BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/Los_Angeles X-WR-TIMEZONE:America/Los_Angeles BEGIN:VEVENT UID:246@escience.washington.edu DTSTART;TZID=America/Los_Angeles:20250109T163000 DTEND;TZID=America/Los_Angeles:20250109T172000 DTSTAMP:20241218T205250Z URL:https://escience.washington.edu/events/uw-data-science-seminar-anamika -agrawal/ SUMMARY:UW Data Science Seminar: Anamika Agrawal DESCRIPTION:\n\nPlease join us for a UW Data Science Seminar on Thursday\, January 9th from 4:30 to 5:20 p.m. PT. The seminar will feature Anamika Ag rawal\, Shanahan Foundation Fellow at the Allen Institute.\n\nThe seminar will be held in the Physics/Astronomy Auditorium (PAA)\, Room A118 - camp us map.\n\n \;\n\n\n"The multi-scale brain: modeling the impact of loc al structure and dynamics on brain function and dysfunction"\nAbstract: Th e brain’s ability to carry out metabolically-intensive complex computati ons\, at the level of neural circuits\, is supported by its local cellular properties.  Local neuronal properties\, such as synaptic organization\, neuronal morphology\, and neurotransmitter dynamics\, have a significant impact on neuroplasticity\, metabolism\, and neuronal health. These local effects translate to upstream effects on brain-wide function\, influencing learning and development\, and even contributing to the initiation and pr ogression of neurodegenerative diseases. However\, there is a notable lack of modeling work\, that can abstract and integrate these scales to reveal the principles of neurobiologically-constrained brain function. My resear ch seeks to fill this gap by developing multi-scale models that connect c ellular mechanisms with brain-wide function\, to provide the basis of biol ogically-plausible and metabolically-efficient brain function.\nIn my talk \, I will be covering two chief research directions from my work. First\, I will be talking about how the morphology of single-neurons\, in particul ar the arborization properties of its dendritic trees\, could impact the ‘computational complexity’ of the calculations that a neuron can perfo rm locally. I will present a novel deep network formalism to link the dend ritic architecture across diverse cell types\, to the neuronal cell type's computational capabilities. I used this model of a `dendritic circuit' to show that a single neuron can locally compute complex functions in very h igh input dimensions. Intriguingly\, the organization of dendritic compart ments in a size-constrained circuit can lead to qualitatively different co mputational outcomes\, thereby mapping the morphological properties of a n euronal cell type to its computational specialization.\nWhile the ability of dendritic trees to perform non-linearly separable computations has been demonstrated earlier\, my work is the first one to offer several order pa rameters to quantify and characterize the computational complexity of a mo rphological cell type along the axes of effective input dimensionality\, a nd the entropy and sensitivity of the computational function that the cell is capable of performing.\nSecondly\, I will be talking about my efforts to integrate the biophysical priors of neuropathology accumulation and pro pagation into a statistical inference framework to estimate the pseudotemp oral disease trajectory of Alzhiemer’s Disease. I have been working with the Seattle Alzheimer’s Disease Consortium (SEA-AD) to develop novel me thods incorporating multimodal neuropathological data\, including multiple proteomic factors (such as Amyloid-Beta and tau polymeric aggregates) and cellular factors (such as local neuronal and non-neuronal densities and c ellular colocalization with pathological proteins)\, measured across multi ple brain regions.  Our findings have successfully defined a pseudotempor al trajectory of Alzheimer’s Disease in the brain's MTG region\, and wil l be applied to multiple brain regions in upcoming studies.\nIn conclusion \, my research has been guided by a desire to connect small-scale neurobio logical processes with larger brain functions. By employing multi-scale mo deling techniques and collaborating across disciplines\, I have been able to tackle questions that span from cellular dynamics to brain-wide health. Looking forward\, I am excited to continue building on these foundational insights through novel quantitative and theoretical method development.\n Bio: Anamika Agrawal joined the Allen Institute and the University of Wash ington as a Shanahan Foundation Fellow in August 2022. She received her Ph .D. in Physics from UC San Diego\, working with Prof. Elena Koslover. Duri ng her graduate studies\, she became interested in the interplay of intrac ellular dynamics and neuronal morphology\, with her work focusing on mitoc hondrial organization. Drawing inspiration from her specialization in Quan titative Biology at UCSD\, she enjoys working with simple analytical and c omputational models that still preserve the relevant biological complexity of the involved data. During the fellowship\, she is interested in unders tanding how cell-type-specific neuronal properties\, such as morphology an d transcriptomics\, contribute to neuronal function and dysfunction.\n\nTh e UW Data Science Seminar is an annual lecture series at the University o f Washington that hosts scholars working across applied areas of data scie nce\, such as the sciences\, engineering\, humanities and arts along with methodological areas in data science\, such as computer science\, applied math and statistics. Our presenters come from all domain fields and includ e occasional external speakers from regional partners\, governmental agenc ies and industry.\n \;\n\nThe 2024-2025 seminars will be held in perso n\, and are free and open to the public. ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa ds/2024/12/Anamika_Argawal_SQUARE.jpg LOCATION:Physics/Astronomy Auditorium\, room A118\, Seattle\, WA\, X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Seattle\, WA\, ;X-APPLE-RAD IUS=100;X-TITLE=Physics/Astronomy Auditorium\, room A118:geo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:America/Los_Angeles X-LIC-LOCATION:America/Los_Angeles BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST END:STANDARD END:VTIMEZONE END:VCALENDAR