Bio
Sedar was a PhD student at the University of Leeds, department of Geography. He graduated in Computer Science at King’s College London 2018. From a very early stage of his degree, he focused on artificial intelligence planning implementations on drones in a search and rescue domain, and this was his first formal attempt to study artificial intelligence. He participated in summer school at Boğaziçi University in Istanbul working on programming techniques to reduce execution time. During his final year, he concentrated on how argumentation theory with natural language processing can be used to optimise political influence. In the midst of completing his degree, he applied to Professor Alison Heppenstall's research proposal focusing on data analytics and society, a joint endeavour with the Alan Turing Institute and the Economic and Social Research Council. From 2018 - 2023 he will be working on his PhD at the Alan Turing Institute and Leeds Institute for Data Analytics.
Research interests
The main findings from Sedar's doctoral research are split into three parts. Firstly, agents can learn intelligent behaviours from their environment. Secondly, learnt behaviours were in agreement with theoretical and empirical findings from published literature. Thirdly, agents can adapt to previously unbeknown situations and perform relatively well. Overall, this thesis demonstrates that when integrated with agent-based models, neurologically inspired decision-making algorithms can enhance models by introducing learning and adaptability, making these models better placed to support complex real-world decision-making.
Selected publications and papers
CONFERENCES
- GISRUK 2019, Modelling the Supply and Demand of Police in the UK: Sedar Olmez, Dan Birks and Alison Heppenstall - Newcastle.
- GISRUK 2022, Towards Modelling Energy Demand of Vehicles in Cities: an Agent-Based Method: Sedar Olmez, Annabel Whipp, Ellie Marfleet, Jason Thompson, Keiran Suchak, Alison Heppenstall, Rajith Vidanaarachchi - Liverpool - https://zenodo.org/record/6408394.
- AGU Fall Meeting 2019 San Francisco, Application of Statistical Modelling to a Field Experiment Database to Predict N 2 O Emissions in Sugarcane Production: Marcelo Valadares Galdos, Johnny Soares, Sedar Olmez, Vitor Vargas, Iracema Degaspari, Janaina Braga do Carmo, Heitor Cantarella - https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/554099
PAPERS
- Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study, arXiv: Sedar Olmez, Alison Heppenstall, Dan Birks: https://arxiv.org/abs/2201.01099
- An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space, Energies: Sedar Olmez, Jason Thompson, Ellie Marfleet, Keiran Suchak, Alison Heppenstall, Ed Manley, Annabel Whipp, Rajith Vidanaarachchi: https://www.mdpi.com/1996-1073/15/11/4031
- Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent-Based Modelling Approach, Applied Sciences: Sedar Olmez, Liam Douglas-Mann, Ed Manley, Keiran Suchak, Alison Heppenstall, Dan Birks, Annabel Whipp: https://www.mdpi.com/2076-3417/11/12/5336
OPEN-SOURCE MODELS
- PwC UK Housing Market Agent-Based Model in Python 2022: Sedar Olmez, https://github.com/SedarOlmez94/Pythonic_UK_Housing_Market_ABM_2022/tree/v.1.0.0
- An Agent-Based Reinforcement Learning Model of Burglary (ARLMB): Sedar Olmez, https://zenodo.org/record/6722701
- 3D Urban Traffic Simulator (ABM) in Unity: Sedar Olmez, Obi Thompson Sargoni, Alison Heppenstall, Daniel Birks, Annabel Whipp and Ed Manley, https://www.comses.net/codebases/32e7be8c-b05c-46b2-9b5f-73c4d273ca59/releases/1.0.0/
- Energy Calculation Extension for the article: An Agent-Based Simulation of Heterogeneous Driver Behaviour and its Impact on Electric Energy Consumption in Urban Space: Sedar Olmez and Keiran Suchak, https://codeocean.com/capsule/9598578/tree/v1
Achievements and awards
- Broadening Horizons scholarship 2016 - for collaborating with students at the Istanbul Technical University
- King's Planning Competition Award 2017 - for developing JavaFF planner to use a parallel algorithm to solve problems faster and more efficiently.
- Hack King’s 4.0 MLH finalist medal 2018 - for developing a collaborative video game, allowing viewers to change the game's outcome.
