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:820@fds.yale.edu DTSTART;TZID=America/New_York:20241210T150000 DTEND;TZID=America/New_York:20241210T160000 DTSTAMP:20250916T142145Z URL:https://fds.yale.edu/events/fds-project-match/ SUMMARY:FDS Project Match DESCRIPTION:\nThe FDS Data Science Project Match\, hosted by the Yale Insti tute for Foundations of Data Science (FDS)\, is an opportunity for Yale fa culty from any department or school within the university to connect with talented students from the departments of Statistics and Data Science\, Ap plied Mathematics\, and Computer Science. In a series of lightning-round t alks\, faculty will have exactly five minutes to pitch a current research problem\, aiming to team up with students interested in tackling complex d ata challenges. This event facilitates collaboration on current research p rojects\, offering a platform for faculty to present their data-driven ini tiatives and find skilled undergraduate and/or graduate students eager to contribute. It’s also a wonderful way to learn about the research of man y Yale faculty.\n\n\n\nPresenters:\n\n\n\n\nJennifer MarlonSenior Research Scientist\, Lecturer\, and Director of Data Science\,Yale School of the E nvironmentExecutive Director\, Yale Center for Geospatial SolutionsPresent ed by Andrew Gillreath-Brownjennifer.marlon@yale.edu"Aligning Public Perce ption with Environmental Hazards (YPCCC)"This project investigates the gap between public worry about climate-driven hazards like wildfire\, drought \, and extreme heat and the actual vulnerability levels faced by communiti es across the United States. By integrating nationally representative surv ey data on public perception with objective measures of risk—such as wil dfire indices\, drought severity scores\, and heat vulnerability metrics d erived from emergency room visits and environmental data—you’ll help u ncover where perception misaligns with reality. Your work will directly co ntribute to tailoring risk communication\, improving resource allocation\, and building climate resilience. This is a unique opportunity to apply yo ur technical skills in computer science\, data science\, or applied math t o cutting-edge causal inference methods while gaining experience with larg e-scale survey\, environmental\, and health datasets. Join us to help brid ge the gap between perception and reality\, empowering communities and pol icymakers to act effectively in the face of climate change."Geospatial Ana lysis and Earth Observation (YCGS)"The Yale Center for Geographic Solution s (YCGS) is seeking an enthusiastic student to join our research team focu sed on applying Earth observation (EO) data and geospatial analysis to tac kle pressing global challenges. The exact project will be determined based on ongoing research priorities and the student’s skills and interests\, but all projects will be conducted in collaboration with YCGS-affiliated faculty\, staff\, and students\, and will provide valuable experience with state-of-the-art geospatial tools and methods.Examples of potential proje cts include: 1) \;Enhanced Flood Zone Mapping in Connecticut: Work wit h high-resolution LiDAR data to map terrain\, vegetation\, and urban infra structure across Connecticut\, and integrate this data with satellite imag ery and climate datasets to analyze environmental patterns and contribute to projects addressing flood risk\, urban heat islands\, and land cover ch ange\; 2) \;Assessing Infrastructure Development in Developing Countri es: Use satellite imagery and geospatial datasets to monitor infrastructur e construction progress. Collect and preprocess project data (e.g.\, timel ines\, locations\, descriptions) using web scraping techniques\, and integ rate these data with EO data to evaluate policy effectiveness and project outcomes\; and 3) \;Urban Verticality and Social Equity: Integrate a g roundbreaking global building height dataset derived from remote sensing w ith rich social science data to examine whether areas of high-density vert ical growth are more or less resilient to climate challenges\, such as hea t waves and air pollution. In all cases\, the student will gain hands-on e xperience with advanced tools like \;Google Earth Engine\, \;Pytho n\, and \;GIS platforms (e.g.\, QGIS) \;for data processing\, anal ysis\, and visualization. They will also learn techniques for integrating diverse data sources\, including LiDAR\, satellite imagery\, and vector da ta\, to address real-world challenges such as climate resilience\, environ mental conservation\, and sustainable development.\n\n\n\nKartik Pattabira manAssistant Professor\, Child Study Center\, Yale School of Medicinekarti k.pattabiraman@yale.edu | https://medicine.yale.edu/profile/kartik-pattabi raman/"Creating high throughput approaches to causally link psychiatric ri sk genes to circuit vulnerabilities" Neuropsychiatric disorders are curren tly the leading cause of disability in the United States. Limited understa nding of the underlying pathophysiology of these disorders has resulted in non-specific pharmacologic interventions with minimal to moderate efficac y. Patient-based studies combining the fields of psychiatry\, neuroimaging \, and genetics have provided substantial evidence for a shared neurodevel opmental etiology. Translating these research findings into effective outc ome-altering treatments requires establishing direct causal links between the genetic\, neuroimaging\, and behavioral signatures of psychiatric diso rders. However\, the current strategies used to study brain connectivity a re too laborious to characterize the thousands of identified risk alleles and lack the resolution required to identify the single circuit-level dysc onnectivity underlying these disorders. This project aims to create high t hroughput and high-resolution strategies to directly link individual risk alleles to changes in brain connectivity using automated characterization of direct fluorescent labeling of neuronal projections from tissue-cleared mouse lines.\n\n\n\nJohan UganderAssociate Professor at Stanford Universi ty in the Department of Management Science &\; Engineering and the Inst itute for Computational &\; Mathematical EngineeringVisiting faculty at FDSjohan.ugander@yale.edu | https://stanford.edu/~jugander/"Strategic beh avior with
bridging-based fact-checking"Social networks scaffold the dif fusion of information on social media\, both true and false. A recent fact -checking feature adopted by Twitter (now X) asks volunteers to propose an d vet fact-checking notes for false content. An important aspect of this f eature\, called Community Notes\, is its use of a bridging-based decision algorithm whereby fact-checking notes are shown only if they are seen as b roadly informative and helpful by users from across the political spectrum . This design is understood to prevent partisan engagement. The goal of th is proposal is to investigate strategic misuse of the Community Notes feat ure by partisan actors.\n\n\n\nKaren SetoFrederick C. Hixon Professor of G eography &\; Urbanization ScienceFaculty Director\, \;Hixon Center& nbsp\;for Urban SustainabilityCo-Director\, Yale Center for \;Geospati al SolutionsU.S. National Academy of Sciences \;Council on Foreign Rel ationsYale School of the EnvironmentKaren.seto@yale.edu \;| \;http ://urbanization.yale.eduPresented by Juwon Kong\, Postdoctoral Fellow\, YS E"Fusing multi-resolution\, multi-sensor satellite data to explore environ mental change"This project uses satellite imagery with different spatial\, spectral\, temporal and radiometric resolutions from multiple sensors and fuses them into a single high-resolution daily product to capture decades of environmental change in parks across the US. Specifically\, we use sat ellite imagery from the MODIS\, Landsat\, and Planet sensors to create a c oherent dataset that leverages the benefits of each individual dataset. Th e RA will explore different models to fuse satellite imagery of varying re solutions\, including pyramid-based methods\, wavelet transform-based meth ods\, statistical approaches\, and deep learning methods.\n\n\n\nLeandros TassiulasJohn C. Malone Professor of Electrical &\; Computer Engineerin g &\; Computer Science\, Yale School of Engineering and Applied Science Presented by Ali Maatook\, Postdoc in the Tassiulas Lableandros.tassiulas@ yale.edu | "LLM customization"\n\n\n\nKiran TuragaProfessor of Surgery\, C hief of Surgical Oncology\, Yale School of MedicineAssistant Director of C TO\, Yale Cancer Centerkiran.turaga@yale.edu | https://medicine.yale.edu/p rofile/kiran-turaga/"Registry based clinical trials"Clinical trials are th e cornerstone in determining new evidence for healthcare but are incredibl y expensive and difficult to perform. We propose a novel mechanism for dis covery of therapeutics and conduct of clinical trials. \;\n\n\n\nKrist en BrennandElizabeth Mears and House Jameson Professor of Psychiatry \ ;Co-Director\, \;Yale Science Fellows ProgramYale School of Medicinekr isten.brennand@yale.edu | https://brennandlab.org"Using Stem Cells to Expl ore the Genetics Underlying Brain Disease"Each person’s distinct genetic s and environment predispose them to some phenotypes and confers resilienc e to others. How do all the individual variants across the genetic landsca pe combine to yield larger phenotypic impacts in aggregate? How does genet ic variation govern the penetrance of deleterious mutations\, variable exp ressivity\, and pleiotropy? What is the role of the environment across the lifespan? Understanding how these elements interact will advance our know ledge of human development\, aging\, health\, and disease. Our functional genomics approach integrates human induced pluripotent stem cell models wi th CRISPR-based genome engineering to introduce and reverse genetic variat ion\, yielding precision models that can be combined with genetic and phar macological screens. With this approach\, we demonstrated that diverse ris k variants share downstream convergent impacts\, and that when added toget her\, their combinatorial perturbations yield novel non-additive outcomes that cannot yet be predicted by individual manipulations alone. We seek to understand the genetic regulation of phenotype\, and how it is impacted b y developmental\, cellular\, and environmental contexts. Thus\, rather tha n just characterize the impact of trait-associated variants\, we seek to u ncover modifiers that alter it. For example\, we study how genotype-phenot ype relationships vary across people and dynamic conditions. Our goal is t o decipher the frameworks that buffer genetic risk\, in order to confer bi ological resilience and promote healthy development. We are uniquely posit ioned to answer critical questions: How does the environment impact geneti c regulation? Why are there marked sex effects across many human traits an d diseases? What are the molecular mechanisms of resilience\, whereby indi viduals with high genetic risk show no clinical manifestation of disease? Understanding the basic biology governing the complex interplay between ge netic variants and the environment will springboard the development of nov el\, personalized approaches to improve health and prevent disease.\n\n\n\ nPurushottam DixitAssistant Professor\, Department of Biomedical Engineeri ng\,Yale School of Engineering and Applied Sciencepurushottam.dixit@yale.e du | https://sites.google.com/view/dixitlab"Mapping the Waddington landsca pe of mammalian tissues using Hopfield networks"In higher organisms\, diff erent tissue types\, e.g.\, eyes\, the blood\, and the brain originate fro m the same DNA. How the same instruction manual (DNA) is used to create ve ry different machines (tissues) is an open problem in organismal biology. In the 1950s\, Waddington provided a qualitative picture of the process of  \;canalization \;to form tissues using the language of dynamical systems. However\, till recently\, this remained a qualitative picture. Re cent advances in sequencing allow us to collect large amounts of high dime nsional gene expression data across multiple tissues. We propose a project to use this data and Hopfield networks to construct the Waddington landsc ape of mammalian tissue formation.\n\n CATEGORIES:FDS Events,Project Match LOCATION:Yale Institute for Foundations of Data Science\, Kline Tower 13th Floor\, Room 1327\, New Haven\, CT\, 06511\, United States X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Kline Tower 13th Floor\, Ro om 1327\, New Haven\, CT\, 06511\, United States;X-APPLE-RADIUS=100;X-TITL E=Yale Institute for Foundations of Data Science:geo:0,0 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