Events

Larsen, B. (CMPM) - Communal Narrative Play in Perennial Games
2025-08-05 10:00 (America/Los_Angeles) | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Online communities tell stories with the games they play. As continual updates, recurring monetization, and platforms for community discussions have flourished, we have seen a rise in video games using ongoing development to tell stories, and have a community interact with those stories and build upon them. In this dissertation, I study this phenomenon, which I call textit{perennial games}—storytelling experiences, which are perpetual, continuous, and tell an ongoing, communal story, where everyone influences its future in big and small ways. I study this especially as it has grown in the years 2010-2025, as the modern rise of the live-service game has exploded in popularity, and are using this format to tell stories in ways both unique yet also in ways that harks back to serial fiction, professional wrestling, modern television series, traditional mythology, and more. Through a three-pronged focus I study: 1) the games as narrative experiences, and how they facilitate narrative play through their design, 2) the communities who play them, how and why they play with the narrative and stay in these worlds for decades, and 3) the development, investigating the many joys and challenges of telling an ongoing story, following the inevitable oscillations as developers interact with the community. Through this multifaceted approach, I illustrate how perennial games cultivate community by inherently trading their mystery for familiarity, creating strong social bonds through the communal experience of uncovering, cataloging and deciphering mystery. Pushed forward by the inherent myth that these games will continue to change, the communities around them strain against and increasing lack of mystery, both seeking the safety of their social bonds while yearning for that which brought them there in the first place. Perennial games can be alluded to a developed garden, requiring maintenance and care, each year taking a subtly new shape, molded by its inhabitants and its caretakers, always a bit more wild than anyone can manage on their own, and as it grows the people inside it grow ever more dependent on its continued existence, until the promise that kept them there breaks.
Event Host: Bjarke Larsen, PhD Candidate, Computational Media
Advisor: Elin Carstensdottir

Exploring your science major
2025-08-06 17:00
Choosing a major can feel overwhelming, but many science students share a common academic path in their first year. This session breaks down the similarities in core coursework, introduces the range of science majors available, and helps you begin thinking about where your interests align. To receive a Zoom link, register HERE.

Rose, N. (BMEB) - MACHINE LEARNING MODELS FOR T CELL RECEPTOR TARGET DISCOVERY AND AFFINITY ENGINEERING
2025-08-07 10:30 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
T cell receptors (TCRs) mediate antigen-specific immune responses through recognition of peptides presented by major histocompatibility complex (pMHC) molecules. Accurately predicting TCR–pMHC interactions remains a major barrier to TCR-based immunotherapy, due to limitations in current models that fail to generalize beyond common viral epitopes and well-characterized HLA alleles. In this PhD proposal, I outline a computational framework that integrates deep learning, structural modeling, and molecular simulation to improve TCR–pMHC prediction and discovery. I first introduce TRIOPS, a convolutional neural network trained on harmonized, experimentally validated data to predict MHC restriction from TCR sequence alone. TRIOPS outperforms state-of-the-art models in both held-out evaluation and independent patient datasets, demonstrating improved accuracy in assigning TCRs to their correct restricting MHC alleles. I then propose TRILOBITE, a two-part structure-based model combining atomic-resolution graph representations and dynamics-derived biophysical features to classify TCR–pMHC binding and estimate affinity. Finally, I propose an end-to-end pipeline to identify tumor-reactive TCRs from patient-derived sequencing data by integrating HLA typing, antigen prediction, structure generation, and binding assessment. Applied to a pan-cancer atlas of over 1.2 million T cells, this framework will enable high-throughput, structure-informed TCR discovery across diverse HLA backgrounds. Together, these aims address a critical need for scalable, mechanistically grounded methods for mapping T cell specificity to accelerate cancer immunotherapy discovery.
Event Host: Nicholas Rose, PhD student, Biomolecular Engineering & Bioinformatics
Advisor: Vanessa Jonsson

Slugs at Sundown - The Art of the Career Pivot
2025-08-07 17:30 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Thinking about making a career change? Join fellow Banana Slugs for an evening of connection and guidance from professional career coaches. Learn how to navigate transitions with confidence, gain practical strategies, and get inspired to take the next step in your journey—whether you’re pivoting by choice or by chance.
Bonus: Come early for a 15-minute, one-on-one resume review—available from 4:30 to 5:30 PM.

Katte, P. (BMEB) - Interactive and Scalable Frameworks for Pathogen Surveillance and Ancestral Recombination Graph
2025-08-08 14:00 | Biomedical Sciences Building, 575 McLaughlin Drive
The explosive growth of genomic data, driven by advances in sequencing and inference technologies, presents both an opportunity and a challenge for evolutionary biology and public health. Existing visualization and analysis tools often fall short in handling the scale, complexity, and uncertainty of modern genomic datasets—especially in the areas of pathogen surveillance and ancestral recombination inference. This thesis introduces new tools that provide scalable visualization and analysis to bridge these gaps and enable more interpretable and actionable genomic insights.
First, I develop an interactive dashboard within a tool called WEPP for wastewater-based pathogen surveillance. It combines phylogenetic placement with intuitive web-based visualization, allowing public health officials to track variant spread at high resolution. Second, I build Lorax, a browser-based platform for visualizing Ancestral Recombination Graphs (ARGs) at biobank scale. Lorax incorporates a multi-agent system that supports natural language querying, code generation, and interactive tree exploration. Finally, I introduce a novel inference framework based on Generative Flow Networks to sample from posterior distributions over ARGs, addressing key limitations in uncertainty quantification and scalability found in existing methods. Together, these tools aim to make the study of evolution and disease more accessible and effective, helping researchers and public health teams draw clearer conclusions from complex genetic data.
Event Host: Pratik Katte, PhD Student, Biomolecular Engineering & Bioinformatics
Advisor: Russ Corbett-Detig

Vera-Choqqueccota, S. (BMEB) - A CRISPRi-Based platform for multimodal functional analysis of neurodevelopmental and neuropsychiatric disorders risk genes in engineered mouse cortical neurons
2025-08-08 15:00
Neurodevelopmental and neuropsychiatric disorders (NPDs), such as autism spectrum disorder and schizophrenia, are among the most heritable yet mechanistically complex conditions. While large-scale genomic studies have identified hundreds of high-confidence risk genes, the lack of scalable and integrative platforms has limited our ability to functionally characterize these genes. To address this, I am developing a CRISPR interference (CRISPRi)-based screening platform using mouse embryonic stem cells (mESCs) engineered to express Ngn2 for rapid and standardized neuronal differentiation and dCas9-KRAB for gene repression. In Aim 1, I will generate and validate this cell line, enabling reproducible and scalable perturbation of disease-relevant genes. In Aim 2, I will apply this system to characterize the effects of knocking down ten NPD-risk genes using multimodal analysis of morphology, transcriptomics, and electrophysiology. In Aim 3, I will build a framework to crowdsource functional analysis of NPD risk genes to design gRNAs, access experiments remotely, and analyze data using simplified, accessible tools. Together, this project advances both scientific understanding of NPD mechanisms and equitable access to functional analysis.
Event Host: Samira Vera-Choqqueccota, PhD Student, Biomolecular Engineering & Bioinformatics
Advisor: David Haussler

Montenegro, C. (ECE) - Control of Uncertain Hybrid Systems
2025-08-11 14:00
Machine learning endows autonomous systems to uncover underlying structures and physical laws from measured data and to leverage these models for prediction and decision-making. As the costs of data acquisition, processing, and storage decline—and sensors become increasingly widespread alongside ever-improving algorithms—artificial intelligence has attracted significant attention in research and industry.
Machine-learning methods are particularly attractive when an analytical model is too difficult—or even impossible—to derive because the underlying principles are poorly understood. As control engineering enters such domains—for example, physical human-robot interaction and self-driving vehicles—data-driven models offer a practical alternative to classical system-identification techniques for model-based control. In addition, we know that robotic or control systems seldom work in ideal conditions. Sensor noise, incomplete state information, and uncertain parameters are everyday realities, and controllers must be robust—able to attenuate these disturbances—and be backed by formal guarantees of stability and safety.
Coupling physical dynamics with embedded computation and communication introduces new challenges. Hardware elements such as analog-to-digital converters, sample-and-hold circuits, and quantizers, together with events like timers, resets, and impacts, yield an even more complex class of control systems in which designing controllers that remain robust to unmodeled dynamics and disturbances—and providing formal certificates of stability and safety—becomes harder. Cyber-physical systems that have continuous dynamics with event-driven behavior, therefore, require control strategies that explicitly account for these events and stay robust to adversarial uncertainties.
Therefore, the focus of this proposal is to design learning-based certificates and control techniques for hybrid systems with uncertainties in the form of unmodeled dynamics and unknown disturbances. We propose four research thrusts in this proposal. The first one addresses the problem of learning a surrogate model of the unmodeled using learning-based models that are both statistically sound and directly usable for feedback design. In the second thrust, we develop a safety control framework for systems whose dynamics are learned with high probability using a set-valued and variational analysis. In our third thrust, we consider the problem of learning certificates—in particular, Lyapunov functions and cost upper-bound surrogates—for hybrid systems. Finally, we tackle the optimal control problem for hybrid systems under unknown disturbances in our fourth thrust.
Event Host: Carlos Montenegro, PhD Student, Electrical & Computer Engineering
Advisor: Ricardo Sanfelice

August Slugs & Steins with Professor Nancy N. Chen
2025-08-11 18:30
Breathing in the Anthropocene: Reflections on Breath, Air, and Vitality
This presentation examines breathing in the present moment when humans vastly transform Earth ecosystems that impact health and well-being. Atmospheric transformations via worsened air highlight connections of breath with health. How might breath be shaped by cultural and individual experiences? Ethnographic research at the intersections of medical and environmental anthropology have renewed attention on energetic relations between bodies, landscapes, air, and health, especially the role of vital energy in qi, prana, or ha. In addition to these breath centered approaches, we examine recent biomedical research on breath management across a broad range of complementary medicine and health interventions.

Ghosh, S. (CMPM) - Scientific Sensemaking with Spatial Data in Collaborative Virtual Reality
2025-08-12 09:00
Collaborative virtual reality environments have the potential to greatly impact scientific progress, especially those relating to existential human problems. Within these virtual environments, scientists could view and interact with spatial data in applications as part of their sensemaking process, however, there are design challenges and barriers to development. This advancement document presents research questions related to the design of these technologies, and a project schedule for completing the dissertation. In the first part, Virtual Reality for Scientific Sensemaking, I describe prior, current, and future work related to collaboratively designing interfaces with scientific domain experts across civil engineering and marine science. In the second part, Taxonomy, I describe current and future work to categorize and generalize XR input patterns for collaborative virtual environments, as these works have been shown to fundamentally enable the design of many apps. In the final part, Open Source Infrastructure Stability, I describe my effort to support the ecosystem through the contribution of interoperable, low-resource, and extensible open source software.
Event Host: Samir Ghosh, PhD Student, Computational Media
Advisor: Katherine Isbister

Mawhorter, R. (CSE) - Certified Synthesis for Interactive Media: High Assurance Metroidvania Generation
2025-08-12 10:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Program verification has been applied in many contexts (including videogames), but the scale and complexity of the examples that have been analyzed fall short of the ability to analyze many existing games without massive computational costs. My research focuses on automatic analysis and design of one particular game: Super Metroid, with the goal of creating general methods for efficient analysis that address these issues. In pursuit of this goal, I develop novel abstraction strategies that can be reapplied in other contexts. I also show that these same techniques can also be used to synthesize games, and I develop a paradigm for understanding procedural generation problems as verification problems. This paradigm enables generators to certify their output, and these certificates act as a powerful debugging tool. My research expands on existing techniques for applying symbolic search to large state spaces, exploring many different ways of optimizing the state space representation, and reporting on their relative effectiveness in real-world contexts. I also demonstrate how multiple layers of abstraction can be used to enhance existing search algorithms. Using these methods, I show how verifying properties of software with respect to the humans that interact with it can be practically achieved.
Event Host: Ross Mawhorter, PhD Candidate, Computer Science & Engineering
Advsior: Adam Smith

BME Special Seminar: La protein and the RNA Polymerase III transcriptome
2025-08-12 12:00
Presenter: Richard J Maraia, MD, Senior Investigator and Head of the Section on Molecular and Cell Biology in the Intramural Research Program, NICHD
Description: The La protein is a eukaryote-ubiquitous RNA-binding protein that (in the organisms examined) stabilizes newly synthesized RNA polymerase (Pol) III transcripts by transiently protecting their 3’-ends prior to maturation as abundant stable noncoding (nc)RNAs. While Bacteria and archaea use a single RNA polymerase to produce their cellular RNAs, Pol III is specialized to synthesize short RNAs in large amounts, e.g., tRNAs at >10-fold molar levels relative to ribosomal RNA synthesized by Pol I. tRNA genes are the most numerous of Pol III-transcribed genes, followed by 5S rRNA and a few single-to-low copy noncoding (nc)RNA genes. The numbers of tRNA genes have been expansive and dynamic in eukaryotes, presumably facilitated by their “internal split promoter” (A-box and B-box) which roughly correspond to the most conserved regions of tRNAs, the D and T loops. The Pol III-specific transcription factor, TFIIIC binds to this promoter and directs transcription at the upstream initiation site, i.e., even if the tRNA sequence is inserted at a different locus. Transcription termination by Pol III is by a unique mechanism, directed by its second largest subunit, RPC2 which together with help of another Pol III-specific subunit reads the ≥4 consecutive Ts on the nontemplate strand as a pause signal that results in release the RNA. The first part of the talk will be on a paper under revision, A POLR3B-variant reveals a RNA Polymerase III transcriptome response dependent on the SSB/La protein. In this study, we performed extensive molecular characterization on a patient case that we found in the NIH Undiagnosed Disease Program (UDP) with homozygous SNPs in RPC2 and gained new insight as per the title. The second part of the talk will be related to a paper published in July 2025 that reflects diversity of tRNA genes. More specifically, we discovered a tRNA gene with apparent latent noncanonical activity to activate cellular interferon signaling, in addition to its mRNA decoding activity, and characterized its features and those of a minority subset of other human tRNA genes that exhibit this activity from those in a majority that don’t.
Bio: Richard J. Maraia focuses on RNA polymerase III, Genetics, RNA, Small nuclear RNA and RNA polymerase II. The study incorporates disciplines such as Inverted repeat, Genomic organization, Termination factor, Molecular biology and Heterochromatin in addition to RNA polymerase III. His Molecular biology research incorporates elements of Apoptosis, Mdm2, Cancer research and Small interfering RNA.
His studies in RNA integrate themes in fields like Transcription and Protein biosynthesis. The concepts of his Small nuclear RNA study are interwoven with issues in RNase P and RNA recognition motif. Richard J. Maraia has included themes like General transcription factor and DNA polymerase in his RNA polymerase II study.
He is the ongoing chair of the NIH RNA club and serves on the organizing committees of the international biennial conferences on RNA polymerases I & III and the biennial conferences on La and related protein (LARP). He served on the Earl Stadtman Investigator Search committees for Molecular Biology and Biochemistry as chair and for RNA Biology at the NIH. He had speciality training in pediatrics and interinstitute medical genetics at the NIH.

Hosted by: Professor Todd Lowe, BME Department

What the Mouth Remembers: Lost Recipe, Found Words
2025-08-12 12:00 | Eloise Pickard Smith Gallery, 11 Cowell Service Rd, Santa Cruz, CA, 95064
What the Mouth Remembers is a research-creation project that explores how spoken language, especially when fragmented, mistranslated, and passed down across migration, family histories, and colonial ruptures, becomes a site of embodied memory, survival, and imaginative reconstruction. At the heart of the project is the Jeju language, not as a fixed object of preservation but as a way of understanding and relating that vibrantly moves through sound, memory, and gesture.
Join us from noon to 3 p.m. daily during the exhibition, Jeju Seaweed Soup (Mom-guk) will be available for visitors to taste. Participants can choose either the classic pork-based version or a vegetarian alternative, both of which include rice, kimchi, and seaweed (in this case, from Monterey Bay Seaweeds), prepared using Yoon’s mom’s traditional Jeju recipe for mom-guk.
Yoonkyung Lim is a visual artist and Ph.D. student in Film and Digital Media at the University of California, Santa Cruz. Yoon’s art is participatory. The environment she creates around a theme allows an open space for interaction, discussion, and shared responsibility. Visitors are not merely viewers, they become co-creators. Her recent work investigates feminist oral histories, alternative archives, and diasporic modes of translation. She has exhibited at the Gangwon International Triennale (2024), MMCA Seoul and Gwacheon and the Coreana Museum of Art. Lim holds a BFA from Korea National University of Arts, an MFA from UCLA, and completed the Whitney ISP. Artist website: www.yoonkyunglim.com

Effective Multigenerational Communication
2025-08-12 17:30 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Join the Silicon Valley NHRA and learning partner Steps for an interactive session using drama-based learning to improve communication across generations in the workplace. Through scenario-based activities, you'll explore real-world challenges and uncover how different generations approach work, communication, and leadership. Learn how this approach supports HR and talent strategies by equipping teams with the tools to build stronger connections and a greater sense of belonging. Discover how generational differences can become a strength—not a barrier—to organizational success.
This event is sponsored by the UCSC Silicon Valley Extension Human Resource Management Certificate Program.

Asefi, N. (ECE) - Generative Lagrangian Data Assimilation for Ocean Dynamics under Extreme Sparsity
2025-08-13 10:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural operators with denoising diffusion probabilistic models (DDPMs) to reconstruct high-resolution ocean states from extremely sparse Lagrangian observations. By conditioning the generative model on neural operator outputs, the framework accurately captures small-scale, high-wavenumber dynamics even at $99%$ sparsity (for synthetic data) and $99.9%$ sparsity (for real satellite observations). We validate our method on benchmark systems, synthetic float observations, and real satellite data, demonstrating robust performance under severe spatial sampling limitations as compared to other deep learning baselines.
Event Host: Niloofar Asefi, PhD Student, Electrical & Computer Engineering
Advisor: Ashesh Chattopadhyay

Interested in a paralegal career?
2025-08-13 10:00
You are invited to join a free, online informational session to learn more about the Center for Legal Studies Paralegal Certificate Course©, a professional education program taught through the UCSC Silicon Valley Extension Paralegal Studies program.
Topics

Career growth and earning potential

An overview of how CLS works with accredited college and university partners

Course pricing and payment options

Course materials and textbooks

Instructor and peer/student interactions

How the online course works

Q&A: There will be 15 minutes dedicated to Q&A, so please bring any questions you might have.
Register to receive the event recording
If you are unable to attend the webinar at this date and time, please still register and the BARBRI team will send out the recorded webinar for you to view.
This event is co-sponsored by the UCSC Silicon Valley Professional Education Legal Studies program.

Lunch & Learn: Onboarding Graduate Students for Research
2025-08-13 12:00
Join colleagues from the Office of Research Compliance Administration and Baskin Engineering on August 13, 12-1 p.m., for a practical overview of onboarding graduate students in their role as researchers. This session will cover key institutional requirements needed before students begin research activities. Speakers will highlight common pitfalls, share best practices, and offer strategies to set students up for success.

Return to the Redwoods
2025-08-15 11:00 | Porter College, D-Building, Santa Cruz, CA, 95064
Return to the Redwoods is your chance to reconnect with friends, family, and fellow alumni while exploring the beauty of campus. Enjoy a variety of activities for all ages, including a lively kick-off party, Stars & S’mores, guided hikes, hands-on workshops, and plenty of opportunities for fun and relaxation.
You definitely don’t want to miss out!

AI Workshop: Spiking Neural Networks
2025-08-16 09:00 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Welcome to our immersive AI technology workshop series. During these sessions you will be introduced to new and established AI tools that will help you create and manipulate content in new and powerful ways. Each session is led by an industry expert who will guide you through the material and share its real-world implications.
Learning Outcomes
At the conclusion of the workshop, you should be able to

Describe and discuss the fundamental principles of Spiking Neural Networks (SNNs), including spike-based data representation, key neuron models (e.g., Leaky Integrate-and-Fire), synaptic operations, the current state-of-the-art in neuromorphic hardware, and the realistic short-term and long-term potential of SNNs in the broader AI landscape.
Explain the differences between Spiking Neural Networks (SNNs) and traditional Artificial Neural Networks (ANNs), articulating the motivational factors for SNN adoption such as energy efficiency and event-driven computation.
Demonstrate an ability to properly and effectively implement simple SNN applications (e.g., the XOR problem and a more complex use case) using tools like Nengo, understanding how information is encoded and processed through spike trains, and applying different training approaches for SNNs while contrasting them with traditional backpropagation and evaluating the challenges and opportunities in SNN learning.

Topics Include

Introduction to Spiking Neural Networks: What are SNNs? Visualizing spike-based computation.
Why SNNs Matter: Motivations (energy efficiency, neuromorphic hardware), real-world applications (robotics, IoT), and a realistic look at when they are most beneficial.
Core Concepts of SNNs: Spike-based representation (events, timing, frequency), simplified neuron models (LIF), synaptic function, and encoding strategies (rate vs. temporal).
Hands-On with SNNs (Nengo Demo): Interactive exploration of spike generation, parameter tuning, and visualizing network behavior.
Problem Solving with SNNs:
The XOR problem: Understanding challenges with discrete logic.
Applying SNNs to continuous, real-world analog-like problems.
Training Spiking Neural Networks: Exploring alternatives to backpropagation (e.g., evolutionary computation, PSO) and understanding the associated challenges.
The Neuromorphic Landscape: Introduction to key neuromorphic hardware (e.g., Intel Loihi, BrainChip Akida), their architectures, and real-world case studies.
Current Limitations and Future Outlook: Discussing speed vs. efficiency, challenges in tooling and frameworks, and the short-term and long-term vision for SNN adoption.

Students are required to bring laptops for class exercises.
This event is co-sponsored by the UCSC Silicon Valley Professional Education Artificial Intelligence program.

Vats, V. (CSE) - Learning to Remember: Multi-Agent Self-Refinement toward Persistent Machine Perception
2025-08-18 10:30
Modern machine perception is powerful yet brittle: failing in response to subtle data adversaries and lacking mechanisms to learn from their errors. We address this challenge by progressing from diagnosing such failures to developing a framework for persistent learning. We first investigate the sources of this fragility, demonstrating how both naturally occurring adversarial artifacts, like specular highlights, and conditions of data scarcity fundamentally limit model robustness. We then replace a passive reliance on quality data curation with active, multi-agent refinement: a Worker-Supervisor loop that iteratively critiques and corrects outputs to meet complex, rule-rich guidelines at inference time. While this system achieves dynamic error correction, it rarely remembers what was learned. We thus plan to tackle this problem of non-remembrance by proposing an experience memory that records validated fixes as reusable insights, retrieves them when similar contexts recur, and, where available, grounds them across viewpoints and time. Together, these components turn momentary fixes into long-term skills, paving the way for more capable and reliable perception in fields like augmented reality and robotics.
Event Host: Vanshika Vats, PhD Student, Computer Science & Engineering
Advisor: James Davis

Model Context Protocol: Why It Matters
2025-08-18 18:00
The Impact of Model Context Protocol
Join us for an engaging exploration of the Model Context Protocol—a groundbreaking framework designed to improve communication and context-sharing across AI agents. As AI systems become more modular and collaborative, MCP offers a powerful solution for maintaining continuity across tasks, tools, and models. This free, online event is sponsored by the AI Program at UCSC Silicon Valley Extension.

Topics

The origins and motivations behind MCP
How MCP structures and preserves context
The growing importance of MCP in the development of scalable, interoperable AI agent systems
Real-world use cases
Current limitations of MCP
How MCP is shaping the future of AI infrastructure
How to advance your knowledge and skills in this frontier technology.

Speaker
Praveen Krishna, AI Program chair and platform architect for Audio AI/ML Solutions, Performance & Power, Intel, teaches AI Essentials, Deep Learning and Artificial Intelligence, Open Computer AI Agent by Hugging Face, Practical uses of DeepSeek/Llama, Computer Vision and Image Processing, and Capstone Building Integrated AI Applications.

Interested in a deeper dive into this topic?
AI Technology Workshop Series: Model Context Protocol (Aug. 29)
A one-day workshop held in person at the Silicon Valley Campus or online.

Osorio, S. (AM) - Image-Based Wound Infection Classification
2025-08-19 10:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
This thesis investigates the use of deep learning for classifying wound infections from photographic images, using colony-forming unit (CFU) counts as a quantitative labeling standard. Leveraging the visual information in wound photographs and the clinical relevance of bacterial burden, the study implements a multi-task U-Net architecture for both image reconstruction and binary classification in a shared-encoder framework. Three experimental conditions were explored: one using original images with positive class weighting, one incorporating data augmentation to enhance visual diversity, and one employing 5-fold cross-validation with augmentation to improve validation reliability. The non-augmented model achieved 91.7% accuracy at a threshold of 0.8, correctly identifying 4 of 5 infected cases, while Experiment 2 achieved 87.5% accuracy at a moderate threshold of 0.5 but became more conservative at higher thresholds. The third experiment reached 79.6% accuracy at a threshold of 0.3, detecting all 11 infected cases despite signs of overfitting. These results highlight the model's strong performance in minimizing false negatives, particularly in the non-augmented setting, but also reflect limitations from the small dataset, class imbalance, and reliance on a small validation set. These factors suggest results should be interpreted cautiously and motivate further study with larger datasets, improved regularization, and more varied clinical scenarios.
Event Host: Sebastian Osorio, M.S. Candidate, Scientific Computing & Applied Mathematics
Advsior: Marcella Gomez

Programming with Rust
2025-08-19 12:00
Join us in learning more about Rust, one of the fastest-growing programming languages, which continues to be ranked the most-loved language by its users. Its user base, aka “Rustaceans,” has tripled in just two years as more and more software products are being developed in Rust.
In this fast-paced virtual overview with Danesh Forouhari, we’ll talk about:

The history of Rust
Problems Rust solves
Comparing Rust to other programming languages
Benchmarking data (vs. C & Go)
The good, the bad, and the ugly of programming with Rust
Running some sample Rust code, if time permits

Speaker
Danesh Forouhari, M.S., has more than 20 years of experience in the software development industry. He teaches Python for Programmers.
This fall info session is sponsored by the UCSC Silicon Valley Professional Education Computer Programming certificate program.

Moreland, Z. (AM) - Transcriptomic and Computational Analysis of Burn and Excisional Wound Healing
2025-08-19 13:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Accurate assessment of wound healing progress is critical for optimizing patient care and preventing complications, yet clinicians currently lack precise tools to determine where a wound stands in the healing timeline. Wound healing progresses through overlapping stages of inflammation, proliferation, and maturation, each marked by characteristic shifts in gene expression that are difficult to interpret without robust computational methods. This paper proposes to classify wound healing stages from transcriptomic data using support vector machines combined with biologically informed clustering to serve as features for the hierarchical SVM classifiers. This approach is applied to two distinct wound types: excisional wounds in pigs (21-day timeline) and burn wounds in mice (42-day timeline), enabling comparison of classification performance across different injury mechanisms. The models achieved high overall accuracy, with the burn model performing better at the classification of the stages. Both models made mistakes in distinguishing inflammation from early proliferation, highlighting the inherent biological overlap between these transitional healing stages. Overall, we find that transcriptomic-based classification can reliably identify wound healing stages across different wound types, providing a foundation for developing personalized diagnostic tools that could transform clinical wound management and improve patient outcomes.
Event Host: Zoe Moreland, M.S. Candidate, Applied Mathematics
Advisor: Marcella Gomez

Bhatia, N. (CSE) - Building Adaptive Intelligence into Wireless Sensing
2025-08-19 14:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
WiFi-based indoor positioning is a widely researched area focused on determining the location of devices. Accurate indoor positioning has numerous applications, including asset tracking and indoor navigation. Despite advances, their adoption in practice remains limited due to several challenges such as environmental changes that cause signal fading, multipath effects, and interference, all of which reduce positioning accuracy. Moreover, telemetry data vary across WiFi device vendors, presenting distinct features and formats, while use-case requirements can also differ significantly. At present, there is no unified model capable of handling these variations effectively.
We present WiFiGPT, a decoder-only transformer-based system designed to address these variations while achieving high localization accuracy. Our experiments with WiFiGPT show that it can effectively capture subtle spatial patterns in noisy wireless telemetry, making them reliable regressors. Compared to state-of-the-art methods, our approach matches and often surpasses conventional techniques across multiple types of telemetry. Achieving sub-meter accuracy for RSSI and FTM and centimeter-level precision for CSI highlights the potential of LLM-based localization to outperform specialized methods, without the need for handcrafted signal processing or calibration. Other work includes EchoSense, which utilizes CSI to monitor vital signs such as heart rate and respiration with high accuracy.
Event Host: Nayan Bhatia, PhD Student, Computer Science & Engineering
Advisor: Katia Obraczka

Swaby, A. (ECE) - Improving X-ray Medical Imaging using Amorphous Selenium as a Photoconductive Layer
2025-08-19 14:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
The presence of coronary artery calcification is a strong predictor for future cardiovascular events where cardiac risk categories are quantified depending on calcification size. Dual-energy chest X-rays provide high contrast visualization to improve opportunistic screening for quantifying coronary artery calcifications, determining bone mineral density (i.e., osteoporosis) and characterizing lung lesions. As a dual-energy imaging modality, multilayer flat panel detectors acquire low- and high-energy X-ray images as a polyenergetic, single-exposure. Combining two detectors into a dual-layer configuration, weighted subtraction techniques in the resulting images allow for differentiation of soft tissue from the projection of the bone structures and other high attenuating materials. To improve detection of calcifications < 1 mm in size, the performance of a dual-layer X-ray detector is investigated as a means of providing the necessary μm-resolution and spectral separation for enhanced contrast between low- and high-energy X-ray images. A cascaded linear systems model is used to simulate the modulation transfer function, detective quantum efficiency, and noise power spectrum of an amorphous selenium direct conversion top detector and a cesium iodide-based indirect conversion bottom detector. As the framework for system design and optimization, a generalized task-based analysis is used to analyze how the signal projections, noise contributions, task function, and weighting factors contribute to the detectability index of the dual-layer imaging system.
Event Host: Akyl Swaby, PhD Candidate, Electrical & Computer Engineering
Advisor: Dr. Shiva Abbaszadeh

All UC Alumni Networking Mixer in Central California
2025-08-20 17:30
Alumni from all 10 University of California campuses are invited to our friendly and open networking mixer at a UC alumni-owned winery in Central California.

Project and Program Management Info Session
2025-08-20 18:00
Project Leadership with Tim Bombosch
Join Program Chair Tim Bombosch for UCSC Silicon Valley’s Project and Program Management certificate, where you'll learn from PMI®-certified experts leading teams across top Silicon Valley companies. Gain the tools and strategies to define goals, estimate costs, manage risk, and deliver successful outcomes
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Master Real-World Management Skills
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HireUC Alumni Career Summit
2025-08-21 11:00
About the career summit
The HireUC Alumni Career Summit, in partnership with Hire Talent, is a hiring event organized for University of California (UC) alumni who are looking for early- and mid-level career opportunities. It offers a special chance for alumni to connect with employers from diverse fields and industries.

HireUC Alumni Career Fair
2025-08-21 12:00
The HireUC Alumni Career Fair is a hiring event designed for University of California alumni who are looking for early-and mid-level career opportunities. It offers a special chance for alumni to connect with employers from diverse fields and industries. Alumni from all 10 UC campuses are invited. UC alumni can attend the fair for free, but it’s important to register in advance to secure participation.
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Since 2014, over 100,000+ attendees to Hire Talent Events
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50% bachelors / 40% masters / 10% doctorate
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Jeffrey Nortman jeff@gohiretalent.com
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If you work for a company looking to connect with alumni from the University of California graduating from various majors, degrees and career levels, please use the Employer Registration tab to get participate as a recruiter. Employer registration is open to all employers not just UC Alumni.

Briden, M. (CSE) - Representation Learning and Generative Forecasting for Noisy and Limited Clinical Data: Applications in Wound Healing and EEG
2025-08-21 13:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
The rapid integration of artificial intelligence and machine learning into clinical practice has driven advances in disease classification, segmentation, and clinical decision support. However, the complexities of medical data pose a challenge to widespread adoption. The rarity of medical conditions, ethical considerations, and varying acquisition protocols leads to limited and noisy data. The time-intensive process of labeling data, the high degree of accuracy required in clinical settings, and the ill-defined nature of certain medical conditions further complicate the application and deployment of machine learning models. Likewise, high‐stakes medical decisions demand trustworthy and interpretable predictions. However, prioritizing trust and explainability is rarely a primary objective in most model designs.
This thesis addresses three key challenges in machine learning for healthcare. First, we develop methods for learning under noisy and limited medical data, focusing on representation learning strategies that improve generalization when datasets are small or contain mislabeled samples. Second, we explore the prediction of generative outcomes amid label noise and data scarcity, utilizing parameter-efficient and temporal generative models to forecast disease trajectories. Third, we advance trustworthy and explainable medical artificial intelligence by designing deep architectures that provide interpretable outputs suitable for clinical decision-making.
These challenges are addressed in the context of two complementary medical modalities: wound healing images and electroencephalogram signals. Wound healing tasks focus on predicting healing trajectories while enhancing interpretability through segmentation-based explanations and training large models in light of extreme data noise and scarcity. Electroencephalogram-based tasks emphasize representation learning and explainability for non-invasive mental state classification. These experiments demonstrate the clinical relevance of the proposed approaches and their ability to operate under challenging medical conditions across both imaging and physiological signal domains.
Event Host: Michael Briden, PhD Candidate, Computer Science & Engineering
Advisor: Narges Norouzi
