{ "143295-21892653": { "datetime_modified":"20260113T082021", "datetime_start":"20260113T160000", "datetime_end":"20260113T170000", "has_end_time":1, "date_start":"2026-01-13", "date_end":"2026-01-13", "time_start":"16:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"CANCELLED - CM-AMO | Engineering topological bands in materials with chiral cavities", "occurrence_title":"", "combined_title":"CANCELLED - CM-AMO | Engineering topological bands in materials with chiral cavities: Ceren B. Dag (Indiana University Bloomington)", "event_subtitle":"Ceren B. Dag (Indiana University Bloomington)", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Strongly coupling materials to cavity fields can affect the material\u2019s electronic properties altering the phases of matter. In this talk, I will first discuss the hybrid light-matter band topology of the monolayer graphene whose electrons can be coupled to both left and right circularly polarized vacuum fluctuations, and time-reversal symmetry is broken due to an asymmetry between the two polarizations. This discussion will show how the quantum nature of photons affects the topology of the correlated light-matter hybrid wave function. A central finding of Ref. [1] is a relation between the Berry phase and the properties of exchanged photons with matter at light-matter hybridization points in the Brillouin zone. This physics turns out to be generic, as it also emerges in stacked graphene layers [2]. I will then introduce a quantum optics model to capture the properties of the topological light-matter hybridization gaps and show the presence of chiral edge modes in these gaps [3]. For the rest of the talk, I will describe a newly proposed photonic crystal chiral cavity [4], and its recent realization [5]. \r\n\r\n[1] Physical Review B 110 (12), L121101 (2024)\r\n[2] arXiv: 2504.03842\r\n[3] arXiv: 2510.13373\r\n[4] Nature Communications 16 (1), 5270 (2025)\r\n[5] arXiv: 2509.14366", "occurrence_notes":null, "guid":"143295-21892653@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/143295", "building_id":"1000167", "building_name":"West Hall", "campus_maps_id":"163", "room":"340", "location_name":"West Hall", "has_livestream":0, "cost":"", "tags":["Physics","Science"], "website":"", "sponsors":[ { "group_name":"CM-AMO Seminars", "group_id":"3801", "website":"" }, { "group_name":"Department of Physics", "group_id":"1965", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21892653 } , "142085-21889991": { "datetime_modified":"20251128T121323", "datetime_start":"20260113T160000", "datetime_end":"20260113T170000", "has_end_time":1, "date_start":"2026-01-13", "date_end":"2026-01-13", "time_start":"16:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"Colloquium: What Kinds of Functions Do Neural Networks Learn? Low-Norm vs. Flat Solutions", "occurrence_title":"", "combined_title":"Colloquium: What Kinds of Functions Do Neural Networks Learn? Low-Norm vs. Flat Solutions: Rahul Parhi, UC San Diego", "event_subtitle":"Rahul Parhi, UC San Diego", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"This talk investigates the fundamental differences between low-norm and flat solutions of shallow ReLU networks training problems, particularly in high-dimensional settings. We sharply characterize the regularity of the functions learned by neural networks in these two regimes. This enables us to show that global minima with small weight norms exhibit strong generalization guarantees that are dimension-independent. In contrast, local minima that are \u201cflat\u201d can generalize poorly as the input dimension increases. We attribute this gap to a phenomenon we call neural shattering, where neurons specialize to extremely sparse input regions, resulting in activations that are nearly disjoint across data points. This forces the network to rely on large weight magnitudes, leading to poor generalization. Our analysis establishes an exponential separation between flat and low-norm minima. In particular, while flatness does imply some degree of generalization, we show that the corresponding convergence rates necessarily deteriorate exponentially with input dimension. These findings suggest that flatness alone does not fully explain the generalization performance of neural networks.", "occurrence_notes":null, "guid":"142085-21889991@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/142085", "building_id":"1000166", "building_name":"East Hall", "campus_maps_id":"53", "room":"1360", "location_name":"East Hall", "has_livestream":0, "cost":"", "tags":["Mathematics"], "website":"https:\/\/sparsity.ucsd.edu\/rahul\/", "sponsors":[ { "group_name":"Colloquium Series - Department of Mathematics", "group_id":"4884", "website":"" }, { "group_name":"Department of Mathematics", "group_id":"3791", "website":"" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2025\/11\/event_142085_original-1.png", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_thumb\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/group_thumb\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/group_thumb_square\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "group_large":"https:\/\/events.umich.edu\/media\/cache\/group_large\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/group_large_lightbox\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/event_large_crop\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_list":"https:\/\/events.umich.edu\/media\/cache\/event_list\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/event_grid_2x\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/event_feature_large\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_feature_thumb\/media\/attachments\/2025\/11\/crop-event_142085_original-1.png" }, "occurrence_count":1, "first_occurrence":21889991 } }