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DTSTART;TZID=America/Los_Angeles:20251107T140000
DTEND;TZID=America/Los_Angeles:20251107T160000
DTSTAMP:20260417T050250
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251110T130000
DTEND;TZID=America/Los_Angeles:20251110T150000
DTSTAMP:20260417T050250
CREATED:20251028T155007Z
LAST-MODIFIED:20251028T155148Z
UID:10005010-1762779600-1762786800@events.ucsc.edu
SUMMARY:Nguyen\, R. (BMEB) - Development of Computational Methods for Reliable Genetic Identification of Forensic Samples
DESCRIPTION:Advances in sequencing technologies have enabled the recovery of genetic data from minimal\, contaminated\, and highly degraded samples\, overcoming long-standing barriers in forensic analysis. Nevertheless\, many evidentiary samples still yield poor-quality DNA that is unconducive to PCR amplification of short tandem repeats (STRs)\, microarray genotyping\, or deep sequencing necessary for accurate\, complete genotype calls. \nThis dissertation addresses these challenges through the development of computational methods for reliable identity analysis of forensic samples. First\, I present IBDGem\, a fast and robust computational procedure for detecting identity-by-descent (IBD) regions by comparing low-coverage sequence data from an unknown sample against SNP genotype calls from a known individual. Using data from the 1000 Genomes Project and a panel of 8 rootless hairs\, I demonstrate that IBDGem can detect relatedness segments at 1x coverage and achieve high-confidence identifications with as little as 0.01x coverage. \nThe next part of my thesis examines the characteristics of DNA derived from single\, rootless hairs and evaluates their potential as a source of forensic genetic information. Analyses of 80 rootless hair samples reveal DNA fragmentation patterns associated with endonuclease-mediated degradation and nucleosome positioning. This chapter also shows that even short segments of rootless hair shafts can yield adequate sequence data to generate statistical support for or against identity. \nFinally\, I present a comprehensive analysis of IBDGem’s performance across a range of data conditions and program settings. I find that IBDGem is robust to moderate input errors and can identify the major contributor in two-person mixtures. The method also reliably distinguishes self-comparisons from close-relative comparisons\, and remains effective even when limited to 94 target SNPs in the ForenSeq assay. Overall\, these findings establish IBDGem as a practical tool for analyzing trace DNA evidence when conventional methods are unsuccessful. \nEvent Host: Remy Nguyen\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Ed Green \n  \nZoom- https://ucsc.zoom.us/j/91522009894?pwd=JWPSUcIi7IaZ4YOeLDQJohyRApos4T.1 \nPasscode- 854645
URL:https://events.ucsc.edu/event/nguyen-r-bmeb-development-of-computational-methods-for-reliable-genetic-identification-of-forensic-samples/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251113T100000
DTEND;TZID=America/Los_Angeles:20251113T120000
DTSTAMP:20260417T050250
CREATED:20251110T222658Z
LAST-MODIFIED:20251110T222748Z
UID:10005131-1763028000-1763035200@events.ucsc.edu
SUMMARY:Petety\, A. (CSE) -  New Algorithmic Methods for Uncertain Inputs
DESCRIPTION:This dissertation focuses on designing and proving performance guarantees on algorithms when there is uncertainty in the input. The uncertainty could be from the user being unsure or future inputs that have not arrived yet. We look at different methods in which algorithms can be designed to be competitive against the optimal. One of the assumptions that helps in this is to assume that the input arrival order is completely random. We study the online load/graph balancing problem when the input arrival order is uniformly random. We show lower bounds for the greedy algorithm and the general case. In the next part\, we study the online scheduling problem under the assumption that the online algorithm has an additional ϵ speed compared to the machines in offline optimal. We show a meta algorithm generalizing Shortest Remaining Processing Time that gives a scalable algorithm for minimizing total weighted flow time. We show that it achieves scalability for minimizing total weighted flow time when the residual optimum exhibits supermodularity. In the final part we look at the online caching problem when the algorithm has access to ML-augmented predictions. We propose an algorithm that achieves a O(logb k) competitive ratio even when using just b predictions per cache miss. We also prove its robustness and consistency. \nEvent Host: Aditya Petety\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \n 
URL:https://events.ucsc.edu/event/petety-a-cse-new-algorithmic-methods-for-uncertain-inputs/
LOCATION:CA
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251120T090000
DTEND;TZID=America/Los_Angeles:20251120T100000
DTSTAMP:20260417T050250
CREATED:20251118T162058Z
LAST-MODIFIED:20251118T162058Z
UID:10005178-1763629200-1763632800@events.ucsc.edu
SUMMARY:Jorquera\, Z. (CSE) - Quantum Entanglement Bounds and the Approximation Algorithms That Use Them
DESCRIPTION:One of the central challenges in quantum computing is finding or approximating the ground-state energy of a local Hamiltonian\, a quantum analogue of classical constraint satisfaction problems (CSPs). Among these\, the Quantum Max-Cut problem serves as a canonical example\, paralleling the classical Max-Cut problem. Despite its foundational importance in both theoretical computer science and condensed matter physics\, our understanding of approximation algorithms for Quantum Max-Cut and related local Hamiltonian problems remains limited\, primarily due to the difficulty of representing and optimizing over entangled quantum states. \nIn this advancement talk\, we introduce the quantum information background needed to contextualize the results and the significance of the proposed future work by drawing an analogy to classical optimization. We then investigate approximation algorithms for 2-local Hamiltonians beyond qubit systems\, focusing on higher-dimensional qudit analogues\, such as Quantum Max-d-Cut and a new problem we introduce: the Maximal Entanglement problem. We establish new entanglement upper bounds for these problems based on the star bound\, a key tool for analyzing entanglement monogamy in Hamiltonian optimization. For the Maximal Entanglement problem\, we show that these bounds can be efficiently certified via semidefinite programs (SDPs) and that they directly admit a (1/d + O(1/D))-approximation algorithm (where D is the degree of the interaction graph)\, which beats random assignment. For Quantum Max-d-Cut\, the star bound gives a more complicated notion of entanglement\, for which we show that the basic SDP can verify this bound for all reduced marginals on up to five vertices when d=3\, but likely fails for larger subgraphs. We further propose that b-matchings\, with b = d-1\, capture the appropriate notion of entanglement for these higher-dimensional Quantum Max-d-Cut systems\, analogous to matchings in the qubit/Quantum Max-Cut case. Leveraging this insight\, we design a novel 2-matching-based algorithm that outperforms existing approaches for Quantum Max-3-Cut\, giving an approximation ratio of 0.555. \nThe present work advances the theoretical framework for understanding approximations in qudit Hamiltonians and highlights open directions for certifying quantum upper bounds as well as finding lower bounds via approximation algorithms. \n  \nEvent Host: Zack Jorquera\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Alexandra Kolla  \nZoom- https://ucsc.zoom.us/j/98034235739?pwd=k260nd9labWT8xoQ9Cv3m2TATGw7VB.1
URL:https://events.ucsc.edu/event/jorquera-z-cse-quantum-entanglement-bounds-and-the-approximation-algorithms-that-use-them/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251121T123000
DTEND;TZID=America/Los_Angeles:20251121T140000
DTSTAMP:20260417T050250
CREATED:20251118T163526Z
LAST-MODIFIED:20251118T163526Z
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SUMMARY:Ramollari\, H. (ECE) - An Optofluidic Spectrometer and Applications in Biosensing
DESCRIPTION:Miniaturized spectrometers have the potential to replace bulky and expensive benchtop models. We have previously demonstrated a multimode interference (MMI) waveguide-based spectrometer that achieves high performance while minimizing its footprint. \nIn this talk\, the integration of the MMI spectrometer into an optofluidic device is proposed. This integration opens up applications such as the detection of single particle fluorescence spectra and absorption spectra. \nMoreover\, adding a metasurface to the spectrometer waveguide is expected to enhance the sensitivity of single particle detection and simplify the analysis methods. \nFinally\, to improve the MMI waveguide spectrometer a new nanophotonic platform is proposed. \nEvent Host: Helio Ramollari\, Ph.D. Student\, Electrical Engineering  \nAdvisor: Holger Schmidt  \nZoom- https://ucsc.zoom.us/j/99623652977?pwd=j2hy77fV9jdGuEzI0iGa5JVAa35W1b.1 \nPasscode- 576057
URL:https://events.ucsc.edu/event/ramollari-h-ece-an-optofluidic-spectrometer-and-applications-in-biosensing/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251121T140000
DTEND;TZID=America/Los_Angeles:20251121T160000
DTSTAMP:20260417T050250
CREATED:20251021T182427Z
LAST-MODIFIED:20251022T181942Z
UID:10004960-1763733600-1763740800@events.ucsc.edu
SUMMARY:Torres\, S. (ECE) - An Integrated Platform for Real-time Monitoring and Support of 3D Tissue Growth
DESCRIPTION:Organoids are three-dimensional tissue cultures that model real organs and serve as valuable tools for studying development\, disease\, and treatment response. Traditional methods\, which rely on manual handling and incubators\, limit consistency and real-time monitoring. To address these issues\, we developed a modular microfluidic platform that integrates automated feeding\, live fluorescence imaging\, and environmental control without the need for a standard incubator. The core of the system is a vertically oriented PDMS-glass chip that enables precise media delivery and continuous imaging of small 3D structures such as organoids. Using fluorescent dyes to mimic molecules\, such as nutrients or drugs\, we tracked their movement through tissue in real time without invasive sensors. This setup maintains metabolic stability and provides detailed insight into molecular transport\, which improves applications in disease modeling\, drug testing\, and personalized medicine. \n  \nEvent Host- Sebastián Torres\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Mircea Teodorescu \n  \nZoom- https://ucsc.zoom.us/j/2333595627?pwd=aWtwL3V2QnFTMkNDSWowZnRNS0xSQT09 \nPasscode- 579836
URL:https://events.ucsc.edu/event/torres-s-ece-an-integrated-platform-for-real-time-monitoring-and-support-of-3d-tissue-growth/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2025/10/option-3-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251124T093000
DTEND;TZID=America/Los_Angeles:20251124T113000
DTSTAMP:20260417T050250
CREATED:20251112T181924Z
LAST-MODIFIED:20251112T181924Z
UID:10005132-1763976600-1763983800@events.ucsc.edu
SUMMARY:Chen\, Q. (CSE) - New Approximation and Online Algorithms using Novel Combinatorial Structures
DESCRIPTION:Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular\, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead\, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However\, in the modern era\, computational environments have changed drastically\, and we are not able to afford to design new algorithms for each new problem via repeated trial and error. Therefore\, systematic ways to understand the possibilities and limitations of these problems are desired. This dissertation studies several central combinatorial optimization problems\, focusing on understanding the key structural obstacles and developing unified frameworks. Mainly\, we study two types of combinatorial optimization problems:\n(1) Scheduling. The problem is associated with limited resources\, and our target is to find an allocation method to complete all jobs over time that minimizes the overall budget cost.\n(2) Network Design. Different from scheduling problems. In this problem\, we aim to find a minimum-cost topological network that supports routing for demanding communications. \nOur first work is focused on a group-to-group survivable network design problem that generalizes the classic point-to-point network to support routing between any pair of subsets of nodes. Previous research stops at limited faults\, and the difficulty comes from the way to compress the graph into a tree. We propose a new framework via capacitated tree embeddings against arbitrary faults in the network\, which gives the first polylogarithmic approximation algorithm. Further\, this framework captures nearly all the recent models proposed in the area. \nIn contrast to the offline optimization problems mentioned above\, online algorithms are natural adaptations that have been found in tremendous real applications. In online algorithms\, the algorithm wants to compete against arbitrary uncertainty\, which means the instance is unknown at first and revealed over time. We study various scheduling problems and focus on some important metrics – average flow time\, which measures the average time a job stays in the system from its arrival to completion. Real-world demands give online scheduling problems enormously different settings. Computer scientists need to repeat errors and trials to find a provably good solution. We find the key required combinatorial property is supermodularity for the residual objective\, which measures the average completion time for all alive jobs assuming they have the same arrival time. Further\, we relate supermodularity with gross-substitute/linear-substitute (GS/LS)\, which is a well-studied definition in economics. Finally\, we propose a meta-algorithm that solves all captured problems in one shot. \nEvent Host: Qingyun Chen\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \nZoom-  https://ucsc.zoom.us/j/94376536164?pwd=cPloEcyKuQg1C9reIbuh6rejrOaRfR.1
URL:https://events.ucsc.edu/event/chen-q-cse-new-approximation-and-online-algorithms-using-novel-combinatorial-structures/
LOCATION:
CATEGORIES:Ph.D. Presentations
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