Dr Peter JackoLecturer
Peter Jacko is devoted to the development of the science of the solution of problems in the design and management of complex systems such as business decision-making, public health processes, and communications networks. Much of his research work benefits from the interaction of mathematics, statistics, computing, and/or economics. His publications contribute to areas such as operational research, performance evaluation, stochastic modelling, queueing theory, applied probability, and machine learning. These subjects provide foundation for the modern fields of business analytics and data science.
Peter Jacko is a Lecturer in the Department of Management Science at Lancaster University Management School, UK. He is also member of the Data Science Institute and STOR-i Doctoral Training Centre at Lancaster University. He joined Lancaster University in 2013 under the LANCS Initiative. Between 2009 and 2018 he was affiliated with the BCAM - Basque Center for Applied Mathematics, Spain, where he was formerly a postdoctoral fellow, researcher, co-leader, and an external scientific member in the Networks research group, in Data Science research area. He received his Ph.D. in Business Administration and Quantitative Methods (2009) and D.E.A. in Statistics and Operations Research (2006) from the Universidad Carlos III de Madrid, Spain. He received his Mgr. (2003) and Bc. (2002) degrees in Mathematics from the Univerzita P.J. Šafárika v Košiciach, Slovakia.
Peter Jacko is devoted to the development of the science of the solution of problems in the design and management of complex systems such as business decision-making, public health processes, and communications networks. The leading themes of his research activities are stochastic modelling of real problems and the design of tractable and well-performing solutions for efficient allocation of scarce resources over time.
His research interests are in:
- Fields: Mathematics, Computer Science, Economics & Business, Engineering, Business Analytics, Data Science
- Areas: Operational Research, Performance Evaluation, Stochastic Modelling, Queueing Theory, Applied Probability, Machine Learning
- Problems: Resource Allocation, Scheduling, Sequential Learning, Networks Optimisation, Multi-armed Bandits
- Methods: Markov Decision Processes, Dynamic Programming, Stochastic/Bayesian Analysis, Heuristics Design
His research efforts have been motivated by and the results are aimed to apply to:
- Business Decision-Making: Retail Industry, Contact Centres
- Public Health Processes: Adaptive Clinical Trials, Personalised Medicine
- Communications Networks: Wireless Data Networks (D2D, 4G LTE), Internet (TCP, ICN)
Personal webpage: http://www.lancaster.ac.uk/staff/jacko/
PhD Supervision Interests
I always welcome students with strong quantitative background interested in solving problems in the design and management of complex systems, where interaction of mathematics, statistics, computing, and/or economics is often beneficial. In particular, you will be looking for carrying out research in areas such as operational research, performance evaluation, stochastic modelling, queueing theory, applied probability, and/or machine learning, motivated by real-world problems in business decision-making, public health proccesses or communications networks. PhD funding is available through the Department of Management Science and through the STOR-i Doctoral Training Centre. Currently I am specifically looking for students interested in research on the optimal design and conduct of modern adaptive clinical trials (such as platform, umbrella and basket trials), which can be modelled as Bayesian multi-armed bandit problems. If you are a self-funded PhD applicant or a master/PhD student elsewhere interested in visiting me for a short period, please contact me directly by e-mail. I currently co-supervise Faye Williamson on "Bayesian Bandit Models for the Optimal Design of Clinical Trials" at STOR-i (exp. 2019) Jake Clarkson on "Optimal Search Accounting for Speed and Detection Capability" at STOR-i (exp. 2019), Stephen Ford on "Dynamic allocation of assets subject to failure or depletion" at STOR-i (exp. 2020), Francis Garuba on "Robust and Stochastic Optimisation Approaches to Network Capacity Expansion and QoS Improvement" at Dept of Management Science (exp. 2019), and Ugur Satic on "Simulation and Optimization of Scheduling Policies in Dynamic Stochastic Resource-Constrained Multi-Project Environments" at Dept of Management Science (exp. 2020).
Selected Publications Show all 27 publications
Scheduling of multi-class multi-server queueing systems with abandonments
Ayesta, U., Jacko, P., Novak, V. 04/2017 In: Journal of Scheduling. 20, 2, p. 129-145. 17 p.
Maximal flow-level stability of best-rate schedulers in heterogeneous wireless systems
Jacko, P., Morozov, E., Potakhina, L., Verloop, I.M. 01/2017 In: Transactions on Emerging Telecommunications Technologies. 28, 1, 15 p.
Generalized restless bandits and the knapsack problem for perishable inventories
Graczová, D., Jacko, P. 05/2014 In: Operations Research. 62, 3, p. 696-711. 16 p.
Nearly-optimal scheduling of users with Markovian time-varying transmission rates
Cecchi, F., Jacko, P. 05/2016 In: Performance Evaluation. 99-100, p. 16-36. 21 p.
A Bayesian adaptive design for clinical trials in rare diseases
Williamson, F., Jacko, P., Villar, S.S., Jaki, T.F. 09/2017 In: Computational Statistics and Data Analysis. 113, p. 136-153. 17 p.
Development of solutions to tea production problems
02/11/2015 → 31/08/2017
Virtual Machines for the Traffic Analysis in High-Capacity Networks
01/01/2013 → 01/01/2013
Efficient Control Methods and Algorithms for Dynamic Resource-Sharing Systems
01/01/2011 → 01/01/2013
Jake ClarksonPhD student
STOR-i Centre for Doctoral Training
Stephen FordPhD student
STOR-i Centre for Doctoral Training
Francis GarubaPhD student
Ugur SaticPhD student, Associate Lecturer
Faye WilliamsonPhD student
Optimisation, Statistical Methods in Medicine, STOR-i Centre for Doctoral Training
- Health Systems
- Operational Research and Operations Management
- STOR-i Centre for Doctoral Training