Dr Peter JackoSenior Lecturer
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, approximate dynamic programming, 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 Senior 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 (mathematics, computing, statistics, etc.) background interested in solving problems in the design and management of complex systems. In particular, you will be looking for carrying out research in areas such as operational research, performance evaluation, stochastic modelling, approximate dynamic programming, queueing theory, applied probability, and/or machine learning, motivated by real-world problems in business decision-making, public health proccesses or communications networks. Currently I am specifically looking for students interested in research on the optimal design and conduct of sequential experiments and modern adaptive clinical trials (such as platform, umbrella and basket trials), which can be modelled as multi-armed bandit problems. I have co-supervised around 10 PhD students, some of which have received awards for their research. PhD funding is available through the Department of Management Science and through the STOR-i Doctoral Training Centre. 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.
The Finite-Horizon Two-Armed Bandit Problem with Binary Responses: A Multidisciplinary Survey of the History, State of the Art, and Myths
Jacko, P. 1/06/2019 Lancaster : Lancaster University Management School, 44 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.
On model-based time trend adjustments in platform trials with non-concurrent controls
Roig, M.B., Krotka, P., Burman, C., Glimm, E., Gold, S.M., Hees, K., Jacko, P., Koenig, F., Magirr, D., Mesenbrink, P., Viele, K., Posch, M. 15/08/2022 In: BMC Medical Research Methodology. 22, 1, 16 p.
SIMPLE—A modular tool for simulating complex platform trials
Meyer, E., Mielke, T., Parke, T., Jacko, P., Koenig, F. 30/09/2023 In: SoftwareX. 23, p. 101515.
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.
Modelling and Simulation of Clinical Trial Designs
26/02/2018 → 31/08/2018
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
- Centre for Health Futures
- DSI - Foundations
- Health Systems
- Simulation and Stochastic Modelling
- STOR-i Centre for Doctoral Training