Imperial College London

Dr Dante Kalise

Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer in Computational Optimisation and Control



d.kalise-balza Website CV




742Huxley BuildingSouth Kensington Campus





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.

A glimpse into my current research at Ghana's Numerical Analysis Seminar, July 2021.

Please visit for more details!




King AJ, Portugal SJ, Strömbom D, et al., 2023, Biologically inspired herding of animal groups by robots, Methods in Ecology and Evolution, ISSN:2041-210X

Kalise D, Sharma A, Tretyakov M, 2022, Consensus based optimization via jump-diffusion stochastic differential equations, Mathematical Models and Methods in Applied Sciences (m3as), ISSN:0218-2025

Borovykh A, Kalise D, Laignelet A, et al., 2022, Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs, Ifac Papersonline, Vol:55, ISSN:2405-8963, Pages:168-173


Albi G, Bicego S, Kalise D, 2022, Supervised learning for kinetic consensus control, MTNS22, Elsevier BV, Pages:103-108, ISSN:2405-8963

Dolgov S, Kalise D, Saluzzi L, 2022, Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS), ELSEVIER, Pages:510-515, ISSN:2405-8963

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