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DTSTART;TZID=America/Los_Angeles:20240304T140000
DTEND;TZID=America/Los_Angeles:20240304T150000
DTSTAMP:20260530T223135
CREATED:20240304T170615Z
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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
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:20231004T093000
DTEND;TZID=America/Los_Angeles:20231004T103000
DTSTAMP:20260530T223135
CREATED:20231002T164337Z
LAST-MODIFIED:20231004T165423Z
UID:10000400-1696411800-1696415400@datascience.ucsd.edu
SUMMARY:Fireside Chat: Theory in the age of modern AI
DESCRIPTION:TILOS Fireside Chat on “Theory in the age of modern AI“\, which will be a conversation led by TILOS team members: Misha Belkin (UCSD)\, Arya Mazumdar (moderator\, UCSD)\, Tara Javidi (UCSD)\, Visheeth Vishnoi (Yale U). The focus will be on the implications to\, and the roles played by theory\, in modern AI (especially with the recent exciting development in LLMs).  \n***************** \nTitle: TILOS Fireside Chat on Theory in the age of modern AI \nPanelists: Misha Belkin (UCSD)\, Arya Mazumdar(moderator\, UCSD)\, Tara Javidi (UCSD)\, Visheeth Vishnoi (Yale U) \nTime: Oct 4 (Wed) @ 9:30am — 10:00am PT / 12:30pm — 1:30pm ET
URL:https://datascience.ucsd.edu/event/tilos-fireside-chat-theory-in-the-age-of-modern-ai/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Fireside Chat
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230914T100000
DTEND;TZID=America/Los_Angeles:20230914T130000
DTSTAMP:20260530T223135
CREATED:20230818T062040Z
LAST-MODIFIED:20230818T062843Z
UID:10000398-1694685600-1694696400@datascience.ucsd.edu
SUMMARY:Causal Inference symposium
DESCRIPTION:Curious about Causality? \nCausality is increasingly a part of AI\, data science\, robotics\, and more\, but it is not always clear how we can learn causality from data. Halıcıoğlu Data Science Institute (HDSI) will be hosting a Causal Inference symposium featuring leading HDSI Faculty who will be providing an introductory overview on these methods\, followed by domain-specific talks and open discussion. \nWe welcome all UCSD faculty\, graduate students\, HDSI industry partners\, and guests to join us. \nRegister here: https://www.eventbrite.com/e/causality-symposium-tickets-677459017157?aff=oddtdtcreator  \n\n\n\n\nPlease complete your registration by Friday\, Sept. 8\, 2023. \n\n\n\n\n \nEvent details\n\nDate: September 14\, 2023\nTime: 10:00 am\nLocation: Halıcıoğlu Data Science Institute\, Room 123 (Multipurpose Room)\n\n  \nAgenda \n\n9:30 am – 10 am: Check-in & Coffee\n10 am – 10:30 am: Welcome Remarks & Causality Overview – David Danks\n10:30 am – 11:50 am: Faculty Presentations \n\nCausal Inference for Responsible and Reliable Data Science – Babak Salimi\nRobust Causal Inference with Complex Datasets – Jelena Bradic\nDemystifying Neural Networks Through Interpretable Neurons – Lily Weng\nAdvancing Machine Intelligence Through Learning and Using Causal Knowledge – Biwei Huang\n\n\n11:50 am – 12 pm: Break\n12 pm – 1 pm: Discussion Session\n\nLunch will be provided afterwards. 
URL:https://datascience.ucsd.edu/event/causal-inference-symposium/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/08/Event-Flyer-1-e1692339619192.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230503T140000
DTEND;TZID=America/Los_Angeles:20230503T153000
DTSTAMP:20260530T223135
CREATED:20230501T161933Z
LAST-MODIFIED:20230501T161933Z
UID:10000382-1683122400-1683127800@datascience.ucsd.edu
SUMMARY:Security and Privacy in an Everchanging System Landscape
DESCRIPTION:Abstract: From AI and IoT to AR/VR and Web 3.0\, computer systems are evolving at an unprecedented rate. While this evolution has given rise to exciting applications and opportunities\, it has also brought about novel security and privacy challenges within these systems and across their interactions with existing platforms. In this talk\, I will discuss how system security researchers can keep up with this everchanging landscape and showcase some of my lab’s recent work on understanding and detecting malicious web bots. I will explore how we can build and roll out research infrastructure to measure web bot activities and later use our newfound understanding to develop practical solutions to counter them. I will highlight how we can apply similar research principles to areas such as AI and IoT. Finally\, I will conclude my talk by previewing some of my ongoing work and outlining my research roadmap toward achieving “security at inception” for emerging systems. \nBio: Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University\, where he leads the Ethos Security & Privacy lab. He received his Ph.D. in Computer Science & Engineering from the University of Michigan in 2017. His research focuses on understanding emerging threats in computer systems and building practical solutions that can tackle their security and privacy challenges. His work has resulted in tens of publications and patents\, as well as thousands of citations. Rahmati’s research is supported by the Air Force Office of Scientific Research (AFOSR)\, Office of Naval Research (ONR)\, Meta\, and IBM. His research has received frequent attention from media outlets\, including MIT Technology Review\, Washington Post\, and Bloomberg. His work on the security of autonomous driving systems is part of the permanent display at the London Science Museum.
URL:https://datascience.ucsd.edu/event/security-and-privacy-in-an-everchanging-system-landscape/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230419T100000
DTEND;TZID=America/Los_Angeles:20230419T130000
DTSTAMP:20260530T223135
CREATED:20230403T185835Z
LAST-MODIFIED:20230407T172914Z
UID:10000371-1681898400-1681909200@datascience.ucsd.edu
SUMMARY:Chatting GPT
DESCRIPTION:Artificial Intelligence (AI) systems have made astonishing progress in the last year. In particular\, Large Language Models (LLMs) — AI systems trained on massive amounts of text — have reached a surprising level of capability\, with the most recent iterations able to write essays\, poems\, and computer code\, and score near the 90th percentile on standardized tests such as the LSAT and the Math SAT. The most popular interface to this technology\, ChatGPT\, made the power of LLMs readily-available to the general public for the first time\, and in doing so became the fastest-growing consumer application in history. It is clear that ChatGPT and other LLMs will have major impacts on how we work\, learn\, and live — and there is a sense that we have only seen the tip of the iceberg in terms of what these technologies can do. \nIn this series of talks and panels\, targeted to the campus community and open to the general public\, UCSD experts will discuss ChatGPT and other generative artificial intelligence: What is it? How does it work? What are its ethical implications? And what impacts will it have on fields such as medicine\, business\, and education?
URL:https://www.sdsc.edu/event_items/202304-ChatGPT.html
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Symposium,Webinar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/04/UCSD-Lecture-Template_Chatting-GPT-e1680886379636.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T120000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260530T223135
CREATED:20230403T223545Z
LAST-MODIFIED:20230407T172438Z
UID:10000372-1681473600-1681480800@datascience.ucsd.edu
SUMMARY:The Interplay of Technology\, Ethics\, and Policy
DESCRIPTION:Abstract: Technology is often designed and deployed without critical reflection of the values that it embodies. Value trade-offs—between security and privacy\, free speech and dignity\, autonomy and human agency\, and different conceptions of fairness—abound in many technologies that are now achieving great scale in commonly used tech platforms. The decisions made by the people inside the companies deploying those technologies impose their value choices upon millions of users\, often with negative externalities that are now on full display.\n\n\nIn Reich’s work with policy experts and technologists (particularly “System Error: Where Big Tech Went Wrong and How We Can Reboot”)\, Reich tries to provide a multidisciplinary view—the perspectives of a philosopher\, a political scientist\, and a computer scientist\, respectively—to disentangle the systematic drivers that we believe have led to the ethical reckoning that Big Tech is now facing. Reich examines the value trade-offs arising in systems for algorithmic decision-making\, questions related to data gathering and privacy\, the impacts of AI and automation\, and the power of private platforms to control our information eco-system. Reich then discusses the ways we can all play a role in helping to shape technology and the policies that govern it with an eye toward achieving better outcomes for society. Case studies will be used to engage the audience in the conversation.\n\n\nBio: Rob Reich is the Professor of Political Science\, director of the Center for Ethics in Society\, co-director of the Center on Philanthropy and Civil Society\, and associate director of the Institute for Human-Centered AI. He is the author of “System Error: Where Big Tech Went Wrong and How We Can Reboot” (with Mehran Sahami and Jeremy M. Weinstein) and “Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better” (2018); “Digital Technology and Democratic Theory” (edited with Lucy Bernholz and Hélène Landemore\, 2021). His teaching and writing these days focuses on ethics\, policy\, and technology.\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by Apr. 12 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP
URL:https://datascience.ucsd.edu/event/the-interplay-of-technology-ethics-and-policy/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
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DTSTART;TZID=America/Los_Angeles:20230412T140000
DTEND;TZID=America/Los_Angeles:20230412T153000
DTSTAMP:20260530T223135
CREATED:20230407T173252Z
LAST-MODIFIED:20230407T173252Z
UID:10000379-1681308000-1681313400@datascience.ucsd.edu
SUMMARY:Decoding Nature's Message Through the Channel of Artificial Intelligence
DESCRIPTION:Abstract: Nature contains many interesting physics we want to search for\, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature’s message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature’s message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently\, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and\, in turn\, deliver the world’s most sensitive search for 0vββ. This talk will discuss one such algorithm\, KamNet\, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet\, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further\, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues\, AI will enlighten the bright future of experimental particle physics.\n\nBio: Aobo Li received his B.S. in physics at the University of Washington in 2015\, then did his graduate work at Boston University as part of the KamLAND-Zen collaboration. After getting his Ph.D. in 2020\, Aobo joined UNC Chapel Hill as a Postdoctoral Research Associate and COSMS Fellow. He initiates and leads the Ge Machine Learning (GeM) group\, bringing AI solutions to the LEGEND and the Majorana Demonstrator experiment. Aobo has received many awards\, including the American Physical Society 2023 Dissertation Award in Nuclear Physics\, the UNC Postdoctoral Award of Research Excellence\, and the NeurIPS 2022 ML4PS Workshop Outstanding Paper Award.
URL:https://datascience.ucsd.edu/event/decoding-natures-message-through-the-channel-of-artificial-intelligence/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230411T140000
DTEND;TZID=America/Los_Angeles:20230411T153000
DTSTAMP:20260530T223135
CREATED:20230302T000628Z
LAST-MODIFIED:20240402T224727Z
UID:10000351-1681221600-1681227000@datascience.ucsd.edu
SUMMARY:Responsible AI: Privacy and Fairness in Decision and Learning Systems
DESCRIPTION:Differential Privacy has become the go-to approach for protecting sensitive information in data releases and learning tasks that are used for critical decision processes. For example\, census data is used to allocate funds and distribute benefits\, while several corporations use machine learning systems for criminal assessments\, hiring decisions\, and more. While this privacy notion provides strong guarantees\, we will show that it may also induce biases and fairness issues in downstream decision processes. These issues may adversely affect many individuals’ health\, well-being\, and sense of belonging\, and are currently poorly understood. \nIn this talk\, we delve into the intersection of privacy\, fairness\, and decision processes\, with a focus on understanding and addressing these fairness issues. We first provide an overview of Differential Privacy and its applications in data release and learning tasks. Next\, we examine the societal impacts of privacy through a fairness lens and present a framework to illustrate what aspects of the private algorithms and/or data may be responsible for exacerbating unfairness. We hence show how to extend this framework to assess the disparate impacts arising in Machine Learning tasks. Finally\, we propose a path to partially mitigate these fairness issues and discuss grand challenges that require further exploration. \nBio: Ferdinando Fioretto is an assistant professor at Syracuse University. He works at the juncture of Machine Learning\, optimization\, privacy\, and ethics focusing on two themes: (1) Responsible AI: it analyzes the equity of AI systems in support of decision-making and learning tasks and designs algorithms that better align with societal values and (2) ML for Science and Engineering: it develops the foundation to blend deep learning with mathematical optimization to enable the integration of knowledge\, constraints\, and physical principles into learning models. \nHe is a recipient of the 2022 NSF CAREER award\, the 2022 Amazon Research Award\, the 2022 Google Research Scholar Award\, the 2022 Caspar Bowden PET award\, the 2021 ISSNAF Mario Gerla Young Investigator Award\, the 2021 ACP Early Career Researcher Award\, the 2017 AI*AI Best AI dissertation award\, and several best paper awards. He is also actively involved in the organization of several events\, including the Privacy-Preserving Artificial Intelligence workshop at AAAI\, the Algorithmic Fairness through the lens of Causality and Privacy at NeurIPS\, and the Optimization and Learning in multiagent systems workshop at AAMAS.
URL:https://datascience.ucsd.edu/event/ferdinando-nando-fioretto/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T140000
DTEND;TZID=America/Los_Angeles:20230406T150000
DTSTAMP:20260530T223135
CREATED:20230302T000631Z
LAST-MODIFIED:20240402T224726Z
UID:10000354-1680789600-1680793200@datascience.ucsd.edu
SUMMARY:Intelligent mobile systems for equitable healthcare
DESCRIPTION:Access to even basic medical resources is greatly influenced by factors like an individual’s birth country and zip code. In this talk\, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly\, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples\, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems. \nBio: Justin Chan is a Ph.D. candidate at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His work on smartphone-based ear infections is now FDA-listed and is available to select early access healthcare systems. His work on new-born hearing screening has led to an international effort called TUNE with the goal of bringing universal newborn hearing screening across Kenya as well as collaborations with NGOs such as the Global Foundation for Children with Hearing Loss to deploy this technology in Nepal and Mongolia. His work on contactless cardiac arrest detection has been licensed to a startup which has recently been acquired by Google. He was also a lead contributor for CovidSafe (now WA Notify)\, a COVID-19 contact tracing and symptom tracking app\, which became part of official efforts by the WA Department of Health to manage the pandemic. He has authored publications in interdisciplinary journals like Nature Biomedical Engineering\, Science Translational Medicine\, Nature Communications as well as Computer Science and Engineering venues like MobiSys\, MobiCom\, SIGCOMM\, SIGGRAPH Asia and UIST.
URL:https://datascience.ucsd.edu/event/justin-chan/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture,Seminar
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