Cyber-security is a complex and multi-dimensional research field. My research style comprises an inter-disciplinary (primarily rooted in economics, econometrics, data science (AI/ML/Bayesian and Frequentist Statistics), game theory, and network science) investigation of major socially pressing issues impacting the quality of cyber-risk management in modern networked and distributed engineering systems such as IoT-driven critical infrastructures, cloud-based service networks, and app-based systems (e.g., mobile commerce, smart homes) to name a few. I take delight in proposing data-driven, rigorous, and interdisciplinary solutions to both, existing fundamental challenges that pose a practical bottleneck to (cost) effective cyber-risk management, and futuristic cyber-security and privacy issues that might plague modern (networked) engineering systems. I strongly strive for originality, practical significance, and mathematical rigor in my solutions. One of my primary end goals is to conceptually get arms around complex, multi-dimensional information security and privacy problems in a way that helps, informs, and empowers practitioners and policy makers to take the right steps in making the cyber-space more secure.
I am broadly interested in the economics of sports. My current research focuses primarily on two areas, two-sided markets and antitrust policy. Using e-commerce and social media data, I study the effect on consumer and social welfare when sports consumption move from traditional markets to the internet.
I work on the analysis of sports as they relate to economics, business, finance, history, performance modeling, analytics and prediction/forecasting. I typically use panel data econometric techniques to understand team performance in professional sports. I also have interest in forecast models.
Professor Kowalski’s recent research analyzes experiments and clinical trials with the goal of designing policies to target insurance expansions and medical treatments to individuals who stand to benefit from them the most. Her research has also explored the impact of previous Medicaid expansions, the Affordable Care Act, the Massachusetts health reform of 2006, and employer-sponsored health insurance plans. She has also used cutting-edge techniques to estimate the value of medical spending on at-risk newborns.
My research interests lie in design and analysis of randomized controlled trials (RCTs), partial identification, identification and inference with multi-valued treatments and instruments, and quantile regression. In one recent paper I study the optimal stratified randomization procedure in RCTs, and found a certain kind of matched-pair design is optimal. In another paper (coauthored with Joe Romano and Azeem Shaikh), we provide asymptotically exact inference procedure for matched-pair designs. In another paper we study inference with moment inequalities whose dimension grows exponentially fast with the sample size. I also have a paper in which we study the sharp identified sets for various treatment effects with multi-valued instruments and multi-values treatments.
My primary research is focused on measurement and monitoring of risks in banks, both at the individual bank level and at the level of financial system as a whole. In a recent paper, we have developed a high-dimension statistical approach to measure connectivity across different players in the financial sector. We implement our model using stock return data for US banks, insurance companies and hedge funds. Some of my early research has developed analytical tools to measure banks’ default risk using option pricing models and other tools of financial economics. These projects have often a significant empirical component that uses large financial datasets and econometric tools. Of late, I have been working on several projects related to the issue of equity and inclusion in financial markets. These papers use large datasets from financial markets to understand differences in the quantity and quality of financial services received by minority borrowers. A common theme across these projects is the issue of causal inference using state-of-the art tools from econometrics. Finally, some of ongoing research projects are related to FinTech with a focus on credit scoring and online lending.
Professor Manduca’s research focuses on urban and regional economic development, asking why some cities and regions prosper while others decline, how federal policy influences urban fortunes, and how neighborhood social and economic conditions shape life outcomes. He studies these topics using computer simulations, spatial clustering methods, network analysis, and data visualization.
In other work he explores the consequences of rising income inequality for various aspects of life in the United States, using descriptive methods and simulations applied to Census microdata. This research has shown how rising inequality has lead directly to lower rates of upward mobility and increases in the racial income gap.
Screenshot from “Where Are The Jobs?” visualization mapping every job in the United States based on the unemployment insurance records from the Census LODES data. http://robertmanduca.com/projects/jobs.html
Societal control tends to be implemented from the top-down, whether that is a private corporation or a communist state. How can data science empower from the bottom-up? Computational technologies can be designed to replace extractive economies with generative cycles. My research includes AI for the artisanal economy; computational modeling of Indigenous practices; and other means for putting the power of data science in the service of generative justice.
Student moving from her knowledge of braiding algorithms, to her program for braiding patterns, to a mannequin head for installation in adult braider’s shops. https://csdt.org/culture/cornrowcurves/index.html
Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
My research explores the interplay between corporate decisions and employee actions. I currently use anonymized mobile device data to observe individual behaviors, and employ both unsupervised and supervised machine learning techniques.