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SUMMARY:March is Hummingbird Month at the UCSC Arboretum & Botanic Garden
DESCRIPTION:March is Hummingbird Month at the UCSC Arboretum & Botanic Garden \nThis time of year\, the Arboretum hosts both Anna’s and Allen’s hummingbirds\, the two most common species in Northern California. “The density of hummingbirds—the number per area in the Arboretum—is ridiculously high\,” says Bruce Lyon\, Professor Emeriti of Ecology and Evolutionary Biology at UCSC. “You can watch them feeding on flowers\, you can watch their courtship\, you can watch them chasing different species. It’s a great opportunity to see some pretty amazing hummingbird biology.” \nIn celebration of this special time of year\, we invite you to visit the garden as much as possible! We will have presentations\, workshops\, and tours throughout the month. See our webpage for a schedule of activities and more information about hummingbirds and the abundance of plants at the Arboretum that attract them. \nWe will also feature hummingbird merchandise and hummingbird-attracting plants at our gift shop and nursery. Visit Norrie’s Gift & Garden Shop\, Tuesdays thru Sundays from 10 – 4. For more information visit: https://arboretum.ucsc.edu/garden-shop/ \nAll events are free with paid admission: Adults: $10\, Seniors $8 and Youth 4-17 $5. Current UCSC students are free. Rain cancels outdoor activities. \nCurrent Arboretum members are always free and enjoy other great benefits year-round!  Join Today at https://arboretum.ucsc.edu/get-involved/join-us/    \n  \n 
URL:https://events.ucsc.edu/event/march-is-hummingbird-month-at-the-ucsc-arboretum-botanic-garden/
LOCATION:Arboretum\, 122 Arboretum Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20260313T093000
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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/
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CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260313T100000
DTEND;TZID=America/Los_Angeles:20260313T120000
DTSTAMP:20260427T065951
CREATED:20260304T172425Z
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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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DTSTART;TZID=America/Los_Angeles:20260313T140000
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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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