Aleksandr M. Kazachkov

I am an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. Previously, I was a postdoctoral researcher under Andrea Lodi at Polytechnique Montréal. Before that, I graduated with my Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University in May 2018, under Egon Balas.

You can find more information about me and my recent research on my CV. Please contact me if you have any questions about my work.


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Updates


Students

I am actively seeking students, both undergraduate and graduate, to work on discrete optimization, computational economics, or their intersection. If you are interested in working with me, you are welcome (but not required) to send me an email with your CV and describe your research interests. A strong email is personalized to me; I am likely to not respond to a generic request.

Information on the application procedure for graduate programs, for both master's and doctorate students.

I am committed to promoting equity, diversity, and inclusion within the research team. All applications are welcome, including those from women, underrepresented minorities, indigenous people, people with any sexual orientation or gender identity, people with disabilities or health issues, and people from economically disadvantaged backgrounds. Research can be a challenging and taxing endeavor, especially during the initial transition period, and I make concerted efforts to support my students with that in mind. I also encourage students to take advantage of the Office of Graduate Diversity Initatives.


Research

My research is on the methodology and applications of discrete optimization. In particular, I work on improving integer programming techniques, as well as designing and analyzing fair mechanisms for the allocation of indivisible resources.

I am interested in all aspects of discrete decision analytics, including theoretical, computational, and applied projects, as well as applying novel methods such as machine learning algorithms that can substantially outperform hand-engineered components of optimization solvers. I emphasize prosocial applications, such as in the nonprofit or healthcare sectors, though I also do research in sports analytics.

Cutting plane methods

Computational social choice



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