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.

Data Salon

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.

Other Events

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.

All Events

12:00pm - 1:00pm
AA 340 Conference Room

Integrated Dynamic State Estimation in PowerSystems

Dr. Ning Zhou

AssociateProfessor, Electrical and Computer Engineering Department

Watson School ofEngineering & Applied Science, Binghamton University




Tomake well-informed decisions, power system operators need accurate timely estimates of the operational conditions of thepower grid. Up to the present time, conventional static state estimators havebeen widely deployed in utility control centers to improve the estimationaccuracy and expand the monitoring areas. However, these estimators are no longer sufficient for monitoring themodern power grid, which is experiencing increasing uncertainty and variationdriven by the high penetration of intermittent renewable energy sources (mainlysolar and wind). In fact, conventionalstatic state estimation methods for power grids often fail to provide anyuseful information during transmission-line tripping and cascading grid failureswhen the power system rapidly changes,and state estimation results are crucially needed.

In this presentation, the conventional state estimation isreviewed. Also,a dynamic state estimation (DSE) approach is proposed that can not onlyestimate current operational conditions but also predict their future trendsand quantify their uncertainty. To minimize the financial cost of measurementdevices while achieving observability of important system states, observabilityand detectability studies are carried out to guide measurement placement and modelselection. Itis shown that many dynamic states in the power systems are marginallyobservable (virtually unobservable). If an observer model can be chosen to makethe eigenvalues of the corresponding states stable, the DSE can still convergeto the true value of the states.



Ning Zhou (S’01- M’05- SM’08) is currently with the Electrical and Computer EngineeringDepartment at Binghamton University as an associate professor. In 2005, hereceived his Ph.D. in Electrical Engineering with a minor in statistics fromthe University of Wyoming. From 2005 to 2013,Dr. Zhou worked as a power system engineer at the Pacific Northwest NationalLaboratory. His research interests include power system dynamics and statisticalsignal processing. Dr. Zhou is a senior member of the IEEE Power and Energy Society(PES). He has been an associate editor for IET Generation Transmission andDistribution since 2016. He is the lead author of the 2009 Technical Committee Prize Paper from the IEEE/PES Power System DynamicPerformance Committee.  He has been the co-Chair of IEEEPES Working Group on Data Access and secretary of IEEE PES Task Force on OscillationSource Location since 2016. He is the recipient of the 2009 Outstanding Engineer of Year Award from IEEEPower and Energy Society (PES) Richland Chapter. He is the recipient of IEEEPES Outstanding Branch Counselor Award in 2017. He is the PI of the NSF CAREERaward titled “IntegratedDynamic State Estimation for Monitoring Power Systems under High Uncertaintyand Variation” in the year of 2019.

12:00pm - 1:00pm
AA 340 Conference Room
Recent public discussions and legal decisions suggest that school segregation will remain persistent in the United States, but increased transparency may help monitor spending across schools. These circumstances revive an old question: is it possible to achieve an educational system that is separate but equal—or better—in terms of spending? This question motivates further understanding the measurement of spending progressivity and its association with segregation. Focusing on economic disadvantage, we compare two commonly used measures of spending progressivity: exposure-based and slope-based. Using nationwide US school-level data on public education spending from the National Education Resource Database on Schools (NERD$,, and school enrollments and rates of free/reduced-price lunch from the Longitudinal School Demographic Dataset (LSDD,, we empirically examine school spending progressivity and its properties for the 2018-19 school year. Consistent with our theory, the exposure-based measure is the slope-based measure shrunk inversely by economic school segregation. This property makes more segregated school districts look more progressive on the exposure-based measure, representing a seemingly “separate but better” relationship. However, we show that this provocative pattern may be reversed by relatively modest poor-versus-nonpoor differences in unobserved parental contributions. We discuss implications for the measurement of progressivity, and for theory on public educational investments broadly.
3:30pm - 5:00pm
Brad Skopyk (Department of History) David Mixter (Department of Anthropology & Environmental Studies) Vijay Kumar Kadamanchi (Watson's School of Engineering) In this presentation, we demo and explain the digital infrastructure for a new historical crowdsourcing application of geo-located health events for Mexico before the twentieth century. The crowdsourced database facilitates international collaboration on a data-intensive health-related project and is an important teaching resource for undergraduate classrooms. It uses an Angular frontend with Spring Boot microservices connected to a postgresql database, which subsequently feeds a website for data visualization in maps and graphics. The same web portal makes the database freely available to researchers for purposes of analysis, download, and output of custom cartography and other visualizations.