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 | CA
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 | CA
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 | CA
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 | CA
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.
