David Siska

• Mean-Field / McKean Vlasov SDEs,
• Mathematical Theory of Machine Learning,
• Stochastic Control,
• SPDEs (Stochastic Partial Differential Equations),
• Applications in Financial Mathematics, Economics, Game Theory.

Contact Details

School of Mathematics, University of Edinburgh
Room 4611, JCMB, King's Buildings
Tel. 0131 651 9091
Email. d.siska@ed.ac.uk
Online presence: Twitter @dsiska, Google Scholar, Researchgate and MathSciNet Profile.

PhD Projects

Students interested in working on a PhD project (to start in September 2020) in one of my areas of interest should contact me with informal enquires: d.siska@ed.ac.uk. See the School PhD applications website and MAC-MIGS website for available funding (we normally fully fund all students we accept) and for details regarding the formal application process (feel free to contact me to discuss research plan before submitting the application).

Current PhD Students

• William Hammersley, 2016 - present, is working on McKean-Vlasov SDEs, SPDEs and related topics. He is funded by MIGSAA. This is part of work I do jointly with Lukasz Szpruch who is also William's main advisor.
• Bekzhan Kerimkulov, 2017 - present, is working on PDEs on measure spaces, McKean-Vlasov SDEs and stochastic control. He is funded by MIGSAA. This is part of work I do jointly with Lukasz Szpruch.
• Patryk Gierjatowicz, 2018 - present, is working on machine learning in finance. He is funded by MIGSAA. This is part of work I do jointly with Lukasz Szpruch who is also William's main advisor.
• Maria Lefter, 2018 - present, is working on McKean-Vlasov SDEs, particle approximations and stochastic control. She is funded by MIGSAA. This is part of work I do jointly with Lukasz Szpruch.
• Marc Sabate Vidales, 2019 - present, is working on machine learning algorithms for approximaiton of nonlinear and path-dependent PDEs. He is funded by The Alan Turing Institute and The Edinburgh Futures Institute. This is part of work I do jointly with Lukasz Szpruch.

Former PhD Students

• Neelima, 2015 - 2019, wrote a thesis on nonlinear SPDEs with non-standart growth and their regularity. She was funded by University of Edinburgh's Principal's Career Development Scholarship. She is currently an Assistant Professor at Ramjas College, University of Delhi.

Events

Here are some events I am (or was) helping to organize.

• ICMS Workshop on Wasserstein Calculus and Related Topics: 19th - 23rd November 2018.
• MIGSAA Mini-Course: Singular SPDEs and Regularity Structures: 26th - 30th June 2018.
• International Workshop on BSDEs, SPDEs and their Applications: 3rd-7th July 2017.
• Conference on Stochastic Analysis in Honor of Istvan Gyöngy's 65th Birthday: 10th-12th September 2016.
• Maxwell Institute Probability Day: 13th May 2016.

Preprints

• With J.-F. Jabir and L. Szpruch, Mean-Field Neural ODEs via Relaxed Optimal Control, 2019.
• With L. Gonon, P. Grohs, A. Jentzen and D. Kofler, Uniform error estimates for artificial neural network approximations for heat equations, 2019.
• With W. R. P. Hammersley and L. Szpruch, Weak Existence and Uniqueness for McKean-Vlasov SDEs with Common Noise, 2019.
• With K. Hu, Z. Ren and L. Szpruch, Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks, 2019.
• With B. Kerimkulov and L. Szpruch, Exponential Convergence and stability of Howards's Policy Improvement Algorithm for Controlled Diffusions, 2018.
• With M. Sabate-Vidales and L. Szpruch, Unbiased deep solvers for parametric PDEs, 2018.
• With W. R. P. Hammersley and L. Szpruch, McKean-Vlasov SDEs under Measure Dependent Lyapunov Conditions, 2018.

Publications

• With Neelima, $L^p$-estimates and regularity for SPDEs with monotone semilinearity, Stoch PDE: Anal. Comp., 2019, (arXiv version).
• With Neelima, Coercivity condition for higher order moments of nonlinear SPDEs and existence of solution under local monotonicity, Stochastics, 2019, (arXiv version).
• With I. Gyöngy, Itô Formula for Processes Taking Values in Intersection of Finitely Many Banach Spaces, Stoch. PDE: Anal. Comp., 5(3), 428-455, 2017, (open access).
• With E. Emmrich, Nonlinear stochastic evolution equations of second order with damping, Stoch. PDE: Anal. Comp., 5(1), 81-112, 2017, (arXiv version).
• With I. Gyöngy and S. Sabanis, Convergence of tamed Euler schemes for a class of stochastic evolution equations, Stoch. PDE: Anal. Comp., 4(2), 225-245, 2016, (open access).
• With E. Emmrich and A. Wroblewska-Kaminska, Equations of second order in time with quasilinear damping: existence in Orlicz spaces via convergence of a full discretisation, Math. Methods Appl. Sci., 39(10), 2449-2460, 2016, (preprint version)..
• With E. Emmrich and M. Thalhammer, On a full discretisation for nonlinear second-order evolution equations with monotone damping: construction, convergence, and error estimates, Found. Comput. Math., 2015.
• With E. Emmrich, Evolution equations of second order with nonconvex potential and linear damping: existence via convergence of a full discretization, J. Diff. Eq., 255(10), 3719-3746, 2013.
• With E. Emmrich, Full discretization of the porous medium/fast diffusion equation based on its very weak formulation, Commun. Math. Sci., 10(4), 1055-1080, 2012.
• Error estimates for finite difference approximations of American put option price, CMAM, 12(1), 108-120, 2012.
• With E. Emmrich, Full discretization of second-order nonlinear evolution equations: strong convergence and applications to elasticity theory, CMAM, 11(4),441-459, 2011.
• With I. Gyöngy, On Finite-Difference Approximations for Normalized Bellman Equations, Appl. Math. Optim., 60(3), 297-339, 2009, (arXiv version).
• With I. Gyöngy, On Randomized Stopping, Bernoulli, 14(2), 352–361, 2008.

Talk Slides

I will try to keep slides for some recent talks here.

• Learning to price and hedge path-dependent derivatives (Machine learning in finance conference, Oxford - 17th September 2019).
• Mean-field Langevin dynamics in the energy landscape of neural networks (Mittag-Leffler - May 2019, Oxford - June 2019).

Other

• PhD. Thesis: Numerical approximations of stochastic optimal stopping and control problems
• MSc. Thesis: Stochastic Differential Equations Driven by Fractional Brownian Motion – a White Noise Distribution Theory Approach
• Lecture notes for RNAP as taught in 2016/17: Risk-Neutral Asset Pricing.
• Lecture notes for MCM as taught in 2016/17: Monte-Carlo Methods.
• Lecture notes for SCDAA as being taught in 2019/20 (perpetual draft): Stochastic Control and Dynamic Asset Allocation.
• S. Cohen, I. Gyöngy, G. dos Reis, D. Siska and L. Szpruch (eds.) Frontiers in Stochastic Analysis – BSDEs, SPDEs and their Applications, Springer, 2019.
• G. Danezis, D. Hrycyszyn, B. Mannerings, T. Rudolph, D. Siska Vega Protocol Whitepaper. 2018.

Teaching

• In 2019/20 I am teaching Stochastic Control and Dynamic Asset Allocation.
• I am supervising student projects for the MSc in Computational and Financial Mathematics and MSc in Financial Modelling and Optimization.

Past Teaching

• In 2018/19 I taught Stochastic Control and Dynamic Asset Allocation.
• In 2017/18 I taught Stochastic Control and Dynamic Asset Allocation.
• In 2017/18 I was one of the team teaching OOPA (materials on Learn).
• In 2017/18 I was one of the team teaching SMSTC Foundations of Probability for PhD students.
• In 2016/17 I taught Monte Carlo Methods and Simulation.
• In 2016/17 taught Risk Neutral Asset Pricing.
• In 2016/17 taught Object-Oriented Programming with Applications .
• In 2016/17 I was one of the team teaching SMSTC Probability I for PhD students.
• In 2015/16 I taught Risk Neutral Asset Pricing.
• In 2015/16 I taught Object-Oriented Programming with Applications .
• In 2015/16 I was one of the team teaching SMSTC Probability I for PhD students.
• In 2014/15 I taught Risk Neutral Asset Pricing (requires UoE login).