BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.2//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://datascience.ucsd.edu X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20190310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20191103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20200308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20201101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20210314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20211107T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20220313T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20221106T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251112T110000 DTEND;TZID=America/Los_Angeles:20251112T120000 DTSTAMP:20260528T045315 CREATED:20251106T170549Z LAST-MODIFIED:20251106T170549Z UID:10000534-1762945200-1762948800@datascience.ucsd.edu SUMMARY:TILOS-HDSI Seminar: Adam Oberman - AI Safety Theory: The Missing Middle Ground DESCRIPTION:The next TILOS-HDSI seminar will be Wednesday\, November 12 at 11am PST with Adam Oberman (McGill University). The title is AI Safety Theory: The Missing Middle Ground. \nTalk Information \nSpeaker: Adam Oberman (McGill University) \nDate & Time: Wednesday\, November 12 @ 11am PST \nVenue: HDSI 123 \nAbstract: Over the past few years\, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI\, there is also a risk of harm. In order to benefit from AI while mitigating the risks\, we need a grounded theoretical framework. \nThe current AI safety theory\, which predates generative AI\, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”\, “honest” or “dishonest”. But in practice safety is probabilistic\, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms\, and is essential for technical solutions as well as for making good policy decisions. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\nBio: Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University\, a Canada CIFAR AI Chair\, and an Associate Member of Mila. He is a research collaborator at LawZero\, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models\, reinforcement learning\, optimization\, calibration\, and robustness. Earlier in his career\, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago\, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin. URL:https://datascience.ucsd.edu/event/tilos-hdsi-seminar-adam-oberman-ai-safety-theory-the-missing-middle-ground/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:HDSI Event,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250602T080000 DTEND;TZID=America/Los_Angeles:20250602T190000 DTSTAMP:20260528T045315 CREATED:20250515T153502Z LAST-MODIFIED:20250515T153502Z UID:10000520-1748851200-1748890800@datascience.ucsd.edu SUMMARY:TILOS Industry Day 2025 DESCRIPTION:Our 4th Annual Industry Day will be June 2\, 2025\, at the Halıcıoğlu Data Science Institute at UC San Diego\, the campus hub for data science. This year TILOS Industry Day will feature: \n\nTalks from invited industry speakers sharing their perspectives on challenges in AI + Optimization + Use Domains (chips\, robotics\, networking)\nResearch highlights from TILOS team members\nPanel discussions on AI Challenges for Academia—An Industry Perspective and Building Deep Tech Companies\nPoster session featuring the work of TILOS trainees (students and postdoctoral scholars)\n\nMore information can be found at the event site: https://tilos.ai/tilos-industry-day-2025/ \nDate & Time\nMonday\, June 2\, 2025\n8:00am – 7:00pm \nRegistration\nRegistration is complementary but required as space is limited. Register HERE by Wednesday\, May 28\, 2025. \nVenue\nHalıcıoğlu Data Science Institute Room 123\nUniversity of California\, San Diego\n3234 Matthews Lane\nLa Jolla\, CA 92093\n[ MAP ] \nParking\nGilman Parking Structure (252 Russell Ln\, La Jolla\, CA 92093; 5 minute walk to venue). \nHopkins Parking Structure (9800 Hopkins Dr\, La Jolla\, CA 92093; 10 minute walk to venue). \nParking fees are payable at pay stations or pay-by-phone. Note that many visitor spots are limited to two hours. Even though the app allows you to pay for longer periods\, you will get a ticket after that time if parked in a 2-hour space. \nSchedule (subject to change)\n\n\n\n\n\n\n\n\n8:00 – 8:45am\nRegistration & Breakfast\n\n\n8:45 – 9:00am\nOpening Remarks\nYusu Wang\, TILOS Director\nVijay Kumar\, TILOS Associate Director of Translation\n\n\n9:00 – 10:05am\nSESSION 1 Chair: David Pan\, TILOS & UT Austin\n\n\n9:00 – 9:35am\nIndustry Keynote\nMark Ren\, Director of Design Automation Research\, NVIDIA\n\n\n9:35 – 9:50am\nTILOS Faculty Talk\nTajana Rosing\, UC San Diego\n\n\n9:50 – 10:05am\nTILOS Faculty Talk\nHamed Hassani\, University of Pennsylvania\n\n\n10:05 – 10:15am\nShort Break\n\n\n10:15 – 11:20am\nSESSION 2 Chair: Yusu Wang\, TILOS & UC San Diego\n\n\n10:15 – 10:50am\nIndustry Keynote\nVijay Shirsathe\, VP of Engineering\, Qualcomm\n\n\n10:50 – 11:05am\nTILOS Faculty Talk\nAlejandro Ribeiro\, University of Pennsylvania\n\n\n11:05am – 11:20am\nTILOS Faculty Talk\nFarinaz Koushanfar\, UC San Diego\n\n\n11:20 – 11:30am\nShort Break\n\n\n11:30am – 12:15pm\nPanel Discussion: AI Challenges for Academia—An Industry Perspective\nNageen Himayat\, Senior Principal Engineer\, Intel\nSoonho Kang\, Principal Applied Scientist\, Amazon Web Services\nSubarna Tripathi\, Research Scientist\, Intel Labs\nModerator: TBA\n\n\n12:15 – 1:15pm\nLunch\n\n\n1:15 – 2:30pm\nSESSION 3 Chair: TBA\n\n\n1:15 – 2:00pm\nTILOS Research by Industry Partners\nDavid Gonzalez Aguirre\, Research Scientist\, Intel Labs\nMojan Javaheripi\, Senior Researcher\, Microsoft\nTBA\n\n\n2:00 – 2:30pm\nSpotlight Talks for Poster Session\n\n\n2:30 – 3:15pm\nTILOS Trainee Poster Session + Coffee\n\n\n3:15 – 4:05pm\nSESSION 4 Chair: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n3:15 – 3:50pm\nIndustry Keynote\nJonathan Hurst\, Co-Founder & Chief Robot Officer\, Agility Robotics\n\n\n3:50 – 4:05pm\nTILOS Faculty Talk\nCamillo J. Taylor\, University of Pennsylvania\n\n\n4:05 – 4:15pm\nShort Break\n\n\n4:15 – 5:00pm\nPanel Discussion: Building Deep Tech Companies\nJohn Black\, SVP of Strategy\, Brain Corp\nHenrik Christensen\, TILOS & UC San Diego\nKatie Vasquez\, Calibrate Ventures\nModerator: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n5:00 – 7:00pm\nDinner URL:https://datascience.ucsd.edu/event/tilos-industry-day-2025/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Conference,HDSI Event END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250314T110000 DTEND;TZID=America/Los_Angeles:20250314T143000 DTSTAMP:20260528T045315 CREATED:20250313T162027Z LAST-MODIFIED:20250313T162027Z UID:10000511-1741950000-1741962600@datascience.ucsd.edu SUMMARY:HDSI 2025 Senior Capstone Showcase DESCRIPTION:Poster session for the undergraduate data science major’s senior capstone program\n  \nRegistration is now open for the Halıcıoğlu Data Science Institute’s 2025 Senior Capstone Showcase! We invite you to join our senior class in an interactive presentation of the projects they have worked on for the past two quarters.\nRegister here: https://dsc-capstone.org/showcase-25/ URL:https://datascience.ucsd.edu/event/hdsi-2025-senior-capstone-showcase/ LOCATION:Price Center East Ballroom\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States CATEGORIES:HDSI Event,Showcase END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240603T153000 DTEND;TZID=America/Los_Angeles:20240603T170000 DTSTAMP:20260528T045315 CREATED:20240612T154318Z LAST-MODIFIED:20240612T154318Z UID:10000486-1717428600-1717434000@datascience.ucsd.edu SUMMARY:Bhanu Teja Gullapalli’s Dissertation Defense - June 3\, at 3:30 pm. PST. DESCRIPTION:Please join the HDSI PhD Program for Bhanu Teja Gullapalli’s presentation of his dissertation on June 3\, at 3:30 pm. PST. \nThis defense will take place in-person\, (HDSI Conference Room 138 ) but there will also be a Zoom link provided for remote attendees. \nPlease note that the Zoom meeting will be recorded and the committee will meet in closed session after the presentation. \n\nTitle: Harnessing Digital Biomarkers of Substance Use and Addiction with Large-Scale Mobile Sensor Data \nAbstract: Mobile sensors are often used in health to track and monitor health\, ranging from daily activities to diagnosing life-threatening conditions; however\, they are underutilized for substance use and its disorders. Our work is focused on developing digital biomarkers from the physiological data captured from wearable devices for substance use. Specifically\, we build models that combine the multimodal sensor data from wearable devices to detect drug administrations\, predict drug-induced mental states such as drug craving and euphoria. We further show that integrating drug pharmacokinetics information into these data-driven models enhances the accuracy of drug monitoring\, thereby increasing the generalizability and trust. A consistent pattern observed among these models was bias based on drug-usage history; therefore\, we develop a model that screens users and distinguishes opioid misusers from prescription users\, which would allow for more accurate prescription of opioids\, minimizing the risk of addiction. URL:https://datascience.ucsd.edu/event/bhanu-teja-gullapallis-dissertation-defense-june-3-at-330-pm-pst/ LOCATION:https://ucsd.zoom.us/j/92070614513 CATEGORIES:HDSI Event END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20230805T180000 DTEND;TZID=America/Los_Angeles:20230805T210000 DTSTAMP:20260528T045315 CREATED:20230616T085435Z LAST-MODIFIED:20230616T170852Z UID:10000395-1691258400-1691269200@datascience.ucsd.edu SUMMARY:HDSI Alumni Celebration DESCRIPTION:If you are a HDSI alumni\, faculty or staff\, you’re invited to join us in celebrating and socializing with HDSI alumni this summer when we gather for our annual celebration!  \nREGISTER HERE \nRegistration is 18$ which includes a food\, a free drink\, and more! \nThe registration deadline is July 28th. \nPlease note that registration is required and that this is a private event for HDSI alumni\, and HDSI faculty & staff only. \n  \nThank you for all that you do in educating so many young professionals. We hope to see you on August 5th!  URL:https://datascience.ucsd.edu/event/hdsi-alumni-celebration/ LOCATION:Ridgewalk Social\, University of California San Diego\, Rimac Annex\, San Diego\, 92093 CATEGORIES:HDSI Event,Social Event ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/06/hdsi-alumni-celeb-e1686905645343.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20230605T080000 DTEND;TZID=America/Los_Angeles:20230605T170000 DTSTAMP:20260528T045315 CREATED:20230613T210741Z LAST-MODIFIED:20230613T213829Z UID:10000393-1685952000-1685984400@datascience.ucsd.edu SUMMARY:5-Year Anniversary Celebration & Ribbon Cutting DESCRIPTION:On June 5th\, 2023 the Halıcıoğlu Data Science Institute celebrated the 5-year anniversary since becoming its own academic department. In parallel to these celebrations\, HDSI unveiled it’s first dedication space\, the Halıcıoğlu Data Science Institute building. \n  \n[ngg src=”galleries” ids=”1″ display=”basic_thumbnail” thumbnail_crop=”0″ images_per_page=”15″]  URL:https://datascience.ucsd.edu/event/5-year-anniversary-celebration-ribbon-cutting/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220808T150000 DTEND;TZID=America/Los_Angeles:20220808T163000 DTSTAMP:20260528T045315 CREATED:20220805T204313Z LAST-MODIFIED:20220805T204313Z UID:10000345-1659970800-1659976200@datascience.ucsd.edu SUMMARY:Accelerating the Deployment of Renewables with Computation | Andrew Grimshaw DESCRIPTION:Dr. Grimshaw received his BA from UCSD in 1981\, his PhD in Computer Science from the University of Illinois in 1988\, and then joined the Department of Computer Science at the University of Virginia. In his 34-year career at Virginia\, Grimshaw focused on the challenges of designing\, building\, and deploying solutions that meet user requirements on production super computing systems such as those operated by the DoD\, NASA\, DOE\, and the NSF. In addition to his academic career\, Dr. Grimshaw has been a founder\, or very early employee\, of three startups: Software Products International\, Avaki\, and Lancium. Dr. Grimshaw retired this year from the University of Virginia to join Lancium and participate in their transformative mission to change how and where computing is done while decarbonizing the electrical grid. \nEvery MWh of electricity produced in the US today leads\, on average\, to 0.7 metric tons of CO2 emissions. That’s the bad news; The good news is that renewable\, carbon-neutral generation is now the least expensive means to produce electricity in the world. This has led to the development of tens of gigawatts of wind and solar capacity in the US. In West Texas alone\, there is now so much excess power that wind and solar resources are often curtailed\, leaving terawatt-hours of potential power unused. \nIn short\, generating sufficient power with renewables is no longer the problem. Instead\, the problem is leveraging this power. There are two significant challenges. First\, the sun does not always shine\, and the wind does not always blow\, meaning that the current design of the electrical grid will have to change to accommodate fluctuating generation. Second\, the best wind and solar sources are not proximate to sources of load\, namely\, population centers and heavy industry. \nOne way to address this second problem is to identify energy-intensive\, economically viable industries\, that can be feasibly moved to where the power is plentiful and that can accommodate the large amounts of variable generation provided by renewable energy. Computation\, including Bitcoin\, fits these requirements: computation turns electricity into value and requires only power and networking to operate. Computation can be paused\, restarted\, and migrated between sites in response to differences in power availability. In essence\, data centers can act like giant batteries and grid stabilizers\, soaking up power and delivering value when renewable sources are pumping out lots of power\, and reducing consumption and stabilizing the grid when renewable sources are limited. \nIn this talk I begin with some electrical grid basics\, stability\, primary frequency response\, ancillary services\, and the Texas CREZ line. I then show how Bitcoin and computation more generally can be used as a variable and controllable load to stabilize the grid\, consuming energy when it is inexpensive\, and dropping load and releasing energy back to the grid (i.e. humans) when energy prices are high. Further\, buying TWhs of otherwise unused energy causes renewable energy generation to become more profitable by providing a stable base load\, spurring further renewable energy projects. This in turn increasing the availability of renewable energy even on cloudy and windless days. URL:https://datascience.ucsd.edu/event/accelerating-the-deployment-of-renewables-with-computation-andrew-grimshaw/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220512T150000 DTEND;TZID=America/Los_Angeles:20220512T161500 DTSTAMP:20260528T045315 CREATED:20220503T200005Z LAST-MODIFIED:20220503T200005Z UID:10000204-1652367600-1652372100@datascience.ucsd.edu SUMMARY:HDSI Beyond Graduation DESCRIPTION: URL:https://datascience.ucsd.edu/event/hdsi-beyond-graduation/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220503T160000 DTEND;TZID=America/Los_Angeles:20220503T170000 DTSTAMP:20260528T045315 CREATED:20220503T200559Z LAST-MODIFIED:20220503T200559Z UID:10000206-1651593600-1651597200@datascience.ucsd.edu SUMMARY:Demystifying Graduate Admissions: STEM Programs DESCRIPTION:To register\, click here. URL:https://datascience.ucsd.edu/event/demystifying-graduate-admissions-stem-programs/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220309T150000 DTEND;TZID=America/Los_Angeles:20220309T190000 DTSTAMP:20260528T045315 CREATED:20220222T210142Z LAST-MODIFIED:20220222T210142Z UID:10000341-1646838000-1646852400@datascience.ucsd.edu SUMMARY:Data Science Study Jam WI22 DESCRIPTION: URL:https://datascience.ucsd.edu/event/data-science-study-jam-wi22/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20220301T130000 DTEND;TZID=America/Los_Angeles:20220301T163000 DTSTAMP:20260528T045315 CREATED:20220228T215041Z LAST-MODIFIED:20220228T215041Z UID:10000188-1646139600-1646152200@datascience.ucsd.edu SUMMARY:HDSI 4th Anniversary DESCRIPTION: URL:https://datascience.ucsd.edu/event/hdsi-4th-anniversary/ LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20211117T140000 DTEND;TZID=America/Los_Angeles:20211117T150000 DTSTAMP:20260528T045315 CREATED:20211112T223936Z LAST-MODIFIED:20211112T223936Z UID:10000338-1637157600-1637161200@datascience.ucsd.edu SUMMARY:HDSI Seminar Series | Theories of Inference for Visual Analysis DESCRIPTION: URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-theories-of-inference-for-visual-analysis/ CATEGORIES:Colloquium,Guest Lecture,HDSI Event,Industry,Seminar,Webinar,Workshops ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20211028T173000 DTEND;TZID=America/Los_Angeles:20211028T190000 DTSTAMP:20260528T045315 CREATED:20210929T222223Z LAST-MODIFIED:20210929T222223Z UID:10000337-1635442200-1635447600@datascience.ucsd.edu SUMMARY:Data Science Insights Speaker Series: Lily Weng DESCRIPTION: URL:https://www.meetup.com/San-Diego-Machine-Learning/events/280949913/#new_tab CATEGORIES:HDSI Event,Industry ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210614T150000 DTEND;TZID=America/Los_Angeles:20210614T170000 DTSTAMP:20260528T045315 CREATED:20210511T155921Z LAST-MODIFIED:20210511T155921Z UID:10000324-1623682800-1623690000@datascience.ucsd.edu SUMMARY:HDSI Virtual Graduation Celebration: Class of 2021 DESCRIPTION: URL:https://datascience.ucsd.edu/event/hdsi-virtual-graduation-celebration-class-of-2021/ CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210513T130000 DTEND;TZID=America/Los_Angeles:20210513T140000 DTSTAMP:20260528T045315 CREATED:20210511T213341Z LAST-MODIFIED:20230324T155808Z UID:10000326-1620910800-1620914400@datascience.ucsd.edu SUMMARY:Some Results on Label Shift and Label Noise | Zachary Chase Lipton DESCRIPTION:Title: Some Results on Label Shift and Label Noise \nAbstract: In this talk I will discuss distribution shift\, both as an obstacle to be overcome to achieve generalization\, and as a device for obtaining generalization guarantees. In the first part\, I will discuss the problem of label shift\, where the proportion among the labels can shift but the class conditional distributions do not change\, including connections to some practical problems and some theoretical results. Then I will discuss a new work in which we deliberately alter the distribution of training data in order to obtain a generalization guarantee. \nBio\nZachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab\, whose research spans core machine learning methods\, applications to clinical medicine and natural language processing\, and the impact of automation on social systems. Current focuses include robustness under distribution shift\, decision-making\, applications of causal thinking to practical high-dimensional settings that resist stylized causal models\, and AI ethics. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning\, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton)\, GitHub (@zackchase)\, or his lab’s website (acmilab.org). URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-some-results-on-label-shift-and-label-noise/ CATEGORIES:Guest Lecture,HDSI Event,Seminar ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210506T130000 DTEND;TZID=America/Los_Angeles:20210506T140000 DTSTAMP:20260528T045315 CREATED:20210504T195111Z LAST-MODIFIED:20210504T195111Z UID:10000321-1620306000-1620309600@datascience.ucsd.edu SUMMARY:HDSI Seminar Series: Unavoidable Tensions in Explaining Algorithmic Decisions DESCRIPTION:Title: Unavoidable Tensions in Explaining Algorithmic Decisions \nAbstract: Recent developments in methods for explaining the decisions of machine learning models have been widely embraced for their ability to provide transparency and accountability without limiting model complexity or compelling model disclosure. Yet applying these methods is far from straightforward and they rarely prove a cure all. This talk identifies a number of unavoidable tensions that decision makers must navigate as they seek to employ these methods—and the deeply subjective judgment that must go into these considerations. \nPapers: This presentation is based–and builds–on two papers\, which are joint work with Andrew Selbst and Manish Raghavan: \n\nBarocas\, Solon\, Andrew D. Selbst\, and Manish Raghavan. “The hidden assumptions behind counterfactual explanations and principal reasons.” In Proceedings of the 2020 Conference on Fairness\, Accountability\, and Transparency\, pp. 80-89. 2020. https://dl.acm.org/doi/abs/10.1145/3351095.3372830\nSelbst\, Andrew D.\, and Solon Barocas. “The Intuitive Appeal of Explainable Machines.” Fordham Law Review 87\, no. 3 (2018): 1085. https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=5569&context=flr\n\nBio:  Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research\, an Adjunct Assistant Professor in the Department of Information Science at Cornell\, and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. His research explores ethical and policy issues in artificial intelligence\, particularly fairness in machine learning\, methods for bringing accountability to automated decision-making\, and the privacy implications of inference. Solon co-founded the ACM conference on Fairness\, Accountability\, and Transparency (FAccT). URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-unavoidable-tensions-in-explaining-algorithmic-decisions/ CATEGORIES:Guest Lecture,HDSI Event,Seminar ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210503T130000 DTEND;TZID=America/Los_Angeles:20210503T140000 DTSTAMP:20260528T045315 CREATED:20210428T220805Z LAST-MODIFIED:20210428T220805Z UID:10000178-1620046800-1620050400@datascience.ucsd.edu SUMMARY:HDSI Seminar Series: Causal Effect Inference: A Machine Learning Approach by Mihaela van der Schaar DESCRIPTION:Title: Causal Effect Inference: A Machine Learning Approach \nAbstract \nA major challenge in the domain of healthcare is ascertaining whether a given treatment influences or determines an outcome—for instance\, whether there is a survival benefit to prescribing a certain medication. Current treatment guidelines have been developed with the “average” patient in mind\, but the reality is that treatments result in different effects and outcomes from one individual to another. \nUsing AI and machine learning\, we can endeavor to understand the effect of specific treatments on specific patients at specific times\, given their unique characteristics. This is what we call causal effect inference\, or individualized treatment effect inference. This is far from a straightforward undertaking\, however. In this seminar\, I will offer an introduction to individualized treatment effect inference for healthcare. I will explain the importance of this research area\, while also highlighting some key challenges\, formalisms\, methodologies\, and applications. \nBio \n \nMihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning\, Artificial Intelligence and Medicine at the University of Cambridge\, a Fellow at The Alan Turing Institute in London\, and a Chancellor’s Professor at UCLA. \nMihaela was elected IEEE Fellow in 2009. She has received numerous \nawards\, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018)\, a National Science Foundation CAREER Award (2004)\, 3 IBM Faculty Awards\, the IBM Exploratory Stream Analytics Innovation Award\, the Philips Make a Difference Award and several best paper awards\, including the IEEE Darlington Award. \nMihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. \nIn 2019\, she was identified by National Endowment for Science\, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing\, communication networks\, network science\, multimedia\, game theory\, distributed systems\, machine learning and AI. \nMihaela’s research focus is on machine learning\, AI and operations research for healthcare and medicine. \nIn addition to leading the van der Schaar Lab\, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). URL:https://youtu.be/yhUItwTP02o#new_tab CATEGORIES:Guest Lecture,HDSI Event,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210429T130000 DTEND;TZID=America/Los_Angeles:20210429T140000 DTSTAMP:20260528T045315 CREATED:20210427T184915Z LAST-MODIFIED:20210427T184915Z UID:10000177-1619701200-1619704800@datascience.ucsd.edu SUMMARY:HDSI Seminar Series: A Modern Take on Huber Regression by Po Ling Loh DESCRIPTION:Title: A modern take on Huber regression \nAbstract: \nIn the first part of the talk\, we discuss the use of a penalized Huber M-estimator for high-dimensional linear regression. We explain how a fairly straightforward analysis yields high-probability error bounds that hold even when the additive errors are heavy-tailed. However\, the parameter governing the shape of the Huber loss must be chosen in relation to the scale of the error distribution. We discuss how to use an adaptive technique\, based on Lepski’s method\, to overcome the difficulties traditionally faced by applying Huber M-estimation in a context where both location and scale are unknown. \nIn the second part of the talk\, we turn to a more complicated setting where both the covariates and responses may be heavy-tailed and/or adversarially contaminated. We show how to modify the Huber regression estimator by first applying an appropriate “filtering” procedure to the data based on the covariates. We prove that in low-dimensional settings\, this filtered Huber regression estimator achieves near-optimal error rates. We further show that the commonly used least trimmed squares and least absolute deviation estimators may similarly be made robust to contaminated covariates via the same covariate filtering step. This is based on joint work with Ankit Pensia and Varun Jog. \nBio: \nPo-Ling Loh received her PhD in Statistics from UC Berkeley in 2014. From 2014-2016\, she was an Assistant Professor of Statistics at the University of Pennsylvania. From 2016-2018\, she was an Assistant Professor of Electrical & Computer Engineering at UW-Madison\, and from 2019-2020\, she was an Associate Professor of Statistics at UW-Madison and a Visiting Associate Professor of Statistics at Columbia University. She began a position as a Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge in January 2021. Po-Ling’s current research interests include high-dimensional statistics\, robustness\, and differential privacy. She is a recipient of an NSF CAREER Award\, an ARO Young Investigator Award\, the IMS Tweedie and Bernoulli Society New Researcher Awards\, and a Hertz Fellowship. URL:https://youtu.be/5f-5psF6XbA#new_tab CATEGORIES:Guest Lecture,HDSI Event,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210408T130000 DTEND;TZID=America/Los_Angeles:20210408T140000 DTSTAMP:20260528T045315 CREATED:20210331T191442Z LAST-MODIFIED:20210331T191442Z UID:10000172-1617886800-1617890400@datascience.ucsd.edu SUMMARY:HDSI Seminar Series: Deep Learning for Market Design: Fairness\, Robustness\, and Expressiveness by John Dickerson DESCRIPTION:Title\nDeep Learning for Market Design: Fairness\, Robustness\, and Expressiveness \nJohn Dickerson\, Assistant Professor of Computer Science\, University of Maryland; Chief Scientist\, Arthur AI \nAbstractThe design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising\, sourcing\, spectrum allocation\, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson’s 1981 work characterizing single-item optimal auctions\, there has been limited progress outside of restricted settings. A recent paper by Dütting et al. circumvents analytic difficulties by applying deep learning techniques to\, instead\, approximate optimal auctions. Their RegretNet architecture can represent auctions with arbitrary numbers of items and participants; it is trained to be empirically strategyproof\, but the property is never exactly verified leaving potential loopholes for market participants to exploit. In parallel\, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances\, in this talk\, we discuss extensions of these techniques for approximating auctions using deep learning to address concerns of* fairness while maintaining high revenue and strong incentive guarantees;\n* certified robustness\, that is\, verification of claimed strategyproofness of deep learned auctions; and\n* expressiveness via different demand functions and other constraints.To enable that last point\, we propose a new architecture to learn incentive compatible\, revenue-maximizing auctions from sampled valuations\, which uses the Sinkhorn algorithm to perform a differentiable bipartite matching. Our new framework allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. \nThis talk covers hot-off-the-presses work led by PhD students Michael Curry\, Ping-yeh Chiang\, and Samuel Dooley\, and undergraduate students Kevin Kuo\, Uro Lyi\, Anthony Ostuni\, and Elizabeth Horishny. Papers have appeared at NeurIPS-20 or are currently under review; please check arXiv or get in touch for drafts. \nBio\nJohn P Dickerson is an Assistant Professor of Computer Science at the University of Maryland as well as Chief Scientist of Arthur AI\, an enterprise-focused AI/ML model monitoring firm. He is a recipient of awards such as the NSF CAREER Award\, IEEE Intelligent Systems AI’s 10 to Watch\, Google Faculty Research Award\, Google Research Scholar Award\, and paper awards and nominations at venues such as AAAI. His research centers on solving practical economic problems using techniques from computer science\, stochastic optimization\, and machine learning. He has worked extensively on theoretical and empirical approaches to organ exchange where his work has set policy at the UNOS nationwide kidney exchange; worldwide blood donation markets with Facebook; game-theoretic approaches to counter-terrorism and negotiation\, where his models have been deployed; and market design problems in industry (e.g.\, online advertising) through various startups. Dickerson received his PhD in computer science from Carnegie Mellon University. URL:https://youtu.be/nWmI2bs_mcs#new_tab CATEGORIES:Colloquium,HDSI Event,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210401T130000 DTEND;TZID=America/Los_Angeles:20210401T140000 DTSTAMP:20260528T045315 CREATED:20210331T183837Z LAST-MODIFIED:20210331T183837Z UID:10000171-1617282000-1617285600@datascience.ucsd.edu SUMMARY:HDSI Seminar Series: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate by Quanquan Gu DESCRIPTION:Title: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate \nHDSI Seminar Series \nQuanquan Gu\, Assistant Professor of Computer Science at UCLA \nAbstract: Understanding the algorithmic bias of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works\, however\, focus on very small or even infinitesimal learning rate regimes and fail to cover practical scenarios where the learning rate is moderate and annealing. In this talk\, I will introduce our attempt to characterize the particular regularization effect of SGD in the moderate learning rate regime by studying its behavior for optimizing an overparameterized linear regression problem. In this case\, SGD and GD are known to converge to the unique minimum-norm solution; however\, with the moderate and annealing learning rate\, we show that they exhibit different directional bias: SGD converges along the large eigenvalue directions of the data matrix\, while GD goes after the small eigenvalue directions. Furthermore\, we show that such directional bias does matter when early stopping is adopted\, where the SGD output is nearly optimal but the GD output is suboptimal. Our analysis explains several folk arts in practice used for SGD hyperparameter tuning\, such as (1) linearly scaling the initial learning rate with batch size; and (2) overrunning SGD with a high learning rate even when the loss stops decreasing. \nThis talk is based on joint work with Jingfeng Wu\, Difan Zou\, and Vladimir Braverman. \nBio: Quanquan Gu is an Assistant Professor of Computer Science at UCLA. His current research is in the area of artificial intelligence and machine learning\, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning and building the theoretical foundations of deep learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award\, NSF CAREER Award\, Simons Berkeley Research Fellowship\, Adobe Data Science Research Award\, Salesforce Deep Learning Research Award\, and AWS Machine Learning Research Award. URL:https://youtu.be/qLGvn4RJwl4#new_tab CATEGORIES:Colloquium,HDSI Event,Seminar ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210301T100000 DTEND;TZID=America/Los_Angeles:20210301T170000 DTSTAMP:20260528T045315 CREATED:20210224T190853Z LAST-MODIFIED:20210224T190853Z UID:10000320-1614592800-1614618000@datascience.ucsd.edu SUMMARY:HDSI 3rd Anniversary Symposium DESCRIPTION: URL:https://datascience.ucsd.edu/news-and-events/events/3rdanniversary/ CATEGORIES:HDSI Event END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20210113T170000 DTEND;TZID=America/Los_Angeles:20210113T180000 DTSTAMP:20260528T045315 CREATED:20201130T220001Z LAST-MODIFIED:20230712T092601Z UID:10000144-1610557200-1610560800@datascience.ucsd.edu SUMMARY:Re-Imagine Education with AI DESCRIPTION:Talk Contents\n\nHow to apply AI Tech to Education?\nIntroduction of Riiid Labs & Technology\nFuture of AI-enabled Education\n$100K Riiid! AIEd Challenge on Kaggle\n\nDate/Time\n\nJanuary 13 (Wed)\, 5:00PM-6:00PM\nOpen to all members of HDSI Community\nPrerequisite: Intellectual curiosity to explore the future of education with AI\n\nGet More Info\n\nriiid.co\nednetchallenge.ai\nkaggle.com/c/riiid-test-answer-prediction\naaai.org/Conferences/AAAI-21\n\nHow To Join\n\nZoom Webinar Link: https://qrco.de/bbqd5M\nPlease RSVP through HDSI in order to best customize the talk to UCSD student needs URL:https://datascience.ucsd.edu/event/re-imagine-education-with-ai/ CATEGORIES:Guest Lecture,HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20201204T130000 DTEND;TZID=America/Los_Angeles:20201204T140000 DTSTAMP:20260528T045315 CREATED:20201130T215335Z LAST-MODIFIED:20201130T215335Z UID:10000142-1607086800-1607090400@datascience.ucsd.edu SUMMARY:Meet the DSC Reps! DESCRIPTION:Take a break from midterms and come meet your Data Science Student Representatives this Friday! Chat with the Reps and play some games to win awesome HDSI swag. Learn how to balance classes\, get peer insight on finding research and internships\, and talk about any other questions you’d like answered. Please RSVP through the link below to receive the Zoom link.   \n \n RSVP Here for Zoom Link URL:https://datascience.ucsd.edu/event/meet-the-dsc-reps/ CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20201106T080000 DTEND;TZID=America/Los_Angeles:20201106T170000 DTSTAMP:20260528T045315 CREATED:20201105T192203Z LAST-MODIFIED:20201105T192203Z UID:10000283-1604649600-1604682000@datascience.ucsd.edu SUMMARY:Deep-Math Conference 2020 DESCRIPTION: URL:https://datascience.ucsd.edu/event/deep-math-conference-2020/2020-11-06/ CATEGORIES:Colloquium,Conference,HDSI Event,Webinar,Workshops ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20201105T080000 DTEND;TZID=America/Los_Angeles:20201105T170000 DTSTAMP:20260528T045315 CREATED:20201105T192203Z LAST-MODIFIED:20201105T192203Z UID:10000282-1604563200-1604595600@datascience.ucsd.edu SUMMARY:Deep-Math Conference 2020 DESCRIPTION: URL:https://datascience.ucsd.edu/event/deep-math-conference-2020/2020-11-05/ CATEGORIES:Colloquium,Conference,HDSI Event,Webinar,Workshops ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20201015T140000 DTEND;TZID=America/Los_Angeles:20201015T180000 DTSTAMP:20260528T045315 CREATED:20200929T191622Z LAST-MODIFIED:20200929T191622Z UID:10000134-1602770400-1602784800@datascience.ucsd.edu SUMMARY:Talent Day DESCRIPTION: URL:https://datascience.ucsd.edu/event/talent-day/ CATEGORIES:HDSI Event,Webinar ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20200826T150000 DTEND;TZID=America/Los_Angeles:20200826T160000 DTSTAMP:20260528T045315 CREATED:20200813T162936Z LAST-MODIFIED:20230929T214059Z UID:10000128-1598454000-1598457600@datascience.ucsd.edu SUMMARY:HDSI Open House DESCRIPTION: URL:https://datascience.ucsd.edu/event/hdsi-open-house/ CATEGORIES:HDSI Event ATTACH;FMTTYPE=: END:VEVENT END:VCALENDAR