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: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 END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240722T140000 DTEND;TZID=America/Los_Angeles:20240722T150000 DTSTAMP:20260529T123044 CREATED:20240717T171423Z LAST-MODIFIED:20240717T171423Z UID:10000488-1721656800-1721660400@datascience.ucsd.edu SUMMARY:Mayank Garg | Tackling Acute Respiratory Distress Syndrome (ARDS) : Integrated and Holistic Approaches DESCRIPTION:When Monday July 22nd 2:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: “Tackling Acute Respiratory Distress Syndrome (ARDS) : Integrated and Holistic Approaches” \nAbstract: ARDS is a complex heterogenous disorder which forms a significant health care burden. Pathophysiologically\, ARDS is caused by multiple aetiologies which can lead to the diversity in clinical presentation seen. In our work with preclinical rodent models\, we investigate the role of host mitochondrial factors in skewing the inflammation resolution pathways leading to an aggravated and exaggerated state of inflammation and possibly increased mortality. This work elicits another instance of how endotype identification is essential in ARDS (and other diseases)\, to improve translation of basic research. A potential solution for this could be integration of data driven approaches to derive biological insights which would serve as essential context for preclinical disease models. \nMayank Garg Bio: Mayank is a physician scientist who completed his medical graduation and clinical training from IPGME&R and SSKM Hospital\, Kolkata\, India. He gained brief experience in intensive care before switching to experimental research at CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB)\, Delhi\, India. He is affiliated with Ashoka University as a Simons Ashoka Early Career fellow. \nMayank is currently pursuing quantitative health research to explore the heterogeneity of ICU disorders like Sepsis and ARDS. Realising the importance of context in biomedical research\, he aims to derive mechanisms to leverage data science in a clinically relevant manner\, and to validate them using contextual application of experimental models. \nHe also believes in the potential of digital transformation in personal empowerment\, for improving health-care\, sick-care\, and clinical research. He is collaborating on a project to develop a digital framework to assist data collection and aid analysis for personalized lifestyle medicine. He plans to leverage the power of LLMs integrated with such digital frameworks for healthcare and healthcare research. He strongly advocates for collaborative growth and strict ethical standards as the foundation for advancing science. URL:https://datascience.ucsd.edu/event/mayank-garg-tackling-acute-respiratory-distress-syndrome-ards-integrated-and-holistic-approaches/ 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/cropped-HDSI-UCSD-Image-e1712856546428.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240618T080000 DTEND;TZID=America/Los_Angeles:20240618T193000 DTSTAMP:20260529T123044 CREATED:20240612T152605Z LAST-MODIFIED:20240612T152605Z UID:10000485-1718697600-1718739000@datascience.ucsd.edu SUMMARY:TILOS Industry Day DESCRIPTION:EVENT WEBSITE \n\n\n\n8:00 – 8:45am\nBreakfast\n\n\n8:45 – 9:00am\nWelcome Remarks and Introduction to TILOS\nDirector Yusu Wang (UCSD) and AD Translation Vijay Kumar (UPenn)\n\n\n9:00 – 10:30am\nSESSION 1\nIndustry Keynote: Nicholas Roy (Zoox and MIT)\nTILOS Faculty Highlights:\nFarinaz Koushanfar (UCSD)\nNisheeth Vishnoi (Yale U)\n\n\n10:30 – 10:45am\nBreak (15 minutes)\n\n\n10:45am – 12:15pm\nSESSION 2\nIndustry Keynote: Nageen Himayat (Intel Labs)\nTILOS Faculty Highlights:\nAlejandro Ribeiro (UPenn)\nSean Gao (UCSD)\n\n\n12:15 – 2:00pm\nTILOS Trainee Poster Lightning Preview Session + Lunch\n\n\n2:00 – 3:00pm\nPanel on Academic-Industry Relations / Collaborations\nInvited Panelists:\nNing Bi (Qualcomm VP Engineering)\nVitaly Feldman (Apple ML Research)\nKatherine Heller (Google Responsible AI)\nTara Javidi (UCSD)\nSomdeb Majumdar (Intel AI/ML Lab)\nModerator: Vijay Kumar (TILOS AD Translation\, UPenn)\n\n\n3:00 – 3:30pm\nBreak (30 minutes)\n\n\n3:30 – 5:00pm\nSESSION 3\nIndustry Keynote: Carolina Parada (Google DeepMind)\nTILOS Faculty Highlights:\nNikolay Atanasov (UCSD)\nMisha Belkin (UCSD)\n\n\n5:00 – 7:30pm\nBuffet Dinner and Trainee Poster Session URL:https://tilos.ai/events/tilos-industry-day-2024/#new_tab LOCATION:Halıcıoğlu Data Science Institute\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA CATEGORIES:Symposium ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240606T110000 DTEND;TZID=America/Los_Angeles:20240606T170000 DTSTAMP:20260529T123044 CREATED:20240311T193901Z LAST-MODIFIED:20240606T183803Z UID:10000455-1717671600-1717693200@datascience.ucsd.edu SUMMARY:HDSI Anniversary Symposium DESCRIPTION:[vc_row][vc_column][vc_custom_heading text=”JOIN US FOR THE HALICIOGLU DATA SCIENCE INSTITUTE ANNIVESARY SYMPOSIUM!” font_container=”tag:h4|text_align:left|color:%2300629b” css=”” custom_css=”font-family: ‘Refrigerator Deluxe Extrabold’ !important;”][vc_column_text css=””]Date: June 6\nTime: 11:00 AM – 5:00 PM\nLocation: Halıcıoğlu Data Science Institute\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093 \nJoin us to celebrate the advancements and future of the Halıcıoğlu Data Science Institute. The event will feature: \n\nKeynote Speaker: Thomas Andriola\, Vice Chancellor and Chief Digital Officer\, UC Irvine\nFaculty Talks: Tiffany Amariuta\, Haojian Jin\, Sooyhun Liao\nPanel Discussions\nPhD Poster Session\n\nRSVP: Register Below[/vc_column_text][vc_separator color=”custom” css=”” accent_color=”#d462ad”][vc_custom_heading text=”AGENDA” font_container=”tag:h4|text_align:left|color:%2300629b” css=”” custom_css=”font-family: ‘Refrigerator Deluxe Extrabold’ !important;”][vc_column_text css=””]Time HDSI 6-Year Symposium 11:00 amPoster Session & Check In11:30 amLunch12:30 pmWelcome Remarks12:45 pmHDSI Data Planet \n\n \n Arun Kumar \n \n \n Associate Professor\, CSE and HDSI \n \n \n \n\n \n Jingbo Shang \n \n \n Assistant Professor\, CSE and HDSI \n \n \n 1:30 pmModerator Introduction \n\n \n Benjamin Smarr \n \n \n Moderator | Assistant Professor\, HDSI and Bioengineering \n \n \n 1:35 pmKeynote | Data Science: Where do we go from here? \n\n \n Thomas Andriola \n \n \n Vice Chancellor\, Information Technology and Data; Chief Digital Officer\, UC Irvine \n \n \n 2:25 pmQ&A2:35 pmBreak2:45 pmEquity in Healthcare \n\n \n Tiffany Amariuta \n \n \n Assistant Professor\, HDSI and Biomedical Informatics \n \n \n 3:05 pmSecurity & Privacy \n\n \n Haojian Jin \n \n \n Assistant Professor\, HDSI \n \n \n 3:25 pmScalable education using student data \n\n \n Sooyhun Liao \n \n \n Assistant Teaching Professor\, HDSI \n \n \n 3:45 pmPanel Discussion – Impact of Data Science \n\n \n Sooyhun Liao \n \n \n Assistant Teaching Professor\, HDSI \n \n \n \n\n \n Haojian Jin \n \n \n Assistant Professor\, HDSI \n \n \n \n\n \n Tiffany Amariuta \n \n \n Assistant Professor\, HDSI and Biomedical Informatics \n \n \n \n\n \n Benjamin Smarr \n \n \n Moderator | Assistant Professor\, HDSI and Bioengineering \n \n \n 4:10 pmPanel Discussion – Future of Data Science at UCSD \n\n \n Frank Wuerthwein \n \n \n Director\, San Diego Supercomputer Center \n \n \n \n\n \n Rajesh Gupta \n \n \n Founding Director\, Halıcıoğlu Data Science Institute \n \n \n \n\n \n Sorin Lerner \n \n \n Chair\, Computer Science & Engineering \n \n \n \n\n \n Bill Lin \n \n \n Chair\, Electrical and Computer Engineering \n \n \n \n\n \n Shankar Subramaniam \n \n \n Moderator | Distinguished Professor\, Bioengineering\, Computer Science & Engineering\, Cellular & Molecular Medicine\, and Nanoengineering \n \n \n \n\n \n Michael Holst \n \n \n Chair\, Mathematics \n \n \n 4:50 pmClosing Remarks[/vc_column_text][vc_separator color=”custom” css=”” accent_color=”#d462ad”][/vc_column][/vc_row] URL:https://datascience.ucsd.edu/event/hdsi-6-year-symposium/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Symposium ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/03/HDSI_6th_Anniversary_V2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240605T123000 DTEND;TZID=America/Los_Angeles:20240605T140000 DTSTAMP:20260529T123044 CREATED:20240528T231005Z LAST-MODIFIED:20240528T233634Z UID:10000482-1717590600-1717596000@datascience.ucsd.edu SUMMARY:Inaugural Graduate Commencement Awards Luncheon DESCRIPTION:The 2024  Inaugural Graduate Commencement Awards Luncheon will take place on Wednesday June 5 at HDSI’s Multi-Purpose Room. Join us in celebrating the successes of our graduating MS and PhD. Space is limited and we have to confirm catering numbers\, so please RSVP no later than Friday 5/31!!\nWhen: Wed June 5\, 2024\nTime: 12:30pm – 2:00pm\nWhere: HDSI Ground Floor\, Multi-purpose Room\nDress: Business Casual URL:https://datascience.ucsd.edu/event/inaugural-graduate-commencement-awards-luncheon/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Student Event ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/cropped-HDSI-UCSD-Image-e1712856546428.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240603T153000 DTEND;TZID=America/Los_Angeles:20240603T170000 DTSTAMP:20260529T123044 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:20240529T140000 DTEND;TZID=America/Los_Angeles:20240529T153000 DTSTAMP:20260529T123044 CREATED:20240525T004222Z LAST-MODIFIED:20240525T004904Z UID:10000479-1716991200-1716996600@datascience.ucsd.edu SUMMARY:Forecasting the Antibody Response against the Influenza Virus | Tal Einav DESCRIPTION:Abstract: Although influenza is one of the best-studied viruses\, vaccine effectiveness remains around 20-50%. A sizable fraction of people exhibit a weak or short-lived antibody response following vaccination\, yet we cannot identify these individuals a priori nor ascertain whether a different vaccine would have served them better. In this talk\, we demonstrate how machine learning can leverage the wealth of prior studies to forecast each person’s vaccine response. We will discuss when these predictions are accurate\, when they fall short\, and some of the exciting possibilities for the future of this field. \n\nBio: Tal Einav’s career path has included a year-long sabbatical as a software developer\, teaching courses at the Marine Biology Laboratory (100 hours per week!)\, and training at Caltech and the Fred Hutch Cancer Center. He runs the Computational Immunology Lab at LJI\, and his work blends questions and techniques from computer science and biology to predict how biological systems will behave. In today’s talk\, the biological system is everyone in this room\, and the question is what will happen when you receive your next influenza vaccine. URL:https://datascience.ucsd.edu/event/forecasting-the-antibody-response-against-the-influenza-virus-tal-einav/ 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:20240522T140000 DTEND;TZID=America/Los_Angeles:20240522T150000 DTSTAMP:20260529T123044 CREATED:20240520T222549Z LAST-MODIFIED:20240525T004748Z UID:10000481-1716386400-1716390000@datascience.ucsd.edu SUMMARY:Paths to AI Accountability | Sarah H. Cen DESCRIPTION:Speaker: Sarah H. Cen\n\nAbstract: We have begun grappling with difficult questions related to the rise of AI\, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI. In this talk\, I will discuss the two main components of AI accountability\, then illustrate them through a case study on social media. Within the context of social media\, I will focus on how social media platforms filter (or curate) the content that users see. I will review several methods for auditing social media\, drawing from concepts and tools in hypothesis testing\, causal inference\, and LLMs.\n\nBio: Sarah is a final-year PhD student at MIT in the Electrical Engineering and Computer Science Department advised by Professor Aleksander Mądry and Professor Devavrat Shah. Sarah utilizes methods from machine learning\, statistical inference\, causal inference\, and game theory to study responsible computing and AI policy. Previously\, she has written about social media\, trustworthy algorithms\, algorithmic fairness\, and more. She is currently interested in AI auditing\, AI supply chains\, and IP Law x Gen AI. URL:https://datascience.ucsd.edu/event/paths-to-ai-accountability/ 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:20240522T100000 DTEND;TZID=America/Los_Angeles:20240522T110000 DTSTAMP:20260529T123044 CREATED:20240521T224521Z LAST-MODIFIED:20240521T224521Z UID:10000480-1716372000-1716375600@datascience.ucsd.edu SUMMARY:TILOS Seminar: Large Datasets and Models for Robots in the Real World DESCRIPTION:Large Datasets and Models for Robots in the Real World\nNicklas Hansen\, UC San Diego\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However\, teaching AI agents to reliably interact with our physical world has proven challenging\, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk\, I will cover ongoing efforts of the Open X-Embodiment project–a collaboration between 279 researchers across 20+ institutions–to build a large\, open dataset for real-world robotics\, and discuss how this new paradigm is rapidly changing the field. Concretely\, I will discuss why we need large datasets in robotics\, what such datasets may look like\, and how large models can be trained and evaluated effectively in a cross-embodiment cross-environment setting. Finally\, I will conclude the talk by sharing my perspective on the limitations of current embodied AI agents\, as well as how to move forward as a community. \n\nNicklas Hansen is a Ph.D. student at University of California San Diego advised by Prof. Xiaolong Wang and Prof. Hao Su. His research focuses on developing generalist AI agents that learn from interaction with the physical and digital world. He has spent time at Meta AI (FAIR) and University of California Berkeley (BAIR)\, and received his B.S. and M.S. degrees from Technical University of Denmark. He is a recipient of the 2024 NVIDIA Graduate Fellowship\, and his work has been featured at top venues in machine learning and robotics. Webpage: www.nicklashansen.com URL:https://datascience.ucsd.edu/event/tilos-seminar-large-datasets-and-models-for-robots-in-the-real-world/ 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/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240508T070000 DTEND;TZID=America/Los_Angeles:20240508T200000 DTSTAMP:20260529T123044 CREATED:20240501T163014Z LAST-MODIFIED:20240501T163014Z UID:10000478-1715151600-1715198400@datascience.ucsd.edu SUMMARY:Compassionate constructive laziness | Brad Voytek DESCRIPTION:The Triton Neurotech and TNT Academy team is excited to announce their first event in their Professor Talk series! Join them for a talk put on by Dr. Bradley Voytek on Wednesday\, May 8th from 7-8pm in Henry Booker Room\, Jacobs Hall 2512.\n\nDr. Voytek is a Professor in the Department of Cognitive Science\, the Halıcıoğlu Data Science Institute\, and the Neurosciences Graduate Program at UC San Diego. After his PhD at UC Berkeley\, he joined Uber as their first data scientist—when it was a 10-person startup—where he helped build their data science strategy and team. His research lab combines large-scale data science and machine learning to study how brain regions communicate with one another\, and how that communication changes with aging and disease. \nHe is an advocate for promoting science to the public and speaks extensively with students at all grade levels about the joys of scientific research and discovery. To that end he is going to speak about the following: \nTitle: Compassionate constructive laziness \nDescription: Work hard. Follow your passion. Head down\, nose to the grindstone hustle. Who do we do these things for and why? Yes\, hard work is good if you have a clear set of goals and a vision to make your future life better and easier. Yes\, passion is wonderful\, but it can be blinding. And yes\, grinding hassle can get you far\, but at the cost of making many interpersonal interactions primarily transactional. In this discussion\, Dr. Voytek will talk about his experience with the principle of constructive laziness – at first an accidental discovery\, but later a more mindful ethos. URL:https://datascience.ucsd.edu/event/compassionate-constructive-laziness-brad-voytek/ LOCATION:Henry Booker Room\, Jacobs Hall 2512 CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/05/TNT-Academy-and-Triton-Neurotech-BVoytek-e1714580971303.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240423T110000 DTEND;TZID=America/Los_Angeles:20240423T120000 DTSTAMP:20260529T123044 CREATED:20240501T163343Z LAST-MODIFIED:20240501T164107Z UID:10000474-1713870000-1713873600@datascience.ucsd.edu SUMMARY:Building and Deploying Large Language Model Applications Efficiently and Verifiably | Ying Sheng DESCRIPTION:Abstract:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe applications of large language models (LLMs) are increasingly complex and diverse\, necessitating efficient and reliable frameworks for building and deploying them. In this talk\, I will begin with algorithms and systems for serving LLMs for everyone (FlexGen\, S-LoRA\, VTC)\, highlighting the growing trend of personalized LLM services. My work addresses the need to run LLMs locally for isolated individual needs. It also tackles the problem of efficiency and service fairness when resource sharing among many users is required. Once we have efficient deployment\, a primary concern is the reliability of generation. The second part of this talk aims to address this issue by exploring verifiable code generation. To achieve this\, I adopt tools in formal verification to facilitate LLMs in generating correctness certificates alongside other artifacts (Clover). Finally\, I will touch on future research avenues\, such as integrating formal methods with LLMs and developing programming systems for generative AI. \nBio:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYing Sheng is a Ph.D. candidate in Computer Science at Stanford University\, advised by Clark Barrett. Her research focuses on building and deploying large language model applications\, emphasizing accessibility\, efficiency\, programmability\, and verifiability. Ying has authored numerous papers in top-tier AI\, system\, and automated reasoning conferences and journals\, such as NeurIPS\, ICML\, ICLR\, OSDI\, SOSP\, IJCAR\, and JAR. Her work has received a Best Paper award (as first author) at IJCAR and a Best Tool Paper award at TACAS. As a core member of the LMSYS Org\, she has developed influential open models\, datasets\, systems\, and evaluation tools\, such as Vicuna\, Chatbot Arena\, and SGLang. Ying is a recipient of the Machine Learning and Systems Rising Stars Award (2023) and the a16z Open Source AI Grant (2023). More information about her can be found at https://sites.google.com/view/yingsheng. URL:https://datascience.ucsd.edu/event/building-and-deploying-large-language-model-applications-efficiently-and-verifiably-ying-sheng/ LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240417T100000 DTEND;TZID=America/Los_Angeles:20240417T110000 DTSTAMP:20260529T123044 CREATED:20240409T185225Z LAST-MODIFIED:20240409T185225Z UID:10000469-1713348000-1713351600@datascience.ucsd.edu SUMMARY:TILOS Seminar: Transformers learn in-context by (functional) gradient descent DESCRIPTION:Transformers learn in-context by (functional) gradient descent\nXiang Cheng\, TILOS Postdoctoral Scholar at MIT\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Motivated by the in-context learning phenomenon\, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent\, which enables it to learn linear functions in-context. More generally\, we show that a non-linear Transformer can implement functional gradient descent with respect to some RKHS metric\, which allows it to learn a broad class of functions in-context. Additionally\, we show that the RKHS metric is determined by the choice of attention activation\, and that the optimal choice of attention activation depends in a natural way on the class of functions that need to be learned. I will end by discussing some implications of our results for the choice and design of Transformer architectures. URL:https://datascience.ucsd.edu/event/tilos-seminar-transformers-learn-in-context-by-functional-gradient-descent/ LOCATION:Virtual CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240416T140000 DTEND;TZID=America/Los_Angeles:20240416T150000 DTSTAMP:20260529T123044 CREATED:20240410T170408Z LAST-MODIFIED:20240410T170614Z UID:10000472-1713276000-1713279600@datascience.ucsd.edu SUMMARY:Internships: The Student Perspective DESCRIPTION: URL:https://datascience-ucsd.12twenty.com/Login CATEGORIES:Workshops END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240416T110000 DTEND;TZID=America/Los_Angeles:20240416T120000 DTSTAMP:20260529T123044 CREATED:20240501T164203Z LAST-MODIFIED:20240501T164547Z UID:10000473-1713265200-1713268800@datascience.ucsd.edu SUMMARY:Unlocking musical creativity with generative AI | Chris Donahue DESCRIPTION:Abstract:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn this talk\, I will present my work on developing and responsibly deploying generative AI systems that unlock and augment human creative potential in music. While we all possess a remarkably sophisticated intuition for and appreciation of music\, conventional tools for creative musical expression (e.g.\, instruments\, music notation) are inaccessible to those of us without formal training. To lower the barrier to entry\, I develop generative AI systems with intuitive forms of control (e.g.\, singing) that allow users to easily translate their ideas into music. My research also aims to augment the creative potential of experts. To this end\, I develop generative AI methods that can support realistic co-creation workflows for musicians\, analogous to tools like Copilot for programmers. \nMethodologically\, my work often centers around language models (LMs)\, and involves building new LM methods to confront the unique challenges posed by the domain of music\, such as modeling long sequences and understanding multimodal relationships. Another challenge of working in creative domains is evaluation—to confront this\, my work involves deploying systems to real-world users\, so that we may better understand how systems help users accomplish real creative goals. More broadly\, we are in the midst of a pivotal moment in music AI research\, where technological developments are suddenly translating into real-world impact. Accordingly\, I will discuss how I approach my current and future research goals in a responsible fashion\, commensurate with the broad economic\, cultural\, and social importance of music. \nBio:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChris Donahue is an Assistant Professor in the Computer Science Department at CMU\, and a part-time research scientist at Google DeepMind working on the Magenta project. His research goal is to develop and responsibly deploy generative AI for music and creativity\, thereby unlocking and augmenting human creative potential. In practice\, this involves improving machine learning methods for controllable generative modeling of music and audio\, and deploying real-world interactive systems that allow anyone to harness generative music AI to accomplish their creative goals through intuitive forms of control. Chris’s research has been featured in live performances by professional musicians like The Flaming Lips\, and also empowers hundreds of daily users to convert their favorite music into interactive content through his website Beat Sage. His work has also received coverage from MIT Tech Review\, The Verge\, Business Insider\, and Pitchfork. Before CMU\, Chris was a postdoctoral scholar in the CS department at Stanford advised by Percy Liang. Chris holds a PhD from UC San Diego where he was jointly advised by Miller Puckette (music) and Julian McAuley (CS). URL:https://datascience.ucsd.edu/event/unlocking-musical-creativity-with-generative-ai-chris-donahue/ LOCATION:Computer Science and Engineering Building\, 3235 Voigt Dr\, La Jolla\, CA 92093\, USA\, Room 1242 CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240411T160000 DTEND;TZID=America/Los_Angeles:20240411T170000 DTSTAMP:20260529T123044 CREATED:20240409T185440Z LAST-MODIFIED:20240409T185440Z UID:10000471-1712851200-1712854800@datascience.ucsd.edu SUMMARY:Virtual Alumni Spotlight DESCRIPTION: URL:https://datascience.ucsd.edu/event/virtual-alumni-spotlight/ LOCATION:https://ucsd.zoom.us/j/93048333550 CATEGORIES:Seminar ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2024/03/HDSI_Alumni_Spotlight_Flyer_V1-scaled-e1712855056543.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240410T140000 DTEND;TZID=America/Los_Angeles:20240410T153000 DTSTAMP:20260529T123044 CREATED:20240409T185628Z LAST-MODIFIED:20240409T190531Z UID:10000470-1712757600-1712763000@datascience.ucsd.edu SUMMARY:Making the Most of Your Camera | James Tompkin DESCRIPTION:Abstract: Images are everywhere\, especially images of the real world\, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive tools for visualization and analysis. These methods aid real world sensing and measurement\, scientific and medical imaging\, and media and the arts. The past three years in visual computing have been populated by methods in neural fields – a flexible way to solve the inverse problems required to reconstruct visual scene models. To make the most of the many images from our cameras\, we must be able to scalably reconstruct large scenes from thousands of images\, and I will discuss how to achieve this with hybrid neural fields. Further\, to make the most of active illumination sensors in our cameras\, we must be able to integrate their different signals\, and I will discuss a physically-based neural field to achieve this\, including for dynamic scenes. Finally\, I will contextualize these tools within ongoing discussions around data and 3D learning. \nBio: James Tompkin (jamestompkin.com) is the John E. Savage Assistant Professor of Computer Science at Brown University. His research at the intersection of computer vision\, computer graphics\, and human-computer interaction helps develop new visual computing tools. His doctoral work at University College London studied large-scale video processing and exploration techniques\, and postdoctoral work at Max-Planck-Institute for Informatics and Harvard University helped create new methods to edit content within images and videos. Recent research has developed new techniques for low-level scene reconstruction\, view synthesis for VR\, and content editing and generation. URL:https://datascience.ucsd.edu/event/making-the-most-of-your-camera-james-tompkin/ 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:20240405T123000 DTEND;TZID=America/Los_Angeles:20240405T140000 DTSTAMP:20260529T123044 CREATED:20240329T001842Z LAST-MODIFIED:20240329T001950Z UID:10000468-1712320200-1712325600@datascience.ucsd.edu SUMMARY:"Advancing NLP for Timely and Actionable Feedback in Healthcare Conversations" | Veronica Perez-Rosas DESCRIPTION:Abstract: “Effective communication is crucial in healthcare for ensuring successful clinical interactions\, as it affects how patients respond\, the decisions being made by both patients and clinicians\, and the outcomes of treatments. Recent developments in Natural Language Processing (NLP) aim to improve and support these interactions within clinical settings. In this talk\, I will discuss my research on offering timely and actionable evaluative feedback for mental healthcare interactions\, addressing a crucial bottleneck in effective mental healthcare delivery. I will specifically focus on computational approaches for building conversational systems to aid in psychotherapy training\, and present two NLP tasks to generate language-based feedback: (1) generating counselor responses following established counseling strategies\, and (2) offering alternative rewrites to counseling trainees’ responses to refine their counseling skills. I will conclude the talk by outlining future directions towards my long-term agenda of building computational approaches that understand\, model\, and predict health behaviors while also being human-centric and scalable” \nBio: “Veronica Perez-Rosas is an Assistant Research Scientist at the University of Michigan. She received her Ph.D. in Computer Science and Engineering from the University of North Texas in 2014\, and was a postdoctoral fellow at the University of Michigan until 2016. Her research interests include Natural Language Processing\, Machine Learning\, Affect Recognition\, and Multimodal Processing of Human Behavior. Her research focuses on developing computational methods to analyze\, recognize\, and predict human behaviors during social interactions. She has authored papers in leading conferences and journals in Natural Language Processing and Multimodal Processing\, has mentored numerous students in these research areas\, and has served as workshop chair or area chair for multiple international conferences in the field.” URL:https://datascience.ucsd.edu/event/advancing-nlp-for-timely-and-actionable-feedback-in-healthcare-conversations-veronica-perez-rosas/ 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:20240404T080000 DTEND;TZID=America/Los_Angeles:20240405T120000 DTSTAMP:20260529T123044 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:20240403T140000 DTEND;TZID=America/Los_Angeles:20240403T153000 DTSTAMP:20260529T123044 CREATED:20240326T221709Z LAST-MODIFIED:20240329T001345Z UID:10000462-1712152800-1712158200@datascience.ucsd.edu SUMMARY:"Contextualized learning for adaptive yet persistent AI in biomedicine" | Ben Lengerich DESCRIPTION:Abstract: “In biomedical data analysis\, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution\, context-specific insights by integrating information across datasets\, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim\, this presentation will examine “contextualized learning”\, a meta-learning paradigm which learns relationships between dataset context and statistical parameters. Using contextualized network inference as an illustrative example\, I will show how we can estimate context-specific graphical models\, offering insights such as personalized gene expression analysis for SOTA cancer subtyping. The talk will also discuss trends towards “contextualized understanding”\, bridging statistical and foundation models to standardize interpretability. The primary aim is to illustrate how contextualized learning and understanding contribute to creating learning systems that are both adaptive and persistent\, facilitating cross-context information sharing and detailed analysis.” \nBio: “Ben Lengerich is a Postdoctoral Associate and Alana Fellow at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Broad Institute of MIT and Harvard\, where he is advised by Manolis Kellis. His research in machine learning and computational biology emphasizes the use of context-adaptive models to understand complex diseases and advance precision medicine. Through his work\, Ben aims to bridge the gap between data-driven insights and actionable medical interventions. He holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University\, where he was advised by Eric Xing. His work has been recognized with spotlight presentations at conferences including NeurIPS\, ISMB\, AMIA\, and SMFM\, financial support from the Alana Foundation\, selection as a “”Rising Star in Data Science” by the University of Chicago and UC San Diego\, and “”Next Generation in Biomedicine”” by the Broad Institute.” URL:https://datascience.ucsd.edu/event/special-seminar-ben-lengerich/ LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202 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:20240403T120000 DTEND;TZID=America/Los_Angeles:20240403T133000 DTSTAMP:20260529T123044 CREATED:20240327T215044Z LAST-MODIFIED:20240401T225533Z UID:10000466-1712145600-1712151000@datascience.ucsd.edu SUMMARY:MathWorks & HDSI AI Seminar | Esperanza Linares DESCRIPTION:HDSI! Come and join MathWorks Engineers for a technical seminar on AI (and lunch!) on Wednesday\, April 3! Come learn why data scientists should learn MATLAB – we will highlight the tools that will be serve your role as data scientists and data science students. You can also learn about our engineer’s journey\, roles available at MathWorks\, and the use of our tools in industry! \nMathworks UCSD Technical Seminar Series \nLow-Code AI in MATLAB \nLearn how you can apply AI in your field without extensive knowledge in programming. This hands-on session includes a quick recap on the fundamentals of AI and three exercises where you will learn how to classify human activities using MATLAB® interactive tools and apps: \n1. Accessing and preprocessing data acquired from a mobile device\n2. Applying clustering to the unlabeled data using the Cluster Data Live Editor Task\n3. Classifying the labeled data using two apps: Classification Learner app and the Deep Network Designer app \nAt the end of the seminar\, you will be able to design and train different machine learning and deep learning models without extensive programming knowledge. You will also learn how to automatically generate code from the interactive workflow. This will not only help you to reuse the models without manually going through all the steps but also to learn programming or advance your coding skills. \nAbout the Speaker: \nEsperanza Linares is a Senior Customer Success Engineer at MathWorks. She is part of a global team that partners with academic and research institutions worldwide\, focusing on student and research success. Before joining MathWorks\, she did her postdoctoral work in the pharmaceutical industry\, where she developed a discrete element method model to simulate the compaction of granular materials. She holds a BS in Mechanical Engineering from UNAM (Mexico) and a Ph.D. in Mechanical Engineering from Caltech. \nRegistration Link: https://forms.office.com/Pages/ResponsePage.aspx?id=ETrdmUhDaESb3eUHKx3B5tTIy0i-nn1KjKWuEYZzK09UNVNXNFM4NTA3Q045REVJWUNHNjcxUkZSTi4u \n*Lunch will be provided URL:https://datascience.ucsd.edu/event/mathworks-hdsi-ai-seminar-esperanza-linares/ 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/2023/03/mathworks_logo.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240402T140000 DTEND;TZID=America/Los_Angeles:20240402T153000 DTSTAMP:20260529T123044 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:20260529T123044 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:20240401T110000 DTEND;TZID=America/Los_Angeles:20240401T123000 DTSTAMP:20260529T123044 CREATED:20240328T234406Z LAST-MODIFIED:20240328T234737Z UID:10000467-1711969200-1711974600@datascience.ucsd.edu SUMMARY:How Do We Get There?: Toward Intelligent Behavior Intervention | Xuhai Xu DESCRIPTION:Abstract: As the intelligence of everyday smart devices continues to evolve\, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline for health — combining behavior modeling & interaction design — seems to be within reach. How do we get there? \nIn this talk\, I will introduce a comprehensive intervention pipeline that bridges behavior science theory-driven designs and generalizable behavior models. I will also introduce my efforts on passive sensing datasets\, human-centered algorithms\, and a benchmark platform that drives the community toward more robust and deployable intervention systems for health and well-being. \nBio: Xuhai “Orson” Xu is a postdoc at MIT EECS. He received his PhD at the University of Washington. Specializing in human-computer interaction\, applied machine learning\, and health\, Xu develops intelligent behavior intervention systems to promote human health and well-being. His research covers two aspects — 1) building deployable human-centered behavior models and 2) designing interactive user experiences — to establish a complete system to improve end-users’ well-being. Moreover\, his research also goes beyond end-users and supports health experts by designing new human-AI collaboration paradigms in clinical settings. Xu has earned several awards\, including 9 Best Paper\, Best Paper Honorable Mention\, and Best Artifact awards. His research has been covered by media outlets such as the Washington Post and ACM News. He was recognized as the Outstanding Student Award Winner at UbiComp 2022\, the 2023 UW Distinguished Dissertation Award\, and the 2024 Innovation and Technology Award at the Western Association of Graduate Schools. \nZoom: https://ucsd.zoom.us/j/92792843021\nPassword: 741675 URL:https://datascience.ucsd.edu/event/how-do-we-get-there-toward-intelligent-behavior-intervention-xuhai-xu/ LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 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:20260529T123044 CREATED:20240326T031112Z LAST-MODIFIED:20240326T031112Z UID:10000464-1711634400-1711639800@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. \nSpeaker Bio: 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. He 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/the-emergence-of-reproducibility-and-generalizability-in-diffusion-models-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:20240328T140000 DTEND;TZID=America/Los_Angeles:20240328T153000 DTSTAMP:20260529T123044 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:20260529T123044 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:20260529T123044 CREATED:20240323T081729Z LAST-MODIFIED:20240323T081729Z UID:10000463-1711461600-1711467000@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.” \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/making-machine-learning-predictably-reliable-andrew-ilyas/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240326T140000 DTEND;TZID=America/Los_Angeles:20240326T153000 DTSTAMP:20260529T123044 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:20260529T123044 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 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:20240320T140000 DTEND;TZID=America/Los_Angeles:20240320T153000 DTSTAMP:20260529T123044 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 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:20240320T100000 DTEND;TZID=America/Los_Angeles:20240320T110000 DTSTAMP:20260529T123044 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 ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT END:VCALENDAR