Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer in Machine Learning and Mathematical Finance







801Weeks BuildingSouth Kensington Campus





Lukas is a Senior Lecturer jointly appointed at the Department of Mathematics and the artificial intelligence initiative Imperial-X (I-X).

Lukas holds a doctoral degree from ETH Z├╝rich and has been a postdoctoral researcher at University of St. Gallen and an assistant professor at University of Munich before joining Imperial College London.

His research is at the intersection of mathematics, machine learning and finance. It centers around various machine learning methods (deep learning, reservoir computing, random features, kernel methods, ...) and their applications to time series, stochastic processes, partial differential equations and finance. This encompasses

(i) applying and refining these methods or developing novel methods for practically important applications (for example hedging, forecasting or financial bubble detection) 

(ii) carrying out mathematical analyses (for instance proving bounds on the approximation or generalization errors of deep or recurrent neural networks) in order to gain a more profound theoretical understanding of these methods.

Lukas serves as an action editor for Neural Networks.

For a full list of publications and preprints see Lukas' personal homepage or his google scholar profile.

Selected Publications

Journal Articles

Gonon L, Grigoryeva L, Ortega JP, 2023, APPROXIMATION BOUNDS FOR RANDOM NEURAL NETWORKS AND RESERVOIR SYSTEMS, Annals of Applied Probability, Vol:33, ISSN:1050-5164, Pages:28-69

Biagini F, Gonon L, Reitsam T, 2023, Neural network approximation for superhedging prices, Mathematical Finance, Vol:33, ISSN:0960-1627, Pages:146-184

Gonon L, 2023, Random feature neural networks learn Black-Scholes type PDES without curse of dimensionality, Journal of Machine Learning Research, Vol:24, ISSN:1532-4435, Pages:1-51

Cuchiero C, Gonon L, Grigoryeva L, et al., 2022, Discrete-Time Signatures and Randomness in Reservoir Computing, IEEE Transaction on Neural Networks and Learning Systems, Vol:33, ISSN:2162-237X, Pages:6321-6330

Gonon L, Schwab C, 2021, Deep ReLU network expression rates for option prices in high-dimensional, exponential Levy models, Finance and Stochastics, Vol:25, ISSN:0949-2984, Pages:615-657

Gonon L, Grigoryeva L, Ortega J-P, 2020, Risk Bounds for Reservoir Computing, Journal of Machine Learning Research, Vol:21, ISSN:1532-4435

Buehler H, Gonon L, Teichmann J, et al., 2019, Deep hedging, Quantitative Finance, Vol:19, ISSN:1469-7688, Pages:1271-1291

More Publications