Title: The many complicated ways how ice forms in the atmosphere
How ice crystals form in the atmosphere is considered one of the grand challenges in the atmospheric sciences. Accurate prediction of ice nucleation is crucial to improve simulations of cloud formation and, thus, climate predictions. In addition, most precipitation is initiated by ice crystals. Hence, prediction of rain or extreme weather events are bound to a fundamental understanding of atmospheric ice formation. This presentation introduces the most common pathways how we think ice forms in the atmosphere. Ice nucleates on nano- to micrometer-sized airborne particles, so-called ice-nucleating particles, that are physicochemically complex. Experiments that aim to resolve on the nanoscale the ice-nucleating particles and relate the particles’ properties to their ice formation ability are presented. Current debates on how to interpret ice nucleation experiments and the consequences for application in cloud-resolving models will be discussed.
Bio:
Dr. Daniel Knopf is a Full Professor in the School of Marine and Atmospheric Sciences with an affiliated appointment in the Department of Chemistry. His research interests revolve around the role of airborne particles in various atmospheric processes. This includes the oceans or wildfires serving as sources of particles, the interaction of particles with gases, the formation of clouds, and air quality and climate relevant issues. Dr. Knopf was trained in physics at the Ruprecht-Karls University Heidelberg and Max-Planck Institute for Nuclear Physics (M.Sc.) and in atmosphere chemistry at the Swiss Federal Institute of Technology (Ph.D.). Dr. Knopf joined SBU as an assistant professor in 2007.
Date: Tuesday, September 26, 2023
Time: 1pm-2pm
Location: Special Collections Seminar Room, E-2340, second floor of the Melville Library
Title: "Measuring Self-Identity"
Social media platforms encourage users to provide short biographies or descriptions of themselves, providing a mechanism for self identity or to define how they represent themselves to others. We have assembled a seven+ year longitudinal corpus observing how the self-descriptions of millions of people on Twitter change and evolve. This provides a unique resource to study notions of self-identity.
I report on the general infrastructure behind this developing data resource, and the observations of social behavior that can be made using it:
-- By studying user references to employment, we can document career progressions, and demonstrate that higher status jobs disproportionately become part of self-identity, more so than highly paid jobs.
-- In the United States, there has been a steady trend towards increased expression of political identities, at the expense of religious identity.
-- We measure the power of self-descriptions to predict user attributes, including which celebrities they are likely to follow.
This talk represents work with my collaborators Jason Jones, Dakota Handzlik, and Xingzhi Guo.
Bio:
Dr. Steven Skiena is Distinguished Teaching Professor of Computer Science and Director of the Institute for AI-Driven Discovery and Innovation at Stony Brook University. His research interests include data science, bioinformatics, and algorithms. He is the author of six books, including "The Algorithm Design Manual", "The Data Science Design Manual", and "Who's Bigger: Where Historical Figures Really Rank", and over 150 technical papers.
Dr. Skiena received his B.S. in Computer Science from the University of Virginia and his Ph.D. in Computer Science from the University of Illinois in 1988. He is a Fellow of the American Association for the Advancement of Science (AAAS), a current and former Fulbright scholar, and recipient of the University of Virginia Engineering Distinguished Alumni Award (WahooWa!), the ONR Young Investigator Award and the IEEE Computer Science and Engineer Teaching Award. More info is availableat http://www.cs.stonybrook.edu/~
Date: Tuesday, October 24, 2023
Time: 1pm-2pm
New Location: North Reading Room Learning Lab, N1005, first floor inside the North Reading Room of the Melville Library
Title: "From Macro to Micro: Understanding Human Motion In the Wild"
Modern environments are alive with sensors, such as smartphones, wearables, vehicles, and cameras. However, despite the plethora of sensors, direct sensing of desired events is difficult in the real-world. This is primarily due to large amounts of noise introduced by our movements during everyday tasks, in addition to lack of dedicated sensors and deployment difficulties. We address this challenge by modeling movements to design and develop mobility-aware systems. This has opened up new opportunities in both urban tech and mobile health. This talk will explore our research efforts in leveraging pervasive sensing devices, towards developing a multi-modal data integration and interpretation framework. I will discuss sensing frameworks for urban spaces and the mHealth space. We have designed real-time monitoring tools that can capture fine-grained measures of users’ health in uncontrolled environments. In building these data-driven frameworks that introduce mobility-awareness in non-homogeneous sensors, we introduce new avenues for smart cities and healthcare analytics in the real-world.
Bio:
Dr. Shubham Jain is an Assistant Professor in the Department of Computer Science at Stony Brook University where she leads PiCASSo (Pervasive Computing and Smart Sensing) Lab. Her research interests lie in cyber-physical systems, mobile health, and data analytics in smart environments. Her research has been published in conferences like ACM MobiSys, ACM/IEEE IPSN, and IMWUT. Her work on pedestrian safety has been featured in several media outlets, including The Wall Street Journal. She received her PhD in Electrical & Computer Engineering from Rutgers University in 2017.
Date: Tuesday, November 14, 2023
Time: 1pm-2pm
Location: Special Collections Seminar Room, E-2340, second floor of the Melville Library
Library Administration: 631.632.7100
