BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Astrobiology - ECPv6.18.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Astrobiology
X-ORIGINAL-URL:https://astrobiology.com
X-WR-CALDESC:Events for Astrobiology
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
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:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20261027T080000
DTEND;TZID=America/Denver:20261027T113000
DTSTAMP:20261027T013614Z
CREATED:20261027T013614Z
LAST-MODIFIED:20261027T013614Z
UID:64062-1793088000-1793100600@astrobiology.com
SUMMARY:Westminster University Presents: Astrobiology and Utah with Dr. Bonnie Baxter
DESCRIPTION:Mars orbiters and rovers have mapped extensive evaporite deposits on Mars\, similar in chemistry to the Bonneville Salt Flats and the playa around Great Salt Lake. Where the Perseverance rover is coring samples\, the sulfate minerals are much like those we are exploring in the lakebed. The lake mineral deposits can entomb living microorganisms and biological molecules. If life was ever in the water that evaporated from Jezero Crater\, could Perseverance discover microorganisms or their remnants in the mineral cores? Come hear how our research at Westminster’s Great Salt Lake Institute is informing these astrobiology investigations.
URL:https://astrobiology.com/event/westminster-university-presents-astrobiology-and-utah-with-dr-bonnie-baxter/
LOCATION:Westminster University\, 1840 S 1300 E\, Salt Lake City\, UT\, United States
ATTACH;FMTTYPE=image/png:https://astrobiology.com/wp-content/uploads/2026/10/westminster.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261027T150000
DTEND;TZID=America/New_York:20261027T170000
DTSTAMP:20261027T001158Z
CREATED:20261027T001158Z
LAST-MODIFIED:20261027T001158Z
UID:64049-1793113200-1793120400@astrobiology.com
SUMMARY:NASA Ames Machine Learning Seminar INDUS and INDUS-SDE: Scientific Language Models for Metadata Curation and Discovery Across NASA Science Divisions
DESCRIPTION:JOIN THIS ML SEMINAR SERIES HERE \nTime: October 27th\, 12PM PDT/3PM EDT \nEveryone is invited to the monthly machine learning virtual seminar hosted by the ML-Astrobiology Initiative at NASA Ames Research Center. Previously recorded seminars can be found on the ML-Astrobiology website. \nThe speaker for this event will be Dr. Kumar Ramasubramanian from the NASA-OSDI team and University of Alabama. \nTitle: INDUS and INDUS-SDE: Scientific Language Models for Metadata Curation and Discovery Across NASA Science Divisions \nAbstract: \nScientific discovery in astrobiology depends on content drawn from planetary science\, Earth science\, biology\, and astrophysics\, each with its own terminology\, and distributed across mission websites\, data portals\, technical reports\, and publications with incomplete or inconsistent metadata. \nGeneral-purpose large language models do not resolve this fragmentation. Their answers are bounded by what their training data or retrieval layer happened to cover\, and content that was never indexed or tagged remains invisible regardless of the interface used to query it. A query for all historical Raman spectroscopy data from Mars therefore returns a plausible subset with no guarantee of completeness. \nWe present INDUS\, a family of compact encoder language models developed to address this problem at the level of the archive rather than the query. INDUS models are pretrained on NASA science text across five science divisions and are designed to curate heterogeneous scientific content at scale: assigning controlled-vocabulary keywords\, classifying documents into topical portals\, routing content to the appropriate science division\, and producing embeddings for semantic retrieval. Because they are encoders\, inference cost per document is low enough to process the full corpus continuously rather than sample from it. \nThe first generation of INDUS outperformed general-purpose and scientific baselines on NASA tasks and is deployed in NASA’s Science Discovery Engine. The second generation\, INDUS-SDE\, extends this work to the noisy\, web-sourced content that constitutes most of the archive. \nWe introduce Weighted Dynamic Masking\, a pretraining strategy that prioritizes domain-relevant terminology over structural boilerplate such as website headers\, and an extended 1024-token context window suited to full metadata records. Evaluated on approximately 600\,000 documents\, INDUS-SDE matches domain-specific models trained on substantially more in-domain data\, and its companion sentence transformer\, INDUS-SDE-ST\, supports semantic discovery over the curated corpus. \nFor astrobiology\, this yields a shared representation space in which terminology from different disciplines aligns. In this talk\, we discuss the utility of INDUS models for astrobiology\, with examples of how they can be adapted to a new vocabulary and downstream task from a set of expert-labeled examples. We describe the deployed system\, the requirements for such adaptation\, and the role of community-contributed labeled data in extending INDUS to astrobiology-focused mission development.
URL:https://astrobiology.com/event/nasa-ames-machine-learning-seminar-indus-and-indus-sde-scientific-language-models-for-metadata-curation-and-discovery-across-nasa-science-divisions/
LOCATION:Online – Webinar
ATTACH;FMTTYPE=image/png:https://astrobiology.com/wp-content/uploads/2026/03/NASA-Ames-AIML-Seminar-Series-Machine-Learning.png
END:VEVENT
END:VCALENDAR