My research is an N-headed monster (not necessarily a good thing) involving scientific computation and machine learning, optimal control, high-dimensional approximation and agent-based models across scales. I am mostly interested in:
Hamilton-Jacobi-Bellman PDEs: circumventing the curse of dimensionality in very high-dimensional dynamic programming and reinforcement learning problems using scientific computing and machine learning techniques. Applications in optimal feedback control/estimation of nonlinear ODE/PDE dynamics.
Controlling agent-based dynamics across scales: opinion dynamics, pedestrians, swarm robotics, interacting particle systems in general. Optimal control for microscopic, kinetic, and mean-field descriptions. Numerical methods for mean field control and games.
Optimisation and control with PDE constraints: nonlocal dynamics, vibration control, optimal actuator/sensor design, fluid flow control.
PhD supervision: if you are interested in following a PhD in any of the above, please do not hesistate to get in touch! If you can already tell me a bit about your interests, that's much better, but if you have a general idea I'm also happy to talk.
Collaborations, projects: if you need some expertise in optimisation and/or control, computation, or learning, let's talk.
Please visit https://www.dkalise.net for more details!
et al., 2022, Moment-driven predictive control of mean-field collective dynamics, Siam Journal on Control and Optimization, Vol:60, ISSN:0363-0129
et al., 2022, Controlling swarming models towards flocks and mills, Siam Journal on Control and Optimization, ISSN:0363-0129
et al., 2021, Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic, Plos Computational Biology, Vol:17, ISSN:1553-734X
et al., 2021, Optimal actuator design for the Euler-Bernoulli vibration model based on LQR performance and shape calculus, Ieee Control Systems Letters, Vol:6, ISSN:2475-1456, Pages:1334-1339
Albi G, Bicego S, Kalise D, 2021, Gradient-augmented supervised learning of optimal feedback laws using state-dependent Riccati equations, Ieee Control Systems Letters, Vol:6, ISSN:2475-1456, Pages:836-841