For virtual event links or to be added to the colloquium mailing list please contact mathstat@american.edu.

See also previous colloquium Abstracts and YouTube Playlist.

If interested in presenting to the Colloquium, please contact the organizers: Stephen D. Casey (scasey@american.edu), Nimai Mehta (mehta@american.edu).

Fall 2022

• December 7, 2022, 2:30 PM:
  Zois Boukouvalas, AU,
  Efficient and Explainable Multivariate Data Fusion for Misinformation Detection During High Impact Events
  Abstract: With the evolution of social media, cyberspace has become the de-facto medium for users to communicate during high-impact events such as natural disasters, terrorist attacks, and periods of political unrest. However, during such high-impact events, misinformation on social media can rapidly spread, affecting decision-making and creating social unrest. Identifying the spread of misinformation during high-impact events is a significant data challenge, given the variety of data associated with social media posts. Recent machine learning advances have shown promise for detecting misinformation, however, there are still key limitations that makes this a significant challenge. These limitations include the effective and efficient modeling of the underlying non-linear associations of multi-modal data as well as the explainability of a system geared at the detection of misinformation. In this talk we present a novel multivariate data fusion framework based on pre-trained deep learning features and a well-structured and parameter-free joint blind source separation method named independent vector analysis, that can reliably respond to this set of limitations. We present the mathematical formulation of the new data fusion algorithm, demonstrate its effectiveness, and present multiple explainability case studies using a popular multi-modal dataset that consists of tweets during several high-impact events.
• November 29, 2:30 PM: 
  John T. Rigsby,
  Chief Analytics Officer, Defense Technical Information Center
  "Data Science Projects at the Defense"
  Abstract: The mission of the Defense Technical Information Center (DTIC) is to aggregate and fuse science and technology data to rapidly, accurately and reliably deliver the knowledge needed to develop the next generation of technologies to support our Warfighters and help assure national security. This presentation will cover current efforts of the DTIC Data Science and Analytics Cell to support this mission.

• November 1, Elicia John, AU,
  "Smartphone Data Reveal Neighborhood-Level
  Racial Disparities in Police Presence"
  Abstract : While research on policing has focused on documented actions such as stops and arrests, less is known about patrols and presence. We map the neighborhood movement of nearly ten thousand officers across 21 of America’s largest cities using anonymized smartphone data. We find that police spend more time in neighborhoods with predominantly Hispanic, Asian, and – in particular – Black residents. This disparity persists after controlling for density, socioeconomic, and crime-driven demand for policing, and is lower in cities with a higher share of Black police supervisors (but not officers). It is also associated with a higher number of arrests in some of these communities.

• October 25, "FDA Cybersecurity, Counterintelligence, and Insider:
  Threat Program Overview"
  Craig Taylor, US Food and Drug Administration (FDA) Chief Information Security Officer (CISO)
  Leah Buckley, FDA (Director, Counterintelligence and Insider Threat)
• October 18, Daniel Bernhofen,
  Testing the Invisible Hand with a Natural Experiment  
  Abstract : A central premise of economics is that the market system allocates resources in the right direction, as if directed by an invisible hand. But what is the right direction? The economic subfield of general equilibrium theory has provided an answer to this question via the First Funamental Welfare Theorem, which employs Pareto optimality as a criteria for the right direction and states that a competitive general equilibrium is Pareto optimal. For this reason, the First Fundamental Welfare Theorem has also been labeled the invisible hand theorem and is viewed as a proof of Adam Smith’s famous conjecture that in a market economy individuals are “…led by an invisible hand to promote an end which was no part of (their) intention” (Adam Smith, Wealth of Nations, 1776, vol I, Book IV, Ch II, p.477). A major criticism of the invisible hand theorem is that it holds under very strong conditions and can’t be refuted by the data.
  
  This lecture provides an overview of a research agenda that employs a natural experiment to test some fundamental theorems in international trade, which is a subfield of general equilibrium theory. First, I show that the mathematical structure of these theorems can be summarized as P∙Z>0, which I call the invisible hand inequality. Second, I discuss the relationship between the invisible hand theorem and the invisible hand inequality. Third, I discuss how the 19th century opening up of Japan to international trade after 200 years of self-imposed isolation provides a natural experiment to test the invisible hand inequalities and provide evidence that decentralized markets allocate resources in the (right) direction of comparative advantage.
  
  For a brief background reading for this talk see: Gains from Trade: Evidence from 19th Century Japan.
• September 27, Emmanouil “Manolis” Maragkakis
  Abstract: Aging, while seemingly a universal feature of life, is surprisingly difficult to define. New studies describe aging as a correlated set of declines in functioning with advancing chronological age, which generally begins after sexual maturity. Understanding the regulation of these declines is crucial for the development of interventions and disease therapies but requires the coordinated integration of multidimensional information. Dr. Maragkakis focuses on understanding this high-dimensional regulation of gene expression as a key component of discovering the mechanistic basis of the physiology and diseases of aging. His lab employs experimental and computational approaches to discover fundamental biological mechanisms that control RNA stability and dynamics. They develop tools to integrate high-throughput short- and long-read sequencing and machine learning to decipher the defining features of co-translational mRNA decay, how it is regulated, and the downstream effects of RNA dynamics in aging organs, tissues, and cells.
• September 20, Zeying Wang, AU
  Abstract: The Talk is situated in the area of Combinatorial Design Theory, a subfield of Discrete Mathematics. In particular, I will focus on so-called partial difference sets (PDSs) in finite abelian groups. These are combinatorially defined subsets of a finite group that give rise to highly symmetric graphs called strongly regular Caley graphs. A few years ago we proved a theorem for strongly regular graphs that provides numerical restrictions on the number of fixed vertices and the number of vertices mapped to adjacent vertices under an automorphism. We then used this result to develop some new techniques to study regular partial difference sets in abelian groups. We have proved several non-existence results and classification results for partial difference sets in abelian groups. Also we completely answered a classical question from the 1990s: "For which odd positive integer v>1, can we find a Paley type partial difference set in an abelian group of order v?" In this talk I plan to give an overview of these results. The talk will be accessible to non-specialists.