BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Events
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:20251205T090000
DTEND;TZID=America/Los_Angeles:20251205T110000
DTSTAMP:20260416T205912
CREATED:20251118T165217Z
LAST-MODIFIED:20251119T192149Z
UID:10005180-1764925200-1764932400@events.ucsc.edu
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
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/11/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T100000
DTEND;TZID=America/Los_Angeles:20251205T123000
DTSTAMP:20260416T205912
CREATED:20251125T212206Z
LAST-MODIFIED:20251125T212206Z
UID:10005646-1764928800-1764937800@events.ucsc.edu
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
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/11/ph.d.-presentation-graphic-option-1-1.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T130000
DTEND;TZID=America/Los_Angeles:20251205T140000
DTSTAMP:20260416T205912
CREATED:20251203T234430Z
LAST-MODIFIED:20251203T234430Z
UID:10005731-1764939600-1764943200@events.ucsc.edu
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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2025/10/option-3.png
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
END:VCALENDAR