Weatherwax, K. (CM) - LoFi to X and Y: Background Media Use as Colloquial Assistive Technology for Neurodivergent People
2025-10-09 10:00 (America/Los_Angeles) | CA
Research in media psychology has often framed background media as a distraction that undermines performance. Such perspectives rely on narrow, output-oriented definitions of success and overlook the emotional, mental, social, and environmental needs that shape how people actually work. They also fail to account for neurodivergent experiences, ignoring the diverse ways people engage with media in daily life.

This dissertation uses a critical disability and neurodiversity lens to examine background media, with a focus on LoFi as a commonly used exemplar, as a form of colloquial assistive technology. Drawing on interviews and large-scale online discourse, I show how LoFi is not primarily used to increase productivity, but to manage affect, sustain attention, and reduce cognitive or sensory overload. Users describe it as a supportive presence—helping them transition into work, recover from fatigue, and feel accompanied in otherwise isolating contexts.

These findings challenge dominant narratives about distraction and media use. Rather than being passively consumed, background media is deliberately shaped and adopted as a source of support. This work rethinks what counts as assistive technology, foregrounds the self-directed practices of neurodivergent people, and offers design directions for systems that legitimize and extend such strategies.

Event Host: Kevin Weatherwax, Ph.D Candidate, Computational Media
Advisor: Kate Ringland

Penumbra de la memoria: Brown Bag with Maya Scherr-Willson
2025-10-22 12:00 - 13:00 | Huerta Center Conference Room (Casa Latina), 641 Merrill Rd, Santa Cruz,, CA, 95064
During this presentation, Maya Scherr-Willson (PhD Student in the Film and Media Department) will show material and reflect on insights from a research trip that laid the groundwork for Penumbra de la memoria, a feature documentary to be shot this summer. The project reunites eight women fifty years after they were held as political prisoners together during Argentina’s last military dictatorship to film an adaptation from memory of their prison-era performance of The House of Bernarda Alba by Federico García Lorca. The group, engaged in collective work, will be the protagonist of the film that chronicles the political memory that erupts through their creative process.

Alatawi, A. (ECE) - Learning-Based Channel Estimation for Next-Generation Wireless Communications
2025-10-28 10:00 - 12:00
Accurate Channel State Information (CSI) is critical for coherent detection, equalization, and adaptive resource allocation in modern wireless systems. Traditional estimators rely on stationary statistical models, and many learning-based methods assume training and deployment conditions are matched. In practice, these assumptions break down under user mobility and environmental dynamics, leading to degraded performance. This proposal explores machine-learning approaches for channel estimation that address two complementary challenges.
First, we develop an adaptive deep neural network (ADNN) for single-input single-output links over slowly time-varying channels. The method converts readily available physical-layer feedback—cyclic redundancy check (CRC) and automatic repeat request (ARQ)—into reliable self-supervision. Specifically, packets decoded without errors are re-estimated using least squares (LS) across all symbols to generate high-quality labels, and the DNN weights are periodically updated online. This design eliminates the need for ground-truth labels at deployment and enables continual learning. Simulations show that the ADNN tracks distributional shifts and recovers near–linear minimum mean-square error (LMMSE) performance in both mean-square error (MSE) and symbol error rate (SER), whereas a fixed offline-trained DNN degrades as channel statistics change.
Second, we propose a sequence-to-sequence LSTM estimator for orthogonal frequency-division multiplexing (OFDM). The model exploits both temporal and frequency correlation by taking LS pilot estimates from several previous OFDM blocks as input and reconstructing the full channel frequency response of the current block. Trained on realistic time-selective channels such as WINNER II, the LSTM outperforms LS interpolation and recent super-resolution–based methods across a wide range of SNRs, pilot densities, and temporal window sizes.
Finally, the proposal outlines future research on semantic-aware channel estimation using CSI timeliness, and enhanced sequence models with DNN-refined pilots, whole-block inputs, and efficient GRU architectures.
Event Host: Abdulaziz Alatawi, Ph.D. Student, Electrical & Computer Engineering
Advisor: Hamid Sadjadpour & Zouheir Rezki
Zoom- https://ucsc.zoom.us/j/94895993579?pwd=[REDACTED]
Passcode- 884927

Wang, S. (CSE) - Learned Hashing and Overlay Networks for AI-native Retrieval and Serving at Scale
2025-11-07 14:00 - 16:00 | https://events.ucsc.edu/event/wang-s-cse-learned-hashing-and-overlay-networks-for-ai-native-retrieval-and-serving-at-scale/
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.
Event Host: Shengze Wang, Ph.D. Student, Computer Science & Engineering
Advisor: Chen Qian

Zoom: https://ucsc.zoom.us/j/5455463199?pwd=[REDACTED]
Passcode: [REDACTED]

Nguyen, R. (BMEB) - Development of Computational Methods for Reliable Genetic Identification of Forensic Samples
2025-11-10 13:00 - 15:00
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.
This 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.
The 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.
Finally, 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.
Event Host: Remy Nguyen, Ph.D. Candidate, Biomolecular Engineering & Bioinformatics
Advisor: Ed Green

Zoom- https://ucsc.zoom.us/j/91522009894?pwd=[REDACTED]
Passcode- 854645

Petety, A. (CSE) - New Algorithmic Methods for Uncertain Inputs
2025-11-13 10:00 - 12:00 | CA
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.
Event Host: Aditya Petety, Ph.D. Student, Computer Science and Engineering
Advisor: Sungjin Im

Jorquera, Z. (CSE) - Quantum Entanglement Bounds and the Approximation Algorithms That Use Them
2025-11-20 09:00 - 10:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
In 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.
The 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.

Event Host: Zack Jorquera, Ph.D. Student, Computer Science and Engineering
Advisor: Alexandra Kolla
Zoom- https://ucsc.zoom.us/j/98034235739?pwd=[REDACTED]

Ramollari, H. (ECE) - An Optofluidic Spectrometer and Applications in Biosensing
2025-11-21 12:30 - 14:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
In 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.
Moreover, adding a metasurface to the spectrometer waveguide is expected to enhance the sensitivity of single particle detection and simplify the analysis methods.
Finally, to improve the MMI waveguide spectrometer a new nanophotonic platform is proposed.
Event Host: Helio Ramollari, Ph.D. Student, Electrical Engineering
Advisor: Holger Schmidt
Zoom- https://ucsc.zoom.us/j/99623652977?pwd=[REDACTED]
Passcode- 576057

Torres, S. (ECE) - An Integrated Platform for Real-time Monitoring and Support of 3D Tissue Growth
2025-11-21 14:00 - 16:00
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.

Event Host- Sebastián Torres, Ph.D. Candidate, Electrical & Computer Engineering
Advisor: Mircea Teodorescu

Zoom- https://ucsc.zoom.us/j/2333595627?pwd=[REDACTED]
Passcode- 579836

Chen, Q. (CSE) - New Approximation and Online Algorithms using Novel Combinatorial Structures
2025-11-24 09:30 - 11:30
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:
(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.
(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.
Our 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.
In 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.
Event Host: Qingyun Chen, Ph.D. Student, Computer Science and Engineering
Advisor: Sungjin Im
Zoom- https://ucsc.zoom.us/j/94376536164?pwd=[REDACTED]

Littschwager, N. (CSE) - A Proposal for Characterizing Replicated Systems and Emulators
2025-12-05 09:00 - 11:00
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.
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.
We 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.
Host: Nathan Littschwager, Ph.D. Student, Computer Science and Engineering
Advisor: Lindsey Kuper

DeGrendele, C. (AM) - Learning-Augmented and Structure-Preserving Methods for Conservation Law Solvers
2025-12-05 10:00 - 12:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
Event Host: Chris DeGrendele, Ph.D. Candidate, Applied Mathematics
Advisor: Dongwook Lee
Zoom- https://ucsc.zoom.us/j/96308438100?pwd=[REDACTED]
Passcode- 123456

Garg, S. (CSE) - MAPPING ANNOTATIONS FROM NETLIST TO SOURCE CODE
2025-12-05 13:00 - 14:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Hardware design flows have become increasingly complex as modern chips integrate billions
of transistors and rely on aggressive synthesis optimizations to meet performance,
area, and power targets. While these transformations improve circuit efficiency, they
also erase the correspondence between gate-level netlists and their originating HDL
source lines. The loss of traceability makes post-synthesis debugging, timing backannotation,
and root-cause analysis extremely difficult. Existing solutions depend on
tool-specific metadata or preserved signal names, which are often lost after flattening,
retiming, or logic restructuring.
To address this long-standing problem, this thesis presents SynAlign, a structural
alignment framework that restores the mapping between optimized netlists and
source code without relying on synthesis metadata. SynAlign treats both the reference
RTL and synthesized designs as graphs and iteratively aligns them using shared
structural cues—such as sequential boundaries, fan-in/fan-out relationships, and partial
naming patterns. The algorithm employs anchor-based seeding, multi-stage neighborhood
matching, and a lightweight scoring function to propagate correspondences
efficiently across large designs.
Extensive evaluation demonstrates that SynAlign achieves over 90% line-level
alignment accuracy across diverse designs, maintaining robustness even when 60% of
signal names are obfuscated or removed. The framework scales linearly with design size,
completing alignment on multi-million-node circuits within minutes. Controlled tests
confirmed structural stability under synthetic noise, while production-level validation
on real processor and accelerator modules verified industrial applicability.
By recovering structural visibility lost during synthesis, SynAlign bridges a
critical gap between front-end design intent and post-synthesis implementation. Its explainable
alignment enables faster debug cycles, more accurate timing correlation, and
provides a foundation for next-generation EDA tools that integrate traceability, optimization
transparency, and source-level introspection into the hardware development
process.
Host: Sakshi Garg, Ph.D. Candidate, Computer Science and Engineering
Advisor: Jose Renau
Zoom- https://ucsc.zoom.us/j/96207792766?pwd=[REDACTED]
Passcode- 669162

Jamilan, S. (CSE) - Profile-guided Compiler Optimizations for Data Center Workloads
2025-12-08 09:15 - 10:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
Host: Saba Jamilan, Ph.D. Candidate, Computer Science and Engineering
Advisor: Heiner Litz
Zoom- https://ucsc.zoom.us/j/95818759324?pwd=[REDACTED]
Passcode- 652917

de Priester, J. (ECE) - Hybrid Reinforcement Learning
2025-12-08 13:00 - 14:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
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.
To 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.
Host: Jan de Priester, Ph.D. Student, Electrical and Computer Engineering
Advisor: Ricardo Sanfelice
Zoom- https://ucsc.zoom.us/j/95229790206?pwd=[REDACTED]
Passcode- 981137

Ferdous, N. (CSE) - SPECSIM : A Simulation Infrastructure Mitigating Transient Timing Attacks
2025-12-08 13:00 - 14:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
This 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.
Host: Nilufar Ferdous, Ph.D. Student, Computer Science and Engineering
Advisor: Jose Renau
Zoom- https://us06web.zoom.us/j/84111701472?pwd=[REDACTED]
Passcode- Qi1pAk

Wang, Y. (CSE) - Toward Practical and Effective Large Language Model Unlearning
2025-12-08 14:00 - 15:00
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.
This 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.
Together, 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.
Host: Yaxuan Wang, Ph.D. Student, Computer Science and Engineering
Advisor: Yang Liu
Zoom- https://ucsc.zoom.us/j/94186242839?pwd=[REDACTED]
Passcode- 786334

Zhu, R. (ECE) - From Neuromorphic Principles to Efficient Neural Language Architectures
2025-12-09 16:00 - 17:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
Event Host: Ridger Zhu, Ph.D. Student, Electrical and Computer Engineering
Advisor: Jason Eshraghian
Zoom- https://ucsc.zoom.us/j/95241268060?pwd=[REDACTED]
Passcode- 256794

Singh, A. (ECE) - Quantum Key Distribution Using Entangled Pairs with Random Grouping
2025-12-10 13:00 - 15:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
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.
Host: Archana Jayprakash Singh, Ph.D. Student, Electrical and Computer Engineering
Advisor: Zouheir Rezki
Zoom- https://ucsc.zoom.us/j/92875779810?pwd=[REDACTED]
Passcode- 530049

Tran, L. (BMEB) - Polysome Shadowing: A Long-Read Sequencing Approach to Study Translation
2025-12-11 09:00 - 11:00 | Biomedical Sciences Building, 575 McLaughlin Drive
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.
Host: Liam Tran, Ph.D. Student, Biomolecular Engineering and Bioinformatics
Advisor: Joshua Arribere

Chambers, K. (BMEB) - Using Genomics and Artificial Intelligence to improve prognosis for osteosarcoma patients
2025-12-11 12:00 - 14:00
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.
Host: Krizia Chambers, Ph.D. Candidate, Biomolecular Engineering & Bioinformatics
Advisor: Olena Vaske
Zoom- https://ucsc.zoom.us/j/93569812001?pwd=[REDACTED]
Passcode- 915392

Laffan, N. (CM) - Digital Memory Tools and Their Impact On Collective Remembering
2025-12-11 13:00 - 15:00
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.
During 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.
Host: Nate Laffan, Ph.D. Student, Computational Media
Advisor: Nathan Altice
Zoom- https://ucsc.zoom.us/j/93762016105?pwd=[REDACTED]
Passcode- 668969

HSI Equity Talk
2026-01-22 08:00 - 17:00 | CA
Title: Understanding the advising praxes central to student success at a four-year Hispanic-Serving Research Institution
Presenter: Dr. Lydia Iyeczohua Zendejas
Location: Via Zoom (link provided via RSVP)
Abstract: Higher education scholars increasingly recognize academic advising as a critical strategy for supporting the persistence of systemically marginalized students. Since the 1990s, UC Santa Cruz has undergone significant growth and demographic shifts—undergraduate enrollment grew from 10,269 in 1999 to 17,517 in 2019, with sharp increases in underrepresented, first-generation, and Hispanic students—creating both challenges and opportunities for advancing equitable outcomes.
Dr. Zendejas’s interview-based qualitative study examines how UCSC’s decentralized, dual shared advising model shapes advisors’ ability to provide holistic, culturally responsive advising. In this HSI equity talk, she will share how advising structures, practices, and policies impact advisors’ capacity to support students, how the current model can act as a structural barrier to collaboration, and the advising praxis advisors identify as essential to student success, persistence, and retention.
Please complete this RSVP form if you plan to attend. The Zoom information and a calendar invitation will be sent to those who RSVP.

Sharma, R. (CSE) - Automatically Evolving GPU Libraries for Performance Portable AI Kernels
2026-01-23 09:30 - 11:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
GPUs are the workhorses of modern AI, widely deployed and developed by many vendors including Apple, Qualcomm, Intel, AMD, and NVIDIA. While these GPUs all offer high compute potential, programming them effectively is difficult because they differ in performance-critical features like SIMT width, cache capacity, and memory bandwidth, demanding different optimization strategies. Tunable kernels address this by exposing parameters such as tiling dimensions and workgroup sizes, enabling per-device specialization. Yet this produces static libraries: tuned once, then frozen, degrading as new hardware emerges. We propose automatically evolving libraries that expand their tuning knowledge as new hardware emerges, with minimal impact on user experience.
To build such libraries, we first need to understand the tuning landscape. We address this through GPU Goldmines, a WebGPU-based framework for exhaustively collecting tuning data across diverse devices. Our tuned matrix multiplication kernels outperform an optimized baseline by 8.4x on average, while matrix-vector kernels achieve 93% of platform bandwidth. We find that hyper-tuning for a single GPU causes 50% performance degradation on other devices, whereas data-driven portability methods recover 88% of peak performance. These kernels are fundamental to the prefill and decode phases of LLM inference. We integrate them into llama.cpp as our evaluation platform, where they outperform CPU and Vulkan backends.
Building on this data, we are developing Living Libraries to improve performance continuously without disrupting users. This means choosing good parameters upfront, learning from real-world execution, and knowing when to keep searching versus when to stop, though hand-designed parameter spaces remain inherently bounded. To move beyond this, we extend toward LLM-based kernel evolution, where language models propose entirely new kernel variants, opening a less structured but higher potential search space.
Event Host: Rithik Sharma, Ph.D. Student, Computer Science and Engineering
Advisor: Tyler Sorensen & Yuanchao Xu

Zoom: https://ucsc.zoom.us/j/92739836317?pwd=[REDACTED]
Passcode: [REDACTED]

Johnstone, J. (AM) - The Effects of Asymmetry on Overshooting and Magnetic Pumping from Compressible Convection Zones
2026-02-06 11:00 - 12:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
We present a comprehensive numerical investigation examining how vertical asymmetry in compressible convection affects overshooting and the transport of large-scale magnetic fields from convective to stably stratified regions. Using three-dimensional direct numerical simulations, we systematically vary the superadiabaticity and stratification of a convective layer to control the vertical asymmetry of the flow and analyze its influence on overshooting depth and magnetic pumping efficiency. We extend previous work by Tobias et al. (2001) and draw guidance from the asymmetry regimes identified by John & Schumacher (2023), investigating whether similar asymmetric convecting regimes emerge in our overshooting model that incorporates a stably stratified region below. We find that vertical asymmetry increases significantly with stratification at a moderate, fixed Rayleigh number, while superadiabaticity contributes primarily through enhanced downflow velocities, with both combined leading to increasing overshooting depths reaching approximately 0.46 − 0.7 pressure scale heights. Magnetic pumping efficiency initially increases with stratification but unexpectedly decreases at higher stratification, despite increasing overshooting depths. We find that this behavior arises from the increasing thermal and magnetic diffusivities that result from increasing stratification at fixed Ra. When instead either holding these diffusivities constant or increasing Ra sufficiently, we find that then both overshooting and magnetic pumping depths both decrease with increasing stratification. This behavior is explained by a change of dynamical state from one of laminar downflows to one of turbulent downflowing plumes leading to a high degree of turbulent mixing and entrainment. We thus find two distinct regimes that might be described as a microscopically diffusive regime and a turbulently diffusive one. These results suggest that, in the highly turbulent regime expected in the Sun, magnetic pumping efficiency may decrease with increasing stratification due to enhanced turbulent entrainment, with important implications for solar dynamo theory and the transport of large-scale magnetic fields in the solar interior.

Event Host: Jason Johnstone, Ph.D. Student, Applied Mathematics
Advisor: Nic Brummell
Zoom- https://ucsc.zoom.us/j/5428987373?pwd=[REDACTED]
Passcode- 778899

Yang, J. (CSE) - Towards Controllable and Compositional Generative Vision
2026-02-06 16:00 - 18:00
Diffusion-based text-to-image models can generate impressive images, but they largely treat an image as a single, flat output, which makes precise editing of individual elements difficult. This proposal studies layered generative representations that align with professional editing workflows, enabling users to manipulate foreground objects while preserving the rest of the scene. A central focus is visual effects such as shadows and reflections, which are essential for realistic composition yet are often missing or inconsistent in current generative pipelines. This proposal outlines a research program toward controllable, compositional image generation that supports practical, edit-ready content creation.
Event Host: Jinrui Yang, Ph.D. Student, Computer Science and Engineering
Advisor: Yuyin Zhou
Zoom- https://ucsc.zoom.us/j/91510964517?pwd=[REDACTED]
Passcode- 544143

Li, X. (CSE) - Compute-Efficient Scaling of Fully-Open Visual Encoders
2026-02-09 13:00 - 14:30
Vision encoders have demonstrated significant performance gains in visual generation and multimodal reasoning. These improvements are primarily attributed to the scaling of data, model capacity, and compute. However, this progress is becoming less accessible due to a lack of transparency in data curation and training recipes. In combination with the high compute requirements of foundation-scale pre-training, these factors hinder independent reproducibility.
In this dissertation, we democratize large-scale visual encoder training by developing compute-efficient, reproducible training recipes for video encoders, vision-language models (VLMs), and multimodal large language models (MLLMs). First, we challenge the common belief that scaling necessarily requires proportionally more resources. Specifically, we show that decoupled pre-training separates key factors such as space/time and token length, and learns strong priors first. This design yields dramatic efficiency gains across image, video, and generative modeling. Next, we address the challenge of undisclosed or inaccessible training data by releasing and systematically studying the curation of high-quality, large-scale datasets. We demonstrate that high-quality synthetic captions at scale enable vision-language models to learn stronger visual representations, especially when paired with training frameworks that unify contrastive and generative objectives. Lastly, building on these findings, we develop fully open vision encoders with complete training data, recipes, and checkpoints, and show that transparency can enable rather than hinder state-of-the-art performance as an MLLMs’ visual backbone.
Together, these contributions establish that openness and efficiency are mutually reinforcing, providing a reproducible foundation for the next generation of visual intelligence.
Event Host: Xianhang Li, Ph.D. Candidate, Computer Science and Engineering
Advisor: Cihang Xie
Zoom- https://ucsc.zoom.us/j/95801462664?pwd=[REDACTED]
Passcode- 782017

Centering the Experiences of Undocumented Transfer Students at HSIs: A Brown Bag Presentation by Valeria Alonso Blanco
2026-02-11 12:00 - 13:00 | Huerta Center Conference Room (Casa Latina), 641 Merrill Rd, Santa Cruz,, CA, 95064
The Huerta Center is proud to present a brown bag presentation by Graduate Student Research Awardee Valeria Alonso Blanco. She will present on a qualitative study that explores how undocumented Latinx transfer students navigate institutional support, belonging, and barriers at a four-year Hispanic Serving Institution (HSI). Findings reveal gaps between institutional commitments and student realities, and she offers actionable recommendations for more equitable, transfer-receptive practices.

Sambamurthy, A. (AM) - Lazy Diffusion: Resolving Spectral Collapse in Generative Models for Turbulence
2026-02-12 16:30 - 17:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Diffusion-based generative models offer a principled framework for probabilistic forecasting, but we show they suffer from a fundamental spectral collapse when applied to turbulent flows. A Fourier-space analysis of the forward SDE reveals that the mode-wise signal-to-noise ratio decays monotonically in wavenumber for power-law spectra, rendering high-wavenumber content indistinguishable from noise. We reinterpret the noise schedule as a spectral regularizer and introduce power-law schedules that preserve fine-scale structure deeper into diffusion time. We further propose Lazy Diffusion, a one-step distillation method that leverages the learned score geometry to bypass long reverse trajectories and prevent high-wavenumber degradation. Applied to high-Reynolds-number 2D Kolmogorov turbulence and ocean reanalysis data, these methods resolve spectral collapse and enable stable long-horizon autoregressive emulation.
Event Host: Anish Sambamurthy, Ph.D. Student, Applied Mathematics
Advisor: Ashesh Chattopadhyay
Zoom- https://ucsc.zoom.us/j/5144530307?pwd=[REDACTED]
Passcode- 55555

Fredrickson, K. (CSE) - Practical Anonymity with Formal Resistance to Traffic Analysis
2026-02-20 14:00 - 16:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Anonymous communication systems hide who is talking to whom, not just what is said. However, existing systems are either vulnerable to traffic analysis attacks–attacks where adversaries observe and correlate the network traffic of users–or are forced to rely on unrealistic and unenforceable assumptions about how users behave. Worse, existing theory lacks tools to rigorously model traffic analysis attacks, much less inform whether if a system is secure against traffic analysis or how to design systems that are.
We make several contributions toward our goal of practical anonymity systems that resist traffic analysis. First, we develop the first formal framework for describing the security of systems against traffic analysis attacks, allowing us to quantitatively describe and compare the security of all existing works. Second, leveraging this framework, we develop a security definition that distinguishes between systems that are and are not susceptible to traffic analysis. We call this property input/output independence. We use this definition to prove that the dominant model of systems–synchronous systems–cannot practically provide input/output independence. We then design a new asynchronous anonymity functionality, deferred retrieval, that achieves input/output independence with far more flexible user assumptions and up to 3400 times less traffic overhead for the same latency compared to prior methods. Finally, we design and implement Sparta, a family of high-throughput, scalable instantiations of deferred retrieval using trusted execution environments and oblivious algorithms, yielding the first practical anonymity systems that are formally resistant to long-term traffic analysis.
Event Host: Kyle Fredrickson, Ph.D. Candidate, Computer Science and Engineering
Advisor: Darrell Long
Zoom – https://ucsc.zoom.us/j/98133127429?pwd=[REDACTED]
Passcode – 242206
