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SUMMARY:Littschwager\, N. (CSE) - A Proposal for Characterizing Replicated Systems and Emulators
DESCRIPTION:Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems\, concurrent process calculi\, and programming languages\, since the 1970’s. We have also utilized simulation in our prior work\, where we formalized and proved a folklore claim that the state-based and operation-based approaches to Conflict-free Replicated Data Types (CRDTs) are ‘equivalent’ since they can ‘emulate each other’. More specifically\, a CRDT system consists of a collection of nodes called replicas. Clients interact with individual replicas by querying or updating their state\, and replicas interact by message passing over a network to eventually reach a convergent state. There are two main approaches to implementing a CRDT: operation-based\, and state-based. We showed that the main state-based and operation-based approaches to CRDTs do indeed ‘emulate each other’ since one can exhibit a pair of weak simulations between the original type of CRDT\, and its corresponding translation into the other type. We then leveraged the existence of these weak simulations to formally prove a ‘representation independence’ result\, in the sense that when access to the CRDTs is mediated by an imperative programming language\, the programmer cannot discern the underlying CRDT implementation by producing a program that terminates when run using one type of CRDT implementation\, but not when run with the other. \n Unfortunately\, our results are impractical for the purpose of being reapplied to asserting the equivalence of other replicated systems\, since the simulation relations (that one needs to exhibit in order to prove the necessary representation-independence) are non-modular\, requiring the user to reason about the potential executions of their entire replicated system. Additionally\, we observed that behavioral equivalence of state-based and operation-based CRDTs is a specific instance of the more general paradigm of ‘emulation’\, which is the process by which an ‘emulator’ translates the behavior of one system into the behavior of a different system. \nWe propose to generalize the techniques of our prior work to be applicable for any pair of replicated    systems\, and correct the ‘non-modularity’ issue by decomposing the overall proof structure into compositional simulation proofs about the local behavior of a replica\, and the behavior of the communication medium. Our second proposal comes from the observation that\, to our knowledge\, ‘emulation’ has not been given a formal and general mathematical semantic model that adequately captures the practical nuances faced by researchers and practitioners working on emulators. With that in mind\, we propose a notion of a faithful emulator\, inspired by the concept of a faithful functor 𝐹 ∶ C → D which lets us regard objects in C as ‘the same as’ the objects in D\, but with additional structure. \nHost: Nathan Littschwager\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Lindsey Kuper  \n 
URL:https://events.ucsc.edu/event/littschwager-n-cse-a-proposal-for-characterizing-replicated-systems-and-emulators/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T100000
DTEND;TZID=America/Los_Angeles:20251205T123000
DTSTAMP:20260417T012432
CREATED:20251125T212206Z
LAST-MODIFIED:20251125T212206Z
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SUMMARY:DeGrendele\, C. (AM) - Learning-Augmented and Structure-Preserving Methods for Conservation Law Solvers
DESCRIPTION:In this work\, we develop numerical methods for conservation laws that explore statistical\, structure-preserving\, and machine-learning-based approaches\, each built on top of traditional numerical solvers. First\, we develop a general Gaussian-process-based “recipe’’ for constructing high-order linear operators such as interpolation\, reconstruction\, and derivative approximations. Building on this recipe\, we derive a kernel-agnostic convergence theory for GP-based operators that interprets them as generalized finite-difference schemes\, defines an effective order-of-accuracy proxy that captures non-ideal truncation-error structure\, and uses this metric to select stencil geometries and kernel hyperparameters analytically. We then introduce a new second-order kernel\, Discontinuous Arcsin (DAS)\, that is stationary and prevents oscillations. DAS is integrated into a shock-capturing framework called the Multidimensional Optimal Order Detection (MOOD) method and shows an increase in efficiency by admitting less first order cascades. Next\, we address the long-standing problem of spurious pressure oscillations in compressible multi-component and real-fluid simulations by introducing a fully conservative pressure-equilibrium-preserving scheme and a high-order fully conservative approximate variant that apply to arbitrary equations of state. Unlike existing approaches\, these methods avoid non-conservative updates or EOS-specific constructions\, and on smooth interface advection tests with ideal-gas\, stiffened-gas\, and van der Waals fluids they reduce spurious pressure oscillations by orders of magnitude relative to current schemes. We then propose a hybrid numerical–machine learning framework for mixed hyperbolic–parabolic systems in which only the diffusive contribution is learned while the hyperbolic fluxes are advanced with standard shock-capturing methods\, enabling timesteps at a hyperbolic CFL. Within this framework\, we compare several neural architectures and loss designs on viscous Burgers tests and on the one-dimensional Euler equations with heat conduction\, showing that U-shaped neural operators combined with multi-step and TVD-style regularization improve long-time stability and spectral behavior\, and we analyze the resulting coupled schemes via eigenvalue-based stability diagnostics. Finally\, we apply high-order\, shock-capturing finite-difference methods within NASA’s Launch Ascent and Vehicle Aerodynamics (LAVA) framework to quantify acoustic and pressure loads on the Artemis Mobile Launcher\, including multiphase simulations of water-suppression systems and comparisons to flight data that inform hardware design for future missions. Collectively\, this work offers a set of targeted advances in kernel-based numerical operators\, conservative schemes and learning-augmented solvers each aimed at improving accuracy\, stability\, or efficiency in complex multiphysics flow simulation. \nEvent Host: Chris DeGrendele\, Ph.D. Candidate\, Applied Mathematics \nAdvisor: Dongwook Lee  \nZoom- https://ucsc.zoom.us/j/96308438100?pwd=9El4idgPoaVnAd9m8M6As6uaSbcojp.1 \nPasscode-  123456
URL:https://events.ucsc.edu/event/degrendele-c-am-learning-augmented-and-structure-preserving-methods-for-conservation-law-solvers/
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:20251205T130000
DTEND;TZID=America/Los_Angeles:20251205T140000
DTSTAMP:20260417T012432
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SUMMARY:Garg\, S. (CSE) - MAPPING ANNOTATIONS FROM NETLIST TO SOURCE CODE
DESCRIPTION:Hardware design flows have become increasingly complex as modern chips integrate billions\nof transistors and rely on aggressive synthesis optimizations to meet performance\,\narea\, and power targets. While these transformations improve circuit efficiency\, they\nalso erase the correspondence between gate-level netlists and their originating HDL\nsource lines. The loss of traceability makes post-synthesis debugging\, timing backannotation\,\nand root-cause analysis extremely difficult. Existing solutions depend on\ntool-specific metadata or preserved signal names\, which are often lost after flattening\,\nretiming\, or logic restructuring.\nTo address this long-standing problem\, this thesis presents SynAlign\, a structural\nalignment framework that restores the mapping between optimized netlists and\nsource code without relying on synthesis metadata. SynAlign treats both the reference\nRTL and synthesized designs as graphs and iteratively aligns them using shared\nstructural cues—such as sequential boundaries\, fan-in/fan-out relationships\, and partial\nnaming patterns. The algorithm employs anchor-based seeding\, multi-stage neighborhood\nmatching\, and a lightweight scoring function to propagate correspondences\nefficiently across large designs.\nExtensive evaluation demonstrates that SynAlign achieves over 90% line-level\nalignment accuracy across diverse designs\, maintaining robustness even when 60% of\nsignal names are obfuscated or removed. The framework scales linearly with design size\,\ncompleting alignment on multi-million-node circuits within minutes. Controlled tests\nconfirmed structural stability under synthetic noise\, while production-level validation\non real processor and accelerator modules verified industrial applicability.\nBy recovering structural visibility lost during synthesis\, SynAlign bridges a\ncritical gap between front-end design intent and post-synthesis implementation. Its explainable\nalignment enables faster debug cycles\, more accurate timing correlation\, and\nprovides a foundation for next-generation EDA tools that integrate traceability\, optimization\ntransparency\, and source-level introspection into the hardware development\nprocess. \nHost: Sakshi Garg\, Ph.D. Candidate\, Computer Science and Engineering  \nAdvisor: Jose Renau \nZoom- https://ucsc.zoom.us/j/96207792766?pwd=bjBfusfaucoqMGZNgayum2te4tsLc5.1 \nPasscode- 669162
URL:https://events.ucsc.edu/event/garg-s-cse-mapping-annotations-from-netlist-to-source-code/
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:20251208T091500
DTEND;TZID=America/Los_Angeles:20251208T103000
DTSTAMP:20260417T012432
CREATED:20251205T173457Z
LAST-MODIFIED:20251205T174005Z
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SUMMARY:Jamilan\, S. (CSE) -  Profile-guided Compiler Optimizations for Data Center Workloads
DESCRIPTION:Modern applications\, such as data center workloads\, have become increasingly complex. These applications primarily operate on massive datasets\, which involve large memory footprints\, irregular access patterns\, and complex control and data flows. The processor-memory speed gap\, combined with these complexities\, can lead to unexpected performance inefficiencies in these applications\, preventing them from achieving optimal performance. Considering the complexity and size of data center applications\, manually identifying and resolving performance issues is often impractical or impossible. Instead\, developing new compiler optimization techniques can be a more effective and scalable solution to boost both performance and energy efficiency. In this thesis\, we focus on identifying the root causes that limit the performance of data center workloads. We analyze the limitations of current profile-guided compiler optimization techniques for addressing these performance gaps. Finally\, we propose two profile-guided optimization techniques\, APT-GET and RIFS\, which can be integrated into the LLVM optimization pipeline to deliver further improvements. To hide the long latency of memory accesses\, we introduce APT-GET\, a profile-guided technique that ensures timely prefetches by leveraging dynamic execution-time information to build a novel analytical model that finds the optimal prefetch distance and injection site based on the collected profile. We study APT-GET across 10 real-world applications and demonstrate that it achieves a speedup of up to 1.98× and an average of 1.30×. To enable runtime value-invariant function specialization to reduce redundant operations\, we introduce RIFS\, a profile-guided compiler technique that specializes functions based on runtime-invariant call-site-specific argument values. RIFS introduces a novel value-profiling LLVM pass to identify runtime invariant arguments and a subsequent LLVM transformation pass to generate specialized function variants tailored to these value profiles. To efficiently select among potentially thousands of specialization candidates\, we develop a predictive cost model that estimates each candidate’s performance benefit before code generation. RIFS achieves an average speedup of 5.3% and an instruction reduction of 2.5% over the LLVM -O3+PGO baseline across 12 real-world applications. \nHost: Saba Jamilan\, Ph.D. Candidate\, Computer Science and Engineering  \nAdvisor: Heiner Litz  \nZoom- https://ucsc.zoom.us/j/95818759324?pwd=rdaS7G1V7O6faRhNOgFyq1OR50eSLK.1 \nPasscode- 652917 \n 
URL:https://events.ucsc.edu/event/jamilan-s-cse-profile-guided-compiler-optimizations-for-data-center-workloads/
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:20251208T130000
DTEND;TZID=America/Los_Angeles:20251208T140000
DTSTAMP:20260417T012432
CREATED:20251202T163305Z
LAST-MODIFIED:20251202T163305Z
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SUMMARY:de Priester\, J. (ECE) - Hybrid Reinforcement Learning
DESCRIPTION:Reinforcement Learning (RL) is a machine learning paradigm that trains a decision maker\, or policy\, by learning from interaction with an environment. The power of RL lies in its ability to learn complex strategies without explicit human instruction\, which can lead to better solutions that human designers overlook in domains ranging from robotics to scientific discovery. Despite these successes\, applying RL to safety-critical control systems remains a significant challenge due to the fragility of black-box policies. Standard RL controllers are prone to “chattering” or indecisiveness\, which is rapid\, detrimental switching between decisions induced by small disturbances\, and lack formal closed-loop safety\, stability\, and robustness guarantees. Furthermore\, existing discrete and continuous-time RL paradigms struggle to model hybrid systems\, where continuous state evolution is intertwined with instantaneous discrete updates. Consequently\, standard RL approaches cannot effectively be applied to safety-critical hybrid dynamical systems\, as such approaches suffer from discretization artifacts\, computational inefficiency\, and a lack of closed-loop safety\, stability\, and robustness guarantees. \nTo bridge the gap between hybrid control theory and RL\, this research proposal is organized into four interconnected thrusts. Thrust 1 addresses the fragility of existing standard RL-based policies by designing RL algorithms to construct robust hybrid supervisors to eliminate chattering. Thrust 2 establishes the theoretical bedrock of a native hybrid RL formulation. By leveraging insights from discounted MPC\, the hybrid RL problem is formulated with intrinsic closed-loop stability\, safety\, and robustness properties. Thrust 3 extends standard RL components to the hybrid domain to create RL algorithms capable of solving the hybrid RL problem defined in Thrust 2. Finally\, Thrust 4 provides comprehensive empirical validation\, confirming the robustness of the supervisors from Thrust 1 and demonstrating the advantages of the native hybrid RL formulation developed in Thrusts 2 and 3 over a standard RL formulation. \nHost: Jan de Priester\, Ph.D. Student\, Electrical and Computer Engineering  \nAdvisor: Ricardo Sanfelice \nZoom- https://ucsc.zoom.us/j/95229790206?pwd=ICevzd4QdEE7ZAlYALZIYbhU2bCU4W.1 \nPasscode-  981137
URL:https://events.ucsc.edu/event/de-priester-j-ece-hybrid-reinforcement-learning/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T130000
DTEND;TZID=America/Los_Angeles:20251208T140000
DTSTAMP:20260417T012432
CREATED:20251203T220535Z
LAST-MODIFIED:20251203T220535Z
UID:10005728-1765198800-1765202400@events.ucsc.edu
SUMMARY:Ferdous\, N. (CSE) - SPECSIM : A Simulation Infrastructure Mitigating Transient Timing Attacks
DESCRIPTION:   Transient execution attacks are serious security threats in modern-day processors. Out-of-order execution compels the processor to access data that should not be otherwise perceived. Leakage of that secret information creates a covert channel for the attacker for various types of transient and speculative attacks. Transient based execution attacks emanate when the secret information is leaked by the execution of transient instructions which are executed by the processor but never got committed from the processor pipeline. However\, on the microarchitectural level\, the effect of these transient instructions is noticeable. Generally\, microarchitectural state is the state that a processor maintains to improve performance which is transparent to software. The secret data retained in the microarchitectural state are susceptible to create a covert channel and thereby are at higher risk to be observed by the attacker for transient attacks.\nThis research work presents a robust and secure simulation infrastructure that implements multiple strategies to mitigate transient attacks in the timing domain. This work proposes various strategies e.g.\, Reorder Buffer Transient Flushing Technique in Randomized Transient Pipeline\, SpecSCB for making the speculative instructions invisible to the architectural state\, for the mitigation of the timing attack. In this work\, transient instructions are added in the proposed Randomized Transient Pipeline and are flushed effectively\, using Transient Flushing Techniques\, squashing all the transient instruction residues that could remain in the Randomized Transient Pipeline. This flushing strategy also ensures no difference in the execution time of the base simulation and the proposed Randomized Transient Simulation\, leaving no leakage for transient based timing attacks. In addition to the simulation platform\, a novel Transient Verification Framework is also proposed which consists of Global Time Signature Verification Model and Retirement Time Signature Verification Model. The transient verification framework identifies if there is any anomaly in the timing domain\, related to all existing instructions\, which could leave space for covert channel for timing attacks. Overall\, this work has provided an extensive and robust simulation platform infrastructure for the researchers to explore various types of attacks with their respective mitigating solutions. \nHost: Nilufar Ferdous\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Jose Renau  \nZoom- https://us06web.zoom.us/j/84111701472?pwd=l3s5sQszKt35paVOWNxxLaE8jphG80.1 \nPasscode- Qi1pAk
URL:https://events.ucsc.edu/event/ferdous-n-cse-specsim-a-simulation-infrastructure-mitigating-transient-timing-attacks/
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:20251208T140000
DTEND;TZID=America/Los_Angeles:20251208T150000
DTSTAMP:20260417T012432
CREATED:20251205T175704Z
LAST-MODIFIED:20251205T175952Z
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SUMMARY:Wang\, Y. (CSE) - Toward Practical and Effective Large Language Model Unlearning
DESCRIPTION:The growing integration of Large Language Models (LLMs) into real-world applications has heightened concerns about their trustworthiness\, as models may reveal private information\, reproduce copyrighted content\, propagate biases\, or generate harmful instructions. These risks\, alongside emerging privacy regulations\, motivate the need for LLM unlearning\, methods that remove the influence of specific data while preserving overall model capability.\nThis proposal investigates how to design practical and effective unlearning methods that enable LLMs to produce reliable and responsible outputs. We study both training-free and training-based paradigms. On the training-free side\, we introduce ECO\, which achieves unlearning via embedding-corrupted prompts detected by a lightweight classifier\, and DRAGON\, a generalizable black-box framework that combines detection with chain-of-thought guard reasoning for safe in-context intervention. On the training-based side\, we present FLAT\, a forget-data-only loss adjustment method grounded in a variational $f$-divergence formulation.\nTogether\, these approaches provide complementary strategies for aligning LLM behavior with safety and regulatory requirements while maintaining general utility. This proposal outlines their motivation\, design\, empirical performance\, and the broader research plan toward responsible and accountable LLM systems. \nHost: Yaxuan Wang\, Ph.D. Student\, Computer Science and Engineering  \nAdvisor: Yang Liu \nZoom- https://ucsc.zoom.us/j/94186242839?pwd=ubGMNF25W8gABNIl2S7EaIBHEXletV.1 \nPasscode- 786334
URL:https://events.ucsc.edu/event/wang-y-cse-toward-practical-and-effective-large-language-model-unlearning/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251209T160000
DTEND;TZID=America/Los_Angeles:20251209T170000
DTSTAMP:20260417T012432
CREATED:20251202T204536Z
LAST-MODIFIED:20251209T182652Z
UID:10005719-1765296000-1765299600@events.ucsc.edu
SUMMARY:Zhu\, R. (ECE) -  From Neuromorphic Principles to Efficient Neural Language Architectures
DESCRIPTION:While Large Language Models exhibit remarkable capabilities\, their reliance on the standard Transformer architecture imposes prohibitive computational costs and quadratic memory complexity. To bridge the gap between biological efficiency and high-performance AI\, we have established foundational work in linearizing attention and maximizing hardware utilization through architectures such as RWKV and MatMul-Free networks. Addressing the remaining bottlenecks in long-term memory consolidation and optimization stability\, we propose a research roadmap focused on “In-Place Test-Time Training” (TTT) to enable compositional memory via dynamic weight updates\, and the Muon optimizer to stabilize deep reasoning through orthogonal gradient updates. Ultimately\, this work aims to unify neuromorphic principles with scalable deep learning to enable robust performance in resource-efficient environments. \nEvent Host: Ridger Zhu\, Ph.D. Student\, Electrical and Computer Engineering  \nAdvisor: Jason Eshraghian \nZoom- https://ucsc.zoom.us/j/95241268060?pwd=WDMgDWhhSyXNh8NZpBDvgpbcMVbvUz.1 \nPasscode- 256794
URL:https://events.ucsc.edu/event/ridger-z-ece-from-neuromorphic-principles-to-efficient-neural-language-architectures/
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:20251210T130000
DTEND;TZID=America/Los_Angeles:20251210T150000
DTSTAMP:20260417T012432
CREATED:20251204T161744Z
LAST-MODIFIED:20251205T222136Z
UID:10005732-1765371600-1765378800@events.ucsc.edu
SUMMARY:Singh\, A. (ECE) - Quantum Key Distribution Using Entangled Pairs with Random Grouping
DESCRIPTION:Quantum Key Distribution (QKD) provides information-theoretic security for cryptographic key establishment\, but existing protocols exhibit limited noise tolerance\, restricting their applicability in practical quantum channels with finite resources. This work introduces a QKD protocol based on entanglement swapping that significantly enhances error tolerance and key generation rates. The protocol encodes six-bit classical symbols into six-qubit entangled states organized as three Bell pairs. Key contributions include: (1) maintaining positive secrecy rates under 100% intercept-resend attacks\, unprecedented among existing protocols\, (2) proven security against collective attacks up to 29.29% quantum bit error rate (QBER)\, substantially exceeding BB84’s 11% threshold\, and (3) finite-key security analysis demonstrating viable key generation under practical block size constraints. These results establish that structured multi-qubit encoding fundamentally broadens the operational capabilities of quantum key distribution\, enabling secure communication in high-noise environments such as free-space satellite links and urban channels where conventional protocols fail. \nHost: Archana Jayprakash Singh\, Ph.D. Student\, Electrical and Computer Engineering  \nAdvisor: Zouheir Rezki  \nZoom- https://ucsc.zoom.us/j/92875779810?pwd=xIWhFkOw5WR3vyBvVhBCkd7ueJs2m2.1 \nPasscode- 530049
URL:https://events.ucsc.edu/event/singh-a-ece-quantum-key-distribution-using-entangled-pairs-with-random-grouping/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T090000
DTEND;TZID=America/Los_Angeles:20251211T110000
DTSTAMP:20260417T012432
CREATED:20251202T162054Z
LAST-MODIFIED:20251209T161343Z
UID:10005717-1765443600-1765450800@events.ucsc.edu
SUMMARY:Tran\, L. (BMEB) -  Polysome Shadowing: A Long-Read Sequencing Approach to Study Translation
DESCRIPTION:Translation is a central and highly regulated step of gene expression\, yet there are few quantitative\, high-throughput tools to study translation. Existing methods such as sucrose gradients provide only bulk ribosome counts\, while Ribo-Seq offers positional information in the genome but destroys long-range structure and transcript expression information. Because of these limitations\, many fundamental questions about mRNA translation into protein remain difficult to assay. In this proposal\, I outline my plans to develop a novel technology\, deemed Polysome Shadowing\, that covalently marks ribosome-unprotected regions of RNA with hyperactive base editors. Because ribosomes protect ~21–30 nt regions of mRNAs\, ribosome “shadows” appear as tracts of unedited bases in long-read sequencing. In Aim 1\, I will identify ribosome shadows on single molecules by increasing editing efficiency through optimization of dual cytosine and adenosine base editors and statistical modeling. In Aim 2\, I will maximize the accuracy of information recovered from highly-edited RNAs by developing a multipass library preparation protocol to generate high-confidence reads. In Aim 3\, I will apply the tools I have already developed to examine previously difficult-to-assay paradigms of translational control in the form of viral frameshifting mechanisms. Together\, completion of these aims will build an information-rich sequencing technology capable of positioning ribosomes on intact mRNAs while preserving long-range information and establish feasibility to study nascent paradigms. \nHost: Liam Tran\, Ph.D. Student\, Biomolecular Engineering and Bioinformatics  \nAdvisor: Joshua Arribere 
URL:https://events.ucsc.edu/event/tran-l-bmeb-polysome-shadowing-a-long-read-sequencing-approach-to-study-translation/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T120000
DTEND;TZID=America/Los_Angeles:20251211T140000
DTSTAMP:20260417T012432
CREATED:20251209T224244Z
LAST-MODIFIED:20251209T224244Z
UID:10005759-1765454400-1765461600@events.ucsc.edu
SUMMARY:Chambers\, K. (BMEB) - Using Genomics and Artificial Intelligence to improve prognosis for osteosarcoma patients
DESCRIPTION:Transcriptomic profiling has been transformative in pediatric oncology. Pediatric cancers arise from disrupted developmental programs. Their impaired transcriptional states reflect cell lineage infidelity\, aberrant differentiation\, and immune-microenvironment interactions distinct from those of adult tumors(Gröbner et al.\, 2018; X. Ma et al.\, 2018). Within the osteosarcoma (OS) landscape\, despite being the most common bone tumor of childhood\, it remains one of the least genomically characterized pediatric cancers. Advancements in survival for localized disease\, outcomes for metastatic or recurrent OS have remained stagnant for decades. Transcriptomics characterization of OS has facilitated the exposure of the unique chromothripsis patterns associated with the disease (Sayles et al.\, 2019; Schott et al.\, 2023). Largely\, progress in OS genomics is still limited by the lack of harmonized\, cross-study datasets accessible to researchers. I detail my contributions to OS research\, beginning with the curation of the largest publicly available and harmonized RNA-sequencing osteosarcoma dataset (Chapter 2). A continuous part of my research involved the systematic democratization\, aggregation\, harmonization\, and open sharing of pediatric cancer transcriptomic datasets within the Treehouse Childhood Cancer Initiative (Beale et al.\, 2025). This dataset provided a foundation for the analyses and discoveries presented in this dissertation. I utilize the multi-cohort and transcriptomic multi-omic public OS dataset to discover and define biologically meaningful subtypes that may explain differences in progression and treatment response (Chapter 3). Finally\, I expand these advanced computational approaches into the realm of diagnostic pathology by evaluating strategies for integrating generative AI into rare cancer classification. I leverage both general and domain-specific diffusion models alongside GPT-4o–generated pathology prompts to guide histologic image synthesis (Chapter 4). In summary\, my work advances transcriptional subtyping in OS by leveraging transcriptomic data to identify molecular subtypes of OS that could inform treatment strategies. \nHost: Krizia Chambers\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Olena Vaske \nZoom- https://ucsc.zoom.us/j/93569812001?pwd=RWBuZUdQq2Yo1K4kQ75WRmP0uKjYAH.1&jst=3 \nPasscode- 915392
URL:https://events.ucsc.edu/event/chambers-k-bmeb-using-genomics-and-artificial-intelligence-to-improve-prognosis-for-osteosarcoma-patients/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T130000
DTEND;TZID=America/Los_Angeles:20251211T150000
DTSTAMP:20260417T012432
CREATED:20251202T232256Z
LAST-MODIFIED:20251202T232256Z
UID:10005722-1765458000-1765465200@events.ucsc.edu
SUMMARY:Laffan\, N. (CM) - Digital Memory Tools and Their Impact On Collective Remembering
DESCRIPTION:Today\, both individual and collective memories are increasingly mediated by digital platforms. Both are fundamentally enmeshed in platform ecosystems that orient around commercial imperatives very much at odds with community cohesion. The digital archive where our mediated memories are stored does not merely store information but actively inscribes it\, often privileging narratives aligned with commercial incentives rather than community cohesion. This invisibility is a problem: as we offload our personal memories onto commercial tools\, we unwittingly subject our shared past to algorithmic curation and “algo-time\,” which raises serious questions about how the use of our personal devices is quietly restructuring the way societies remember. \nDuring this presentation\, I will propose a three-pronged method of investigating and engaging in this conceptual space. All three prongs revolve around a shared question : how do the technologies that extend our personal memories affect what we remember collectively? The research first establishes a conceptual ecology around the question by tracing the lifecycle of a single image from individual capture to platform archive. Second\, it employs Research through Design (RtD) and speculative design methods to prototype tools explicitly built for collective remembrance rather than commercial extraction. Finally\, it utilizes artistic practice to “diffract” these concepts\, creating interactive installations that expose the distortions and contradictions inherent in digital memory. Together\, these projects aim to make visible the hidden dynamics that shape the memories we construct together. \nHost: Nate Laffan\, Ph.D. Student\, Computational Media  \nAdvisor: Nathan Altice  \nZoom- https://ucsc.zoom.us/j/93762016105?pwd=RBXDHnuleAECZdVghEaAz9L4KK4p1d.1 \nPasscode- 668969
URL:https://events.ucsc.edu/event/laffan-n-cm-digital-memory-tools-and-their-impact-on-collective-remembering/
LOCATION:
CATEGORIES:Ph.D. Presentations
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