Shahin is a Data Scientist with Software Engineering skills that have been honed over two decades. He has multidisciplinary experience in both industry and academia, where he has demonstrated innovation and leadership.
He has authored books and peer-reviewed publications on the subjects of Data Science, Visualisation, and Evolutionary Computation. He is
the founder of PlotAPI.com, enabling the creation of engaging interactive visualisations that have been used in publications by academia, government, and industry.
Academic Qualifications & Recognitions
PhD, Artificial Intelligence
Manchester Metropolitan University, UK
Preference Driven Multi-Objective Evolutionary Computation of Neural Networks
for Concealed Weapon
Leadership & Management
Oracle (SQL, PL/SQL)
(NumPy, Pandas, Scikit-learn)
Engineer, Data Science
May 2022–Apr 2023
Datapane Inc., US
Worked on the core SDK and looked after everything related to the data science experience.
Head of Data Science
Feb 2018–Aug 2021
Xim Limited (Digital Health), UK
Formerly Lead Data Science Consultant. Innovation, R&D leadership, Data science
growth strategy, Technical oversight.
in Data Science
Aug 2014–Mar 2021
Bournemouth University, UK
Research leadership, Funded projects, Course
design & leadership, Staff mentor.
Selected Consulting History
Jun 2021–Jan 2022
Digital Health Company, UK
Experiment design, Technology roadmap, Strategy; Risk.
Apr 2021–May 2021
Medical University of Vienna, Austria
Custom visualisation for time series from multiple organs.
Dec 2019–Jan 2020
DSTL Project, Defence Company, UK
Modelling multi-sensor data to detect unexploded ordnance.
Data Science Consultant
Sep 2019–Feb 2020
Digital Health Company, UK
Modelling EEG & CBF data to diagnose cognitive impairment.
May 2020–July 2020
Strapi Inc., USA
Design & production of training videos for enterprise offering.
Dr. Shahin Rostami · (+44) 75151 27377 · firstname.lastname@example.org
Data Visualisation SaaS (App)
Producing engaging interactive visualisations that have been used in publications by academia, government, and industry.
A book on evolutionary algorithms that teaches you the concepts and how they’re implemented in practice.
with Rust Notebooks
A book on data analysis with rust notebooks that teaches you the concepts and how they’re implemented in practice.
Data is Beautiful
A practical book on data visualisation that shows you how to create visualisations that are engaging and beautiful.
Visualisation with D3.js
A book on visualisation with D3.js that shows you how to create visualisations from the ground up.
- Daniel Dimanov, Bournemouth University, Thesis: “Multi-Objective Concealed Weapon Detection”, 1st supervisor. Completed.
- Mohammad Naiseh, Bournemouth University, Thesis: “Explainable AI Interfaces to enhance trust calibration”, 3rd supervisor. Completed.
- Mohammed Alqurashi, Bournemouth University, Thesis: “An IDS for IoT Enabled Devices”, 2nd supervisor. Completed.
- Waqas Jamil, Bournemouth University, Thesis: “Sketches and Online Learning”, 2nd supervisor. Completed.
Selected Refereed Journal Articles & Reports
- Neri, F., & Rostami, S. (2021). Generalised Pattern Search Based on Covariance Matrix Diagonalisation. SN Computer Science, 1-22.
- Rostami, S., Neri, F., & Gyaurski, K. (2020). On Algorithmic Descriptions and Software Implementations for Multi-objective Optimisation: A Comparative Study. SN Computer Science, 1(5), 1-23.
- Katos, V., & Rostami, S. et al (2019). STATE OF VULNERABILITIES 2018/2019 - Analysis of Events in the life of Vulnerabilities. European Union Agency for Cybersecurity (ENISA).
- Rostami, S., & Neri, F. (2017). A fast hypervolume driven selection mechanism for many-objective optimisation problems. Swarm and evolutionary computation, 34, 50-67.
- Rostami, S., Neri, F., & Epitropakis, M. (2017). Progressive preference articulation for decision making in multi-objective optimisation problems. Integrated Computer-Aided Engineering, 24(4), 315-335.
- Tsimperidis, I., Rostami, S., & Katos, V. (2017). Age detection through keystroke dynamics from user authentication failures. International Journal of Digital Crime and Forensics (IJDCF), 9(1), 1-16.
- Rostami, S., & Neri, F. (2016). Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm. Integrated Computer-Aided Engineering, 23(4), 313-329.
- Rostami, S., & Shenfield, A. (2017). A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing, 21(17), 4963-4979.
- Rostami, S., O’Reilly, D., Shenfield, A., & Bowring, N. (2015). A novel preference articulation operator for the evolutionary multi-objective optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520.
Selected Refereed Conference Proceedings
- Rostami, S., Kleszcz, A., Dimanov, D., & Katos, V. (2020, October). A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities. In International Conference on Multimedia Communications, Services and Security (pp. 25-36). Springer, Cham.
- Tsimperidis, I., Rostami, S., Wilson, K., & Katos, V. (2020, September). User Attribution Through Keystroke Dynamics-Based Author Age Estimation, International Networking Conference (pp. 47-61). Springer, Cham.
- Neri, F., & Rostami, S. (2020, April). A Local Search for Numerical Optimisation Based on Covariance Matrix Diagonalisation, International Conference on the Applications of Evolutionary Computation (Part of EvoStar) (pp. 3-19). Springer, Cham.
- Dimanov, D. and Rostami, S. (2019). KOSI- Key Object Detection for Sentiment Insights, 19th annual UK workshop on computational intelligence, 4-6 September 2019, Portsmouth, UK.
- Stubbs, R. and Rostami, S. (2019). Hyper-parameter Optimisation by Restrained Stochastic Hill Climbing, UKCI 2019: 19th annual UK workshop on computational intelligence, 4-6 September 2019, Portsmouth, UK.
- Saul, M.A. and Rostami, S. (2018). A Comparison of Resampling Techniques for Pattern Classification in Imbalanced Data-Sets, UKCI 2018: 18th annual UK workshop on computational intelligence, 5-7 September 2018, Nottingham, UK.
- Wilson, K. and Rostami, S. (2018). On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation Algorithms, UKCI 2018: 18th annual UK workshop on computational intelligence, 5-7 September 2018, Nottingham, UK.
- Nava, T., Rostami, S. and Smyth, B. (2018). Knowing the unknown: visualising consumption blind-spots in recommender systems. In: SAC 2018 The 33rd ACM/SIGAPP Symposium On Applied Computing, 9-13 April 2018, Pau, France.
- Shenfield, A., & Rostami, S. (2017). Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)..
- Shenfield, A., & Rostami, S. (2015). A multi objective approach to evolving artificial neural networks for coronary heart disease classification. 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Niagara Falls, ON.
- Rostami, S., Shenfield, A., Sigurnjak, S., & Fakorede, O. (2015). Evaluation of mental workload and familiarity in human computer interaction with integrated development environments using single-channel EEG. In Proceedings of PPIG 2015-26th Annual Workshop.
- Rostami, S., Delves, P., & Shenfield, A. (2013). Evolutionary Multi-Objective Optimisation of an Automotive Active Steering Controller. In Science and Engineering Research Symposium (pp. 1-3).
- Rostami, S., & Shenfield, A. (2012, September). Cma-paes: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation. In Computational Intelligence (UKCI), 2012 12th UK Workshop on (pp. 1-8). IEEE.
- Rostami, S., & Shenfield, A. (2012). Adaptive Grid Archiving Combined with the Covariance Matrix Adaptation Evolution Strategy.
2 of 2