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Using literature review, stakeholder engagement, and rigorous mathematical modeling, we developed a comprehensive framework to identify high impact use cases for this emerging technology. The findings suggest that IVC is not a universal solution, but a targeted tool within a rapidly evolving electrification landscape. It may serve as a bridging technology or a specialized solution for high utilization fleets, rather than a permanent requirement for all electric mobility.\n---\nAbout the speaker: Sina Bahrami is an Assistant Research Scientist in the Department of Civil and Environmental Engineering at the University of Michigan. He earned his Ph.D. in Civil Engineering from the University of Toronto in 2019. His research develops optimization and decision-support tools for emerging mobility systems in smart cities, with a focus on electric and automated vehicles. 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"date_end":"2026-05-12", "time_start":"13:00:00", "time_end":"15:00:00", "time_zone":"America\/Detroit", "event_title":"Multimodal Fusion and Temporal Reasoning for Intelligent Robot Perception", "occurrence_title":"", "combined_title":"Multimodal Fusion and Temporal Reasoning for Intelligent Robot Perception: PhD Defense, Jingyu Song", "event_subtitle":"PhD Defense, Jingyu Song", "event_type":"Presentation", "event_type_id":"16", "description":"Committee chair: Katie Skinner\n\nAbstract:\nReliable autonomy for field robots depends on perception systems that can operate under difficult sensing conditions. In real-world environments, robot perception is often degraded by low-texture visual patterns, environmental disturbances, adverse weather, occlusions, and sensor failures. This dissertation develops multimodal fusion and temporal reasoning methods that improve the robustness, scalability, and accuracy of robot perception across challenging environments.\n\nThe first part of this thesis addresses state estimation and dense mapping for underwater robots, where wave disturbance and low-texture environments often cause vision-based localization to fail. We introduce TURTLMap, a real-time localization and dense mapping framework for low-cost underwater robots. TURTLMap fuses Doppler velocity log, inertial, and pressure measurements for robust localization, while using stereo depth to construct dense 3D maps. Real-world experiments in a water tank environment, evaluated with underwater motion capture and a reference 3D structure, demonstrate accurate robot tracking and mapping under low-texture and wave-disturbed conditions.\n\nThe second part studies adaptive multimodal fusion for autonomous vehicle perception. We introduce LiRaFusion, a LiDAR-radar fusion network that combines joint feature encoding with adaptive feature weighting to better exploit the complementary strengths of LiDAR and radar. Experiments on large-scale 3D object detection benchmarks show that this design improves detection performance over existing fusion methods. Building on this direction, we develop CRKD, a cross-modality knowledge distillation framework that transfers knowledge from a high-performing LiDAR-camera teacher to a scalable camera-radar student. This approach provides a practical pathway for using high-quality sensor data from test fleets to improve cost-effective sensing configurations for consumer vehicles, achieving state-of-the-art camera-radar object detection performance.\n\nThe third part explores temporal reasoning for road scene understanding. We introduce MemFusionMap, a memory-based framework for online vectorized HD map construction that improves temporal fusion by combining current BEV features with multiple working-memory features. MemFusionMap further maintains a temporal overlap heatmap, which provides a spatiotemporal cue for how historical observations overlap with the current field of view and helps the model reason over memory more adaptively. Together, these designs improve map construction under challenging and complex road conditions, including occlusion and dynamic scene changes, while preserving efficient runtime and compatibility with multiple perception models.\n\nFinally, this thesis develops CRISP, a spatiotemporal camera-radar pretraining framework for autonomous driving. CRISP learns transferable bird\u2019s-eye-view representations by forecasting future LiDAR point clouds from historical camera and radar observations, using LiDAR as privileged supervision only during pretraining. At deployment, the model operates using camera-radar inputs alone. Experiments on real-world benchmarks show that CRISP improves long-horizon point cloud forecasting and transfers effectively to downstream tasks including 3D object detection, tracking, online mapping, motion forecasting, future occupancy prediction, and planning.\n\nTogether, these contributions show how multimodal sensing, cross-modality knowledge transfer, temporal memory, and predictive pretraining can make robot perception more reliable under practical sensing constraints. 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We identify the full subcategory of F-gauges with Hodge-Tate weights in the range from 0 to p-2 with the category of Fontaine-Laffaille modules satisfying the analogous weight constraint.", "occurrence_notes":null, "guid":"148194-21903218@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/148194", "building_id":"1000166", "building_name":"East Hall", "building_official_id":1000166, "campus_maps_id":"53", "campus_maps_link_path":"east-hall", "campus_maps_link":"https:\/\/maps.studentlife.umich.edu\/building\/east-hall", "room":"4096", "location_name":"East Hall", "has_livestream":0, "cost":"", "tags":["Dissertation","Graduate","Graduate Students","Mathematics"], "website":"", "links":[ ], "sponsors":[ { "group_name":"Dissertation Defense - Department of Mathematics", "group_id":"4889", "website":"" }, { "group_name":"Department of Mathematics", "group_id":"3791", "website":"" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "image_description":"Math equations on a chalkboard", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_thumb\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb_square\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "group_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large_lightbox\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_large_crop\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_list":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_list\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_grid_2x\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_large\/media\/attachments\/2026\/05\/event_148194_original-1.jpg", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_thumb\/media\/attachments\/2026\/05\/event_148194_original-1.jpg" }, "qr_code_url":"http:\/\/events.umich.edu\/event\/148194\/qrcode.png", "occurrence_count":1, "first_occurrence":21903218 } , { "id":"148195-21903219", "datetime_modified":"20260508T145250", "datetime_start":"20260521T140000", "datetime_end":"20260521T160000", "has_end_time":1, "date_start":"2026-05-21", "date_end":"2026-05-21", "time_start":"14:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Mixing at Double Exponential Rate and Rigidity in Analytic Dynamics", "occurrence_title":"", "combined_title":"Mixing at Double Exponential Rate and Rigidity in Analytic Dynamics: Katia Shchetka", "event_subtitle":"Katia Shchetka", "event_type":"Presentation", "event_type_id":"16", "description":"Abstract:\n\nThis work studies mixing in dynamical systems, focusing on how fast correlations between observables decay. 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In higher dimensions, we construct families of examples of nonlinear maps with and without double exponential rate of mixing, and prove that certain partially hyperbolic systems never have this property.\n\nOur approach uses the Koopman precomposition operator acting on spaces of hyperfunctions (the dual space of analytic functions). In this setting, the operator is non-self-adjoint, compact, and quasi-nilpotent, with its spectrum reduced to zero, which can be considered an indicator of double exponential decay.\n\nFinally, we apply mixing rates to the cohomological equation. 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Their outskirts contain a hidden ecosystem of faint stars and stellar systems that trace the history of their hierarchical growth through mergers --- one of the most important drivers of galaxy evolution. This dissertation aims to uncover this historical record and constrain the processes that govern galaxy formation and evolution across a wide range of mass scales, from Milky Way (MW)\u2013like systems to their ultra-faint dwarf companions. Although mergers can strongly influence the diversity of structural properties seen in galaxies, the resulting dynamical response often erases the observational markers needed to infer the characteristics of the merger. However, simulations show that material accreted into a galaxy is retained by its stellar halo, preserving a \"fossil record\" that we can trace with resolved-star observations. I present the deepest stellar halo map of the nearby galaxy M94, revealing that it has one of the smallest and most metal-poor stellar halos among MW-mass galaxies (M*=2.8x10^10 M\u2609, [M\/H] ~-1.4) and indicating that its dominant merger was with a galaxy less massive than the Small Magellanic Cloud (SMC). M94 also hosts the largest pseudobulge in the Local Universe, but this work suggests that it was shaped primarily by secular processes rather than by this dominant merger. I also illuminate the structural diversity of faint satellite galaxies around M81, finding among them the most compact (DWJ0954+6821), most concentrated (D1006+69, n ~ 5), and one of the most elliptical (D1009+68, \u03f5 ~ 0.57) dwarfs known in the Local Volume. This work improves on ground-based characterizations of these systems and reveals that all four satellites are metal-poor and quenched, with no evidence for tidal stripping despite their varied ellipticities. Lastly, I successfully demonstrate the feasibility of wide-field, multi-object fiber-fed spectroscopy in a low signal-to-noise regime for probing halo kinematics beyond the Local Group, presenting the first measurement of the line-of-sight (LOS) velocity and velocity dispersion of NGC 253's stellar halo. I find that the stellar halo exhibits prograde angular momentum and detect kinematic substructure coincident with its known southwestern shell, consistent with a recent accretion event. 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