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DTSTART;TZID=America/Los_Angeles:20261023T144000
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DTSTAMP:20261022T211345Z
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SUMMARY:CSE Colloquium: Relational Attention for Data-Efficient Language Modeling
DESCRIPTION:Title: Relational Attention for Data-Efficient Language Modeling \nPresenter: Adrian Brasoveanu\, UCSC Linguistics (Joint work with Ece Takmaz and Jakub Dotlačil) \nAbstract: We present Relational BabyLM\, a system submission to the BabyLM 2026 challenge that combines two cognitively motivated inductive biases in a single decoder-only Transformer. Architecturally\, we replace standard self-attention with a Dual Attention Transformer (DAT)\, which separates the routing of object-level (“sensory”) lexical features from structural/relational information (Altabaa and Lafferty\, 2025; Altabaa et al.\, 2024; Webb et al.\, 2024; Kerg et al.\, 2022; Webb et al.\, 2021). Relational attention (RA) disentangled from self-attention greatly increases data efficiency and out-of-training-sample generalization on purely relational tasks\, but language modeling requires object-level and relational information to be integrated as well as disentangled\, and RA-based LMs have remained largely unexplored. BabyLM’s data-constrained training and comprehensive evaluation is an ideal testing ground for whether that data efficiency transfers. As a training intervention\, we add a Next-Latent Prediction (NextLat; Teoh et al. 2026) objective that encourages hidden states to compress history incrementally into a dense belief state. Architecture is the dominant factor for structural linguistic generalization; the objective is secondary but still significant. DAT’s three relational attention types (full RA vs. the simpler RCA and DisRCA variants) are largely interchangeable at 10M words; full RA pulls ahead at 100M. We also introduce a novel symbol-retrieval mechanism (RoPE-based\, as opposed to learned\, relative symbols) that matches learned symbol libraries while adding no parameters. On the strict (100M-word) track\, our best model ranks 6th of 55 overall and 3rd of 55 on the leaderboard’s NLP-task subset at the time of writing; our two strongest models outperform the GPT-2 baseline on most benchmarks\, with one attaining the highest EWoK score among strict-track entries. \nPaper: https://arxiv.org/abs/2609.20530 \nCode: https://github.com/abrsvn/babylm_dat_2026 \nBio: I am a  professor in computational (psycho)linguistics focusing largely on natural language semantics and pragmatics at UCSC. I was also associate campus provost (tout court for 2 years\, then of strategic initiatives for 1 year) in the UCSC EVC office between 2021 and 2024.  For some more of my recent work\, see https://github.com/abrsvn/. \nHosted by: Professor Daniel Fremont and Professor Leilani Gilpin \nDate and Time: Fridays from 2:40 – 3:45 pm \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/95300285113?pwd=62tnEPUtRrsmbW7SHTavaRbaF2afSr.1
URL:https://live-events-ucsc.pantheonsite.io/event/cse-colloquium-relational-attention-for-data-efficient-language-modeling/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20261026T104000
DTEND;TZID=America/Los_Angeles:20261026T114500
DTSTAMP:20261019T182637Z
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SUMMARY:ECE 290 Seminar: "Learning Dexterous Manipulation: From Simulation to Human Demonstrations"
DESCRIPTION:Presented by: Joe Dong\, Co-Founder & CTO @ Chestnut Robotics \nDescription: “Learning dexterous manipulation requires robot hardware\, training environments\, and data to work together. This talk explores two complementary approaches to making robotic hands learnable: training in simulation and learning from human demonstrations.\nFirst\, I will present Aero Hand Open\, an open-source tendon-driven hand developed with a simulation and reinforcement learning stack. I will discuss its design\, transmission modeling in MuJoCo\, the actuation mapping between simulation and hardware\, and experimental validation\, including an in-hand cube rotation policy transferred to the physical hand without policy fine-tuning. \nSecond\, I will introduce the newer Aero Hand and Aero UMI\, a robotic hand and wearable demonstration interface developed as a matched pair. I will discuss how co-designing the hand and capture interface connects human demonstrations to robot actions\, with particular attention to motion\, contact sensing\, calibration\, and data alignment. Through experimental results and demonstrations\, the talk will examine the engineering tradeoffs and open research questions in connecting robot embodiment with the simulation and data used to train it.” \nBio: Xu (Joe) Dong is cofounder and CTO of Chestnut Robotics\, where he develops robotic hands and the data\, simulation\, and learning infrastructure for physical AI. He previously worked on autonomous driving at Waymo and XPeng Motors\, including learning-based motion planning and production end-to-end driving models. He holds a Ph.D. in Biomedical Engineering and an M.S. in Computer Science from Virginia Tech\, and a B.S. in Physics from the University of Science and Technology of China. Personal Website: http://joedong.ai/ \n  \nHosted by: Professor Zhicong Yu\, Electrical & Computer Engineering Department \nWhen: Monday\, October 26th from 10:40 to 11:45 AM PST \nLocation: Classroom E2-194
URL:https://live-events-ucsc.pantheonsite.io/event/ece-290-seminar-learning-dexterous-manipulation-from-simulation-to-human-demonstrations/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261030T103000
DTEND;TZID=America/Los_Angeles:20261030T120000
DTSTAMP:20261009T184943Z
CREATED:20261009T184943Z
LAST-MODIFIED:20261009T184943Z
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SUMMARY:ECE Special Seminar: "Bilevel Optimization for Agentic AI: From Theory to Retrieval-augmented Agentic Reinforcement Learning"
DESCRIPTION:Presented by: Quan Xiao\, PhD Candidate @ Cornell University \nDescription: “Agentic AI systems combine large language models with system-level design choices such as post-training data selection\, retrieval\, tool use\, and memory management in augmented inference. Training these systems involves multiple coupled objectives beyond task success\, including safety\, retrieval quality\, and adaptation efficiency. Sequential tuning can cause catastrophic forgetting\, while direct objective mixing training can introduce unit mismatch and obscure credit assignments across components. \nIn this talk\, I will present bilevel optimization as a framework for jointly learning LLM parameters and agentic-system design variables. I will introduce an efficient first-order penalty formulation that replaces lower-level optimality with a merit constraint\, avoiding Hessian inverses and differentiation through full inner trajectories. By turning the bilevel problem into a penalized objective with analyzable structure\, this formulation enables landscape analysis that leads to global convergence guarantees. \nI will conclude with an application to bilevel retrieval optimization for tool-using agents\, where retrieval enables LLMs to access up-to-date information and manage external memory. In this setting\, landscape analysis also guides the design of memory-efficient variants of the penalty method tailored to the application. Experiments on multi-hop and medical question-answering tasks demonstrate the effectiveness of jointly optimizing retrieval and the LLM. Using only a 3B LLM backbone\, the proposed approach outperforms direct inference with GPT-5.6 Luna\, as well as methods based solely on agentic reinforcement learning or retrieval optimization.” \nBio: Quan Xiao (https://jenniferquanxiao.github.io) is a Ph.D. candidate in the Department of Electrical and Computer Engineering at Cornell University. She received her M.S. in Electrical Engineering from Rensselaer Polytechnic Institute in 2023\, and her B.S. in statistics from the University of Science and Technology of China in 2020. Her research focuses on the theoretical foundations of bilevel and multi-objective optimization\, as well as their applications to generative\, agentic AI and hardware-algorithm co-design. Her work has appeared in leading machine learning and signal processing venues\, including NeurIPS\, ICML\, and IEEE Transactions on Signal Processing\, with papers selected for oral and spotlight presentations. \nQuan has received several awards\, including the IEEE Signal Processing Society Scholarship in 2024 and the Belsky Award for Computational Sciences and Engineering in Rensselaer Polytechnic Institute in 2023. Her research has also received supports from industries\, including Google and Lambda Research. \n  \nHosted by: Professor Yu Zhang\, Electrical & Computer Engineering Department \nWhen: Friday\, October 30th from 10:30AM to 12:00PM \nLocation: Engineering 2\, Room 506
URL:https://live-events-ucsc.pantheonsite.io/event/ece-special-seminar-bilevel-optimization-for-agentic-ai-from-theory-to-retrieval-augmented-agentic-reinforcement-learning/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261030T130000
DTEND;TZID=America/Los_Angeles:20261030T140000
DTSTAMP:20261021T162533Z
CREATED:20261021T162533Z
LAST-MODIFIED:20261021T162533Z
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SUMMARY:Asri\, M. (BMEB) - Genome Inference in the Pangenome Era
DESCRIPTION:Human genome references have provided unified coordinate systems for mapping genetic variation\, enabling numerous discoveries in genomics. However\, mapping to a single linear reference can be misleading or even impossible when a sample’s genome is highly divergent from the reference\, an issue known as reference bias. Recent advances in long-read sequencing technologies and computational techniques have made generating high-quality genome assemblies at scale more feasible than ever. This shift has paved the way for efforts such as the Human Pangenome Reference Consortium (HPRC)\, whose mission is to build a human pangenome reference that represents human genetic diversity and mitigates reference bias. In this dissertation\, I present methods I developed that contribute to building more accurate pangenome references and advancing their applications in variant calling\, ultimately enabling more accurate genome inference. \nFirst\, I introduce Secphase\, a tool for correcting read mappings with direct applications in fixing assembly errors. Secphase corrects mismapped Pacific Biosciences (PacBio) HiFi reads by leveraging long-range phasing information from Oxford Nanopore Technologies (ONT) data. As a core component of the PHARAOH pipeline\, Secphase was coupled with DeepPolisher\, a transformer-based polisher\, to polish 230 HPRC assemblies\, reducing errors by half. \nSecond\, I present HMM-Flagger\, a reference-free tool for detecting structural misassemblies based on the coverage of mapped long reads. It models read coverage using a hidden Markov model (HMM)\, enabling it to classify coverage anomalies as erroneous blocks\, false duplications\, or collapsed blocks. HMM-Flagger has been used extensively in the evaluation and selection of genome assemblies for the HPRC releases. \nFinally\, as an application of pangenomes\, I highlight work on extending DeepVariant to build a pangenome-aware variant caller by augmenting sample-specific input data with pangenome alignments. Using Element Biosciences reads\, pangenome-aware DeepVariant generated call sets with up to 46.5% fewer errors than existing methods. \nEvent Host: Mobin Asri\, PhD Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Benedict Paten \nZoom: https://ucsc.zoom.us/j/91210219698?pwd=HA0b0by9HcKlKiBua7anVBZ7ZfSfJu.1 \nPasscode: 594980
URL:https://live-events-ucsc.pantheonsite.io/event/asri-m-bmeb-genome-inference-in-the-pangenome-era/
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:20261103T100000
DTEND;TZID=America/Los_Angeles:20261103T110000
DTSTAMP:20261020T173621Z
CREATED:20261020T173621Z
LAST-MODIFIED:20261020T173621Z
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SUMMARY:Rondoni\, N. (AM) - Inverse Problems and Control of Nonlinear Systems with Applications in Biology
DESCRIPTION:Steering living systems toward a desired behavior is a central goal in synthetic and quantitative biology. Levers of control range from electric fields and optogenetic stimulation to engineered gene circuits\, chemical dosing\, and tuning the adhesive attraction and repulsion between cells. Biology makes it hard to know how much each lever should be pulled\, or even to discern the current state of the system. Dynamics are nonlinear\, and observations are noisy and indirect. Models are often incomplete\, as exact replication of dynamics requires an immense number of parameters. This dissertation solves inverse problems and develops control methods within this setting across three biological testbeds. Here we take steps toward more reliably engineering tissues to form patterns on demand\, cells to manufacture therapeutics to desired specifications\, and neural experiments to respond to activity in real time. \nIn the first chapter\, we invert the forward mapping between the firing of a neuron and the noisy calcium indicator traces actually recordable in high throughput imaging. This work utilizes model predictive control on a novel mechanistic set of ODEs\, competes with the state of the art\, and is the first of its kind to our knowledge able to provide real time inference of this type with no variable backtracking. While other methods may edit their answers based on future information\, ours operates with 60ms of lag\, then leaves predictions fixed. \nIn the second chapter\, we suppose direct control of the forward mapping\, a nonlinear model representing production of thyroxine by engineered cellular factories\, and ensure the measured output tracks a desired value even when the plant and observations are confounded by stochasticity. We use a radial basis function neural network for online function approximation\, and introduce numerous modifications to this classic architecture to empirically track setpoints stably even in the presence of immense delays in the forward model’s output. We show the median steady state error in terms of normalized root mean square error to be 12.5% or less across setpoints in a Monte Carlo study. \nIn the final chapter\, we again solve an inverse problem\, this time in spatial cellular patterning. Treating a black box agent based model as the forward map\, we recover attraction and repulsion rates that reproduce a target pattern up to rotation\, using an objective defined only on summary entropies of the pattern\, rather than on target cell positions or the true parameters. We show convergence from 100 initial layouts\, for two different target patterns. For a layout where convergence is marginal\, we show that converging and nonconverging initial rates have statistically distinct centroids\, then build a classifier that predicts which initializations are likely to succeed under this iterative intervention strategy. \nApplications such as engineered spatial patterns\, bioelectronic interventions\, and closed loop neural experiments each depend on three tasks. The first is deriving a forward model\, potentially black box or explicit in equations. The second task is computing the input that drives the biological system to a target\, while the third is recovering hidden inputs from what observations are actually available. More broadly\, this dissertation advances control of living systems that are only partially observed and partially understood. \nEvent Host: Nicholas Rondoni\, PhD Candidate\, Applied Mathematics \nAdvisor: Marcella Gomez  \nZoom: https://ucsc.zoom.us/j/97002422471?pwd=BarPJkyaLcJ6OUA1a2Za74GyrAb274.1 \nPasscode: 970 0242 2471
URL:https://live-events-ucsc.pantheonsite.io/event/rondoni-n-am-inverse-problems-and-control-of-nonlinear-systems-with-applications-in-biology/
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:20261116T104000
DTEND;TZID=America/Los_Angeles:20261116T114500
DTSTAMP:20261013T195115Z
CREATED:20261013T195115Z
LAST-MODIFIED:20261013T195115Z
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SUMMARY:ECE 290 Seminar: "From Stochastic Planning to Safe and Resilient Autonomy: Learning and Control for Robotic Systems"
DESCRIPTION:Presented by: Francesco Trotti\, Marie Skłodowska-Curie Postdoctoral Fellow @ University of Bologna\, Italy \nDescription: “Developing autonomous robotic systems capable of operating reliably in uncertain and dynamic environments requires the integration of planning\, learning\, and control while addressing fundamental challenges related to safety\, robustness\, and computational efficiency. This talk will present a research journey from stochastic optimal control and online decision-making to safety-critical learning and real-time robotic autonomy. First\, I will discuss model-based planning methodologies based on Markov Decision Processes and Monte Carlo Tree Search\, with applications to autonomous aircraft guidance\, constrained optimal control\, and decision-making under uncertainty. Building on these foundations\, I will introduce recent advances in safe reinforcement learning\, focusing on the integration of Control Barrier Functions with learning-based policies to improve safety and reduce unnecessary interventions. I will then address computational challenges in predictive safety and collision avoidance\, highlighting approaches aimed at enabling efficient real-time operation. \nFinally\, I will introduce NEMORE\, a Marie Skłodowska-Curie Postdoctoral Fellowship project investigating energy-aware and morphologically adaptive aerial systems. By integrating hybrid multi-mode control\, regenerative actuation\, and wind-adaptive autonomy\, NEMORE aims to advance resilient and energy-efficient flight. The talk will conclude by discussing open research questions at the intersection of hybrid systems\, optimization\, learning\, and control\, toward autonomous aerial robots capable of adapting safely and efficiently to changing operating conditions.” \nBio: Francesco Trotti is a Marie Skłodowska-Curie Postdoctoral Fellow at the University of Bologna\, Italy\, currently conducting the outgoing phase of his fellowship at the University of California\, Santa Cruz\, under the supervision of Prof. Ricardo Sanfelice.\nHe received his Ph.D. in Computer Science from the University of Verona\, Italy\, in 2025\, under the supervision of Profs. Riccardo Muradore and Alessandro Farinelli. During his doctoral studies\, he was a visiting researcher at The Ohio State University\, where he worked with Prof. Andrea Serrani on adaptive control. \nHis research interests include stochastic optimal control\, autonomous planning\, reinforcement learning\, safety-critical control\, and robotic systems. His current MSCA project\, NEMORE\, investigates hybrid control\, energy-aware actuation\, and adaptive autonomy for resilient aerial vehicles. \n  \nHosted by: Professor Zhicong Yu & Professor Ricardo Sanfelice\, Electrical & Computer Engineering Department \nWhen: Monday\, November 16th from 10:40 to 11:45 AM PST \nLocation: Classroom E2-194
URL:https://live-events-ucsc.pantheonsite.io/event/ece-290-seminar-from-stochastic-planning-to-safe-and-resilient-autonomy-learning-and-control-for-robotic-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
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DTSTART;TZID=America/Los_Angeles:20261123T080000
DTEND;TZID=America/Los_Angeles:20261123T090000
DTSTAMP:20261013T161927Z
CREATED:20261013T161821Z
LAST-MODIFIED:20261013T161927Z
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SUMMARY:Bondar\, G. (AM) - Geometric and Graph Structured Optimal Transport for Learning and Control
DESCRIPTION:We motivate\, formulate\, and solve several structured optimal transportation (OT) problems for learning and control. The field of OT had its inception in 1781\, when Gaspard Monge sought the optimal map to transport mass from a given initial distribution to a desired terminal distribution. Both OT and its entropy-regularized variant\, the Schrödinger bridge problem (SBP)\, have catalyzed significant developments across mathematics. More recently\, theoretical and algorithmic developments in OT and SBP have found myriad applications in stochastic optimal control and generative AI. \nThe connecting theme across the problems studied in this dissertation is the presence of structure which can be exploited in their solution — for our work\, this structure arises from geometry underlying a solution space or from a graph capturing a correlation structure between distributional data. Our original contributions begin with automated verification and discovery of OT regularity\, followed by stochastic learning and control for resource allocation and scheduling in multi-core real-time systems\, where the problem has a natural graph structure. We also formulate and solve the problem of finding an optimal graph structure in a distribution-valued dataset where none is present. \nWhile graph-structured OT is a natural fit for data-driven stochastic learning problems\, the knowledge of system dynamics can impose geometric structure to the problem. In this dissertation\, we establish the equivalence between the stochastic optimal control and path space formulations of SBP over a compact connected Lie group. Additionally\, we develop\, analyze\, and validate a novel numerical solution scheme for the SBP with control-affine dynamics. Finally\, we investigate the application of Gromov-Wasserstein (GW) OT — a variant of the problem allowing for transport between incomparable spaces — for spatial proteomics. We show throughout our work how careful considerations of the underlying geometry and graph-structures can enable bespoke solutions for practical learning and control problems through optimal transport. \nEvent Host: Georgiy Bondar\, Ph.D. Candidate\, Applied Mathematics  \nAdvisor: Abhishek Halder \nZoom: https://ucsc.zoom.us/j/96361886043?pwd=U73TbafhzI7kgS7MYAFko3YEPVIxTb.1 \nPasscode: 564490
URL:https://live-events-ucsc.pantheonsite.io/event/bondar-g-am-geometric-and-graph-structured-optimal-transport-for-learning-and-control/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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