Faculty Profiles
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Highlighted below are faculty from each of the four PhD areas of concentration. Please see MSCS Faculty page for more information on individual faculty research programs.
Ronnie
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Ronnie Nagloo: Associate Professor
My research is in mathematical logic and model theory. My work focuses on applying the techniques developed in model theory – more precisely geometric stability theory – to tackle problems in other areas of mathematics such as functional transcendence and number theory. Over the last decades there has been a surge in interest around functional transcendence results, in part due to their connection with special points conjectures in number theory. Model theory has been a key technique used in the proofs of the main results in that area.
I am currently advising one PhD student and look forward to working with many more. The MSCS department at UIC offers a wide selection of graduate classes in the areas of pure and applied mathematics, computer science and statistics. The department’s area of research in pure mathematics includes algebraic geometry, geometry and topology, combinatorics, logic, to name a few. I am a member of the very active logic group which has a research weekly seminar, other working seminars and the student-run Louise Hay Logic Seminar.
Mimi Dai: Associate Professor
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Mimi Dai: Associate Professor
My research concerns analysis of nonlinear partial differential equations arising in fluid and complex fluid dynamics. Fluid is the most omnipresent substance on our planet; understanding the behaviors of solutions to fluid equations is important not only from the mathematical perspective, but also for comprehending classical issues in physics. Theoretical aspects can also cast light on real-life applications like designing aircraft and predicting weather. I have been concerned with issues in fluid and complex fluid dynamics that form a constellation of related problems to the global regularity problem. For instance, my recent results include justifying Kolmogorov’s 1941 conjecture on phenomenological turbulence theory, building grounds for the study of finite dimensionality of turbulent flows, improving regularity criteria for supercritical equations, and discovering dramatic ill-posedness behaviors of solutions.
In recent work with PhD student, Han Liu, we have completed a few projects on some complex fluid models, focusing on the low modes regularity criterion for a Chemotaxis-Navier-Stokes system. This describes the dynamics of bacterial swimming (example: Bacillus Subtilis) and oxygen transport in a liquid medium. We confirmed that under the low modes part of the solution only, a blow-up will not happen, and the bacteria will not concentrate at one point in the space.
Currently, Han is working on the well/ill-posedness problem for the magneto-hydrodynamics with Hall effect, which is a fundamental model to capture the fast magnetic reconnection phenomena in astrophysics. Our analytical study involves applications of various classical energy methods, Calderon-Zygmund techniques, and modern harmonic analysis tools.
Jie Yang: Professor
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Jie Yang: Professor
I work in the fields of experimental design, big data analysis, bioinformatics and biostatistics, and financial mathematics. On experimental design, I focus on optimal design theory and efficient algorithms, which require both optimization theory and practice. On big data analysis, I focus on developing methods and algorithms for model-based statistical analysis. On bioinformatics and biostatistics, I am interested in high-dimensional classification, clustering, categorical or sparse data analysis with genomic data and microbiome data. On financial mathematics, I work on estimating risk-neutral densities and pricing financial assets. Since 2013, I have supervised 14 PhD students including 1 in experimental design, 4 in big data analysis, 6 in bioinformatics and biostatistics, and 3 in financial mathematics.
The whole MSCS department is like a big family. Welcome to join us!
*Jie is pictured with PhD Alums Yanxi Liu (Senior Statistician, AbbVie), Hani Aldirawi (Assistant Professor, California State University), Keren Li (Assistant Professor, University of Alabama at Birmingham) and Nurlan Abdukadyrov (Data Scientist, Cincinnati Insurance)
Lev Reyzin: Professor
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Lev Reyzin: Professor
My work focuses on foundational questions in computational and statistical learning theory, but I am more broadly interested a variety of topics, ranging from practical issues in machine learning to theoretical computer science. My research also intersects with various areas of combinatorics and optimization. Some of my specific recent projects include work on adaptive data analysis, noise-tolerant algorithms, active and interactive learning, network optimization, boosting and ensemble methods, and multiarmed bandits.
Much of my research is done with Ph.D. students, and I work with them across all of my areas of research interest. A few of my recent students have written their dissertations in areas that involve the intersection of theoretical computer science and new challenges posed in the era of “big data.”
I am also part of broader efforts within the city, and am PI of the NSF Institute for Data, Econometrics, Algorithms, and Learning. Please visit https://ideal-institute.org/ for more information about our data science partnerships across the Chicago area.