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-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
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: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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20250309T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20251102T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20260308T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20261101T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20270314T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20271107T080000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260603
DTEND;VALUE=DATE:20260605
DTSTAMP:20260602T210906
CREATED:20260309T221545Z
LAST-MODIFIED:20260423T155836Z
UID:10000552-1780444800-1780617599@datascience.ucsd.edu
SUMMARY:CVPR Workshop
DESCRIPTION:We are excited to announce the CVPR 2026 Workshop on Trustworthy\, Robust\, Uncertainty-Aware\, and Explainable Visual Intelligence and Beyond (TRUE-V)\, which will be held in Denver\, Colorado\, USA on June 3 or 4\, 2026. \nModern vision and vision-language systems are increasingly deployed in safety-critical and real-world settings\, yet they remain opaque\, brittle\, and difficult to calibrate. TRUE-V aims to advance principled foundations and practical methodologies for trustworthy visual intelligence\, spanning interpretability\, robustness\, uncertainty\, alignment\, and responsible deployment. \nWe welcome theoretical\, methodological\, and applied contributions that push forward the science and practice of trustworthy AI in vision and beyond. The topics of interest includes aspects that can help to advance Trustworthy Visual Intelligence\, including (but not limited to): \n• Interpretable and explainable computer vision\n• Robustness and reliability under distribution shift\n• Uncertainty estimation and trust calibration\n• Concept bottleneck & modular architectures\n• Alignment\, safety\, and ethical considerations in vision models\n• Human-in-the-loop evaluation and Evaluation benchmarks\n• Trustworthy deployment in high-stakes domains\n• Vision-language and multimodal reasoning under uncertainty \nSubmission are now open through OpenReview at: \nhttps://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/TRUE-V. \nWe welcome and encourage the authors to consult the submission guidelines and important dates listed on the workshop website: https://trustworthy-ai-workshop.github.io/cvpr2026-TRUE-V/ \nThe paper submission deadline is Mar 20\, 2026 at 23:59 AoE. Accepted papers will be presented as posters\, with a subset selected for spotlight talks. Please feel free to reach out to the organizers directly if you have any questions. \nWe welcome your submissions and look forward to your contributions! Please feel free to forward this CFP to your networks. \nThis workshop is organized by: \n– Lily Weng (UC San Diego\, lweng@ucsd.edu)\,\n– Nghia Hoang (Washington State University\, trongnghia.hoang@wsu.edu)\,\n– Tammy Riklin Raviv (Ben Gurion University\, rrtammy@bgu.ac.il)\n– Giuseppe Raffa (Intel Labs\, giuseppe.raffa@intel.com)\n– Arno Blaas (Apple\, ablaas@apple.com)\n– Eunji Kim (Amazon\, kce407@snu.ac.kr)\n– Bhavya Kailkhura (LLNL\, kailkhura1@llnl.gov)\n– Kowshik Thopalli (LLNL\, thopalli1@llnl.gov)
URL:https://datascience.ucsd.edu/event/cvpr-workshop-trustworthy-robust-uncertainty-aware-and-explainable-visual-intelligence-and-beyond/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20260603T130000
DTEND;TZID=America/Denver:20260603T170000
DTSTAMP:20260602T210906
CREATED:20260602T220949Z
LAST-MODIFIED:20260602T220949Z
UID:10000563-1780491600-1780506000@datascience.ucsd.edu
SUMMARY:CVPR Tutorial: Principled Interpretability in Vision Models
DESCRIPTION:CVPR 2026 Tutorial\nIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)\nDate: June 3\, 2026 (start at 1 pm) ·  Location: Mile High 3C\, Denver\, CO\, USA \n\nOverview\nAs deep learning systems are increasingly deployed in high-stakes applications\, understanding their behavior is critical for ensuring trust and safety. Interpretability provides essential tools to explain\, debug\, and improve these models. However\, the field remains fragmented\, spanning a wide range of methods and assumptions\, while lacking standardized evaluation protocols. \n\nThis tutorial aims to provide a unified overview of interpretability in deep learning – bridging post-hoc mechanistic understanding and methods to design inherently interpretable deep learning models.\nBy the end of this tutorial\, attendees will gain a solid understanding of modern interpretability methods for deep learning models\, how to rigorously evaluate them\, and open research directions in this critical area.\n\n\nTutorial Outline\n\nPost-hoc mechanistic interpretability: Methods that analyze model internals at different levels of granularity (neurons\, layers\, circuits)\, with strengths and limitations.\nFaithfulness and reliability evaluation: Protocols and standardized metrics for assessing interpretability methods and producing actionable explanations.\nInterpretable DNN models by design: Concept bottleneck models and related approaches that align internal representations with human-understandable concepts.\nApplications: Debugging\, model editing\, and safety auditing in practical settings.\n\n\nAgenda (New!)\nOur tutorial is on Wednesday\, June 3rd afternoon session\, 1-5 pm: \n\nPart 1: Introduction & Backgrounds\nPart 2: Post-hoc model-level interpretability\nPart 3: Faithful and reliability evaluations\nPart 4: Interpretable DNN models by design\nPart 5: Applications\, Demos\, and Technical Q/A\n\nIntended Audience\nThis tutorial is intended for researchers and practitioners working on computer vision and modern deep learning systems\, as well as graduate students entering interpretability research. No prior experience in interpretability is required. \nMaterials\nPlease stay tuned on the Tutorial schedule and Agenda! Slides and supplementary materials will be posted here after the tutorial. \n\nOrganizers and Contact\n✉️ Lily Weng (lweng@ucsd.edu)\, Tuomas Oikarinen (toikarinen@ucsd.edu)\, Ge Yan (geyan@ucsd.edu)\, Akshay Kulkarni (a2kulkarni@ucsd.edu)
URL:https://datascience.ucsd.edu/event/cvpr-tutorial-principled-interpretability-in-vision-models/
LOCATION:Colorado Convention Center
CATEGORIES:Workshops
END:VEVENT
END:VCALENDAR