Leonid Zhukov

Director of Data Science,
Professor, Department of Applied Mathematics and Informatics, Moscow, Russia
PhD, Theoretical Physics
Research Scientist at the SCI Institute 1998-2000

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Leonid Zhukov, a SCI Institute alumnus, currently serves as the director of data science at He is also a professor at the School of Applied Mathematics and Information Science at the National Research University Higher School of Economics in Moscow, Russia, for which he received the best teacher award in 2011 and again in 2013. Leonid received his diploma from the National Research Nuclear University, formerly Moscow Engineering and Physics Institute, in 1993. He received his PhD in Physics in1998 from the University of Utah.

Leonid started working at the SCI Institute in 1996 while pursuing his PhD degree and was then hired as a computational professional and finally a research scientist from 1998 to 2000. Leonid's research during his SCI tenure focused on the development of algorithms for the computational solution of inverse problems and regularization methods in biomedicine. He also participated in the development of computational steering software (SCIRun) for large-scale scientific computations and investigated EEG and MEG source localization problems. Leonid's experiences at the SCI Institute led to senior scientist positions at the California Institute of Technology, Overture, and Yahoo!, followed by work as the chief scientist for Jumptap and director of research at Openstat. He later co-founded and served as a technical director of the information security start-up company Trafica, where he was responsible for developing the company roadmap, hiring the engineering team, and leading the company's product development.

Leonid's current research and teaching interests include data mining, machine learning, information retrieval, social network analysis and visualization. His work for involves leading and managing the data science team and working on large-scale machine learning and information retrieval problems. He is currently developing data-driven products and predictive analytics models, as well as performing an exploratory analysis of structured and unstructured historical data.


Visualization of the downhill simplex algorithm converging to a dipole source. The simplex is indicated by the gray vectors joined by yellow lines. The true source is indicated in red. The surface potential map on the scalp is due to the forward solution of one of the simplex vertices, whereas the potentials at the electrodes (shown as small spheres) are the "measured" EEG values (potentials due to the true source). SCIRun problem solving environment, SCI Institute, 1999. 

Use of Semidefinite Programming (SDP) optimization for high dimensional data layout and graph visualization. We developed a set of interactive visualization tools and used them on music artist ratings data from Yahoo!. The computed layout preserves a natural grouping of the artists and provides visual assistance for browsing large music collections. 
color-treeLeonid Many family trees have complex connectivity structure. We found that for large graphs with more than several thousand nodes, standard force directed graph layout algorithm gives good results. This is an example of a medium size tree with several thousand nodes each - See more at: