I was born in Athens, Greece, in 1985. I received a Diploma in Chemical Engineering in 2007 and an MSc with honours in Applied Mathematics in 2009 from NTU Athens. In December 2012, I defended my PhD thesis titled "Modelling and Control of Biological and Physiological Systems" at NTU Athens. In January 2013 I joined the Dynamical Systems, Control and Optimization research unit at IMT Lucca as a post-doctoral Fellow. Afterwards, I worked as a post-doctoral researcher at ESAT, KU Leuven. I am currently a post-doctoral researcher at KIOS Center of Excellence, University of Cyprus. My research focuses on model predictive control and numerical optimization.

Pantelis Sopasakis

KIOS Research Center

University of Cyprus

1 Panepistimiou Avenue

2109 Aglantzia, Nicosia, Cyprus

Email: my surname dot my name at ucy dot ac dot cy

- P. Sopasakis, A. Themelis, J. Suykens and P. Patrinos, A Primal-dual line search method and applications in image processing, European Signal Processing Conference 2017 (arxiv, bibtex)
- L. Stella, A. Themelis, P. Sopasakis and P. Patrinos, A simple and efficient algorithm for nonlinear model predictive control, IEEE CDC, 2017.
- A.K. Sampathirao, P. Sopasakis, A. Bemporad and P. Patrinos, Proximal Quasi-Newton methods for scenario-based stochastic optimal control, IFAC 2017 (arxiv, bibtex).
- A.K. Sampathirao, P. Sopasakis, A. Bemporad and P. Patrinos, GPU-accelerated stochastic predictive control of drinking water networks, IEEE Trans. Control Systems Technology 26(2):551-562, 2018 (arxiv, bibtex).
- A.K. Sampathirao, P. Sopasakis, A. Bemporad and P. Patrinos, Fast parallelizable scenario-based stochastic optimization, 4th European Conference on Computational Optimization, Leuven, Belgium, Sept. 2016 (slides, bibtex).
- A. K. Sampathirao, P. Sopasakis, A. Bemporad and P. Patrinos (2015), Distributed solution of stochastic optimal control problems on GPUs, 54 IEEE Conf. Decision and Control, Osaka, Japan, Dec 2015 (pdf, slides, errata, bibtex).
- P. Patrinos, P. Sopasakis and H. Sarimveis, A global piecewise smooth Newton method for fast large-scale model predictive control, Automatica 47(9), pp. 2016-2022. (bibtex)
- P. Sopasakis, N.Freris and P. Patrinos, Accelerated reconstruction of a compressively sampled data stream, 24th European Signal Processing conference, Budapest, Hungary, 2016 (pdf, slides, bibtex).

- D. Herceg, P. Sopasakis, A. Bemporad and P. Patrinos, Risk-averse model predictive control, (submitted to Automatica, under review), 2018. (arxiv, bibtex)
- S. Ntouskas, H. Sarimveis, P. Sopasakis, Model predictive control for offset-free reference tracking of fractional order systems, Control Engineering Practice, 2018.
- P. Sopasakis and H. Sarimveis, Stabilising Model predictive control for discrete-time fractional-order systems, Automatica 75, 2017, pp. 24-31 (arxiv, bibtex).
- P. Sopasakis, D. Herceg, P. Patrinos and A. Bemporad, Stochastic economic model predictive control for Markovian switching systems, IFAC World Congress 2017 (arxiv, bibtex).
- P. Patrinos, P. Sopasakis, H. Sarimveis, A. Bemporad (2014), Stochastic Model Predictive Control for Constrained Discrete-time Markovian Switching Systems, Automatica 50(10), 2504-2514, (doi, bibtex).
- P. Sopasakis, P. Patrinos, H. Sarimveis and A. Bemporad (2014), Model predictive control for linear impulsive systems, IEEE Transactions on Automatic Control 60(8), pp. 2277-2282, 2015 (bibtex)
- P. Sopasakis, P. Patrinos, H. Sarimveis and A. Bemporad (2012), Model predictive control for linear impulsive systems, 51st Conference on Decision and Control, Maui, Hawaii. (bibtex)
- P. Sopasakis, P. Patrinos and H. Sarimveis (2014), MPC for sampled-data linear systems: Guaranteeing constraint satisfaction in continuous time, IEEE Transactions on Automatic Control 59(4), pp. 1088-1093, 2014 (bibtex)
- P. Sopasakis, P. Patrinos and H. Sarimveis (2014), Robust model predictive control for optimal continuous drug administration, Computer Methods and Programs in Biomedicine 116(3), pp. 193-204 (bibtex).
- P. Sopasakis, S. Ntouskas and H. Sarimveis (2015), Robust model predictive control for discrete-time fractional-order systems, Mediteranean Conference on Control and Automation (MED 2015, IEEE), Torremolinos, Spain, June 16-19, 2015 (pdf, slides, bibtex).
- A.K. Sampathirao, P. Sopasakis and A. Bemporad (Oct. 2014), Decentralised hierarchical multi-rate control of large-scale drinking water networks 9th Int. Conf. Critical Information Infrastructures Security, Limassol, Cyprus, Oct. 13-15 2014 (pdf, bibtex)
- P. Sopasakis, D. Bernardini and A. Bemporad (Dec. 2013), Constrained model predictive control based on reduced-order models, 52nd IEEE Conference on Decision and Control, Florence, Italy. (slides, bibtex)
- P. Patrinos, P. Sopasakis and H. Sarimveis (2011), Stochastic model predictive control for constrained networked control systems with random time delays, 18th IFAC World Congress, Milano, Italy. (pdf, bibtex)

- P. Sopasakis, H. Sarimveis, P. Macheras, A. Dokoumetzidis, Fractional calculus in pharmacokinetics, Journal of pharmacokinetics and pharmacodynamics 45 (1), 107-125, 2018.
- D. Herceg, S. Ntouskas, P. Sopasakis, A. Dokoumetzidis, P. Macheras, H. Sarimveis and P. Patrinos (2017), Modeling and administration scheduling of fractional-order pharmacokinetic systems, IFAC World Congress 2017 (arxiv, bibtex).
- P. Sopasakis, P. Patrinos, H. Sarimveis (2014), Robust model predictive control for optimal continuous drug administration, Computer Methods and Programs in Biomedicine 116(3), pp. 193-204 (bibtex).
- P. Sopasakis and H. Sarimveis (Aug. 2014), Controlled Drug Administration by a Fractional PID, 19th IFAC World Congress, Cape Town, South Africa (pdf, slides, bibtex)
- P. Sopasakis and H. Sarimveis (2012), An integer programming approach for optimal drug dose computation, Computer Methods and Programs in Biomedicine, 108 (3). pp. 1022-1035. (bibtex)
- N. Jeliazkova, C. Chomenides, P. Doganis, B. Fadeel, R. Glafstrom, B. Hardy, J. Hastings, M. Hegi, V. Jeliazkov, N. Kochev, P. Kohonen, C. Munteanu, H. Sarimveis, B. Smeets, P. Sopasakis, G. Tsiliki, D. Vorgimmler and E. Willighagen (2015), The eNanoMapper database for nanomaterial safety information, Beil. Jour. Nanotechnology (pdf, bibtex)
- B. Hardy, P. Sopasakis et al. (2010), Collaborative development of predictive toxicology applications, Journal of Cheminformatics, 2(7). (bibtex)
- I. Tetko, P. Sopasakis, P. Kunwar, S. Brandmaier, S. Novotarskyi, L. Charochkina, V. Prokopenko and W. Peijenburg (2013), Prioritization of Polybrominated Diphenyl Ethers (PBDEs) using the QSPR-Thesaurus Web Tool, ATLA, 40, pp. 1-9. (bibtex)

- A.S. Sathya, P. Sopasakis, R. Van Parys, A. Themelis, G. Pipeleers and P. Patrinos, Embedded nonlinear model predictive control for obstacle avoidance using PANOC, European Control Conference (ECC), Limassol, Cyprus, June 2018.
- P. Sopasakis, A.K. Sampathirao, A. Bemporad and P. Patrinos, Uncertainty-aware demand management of water distribution networks in deregulated energy markets, Environmental Modelling & Software, 2018. (github).
- A.K. Sampathirao, J.M. Grosso, P. Sopasakis, C. Ocampo-Martinez, A. Bemporad and V. Puig (2014), Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study, 19th IFAC World Congress, Cape Town, South Africa (pdf, slides, bibtex)
- C. A. Hans, P. Sopasakis, A. Bemporad, J. Raisch and C. Reincke-Collon, Scenario-based model predictive operation control of islanded microgrids, 54 IEEE Conf. Decision and Control, Osaka, Japan, Dec 2015 (pdf, bibtex).
- D. Herceg, G. Georgoulas, P. Sopasakis, M. Castano, P. Patrinos, A. Bemporad, J. Niemi and G. Nikolakopoulos, Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry, IEEE MED 2017, 2017 (bibtex).
- P. Sopasakis, D. Bernardini, H. Strauch, S. Bennani and A. Bemporad, Sloshing-aware attitude control of impulsively actuated spacecraft, (tutorial paper), European Control Conference (IFAC), Linz, Austria, July 15-17, 2015 (pdf, slides, bibtex).
- P. Sopasakis, D. Bernardini, H. Strauch, S. Bennani and A. Bemporad, A Hybrid Model Predictive Control Approach to Attitude Control with Minimum-Impulse-Bit Thrusters, European Control Conference (IFAC), Linz, Austria, July 15-17, 2015 (pdf, slides, bibtex).

I am currently a TA for the students' project P&O Eagle at ESAT, KU Leuven. P&O Eagle is a drone with computer vision and navigation capabilities.

In the winter semester of 2016 I gave the PhD course
advanced topics in control systems
in IMT Lucca. The course focused on **economic model
predictive control**, control of linear and nonlinear **Markovian switching systems**,
**risk measures** and **risk-averse optimal control**.

We proudly present **SuperSCS**: a super-(linearly) fast solver for conic
optimization problems. Optimization software often cast convex problems into
conic ones and delegate their solution to solvers in the backend.
Based on the implementation of the popular solver **SCS**, we implemented
a new algorithm which leads to manifold speed-ups.

This is a CUDA-implementation of an dual accelerated proximal gradient algorithm for scenario-based model predictive control problems. The structure of the optimization problem is employed to accelerate computations; very high speedups (ranging from x10 up to x100) are observed compared to the corresponding GPU implementation.

GPU, Numerical OptimizationA remarkably fast algorithm for recursive compressive sensing which is about one order of magnitutde faster compared to state-of-the-art algorithms (L1LS, ISTA, FISTA).

Numerical OptimizationDrinking water networks are large-scale systems whose operation is characterised by complex uncertainty patterns. Pressures across the network need to be kept in certain limits, tanks must not overflow and water demand requirements must be met, while unexpectedly high demands and electricity prices may signal the alarm in the control centre of the network. Scenario-based stochastic model predictive control is an advanced control methodology which can address the problem of controlling such systems very efficiently leading to a considerable reduction in operating costs and a higher quality of service.

Stochastic MPC, Control, DWNJAQPOT Quattro is a predictive toxicology platform on the web which combines machine learning, cheminformatics, linked data and web technologies with the OpenTox and eNanomapper APIs to deliver a generic and interoperable solution for drug discovery.

Software, ChemiformaticsThis is an ANSI C embedded control framework for nonlinear model predictive control which implements a very fast nonconvex optimization solver with convergence guarantees that significantly outperforms SQP/SCP approaches. It is particularly suitable for robotic applications.

Software, Optimization, Control