The Data Science Transdisciplinary Area of Excellence (TAE) organizes and sponsors different kinds of events, which are described below. Explore events in the menu on the right hand side of the page. You can find webcasts of some talks on the "Webcasting" page. Subscribe to our Google Calendar to keep in touch.
The Data Salon is our signature event. It is an informal gathering where researchers on campus share ongoing data-related work with the data science community, with the objective of communicating with those outside of their immediate field. We then open discussion to all attendees with the goal of developing and identifying strategies, methods and related questions of interest to the researcher and data scientists. The concept of a "salon" is for a researcher to present problems and research opportunities in a domain-independent fashion that invites contributions and collaborations from different disciplines, rather than an evaluation of the merits of a specific result, as would be the case for a conventional research seminar.
Invited Speaker Series
For our Invited Speaker Series, we invite leaders in data science from off campus, including researchers, administrators, executives and policy makers from universities, institutes and organizations, to share their recent research developments or their insights on the data science movement.
We organize events such as faculty-student mixers, workshops, lectures and data competitions.
We also sponsor/endorse seminars and colloquiums organized by various departments and units on campus. These events are often not initiated by the Data Science TAE, but by the individual departments or units.
Contact us if you would like to lead a discussion in a Data Salon event, nominate an invited
speaker or request our sponsorship of a seminar that your department/unit is organizing.
Data Science Seminar
Hosted by the Department of Mathematical Sciences
Date: Tuesday, October 19, 2021
Time: 12:00pm – 1:00pm
Zoom link: https://binghamton.zoom.us/j/98625073234?pwd=M2ZQYzRQR2I5b0xGQUNWOGNRN0NPUT0
Meeting ID: 986 2507 3234
Speaker: Dr. Damla Senturk (UCLA)
Title: Multilevel Modeling of Spatially Nested Functional Data: Spatiotemporal Patterns of Hospitalization Rates in the U.S. Dialysis Population
End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the U.S. contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loeve expansion is utilized to model the two-level (facility and region) functional data, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the U.S. and over the post-transition time on dialysis. Finite sample performance of the proposed method is studied through simulations.
Biography of the speaker: Dr. Senturk is a Professor in the Department of Biostatistics at UCLA. Dr. Senturk’s areas of statistical methodology research are longitudinal and functional data analysis. Her program of independent and creative research is motivated by her collaborative research in psychiatry and nephrology. She has been elected ASA fellow in 2020 for methodological contributions in semi-parametric modeling and functional data analysis, for innovative applications in neuroscience and other allied disciplines, for outstanding teaching and mentoring, and for exemplary service to the profession. Over the years she has served on 5 NIH R01s as PI and currently serves as the AE for Biometrics.
K-mer identification of allopolyploid subgenomes and chromosomal subcompartments
Wednesday, October 20, 2021, 11:00-12:00 (EST)
Allopolyploids are species that have recently undergone a whole genome duplication through interspecies hybridization. These species, despite having a duplicated genome, are genetically diploid and have unique characteristics compared to unduplicated and autopolyploid (genome duplication within a single species). Previously allopolyploidy was confirmed by comparing to living diploid outgroups that contribute one of the subgenomes of the allopolyploid. Recently, this has been proven through the identification of differentially retained transposable elements. This signal is intrinsic to the polyploid, and does not require comparison to diploid outgroups, allowing cheaper identification of subgenomes in polyploids.
I will describe our algorithm for subgenome identification through the mapping of k-mer density along assembled chromosomes, as well as describe how we use Hidden Markov Modeling to identify post-hybridization rearrangements between the subgenomes. In addition to identification of polyploid subgenomes, we have recently started using our k-mer method to identify sub-chromosomal compartments with unique transposon signatures.
About the speaker: Adam Sessions started his research career in behavioral genetics at Rutgers University, but during internships at Duke and UC Berkeley, became inspired to study genome evolution. Adam Sessions’s PhD focused on studying the genome of Xenopus laevis, a tetraploid frog commonly used in developmental biology. He was able to prove the allotetraploid hypothesis of X. laevis using data intrinsic to the genome, as well as detail the asymmetric evolution that followed. For his postdoc, he focused on plant genome duplications as polyploidy is more common in plants. Adam helped sequence the Brachypodium hybridum, Miscanthus sinensis, and Panicum virgatum genomes, as well as develop new methods for identifying allopolyploids, and timing their hybridization through studying transposon evolution. Looking forward, he hopes to continue to study polyploids in plants, animals, and fungi, with the goal of unifying the techniques used to study these genomes in different lineages, as well as use allopolyploids to further our understanding of speciation.