Alex Kurzhanskiy received his M.S in Applied Mathematics & Computer Science from Lomonosov Moscow University and Ph.D. in Electrical Engineering & Computer Science at Berkeley. He joined PATH as a Postdoctoral Researcher in 2008 and in 2011 he became a part of PATH research staff.
Presently, Dr. Kurzhanskiy is co-leading projects “Augmenting Connected Vehicle’s Awareness with I2V Information at Intersections” under Berkeley Deep Drive program and “Safe Operation of Automated Vehicles at Intersections” sponsored by Caltrans. He also manages 4-year NSF project “Traffic Operating System for Smart Cities” with transfer to practice.
In 2015-16 Dr. Kurzhanskiy conducted Caltrans projects “Modeling and Control of High Occupancy or Tolled (HOT) Lanes”, “HOT Lane Simulation Tools” and “Sustainable Operation of Arterial Networks”. The focus of these projects were: (1) measurement data analysis; (2) model development and identification of its parameters; and (3) simulation and evaluation of performance measures.
In 2013-14 Dr. Kurzhanskiy was a technical lead from UC Berkeley side of the pilot Interstate 680 Corridor System Management Plan project conducted by Connected Corridors for Caltrans District 4, which aimed at trying out novel approaches of traffic modeling and simulation for operations planning. He was responsible for developing calibration and simulation model quality evaluation tools for operations planning.
In 2012-14 Dr. Kurzhanskiy had actively contributed to various parts of the Connected Corridors program. He educated the team of software developers about the traffic engineering problems the program is solving and made significant impact on the software system design. Participating in research meetings and working with graduate students individually, he provides valuable guidance and mentorship to junior researchers.
- Transportation: Traffic Engineering, Traffic Modeling & Simulation, Traffic Signal Control, GIS, Traffic Data Collection & Processing
- Dynamical Systems: Automatic Control Theory and Applications, Stochastic Processes
- Partial Differential Equations: Numerical Methods, Inverse Problems
- Cyber-Physical Systems: Dynamic Modeling & Simulation, Identification, State Estimation & Control
- Data Science: Time Series Analysis, Cluster Analysis, Convex Optimization, Compressed Sensing
- Information Technology: Distributed Computation, Parallelization, MapReduce, CUDA and GPU computing2
