BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://events.ucsc.edu
X-WR-CALDESC:Events for Events
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: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:20251107T140000
DTEND;TZID=America/Los_Angeles:20251107T160000
DTSTAMP:20260418T021041
CREATED:20251021T162001Z
LAST-MODIFIED:20251023T212553Z
UID:10004958-1762524000-1762531200@events.ucsc.edu
SUMMARY:Wang\, S. (CSE) - Learned Hashing and Overlay Networks for AI-native Retrieval and Serving at Scale
DESCRIPTION:Modern AI systems demand low-latency high-quality retrieval and serving over billion-scale keys and vectors. This proposal studies learned hashing and overlay networks to co-locate semantically related items and steer queries with minimal coordination. We first present LEAD\, to our knowledge the first use of order-preserving learned hash functions in distributed key-value overlays\, enabling efficient range queries and cutting hops/messages by 80–90% in prototypes while retaining balance and churn resilience. Second\, Vortex applies learned hashing to approximate nearest-neighbor retrieval: a self-organizing overlay binding learned keys to distributed HNSW indexes to achieve high recall at low fan-out. Third\, PlanetServe introduces onion-style path setup with multi-path dispersal and cache-aware forwarding for open LLM serving\, reducing TTFT and latency while preserving privacy. Planned work generalizes learned hashing to embedding partitions\, token/KV caches\, programmable switches\, and storage tiers\, and provides formal convergence\, load-balancing\, and monotonic-progress guarantees under skew and churn. We are also working to design the first knowledge delivery network for LLM serving: an overlay that unifies data placement\, retrieval\, and policy-aware routing across clusters and providers with tunable cost\, privacy\, and quality. Evaluation on real workloads at scale will measure recall\, tail latency\, cost\, and robustness\, targeting a predictable\, elastic\, scalable AI-native retrieval and serving stack. \nEvent Host: Shengze Wang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian \n  \nZoom: https://ucsc.zoom.us/j/5455463199?pwd=bHRVM01Vd20rcVpkc0FQY01kZG1UUT09&omn=98106984546 \nPasscode: 2121
URL:https://events.ucsc.edu/event/wang-s-cse-learned-hashing-and-overlay-networks-for-ai-native-retrieval-and-serving-at-scale/
LOCATION:https://events.ucsc.edu/event/wang-s-cse-learned-hashing-and-overlay-networks-for-ai-native-retrieval-and-serving-at-scale/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2025/10/option-3-1.png
END:VEVENT
END:VCALENDAR