Earlier\, he pioneered general and scalable unsupervised exploration (APT and APS). His work on million-length contexts has been influential at Google\, Meta\, and the broader industry. Several of his papers have been presented as spotlight and oral presentations at top-tier machine learning conferences\, and have also been featured in popular media\, including MarkTechPost\, Business Insider\, and ZDNet.
URL:https://datascience.ucsd.edu/event/special-seminar-hao-liu/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240326T140000
DTEND;TZID=America/Los_Angeles:20240326T153000
DTSTAMP:20260601T155126
CREATED:20240304T171618Z
LAST-MODIFIED:20240326T030740Z
UID:10000451-1711461600-1711467000@datascience.ucsd.edu
SUMMARY:Making machine learning predictably reliable | Andrew Ilyas
DESCRIPTION:Abstract: “Despite ML models’ impressive performance\, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk\, I overview my work on making ML “predictably reliable”—enabling developers to know when their models will work\, when they will fail\, and why. \nTo begin\, we use a case study of adversarial inputs to show that human intuition can be a poor predictor of how ML models operate. Motivated by this\, we present a line of work that aims to develop a precise understanding of the ML pipeline\, combining statistical tools with large-scale experiments to characterize the role of each individual design choice: from how to collect data\, to what dataset to train on\, to what learning algorithm to use.” \n\nBio “Andrew Ilyas is a PhD student in Computer Science at MIT\, where he is advised by Aleksander Madry and Constantinos Daskalakis. His research aims to improve the reliability and predictability of machine learning systems. He was previously supported by an Open Philanthropy AI Fellowship.”
URL:https://datascience.ucsd.edu/event/special-seminar-andrew-ilyas/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240322T140000
DTEND;TZID=America/Los_Angeles:20240322T153000
DTSTAMP:20260601T155126
CREATED:20240313T190501Z
LAST-MODIFIED:20240321T174552Z
UID:10000457-1711116000-1711121400@datascience.ucsd.edu
SUMMARY:Efficient Deep Learning with Sparsity: Algorithms\, Systems\, and Applications | Zhijian Liu
DESCRIPTION:Abstract: Deep learning is used across a broad spectrum of applications. However\, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the demand side\, the computational costs of deep learning models have surged dramatically\, driven by ever-larger input and model sizes. On the supply side\, as Moore’s Law slows down\, hardware no longer delivers increasing performance within the same power budget. \nIn this talk\, I will discuss my research efforts to bridge this demand-supply gap through the lens of sparsity. I will begin with my research on input sparsity. First\, I will introduce algorithms that systematically eliminate the least important patches/tokens from dense input data\, such as images\, enabling up to 60% sparsity without any loss in accuracy. Then\, I will present the system library that we have developed to effectively translate the theoretical savings from sparsity to practical speedups on hardware. Our system is up to 3 times faster than the leading industry solution from NVIDIA. Following this\, I will touch on my research on model sparsity\, highlighting a family of automated\, hardware-aware model compression frameworks that surpass manual solutions in accuracy and reduce the design cycle from weeks of human efforts to mere hours of GPU computation. Finally\, I will demonstrate the use of sparsity to accelerate a wide range of computation-intensive AI applications\, such as autonomous driving\, language modeling\, and high-energy physics. I will conclude this talk with my vision towards building more efficient and accessible AI. \nBio: Zhijian Liu is a Ph.D. candidate at MIT\, advised by Song Han. His research focuses on efficient machine learning and systems. He has developed efficient ML algorithms and provided them with effective system support. He has also contributed to accelerating computation-intensive AI applications in computer vision\, natural language processing\, and scientific discovery. His work has been featured as oral and spotlight presentations at conferences such as NeurIPS\, ICLR\, and CVPR. He was selected as the recipient of the Qualcomm Innovation Fellowship and the NVIDIA Graduate Fellowship. He was also recognized as a Rising Star in ML and Systems by MLCommons and a Rising Star in Data Science by UChicago and UCSD. Previously\, he was the founding research scientist at OmniML\, which was acquired by NVIDIA.
URL:https://datascience.ucsd.edu/event/special-seminar-zhijian-liu/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240320T140000
DTEND;TZID=America/Los_Angeles:20240320T153000
DTSTAMP:20260601T155126
CREATED:20240313T184800Z
LAST-MODIFIED:20240318T230405Z
UID:10000456-1710943200-1710948600@datascience.ucsd.edu
SUMMARY:Understanding Deep Learning through Optimization Geometry|  Nati (Nathan) Srebro
DESCRIPTION:Abstract: How can models with more parameters than training examples generalize well\, and generalize even better when we add even more parameters\, even without explicit complexity control?  In recent years\, it is becoming increasingly clear that much\, or perhaps all\, of the complexity control and generalization ability of deep learning comes from the optimization bias\, or implicit bias\, of the training procedures.  In this talk\, I will survey our work from the past several years on highlighting the role of optimization geometry in determining such implicit bias\, and understanding deep learning through it\, and how this view influences the study of further deep learning phenomena. \nBio: Nati (Nathan) Srebro is a professor at the Toyota Technological Institute at Chicago\, with cross-appointments at the University of Chicago’s Department of Computer Science\, and Committee on Computational and Applied Mathematics. He obtained his PhD from the Massachusetts Institute of Technology in 2004\, and previously was a postdoctoral fellow at the University of Toronto\, a visiting scientist at IBM\, and an associate professor at the Technion\, and held visiting position at the Weizmann Institute and at École Polytechnique Fédérale de Lausanne. \nDr. Srebro’s research encompasses methodological\, statistical and computational aspects of machine learning\, as well as related problems in optimization. Some of Srebro’s significant contributions include work on learning “wider” Markov networks\, introducing the use of the nuclear norm for machine learning\, introducing the “equalized odds” fairness notion for non-discrimination\, work on fast optimization techniques for machine learning\, and on the relationship between learning and optimization. \nWebsite: https://nati.ttic.edu/
URL:https://datascience.ucsd.edu/event/special-seminar-nathan-srebro/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240320T100000
DTEND;TZID=America/Los_Angeles:20240320T110000
DTSTAMP:20260601T155126
CREATED:20240318T224758Z
LAST-MODIFIED:20240318T224925Z
UID:10000461-1710928800-1710932400@datascience.ucsd.edu
SUMMARY:TILOS Seminar: How Large Models of Language and Vision Help Agents to Learn to Behave
DESCRIPTION:Roy Fox\, Assistant Professor and Director of the Intelligent Dynamics Lab at UC Irvine\nHDSI 123 and Zoom (Link below) \nAbstract: If learning from data is valuable\, can learning from big data be very valuable? So far\, it has been so in vision and language\, for which foundation models can be trained on web-scale data to support a plethora of downstream tasks; not so much in control\, for which scalable learning remains elusive. Can information encoded in vision and language models guide reinforcement learning of control policies? In this talk\, I will discuss several ways for foundation models to help agents to learn to behave. Language models can provide better context for decision-making: we will see how they can succinctly describe the world state to focus the agent on relevant features; and how they can form generalizable skills that identify key subgoals. Vision and vision–language models can help the agent to model the world: we will see how they can block visual distractions to keep state representations task-relevant; and how they can hypothesize about abstract world models that guide exploration and planning. \nBio: Roy Fox is an Assistant Professor of Computer Science at the University of California\, Irvine. His research interests include theory and applications of control learning: reinforcement learning (RL)\, control theory\, information theory\, and robotics. His current research focuses on structured and model-based RL\, language for RL and RL for language\, and optimization in deep control learning of virtual and physical agents.
URL:https://datascience.ucsd.edu/event/tilos-seminar-how-large-models-of-language-and-vision-help-agents-to-learn-to-behave/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240315
DTEND;VALUE=DATE:20240317
DTSTAMP:20260601T155126
CREATED:20240307T172716Z
LAST-MODIFIED:20240307T172716Z
UID:10000454-1710460800-1710633599@datascience.ucsd.edu
SUMMARY:HDSI LLM Workshop
DESCRIPTION:
URL:https://ucsd-hdsi-llm-workshop.github.io/2024/index.html
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Workshops
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240313T130000
DTEND;TZID=America/Los_Angeles:20240313T140000
DTSTAMP:20260601T155126
CREATED:20240313T195909Z
LAST-MODIFIED:20240313T195909Z
UID:10000460-1710334800-1710338400@datascience.ucsd.edu
SUMMARY:Domain Counterfactuals for Trustworthy ML via Sparse Interventions | David I. Inouye
DESCRIPTION:Talk Abstract: \nAlthough incorporating causal concepts into deep learning shows promise for increasing explainability\, fairness\, and robustness\, existing methods require unrealistic assumptions and aim to recover the full latent causal model. This talk proposes an alternative: domain counterfactuals. Domain counterfactuals ask a more concrete question: “What would a sample look like if it had been generated in a different domain (or environment)?”   This avoids the challenges of full causal recovery while answering an important causal query. I will theoretically analyze the domain counterfactual problem for invertible causal models and prove an estimation bound that depends on the sparsity of intervention\, i.e.\, the number of intervened causal variables.  Leveraging this theory\, I will introduce a practical counterfactual estimation algorithm that outperforms baselines. Additionally\, I will showcase the potential of domain counterfactuals for counterfactual fairness and domain generalization through preliminary results. Finally\, I will connect this work to my broader research focus on distribution matching\, highlighting its potential as a foundational tool for building trustworthy machine learning systems. \nBio: \nProf. David I. Inouye is an assistant professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His lab focuses on trustworthy machine learning (ML)\, which aims to make ML systems more robust\, causal and explainable. Currently\, he is interested in advancing distribution matching algorithms and applications such as causality\, domain generalization\, and distribution shift explanations. He is also interested in highly robust distributed learning algorithms on a network of devices\, called Internet Learning. His research is funded by ARL\, ONR\, and NSF. Previously\, he was a postdoc at Carnegie Mellon University working with Prof. Pradeep Ravikumar. He completed his Computer Science PhD at The University of Texas at Austin in 2017 advised by Prof. Inderjit Dhillon and Prof. Pradeep Ravikumar. He was awarded the NSF Graduate Research Fellowship (NSF GRFP).
URL:https://datascience.ucsd.edu/event/domain-counterfactuals-for-trustworthy-ml-via-sparse-interventions-david-i-inouye/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 404\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240312T140000
DTEND;TZID=America/Los_Angeles:20240312T153000
DTSTAMP:20260601T155126
CREATED:20240304T171426Z
LAST-MODIFIED:20240311T181154Z
UID:10000450-1710252000-1710257400@datascience.ucsd.edu
SUMMARY:From Pixels to Measurements: Understanding the Dynamic World ~ Adam Harley
DESCRIPTION:In computer vision\, “video understanding” typically concerns summarization: tracking the main objects\, or describing the main actions. While progress here has been impressive\, many practical applications require extracting information which is much more fine-grained. For example\, biologists are highly interested in tracking specific key points of organisms in long video recordings. Algorithms for such tasks require the generality and precision of low-level vision methods (e.g.\, optical flow)\, but benefit from knowledge about the physical world (e.g.\, things continue to exist while they are occluded). In this talk\, I will present our progress on this crucial space of problems. Our central contribution is to widen the window of “temporal context” used for inference: instead of tracking entities from one frame to the next\, we inspect dozens of frames simultaneously\, and return an answer that makes sense for the full clip. I will discuss the methods and datasets that we have created to drive progress along these lines\, and highlight natural science applications of the work. Finally\, I will introduce our ongoing effort to produce a “foundation model” of motion\, aiming to deliver arbitrary-granularity tracking for a huge variety of real-world situations. \nAdam is a postdoctoral scholar at Stanford University\, working with Leonidas Guibas. He received a Ph.D. in robotics from Carnegie Mellon University\, where he worked with Katerina Fragkiadaki. He received his M.S. in Computer Science at Toronto Metropolitan University\, working with Kosta Derpanis. Adam is a recipient of the NSERC PGS-D scholarship\, and the Toronto Metropolitan University Gold Medal. His research interests lie in Computer Vision and Machine Learning\, particularly for 3D understanding and fine-grained tracking.
URL:https://datascience.ucsd.edu/event/special-seminar-adam-harley/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240311T140000
DTEND;TZID=America/Los_Angeles:20240311T153000
DTSTAMP:20260601T155127
CREATED:20240304T171239Z
LAST-MODIFIED:20240304T171239Z
UID:10000449-1710165600-1710171000@datascience.ucsd.edu
SUMMARY:Special Seminar | Zhuang Liu
DESCRIPTION:Talk info: to be provided
URL:https://datascience.ucsd.edu/event/special-seminar-zhuang-liu/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240304T140000
DTEND;TZID=America/Los_Angeles:20240304T150000
DTSTAMP:20260601T155127
CREATED:20240304T170615Z
LAST-MODIFIED:20240304T170615Z
UID:10000448-1709560800-1709564400@datascience.ucsd.edu
SUMMARY:Evaluating and Designing Computing Systems for the Future of Work | Hancheng Cao
DESCRIPTION:Abstract: From collaborative software to generative AI\, computing technologies are redefining the way we work\, communicate and collaborate. Yet with the growing complexities of computing platforms\, it becomes increasingly challenging to foresee their impacts on human behavior\, leading to not only poor user experience but also problematic applications that mirror and amplify societal issues. How can we better understand machine behavior and machine-mediated user behavior over computing platforms? How can we build applications that align with our needs and values with emerging computing technologies? My research aims to answer these questions through novel measurements and computational methods inspired by social science insights\, such as mining increasingly available large-scale data on how people build\, adopt\, and interact with computing systems. In this talk\, I will present my work demonstrating this approach in the future of work context\, where I develop data-driven\, AI-powered and human-centered methods to understand\, evaluate and design sociotechnical systems at the workplace. I will present an analysis of remote meeting experience through mining millions of meetings\, a study on how an AI algorithm can be built to predict team fracture\, and a development and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation and data to better understand\, support and augment work practices.         \nBio: Hancheng Cao is a final year PhD candidate in computer science (with a PhD minor in management science and engineering) at Stanford University working with Prof. Daniel McFarland and Prof. Michael Bernstein. He works in the field of computational social science and human computer interaction\, where he mines large-scale data\, develops algorithms and builds systems to study human behavior. Recognized as a Stanford Interdisciplinary Graduate Fellow\, he has published 30 academic papers across fields\, with three works he led recognized as Best Paper (CHI 2023) or Honorable Mention (CSCW 2020\, CHI 2021) awards. His research has also appeared in leading social science journals (e.g. American Sociological Review). His research has been widely covered in the media\, including Wired\, Forbes\, New Scientist\, TED among others.
URL:https://datascience.ucsd.edu/event/evaluating-and-designing-computing-systems-for-the-future-of-work-hancheng-cao/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240221T140000
DTEND;TZID=America/Los_Angeles:20240221T153000
DTSTAMP:20260601T155127
CREATED:20240205T171619Z
LAST-MODIFIED:20240220T172031Z
UID:10000436-1708524000-1708529400@datascience.ucsd.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240214T140000
DTEND;TZID=America/Los_Angeles:20240214T153000
DTSTAMP:20260601T155127
CREATED:20240201T193829Z
LAST-MODIFIED:20240220T172238Z
UID:10000434-1707919200-1707924600@datascience.ucsd.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240213T023000
DTEND;TZID=America/Los_Angeles:20240213T160000
DTSTAMP:20260601T155127
CREATED:20240207T223735Z
LAST-MODIFIED:20240220T172208Z
UID:10000437-1707791400-1707840000@datascience.ucsd.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240212T140000
DTEND;TZID=America/Los_Angeles:20240212T153000
DTSTAMP:20260601T155127
CREATED:20240126T182854Z
LAST-MODIFIED:20240220T172343Z
UID:10000430-1707746400-1707751800@datascience.ucsd.edu
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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END:VEVENT
END:VCALENDAR