معرفی
Andrew Lamperski is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, College of Science and Engineering. His career spans postdoctoral research at the University of Cambridge, Johns Hopkins University, and Caltech.
- Ph.D. in Control and Dynamical Systems, Caltech (2011)
- B.S. in Biomedical Engineering and Mathematics, Johns Hopkins (2004)
His research lies at the intersection of Optimal Control, Stochastic Systems, and Biomedical Engineering, with recent work analyzing:
- Control methods for neuromodulation and deep brain stimulation
- Stochastic hybrid systems and moment dynamics
- Network topology identification from corrupted data streams
Key article trends include:
- Advancing Machine Learning for control systems through ReLU networks
- Developing Robust Control frameworks with QSR dissipative properties
- Applying Stochastic Optimization techniques to biomedical devices
Scientific recognition includes:
- Whitaker Postdoctoral Fellowship
- NSF grants for dynamical network reconstruction and non-convex domain control
- NASA collaboration on space vehicle swarms
He has mentored students working on topics like
- Optimal control algorithms
- Neural interfacing
- Stochastic system analysis
- Multi-agent systems
The Lamperski Research Group focuses on translating theoretical control advancements into practical applications for neurological disorders and autonomous systems.
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