BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/New_York X-WR-TIMEZONE:America/New_York BEGIN:VEVENT UID:826@fds.yale.edu DTSTART;TZID=America/New_York:20241219T130000 DTEND;TZID=America/New_York:20241219T140000 DTSTAMP:20250916T142146Z URL:https://fds.yale.edu/events/fds-seminar-vaishali-surianarayanan-uc-san ta-barbara/ SUMMARY:FDS Seminar: Vaishali Surianarayanan (UCSB) DESCRIPTION:\n"Parameterized Algorithms through the Lens of (Hyper)-Graph D ecompositions"\n\n\n\nAbstract: Optimization problems on graphs and hyperg raphs—generalizations of graphs—are foundational in many practical app lications. However\, a huge portion of these problems are NP-hard. This me ans that\, unless P=NP\, no algorithm running in time polynomial in the si ze of the input instance can solve every instance optimally. Despite this\ , real-world instances often exhibit a lot of structure\, enabling tailore d solutions. Parameterized algorithms solve such structurally simple insta nces optimally using time polynomial in the size of the instance. These al gorithms take as input an additional parameter\, which represents some rel evant secondary measurement of the input instance\, such as maximum degree or solution size\, and run in polynomial time in the input size when the parameter is small. \n\n\n\nGraph decompositions also play a pivotal role in algorithm design\, providing a way to break down graphs into manageabl e components. Simple decompositions\, like separating graphs into connecte d components or directed graphs into strongly connected components\, are f undamental in many graph algorithms. More advanced decompositions\, such a s tree decompositions\, are central to modern graph theory and parameteriz ed algorithms.\n\n\n\nIn this talk\, I will provide an overview of my rese arch on parameterized algorithms for (hyper)-graph optimization problems t hrough the lens of (hyper)-graph decompositions. My work focuses both on u sing such decompositions to design algorithms for various problems and on developing algorithms to compute the decompositions themselves. I will beg in by introducing the field of parameterized algorithms\, give an overview of my contributions\, and then highlight one of my recent works on comput ing hypergraph tree decompositions of bounded width. This work provides re sults in both theory and practice and has applications in database query o ptimization. Finally\, I will conclude with my research vision for designi ng parameterized algorithms that are both theoretically sound and scalable in practice\, for problems arising in data mining and database query opti mization.\n\n\n\nBio: Vaishali Surianarayanan is a senior PhD candidate at UC Santa Barbara\, advised by Daniel Lokshtanov. Her research focuses on the parameterization and approximability of graph and hypergraph optimizat ion problems. Her work uncovers structural insights in problem instances a nd leverages small parameters to design theoretically sound algorithms tha t enable practically scalable solutions. Her research has been featured in top AI and CS theory venues such as FOCS\, SODA\, ESA\, and IJCAI. Beyond research\, she is committed to teaching\, mentoring\, and advocating for diversity\, equity\, and inclusion in CS. Her awards include the Best Outg oing Student and Academic Excellence awards during her undergraduate studi es\, as well as the Outstanding Teaching Assistant Award and the Neal Fenz i Resonant Fellowship for her contributions to UC Santa Barbara’s CS dep artment. Before pursuing her PhD\, Vaishali earned an integrated master’ s degree from PSG College of Technology in India.\n CATEGORIES:FDS Events,Postdoctoral Applicants LOCATION:https://yale.zoom.us/j/94654567520%20 END:VEVENT BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST END:STANDARD END:VTIMEZONE END:VCALENDAR