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DTSTART;TZID=America/Los_Angeles:20260304T130000
DTEND;TZID=America/Los_Angeles:20260304T170000
DTSTAMP:20260424T071337
CREATED:20260217T222743Z
LAST-MODIFIED:20260219T210101Z
UID:10009243-1772629200-1772643600@events.ucsc.edu
SUMMARY:Shields\, S. (CM) - Procedural\, Player-Centric Game Balancing
DESCRIPTION:Game balance is a term widely used among players\, researchers\, and designers of games. It is a concept that feels vitally important to how we make and play games – but when we try to define it or implement it\, we seldom get the same definition twice. Balance appears differently to whoever is judging it\, but as researchers and designers we still must translate this element of game design into technical practice. It also is an expensive and time-consuming subject\, one that requires a constant loop of playtesting and design iteration through nearly the entirety of the game development process. \nThis work seeks to focus our understanding of balance while offering procedural methods to either increase speed or improve quality when performing balancing tasks in game design and research. It accomplishes this by offering a taxonomy of balance alongside a generic design framework that can be used to apply balancing strategies to any game context. It additionally provides a catalog of balancing methods\, allowing designers to use common patterns to apply procedural balancing to their games. Finally\, I offer three technical examples using the taxonomy and framework\, putting theoretical knowledge of balance into concrete technical systems. \nBalance ultimately helps us design games that make us feel fairness in our play. By sharpening and optimizing our understanding of the term\, we improve the games we make and open new doors in game systems design. \nEvent Host: Sam Shields\, Ph.D. Candidate\, Computational Media  \nAdvisor: Edward F. Melcer \nZoom- https://ucsc.zoom.us/j/98956788669?pwd=ao7DzYQebCeS3SJ4PsGaZeGYhYMVNI.1 \nPasscode- 713173
URL:https://events.ucsc.edu/event/shields-s-cm-procedural-player-centric-game-balancing/
LOCATION:Merrill College\, College Office\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260305T130000
DTEND;TZID=America/Los_Angeles:20260305T150000
DTSTAMP:20260424T071337
CREATED:20260217T182432Z
LAST-MODIFIED:20260217T182432Z
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SUMMARY:Xu\, Y. (CSE) - Right Place\, Right Time: Accelerating Edge Computation on Modern Heterogeneous SoCs
DESCRIPTION:Modern edge computing increasingly relies on heterogeneous System-on-Chip (SoC) architectures. These chips tightly integrate general-purpose CPUs with various specialized accelerators\, including GPUs\, FPGAs\, and AI accelerators\, all under a shared memory architecture. Although these shared-memory SoCs enable more efficient communication and data sharing between different processing units\, they are notoriously difficult to program and tune due to architectural diversity across vendors and asymmetric compute capabilities within each SoC. \nThis dissertation introduces Redwood and BetterTogether\, two frameworks that rethink CPU-accelerator collaboration on heterogeneous SoCs. Redwood targets a class of algorithms termed traverse–compute\, that combine irregular tree traversals with dense leaf-level computation\, e.g.\, Nearest-Neighbor Search and Barnes–Hut algorithm. \nIt addresses the efficient mapping of these algorithms onto heterogeneous systems by exploiting the architectural strengths of CPUs\, GPUs\, and FPGAs. BetterTogether extends this methodology to a different class of edge workloads\, specifically multi-stage pipelines and neural networks commonly used in computer vision tasks. Furthermore\, it introduces interference-aware analysis and scheduling techniques tailored for mobile SoCs. Finally\, to broaden the scope of heterogeneous acceleration\, we evaluated emerging domain-specific accelerators. We provide a preliminary analysis of Tensor Processing Units and Tensor Cores within the context of modern programming abstractions. \nEvent Host: Yanwen Xu\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Tyler Sorensen \nZoom- https://ucsc.zoom.us/j/5354629158?pwd=0CVhbwLuXDMX5fAGZd63tcfNqDWp0t.1 \nPasscode- 114514
URL:https://events.ucsc.edu/event/xu-y-cse-right-place-right-time-accelerating-edge-computation-on-modern-heterogeneous-socs/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T070000
DTEND;TZID=America/Los_Angeles:20260309T080000
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CREATED:20260303T175533Z
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SUMMARY:Hendawy\, M. (CM) - Autonoming Child Online Safety in the Age of AI: From Control to Digital Co-Agency Across Cultures
DESCRIPTION:Children’s lives are now inextricably linked with AI-driven digital systems that shape learning\, social interaction\, and development. This has elevated child online safety to a central concern for families\, policymakers\, and educators. This makes Child online safety a wicked socio-technical problem\, emerging from the complex interplay of social norms\, platform incentives\, cultural expectations\, and rapidly evolving technologies. Dominant control-based paradigms—monitoring\, blocking\, and surveillance—undermine children’s developmental capacity\, erode family trust\, and foreclose the iterative cycles of self regulated learning necessary for digital resilience. This proposal advances digital co-agency as a new paradigm for child online safety. It reframes safety from an outcome of unilateral control to a shared\, relational practice distributed across children\, caregivers\, technologies\, and governance structures. To be effective\, digital co-agency must be grounded in a clear normative standard. I define this standard as ethical safety: protection is legitimate only when it is rights-respecting and developmentally supportive. Within this boundary\, the dissertation proposes autonoming as a design stance for AI-mediated safety systems. Autonoming systems act as developmental mentors that support children’s judgment over time through explanation\, negotiation\, and graduated support that can fade as competence grows. Autonoming is grounded in Self-Regulated Learning (SRL) as the developmental mechanism for durable safety capacity. SRL models learning as cyclical forethought (planning)\, performance (in-the-moment regulation)\, and reflection (evaluating outcomes). The dissertation adopts a socio-technical interpretivist stance and a Design Science Research orientation to produce actionable artifacts that are theoretically grounded and evaluable.. Its core methodological contribution is localization-first comparative design across Cairo and Berlin. This comparative structure helps distinguish between: localized variables (culturally specific norms regarding authority\, privacy\, risk\, norms\, expectations\, and legitimacy conditions that must be adapted to) from ethical invariants (accountability\, contestability\, proportionality that should hold across contexts). \nEvent Host: Mennatullah Hendawy\, Ph.D. Student\, Computational Media  \nAdvisor: Magy Seif El-Nasr \nZoom- https://ucsc.zoom.us/j/93831600031?pwd=hsnX574bcXVQRZa16sKbX0u7OuaMlu.1 \nPasscode-459844
URL:https://events.ucsc.edu/event/hendawy-m-cm-autonoming-child-online-safety-in-the-age-of-ai-from-control-to-digital-co-agency-across-cultures/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260309T100000
DTEND;TZID=America/Los_Angeles:20260309T110000
DTSTAMP:20260424T071337
CREATED:20260303T174856Z
LAST-MODIFIED:20260303T174856Z
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SUMMARY:Robbins\, A. (ECE) - How to train your organoid: goal-directed learning in biological neural networks
DESCRIPTION:Artificial neural networks can now learn to play games\, control robots\, generate language\, and solve complicated reasoning tasks\, yet we still lack a clear understanding of how to directly guide learning in biological neural networks. We show that brain organoids can learn to solve a fundamental control task\, balancing an inverted pendulum\, through closed-loop electrophysiology. Cortical organoids interfaced with high-density microelectrode arrays received sensory input about the pole’s angle and produced motor output through their neural activity. Training signals selected by a reinforcement learning algorithm significantly outperformed random stimulation and no-stimulation controls. Blocking glutamatergic transmission abolished the learning and washout restored it\, confirming the adaptation depends on synaptic plasticity. To support this work and future experiments\, we developed BrainDance\, an open-source framework for running reproducible biological learning experiments\, and contributed to RT-Sort\, a real-time spike sorting algorithm. This dissertation presents the tools\, experiments\, and findings from pursuing goal-directed learning in biological neural networks. BrainDance makes these experiments easy-to-create\, reproducible and shareable\, letting any lab with compatible hardware start training their own organoids. \nEvent Host: Ash Robbins\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Mircea Teodorescu \nZoom- https://ucsc.zoom.us/j/95839863615?pwd=EmqTWPN9RRBYZRW7rcpoaT9kqacfRP.1 \nPasscode- 069118
URL:https://events.ucsc.edu/event/robbins-a-ece-how-to-train-your-organoid-goal-directed-learning-in-biological-neural-networks/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T140000
DTEND;TZID=America/Los_Angeles:20260309T160000
DTSTAMP:20260424T071337
CREATED:20260303T174216Z
LAST-MODIFIED:20260303T174216Z
UID:10009384-1773064800-1773072000@events.ucsc.edu
SUMMARY:Harrison\, D. (CS) - Multi-Level Control in Neural Dialogue Generation: Style\, Semantics\, and Selection through Over-Generation and Ranking
DESCRIPTION:End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models produce fluent text\, they collapse content planning\, sentence planning\, and surface realization into a single undifferentiated decoding step\, sacrificing the controllable structure that earlier systems provided. This dissertation investigates how that structure can be recovered through the over-generate-and-rank (OGR) paradigm: generating multiple candidate outputs and selecting among them using learned or prompt-based ranking functions that jointly optimize semantic fidelity\, stylistic appropriateness\, and conversational coherence. We instantiate OGR at three levels of natural language generation for dialogue: utterance-level stylistic control\, cross-domain semantic evaluation\, and dialogue-level response selection. \nFirst\, we show that explicit conditioning mechanisms\, specifically decoder-level side constraints for personality variation and discourse contrast\, re-introduce stylistic control into neural sequence-to-sequence models without compromising semantic accuracy. Second\, we demonstrate that prompt-based learning with structured linguistic profiles achieves near-perfect personality accuracy and effectively zero slot error rate when combined with ranking\, establishing that LLM prompting with explicit pragmatic specifications can match or exceed fine-tuning for personality-conditioned generation. Third\, we develop a cross-domain semantic error rate evaluation framework that frames slot error computation as an extraction task\, using a LoRA-adapted language model to extract meaning representations from generated text and a trained ranker to select among candidate extractions\, achieving reliable evaluation across 23 topic domains without domain-specific rules. Fourth\, we build and evaluate a speaker-aware transformer response ranker for Athena\, our Alexa Prize socialbot\, demonstrating that learned ranking over heterogeneous generator pools produces significantly longer conversations and higher user ratings than heuristic rule-based selection in a live A/B study with over 6\,000 conversations. \nA unifying finding emerges across all four contributions: the pragmatic features that control personality style in generation—acknowledgements\, engagement questions\, hedges\, exclamations—are the same features that distinguish high-quality from mediocre responses in open-domain dialogue. This parallel reveals that stylistic control and response ranking are complementary mechanisms for achieving the same goal: making dialogue systems sound more natural and engaging. Together\, these results support the dissertation’s central hypothesis that over-generate-and-rank provides a general\, extensible mechanism for controllable neural language generation\, restoring explicit decision points where competing communicative objectives can be weighed. The ranking function serves a role analogous to the sentence planner in classical NLG architectures\, but operates on the outputs of modern neural and LLM-based generators. \n  \nEvent Host: Davan Harrison\, Ph.D. Candidate\, Computer Science \nAdvisor: Marilyn Walker \n 
URL:https://events.ucsc.edu/event/harrison-d-cs-multi-level-control-in-neural-dialogue-generation-style-semantics-and-selection-through-over-generation-and-ranking/
LOCATION:CA
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260310T163000
DTEND;TZID=America/Los_Angeles:20260310T173000
DTSTAMP:20260424T071337
CREATED:20260217T184921Z
LAST-MODIFIED:20260217T184921Z
UID:10009240-1773160200-1773163800@events.ucsc.edu
SUMMARY:Mashhadi\, N. (CSE) - Compositional\, Clinically Conditioned\, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging
DESCRIPTION:Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes\, but building reliable machine learning models is difficult because data come from different scanners and sites\, some modalities are missing\, labeled cohorts are limited\, and factors such as age and scanner/site effects can bias results. \nThis dissertation develops machine learning methods for robust\, interpretable\, and controllable analysis of AD-related neuroimaging data. First\, I introduce a modular\, graph-based framework for multimodal AD detection that treats datasets and models as nodes and directed edges that can be combined to build more complex predictors. Second\, I propose a clinically conditioned 3D VAE-GAN to synthesize brain MRI\, enhanced with diffusion-driven sampling in clinical feature space to improve realism and control\, supporting data augmentation. Third\, I present a disentangled 3D masked autoencoder (MAE) that learns separated representations for age\, pathology\, and scanner effects\, making it possible to isolate and adjust age\, pathology\, or scanner effects\, while remaining reliable across sites. \nTogether\, these contributions advance practical methods for modular prediction\, controllable image generation\, and confound-aware representation learning in neuroimaging\, with an emphasis on generalization and interpretability for clinically relevant applications. \nEvent Host: Najmeh Mashhadi\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Razvan Marinescu \nZoom- https://ucsc.zoom.us/j/98195204428?pwd=nyfvbmd9t81Xj5Z3yPPVtu4R58CXHq.1 \nPasscode- 688069
URL:https://events.ucsc.edu/event/mashhadi-n-cse-compositional-clinically-conditioned-and-confound-aware-deep-learning-for-alzheimers-disease-neuroimaging/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260311T110000
DTEND;TZID=America/Los_Angeles:20260311T130000
DTSTAMP:20260424T071337
CREATED:20260311T160620Z
LAST-MODIFIED:20260311T160620Z
UID:10011304-1773226800-1773234000@events.ucsc.edu
SUMMARY:Yang\, S. (CSE) - Beyond Image Editing: Building Generalized Image Customization Systems
DESCRIPTION:Current generative vision models struggle with image customization that requires multi-step reasoning or real-world knowledge. This proposal introduces generalized image customization\, enabling systems to execute complex\, inferential modifications rather than just simple edits. The research focuses on the foundational framework required for this generalization\, specifically high-quality training data\, scalable evaluation benchmarks\, self-improving training paradigms that reduce reliance on paired annotations\, and unified multi-modal architectures. Building on two completed studies in data quality and evaluation\, this proposal outlines two future research directions to develop capable\, annotation-efficient\, and reasoning-native image customization systems. \nEvent Host: Siwei Yang\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Cihang Xie \nZoom- https://ucsc.zoom.us/j/3852138080?pwd=Z0MyTVM2WjdCbEM4OXVxWUhhei84dz09 \n 
URL:https://events.ucsc.edu/event/yang-s-cse-beyond-image-editing-building-generalized-image-customization-systems/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260313T093000
DTEND;TZID=America/Los_Angeles:20260313T110000
DTSTAMP:20260424T071337
CREATED:20260217T203948Z
LAST-MODIFIED:20260217T203948Z
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SUMMARY:Fan\, Y. (CSE) - Building Human-Centered Multimodal AI Agents
DESCRIPTION:As multimodal artificial intelligence systems become increasingly embedded in everyday technology\, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace them. This dissertation investigates how to build human-centered multimodal AI agents\, framing human-centeredness as an agent-level objective that requires both accessible\, assistive interaction and reliable\, trustworthy behavior across physical and digital environments. This dissertation explores two complementary dimensions of human-centered agent design. The first focuses on enhancing accessibility through conversational and interactive agents that assist users in everyday tasks. We study both embodied and digital settings in which agents reduce physical and cognitive burdens via natural language interaction\, including hands-free drone control\, navigation assistance in unfamiliar environments\, and interactive access to complex graphical user interfaces. The second dimension focuses on strengthening agent capability to improve reliability and trust. We investigate how agents can acquire environment-specific knowledge through autonomous exploration and how they can reason about visual information in a grounded and transparent manner\, drawing inspiration from human learning and reasoning behaviors. \nEvent Host: Yue Fan\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Xin Eric Wang \nZoom- https://ucsc.zoom.us/j/99619642071?pwd=dwWOlkJxjbamgpB4IbRxYDXbngqXOE.1 \nPasscode- 467959
URL:https://events.ucsc.edu/event/fan-y-cse-building-human-centered-multimodal-ai-agents/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260313T100000
DTEND;TZID=America/Los_Angeles:20260313T120000
DTSTAMP:20260424T071337
CREATED:20260304T172425Z
LAST-MODIFIED:20260304T172425Z
UID:10009393-1773396000-1773403200@events.ucsc.edu
SUMMARY:Moghadam\, M. (CE) - Constraint-Aware Scene Understanding and Trajectory Generation Using Deep Reinforcement Learning for Autonomous Vehicles
DESCRIPTION:Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception\, planning\, and control. While learning-based methods have achieved state-of-the-art performance in perception and environment modeling\, the planning layer remains a key bottleneck for reliable autonomy. Highway driving in particular requires long-horizon reasoning and socially aware interaction with multiple actors\, while also producing smooth and dynamically feasible motion that can be tracked by classical controllers. \nThis thesis focuses on scene understanding and planning for highway driving. We study the problem through two complementary simulation environments: the high-fidelity CARLA simulator for motion planning and continuous trajectory generation under realistic vehicle dynamics and road geometry\, and the lightweight HighwayEnv simulator for interaction-rich behavior planning at high episode throughput. \nWe present three planning contributions that increase autonomy. First\, we introduce a modular hierarchical planning framework in Frenet space that combines long-term decision-making with short-term trajectory optimization. The approach includes a corridor-based dynamic obstacle avoidance strategy that generates spatiotemporal polynomial trajectories and supports diverse driving styles through interpretable parameter tuning. Second\, we propose an end-to-end continuous deep reinforcement learning approach that unifies decision-making and motion planning into a single policy that outputs continuous polynomial trajectories in the Frenet frame. A spatiotemporal observation tensor and a temporal convolutional backbone enable the learned planner to exploit interaction history and outperform optimization-based and discrete RL baselines in CARLA. Third\, we develop an interaction-aware behavior planning neural network architecture that couples trajectory prediction with high-level decision-making via a social pooling scene encoder built on actor histories and an ego-centered BEV representation. This unified design improves RL social awareness\, safety\, and overall driving performance in multi-agent highway scenarios in HighwayEnv. \nAcross extensive simulation studies\, the results show that constraint-aware representations and learning-based policies can improve planning quality beyond hand-crafted objectives\, especially when the policy is equipped with spatiotemporal social context while retaining classical feedback control for stable trajectory tracking. Finally\, we provide supporting simulation and evaluation infrastructure\, including observation tensor and neural network designs\, BEV utilities\, and scalable training and testing pipelines\, to enable reproducible research on learning-based planning in interactive traffic. \nEvent Host: Majid Moghadam\, Ph.D. Candidate\, Computer Engineering  \nAdvisor: Gabriel Elkaim \nZoom- https://ucsc.zoom.us/j/95848602314?pwd=2jlktZ6BChlXcyqT3anX4ZuKrYV4wE.1 \nPasscode- 325939
URL:https://events.ucsc.edu/event/moghadam-m-ce-constraint-aware-scene-understanding-and-trajectory-generation-using-deep-reinforcement-learning-for-autonomous-vehicles/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260313T140000
DTEND;TZID=America/Los_Angeles:20260313T150000
DTSTAMP:20260424T071337
CREATED:20260219T170502Z
LAST-MODIFIED:20260219T170502Z
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SUMMARY:Wang\, H. (CSE) - Accelerating RTL Simulation with Specialized Graph Partitioners
DESCRIPTION:Register transfer level (RTL) simulation is an invaluable tool for developing\, debugging\, verifying\, and validating hardware designs. However\, the performance of RTL simulation has long been a limiting factor in industry. Despite the inherent parallelism of hardware\, current RTL simulators have not achieved practical performance gains due to fundamental challenges in communication\, synchronization\, memory bandwidth\, and architectural mapping. \nThis dissertation addresses the RTL simulation performance problem from three complementary perspectives: optimizing simulation latency through parallelism\, improving aggregate throughput via deduplication\, and enabling efficient GPU acceleration with RTL-native semantics. \nFirst\, we present RepCut\, a parallel RTL simulation methodology that uses replication-aided partitioning to cut circuits into balanced partitions with minimal overlaps. By replicating the overlaps\, RepCut eliminates problematic data dependences between partitions and significantly reduces synchronization overhead. RepCut achieves superlinear speedups of up to 27.10x using 24 threads with only a 3.81% replication cost. \nSecond\, we introduce Simulation Deduplication\, a technique that exploits the extensive reuse of building blocks in modern hardware designs. By generating shared code for duplicated instances and carefully co-scheduling their execution\, we reduce the instruction cache footprint and memory bandwidth pressure. This approach achieves up to 1.95x speedup for single simulations and 2.09x improvement in overall batch simulation throughput. \nThird\, we present Toucan\, a GPU-accelerated RTL simulation framework that preserves RTL semantics rather than flattening designs to gate-level netlists. By leveraging native GPU arithmetic operations and introducing warp-level micro-partitioning with shuffle-based communication\, Toucan achieves efficient mapping of irregular circuit topologies to GPU SIMT architectures while maintaining fast compilation times. Toucan achieves up to 4.73x speedup over the state-of-the-art GPU RTL simulator on large multi-core designs. \nTogether\, these three approaches provide a comprehensive solution to RTL simulation performance optimization\, demonstrating significant improvements over state-of-the-art commercial and open-source simulators across multiple hardware platforms and design scales. \nEvent Host: Haoyuan Wang\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Jose Renau \nZoom- https://ucsc.zoom.us/j/94044618343?pwd=xZkK8GmD28P2Vf8pbyl6aoOaNxxhya.1 \nPasscode- 574772
URL:https://events.ucsc.edu/event/wang-h-cse-accelerating-rtl-simulation-with-specialized-graph-partitioners/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260316T080000
DTEND;TZID=America/Los_Angeles:20260316T100000
DTSTAMP:20260424T071337
CREATED:20260303T180231Z
LAST-MODIFIED:20260303T180253Z
UID:10009388-1773648000-1773655200@events.ucsc.edu
SUMMARY:Teng\, Z. (CM) - Visualizing Player Processes: Towards Design Guidelines for Interactive Process Visualization Tools in Game Analytics
DESCRIPTION:Game analysts face a significant challenge in understanding problem-solving and decision-making processes from the vast and complex sequential data generated by modern video games. Existing visualization tools often fail to adequately support the exploration\, suffering from issues of visual clutter\, inflexible cohort construction\, and a lack of interactive depth. To address this gap\, this dissertation adopts a Research through Design (RtD) methodology to investigate how an interactive process visualization system can be designed and developed to better support the needs of game analysts. \nThe research was conducted in three phases. First\, an initial set of five design guidelines was identified through a breakdown analysis of existing tools and semi-structured interviews with professional game analysts. Second\, these guidelines were iteratively refined through long-term\, collaborative case studies with analysts working on diverse commercial games. This process not only validated the initial guidelines and surfaced one additional guideline concerning interactive inspection\, but also resulted in INSPECT\, an interactive process visualization prototyping system that embodies the refined guidelines. Third\, the guidelines were empirically validated through two complementary user studies of the INSPECT system. A controlled experiment demonstrated that features designed according to the guidelines enabled participants to identify player strategies more efficiently than with a baseline system\, while a qualitative study with professional Dota 2 coaches and players demonstrated the system’s practical value for strategic analysis and strong usability. \nThe primary contributions of this dissertation to the fields of game analytics and information visualization is a set of validated design guidelines for process visualization tools. This contribution provides a durable and transferable framework for the design and development of more effective\, analyst-centered tools for understanding player problem-solving and decision-making processes. \nEvent Host: Zhaoqing Teng\, Ph.D. Candidate\, Computational Media  \nAdvsior: Magy Seif El-Nasr \nZoom- https://ucsc.zoom.us/j/97624383966?pwd=NGolaaTbhdytPcDK6aRIBDIv63b8lm.1 \nPasscode-595285
URL:https://events.ucsc.edu/event/teng-z-cm-visualizing-player-processes-towards-design-guidelines-for-interactive-process-visualization-tools-in-game-analytics/
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CATEGORIES:Ph.D. Presentations
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