Dr Paul Yoo

Positions held:

Reader in Computer Science, School of Computing and Mathematical Sciences
Head, Data Science and AI Group; School of Computing and Mathematical Sciences
Dy Director, BIDA; School of Computing and Mathematical Sciences

Address

Room 168, Malet Street

Email

Birkbeck: p.yoo@bbk.ac.uk

Overview
Overview

Biography

Prior to his current post at Birkbeck (Univ. of London), Paul has held academic and research positions at esteemed institutions such as Cranfield (Defence Academy of the UK), Sydney (USyd) and South Korea (KAIST). He was trained originally as a data scientist with degrees from the University of Sydney, Australia, and has since published over one hundred papers in prestigious journals and conferences. He has also overseen over US$2.5 million in project funding as the PI and has received various national and international awards for his work in advanced data analytics, machine learning, and secure systems research. These include the IEEE Outstanding Leadership Award, Rozetta Award (formerly CMCRC), Emirates Foundation Research Award, and the ICT Fund Award. Most recently, he was awarded the Samsung award for his research on novel abstraction machine-learning approach [news], Research England’s Global Challenge Research Fund (GCRF), as well as the MPU-Macau Fund.
Paul serves as an Associate Editor for several high-ranked journals, including ACM Computing Surveys (Q1), IEEE Transactions on Sustainable Computing (Q1) and IEEE Access (Q1). He has previously served as an Editor for IEEE COMML (Q1) in the areas of big data and machine learning from 2014 to 2019. He is also affiliated with the University of Sydney and Korea Advanced Institute of Science and Technology (KAIST) as a Visiting Professor. Paul is a Senior Member of the IEEE and a Fellow of HEA.
As the Founder and Chair of BIDA's Threat Intelligence Lab, Paul leads a team dedicated to merging advancements in machine learning with the evolution of cybersecurity. The lab is at the forefront of developing the next generation of intelligent cyber defense mechanisms. It fosters a cutting-edge security research environment that focuses on both reactive detection and proactive prediction of threats. The lab currently houses 11 PhD students, and recent photos of their lab meetings can be found here.
Paul’s research is centred around the theory and methodology of machine learning for large-scale real-world problems. He has successfully applied various machine learning and big data analytic approaches to diverse problem domains, such as psychology, security, biology, finance, and manufacturing.
Paul has also supervised a number of PhD/MSc by Research students to completion, and he welcomes inquiries about research degree (PhD/MPhil) and internship opportunities.

Qualifications
- PhD in Engineering, University of Sydney, Australia, 2009
- PgCert in Academic Practice, Cranfield University, 2019
Web profiles
- Personal Web Profile
Administrative responsibilities
- Dy Director (Knowledge Exchange), Birkbeck Institute for Data Analytics (BIDA)
- Head, Data Science and AI Group
- Chair, Threat Intelligence Lab
- Programme Director, Digital Tech Solutions (Software)
Professional memberships
- Senior Member, IEEE
- Fellow, HEA
Honours and awards
- Samsung Global Research Outreach Award, Samsung, January 2017
- IEEE Outstanding Leadership Award, IEEE, January 2013
- Rozetta Award, Rozetta Institute, Australia, July 2006
ORCID
0000-0001-7665-8616
Supervision and teaching
Supervision and teaching

Supervision

Supervisions within Birkbeck:

12

Current doctoral researchers
- ZAID ALMAHMOUD
  
  Nature of supervision:
  Principal Supervisor, Oct 2021 onwards
- JOHN OMOKORE
  
  Nature of supervision:
  Principal Supervisor, Oct 2022 onwards
- THIAGO SUZUKI
  
  Nature of supervision:
  Principal Supervisor, Oct 2022 onwards
- ALBERTO MATUOZZO
  
  Nature of supervision:
  Principal Supervisor, Oct 2020 onwards
  
  Title of thesis:
  Machine Learning techniques for equity markets: from custom loss functions in predictive modelling, to dynamic network ensemble learning for capital deployment.
  
  Description:
  Machine learning techniques are the underpinning of the transition to data driven finance: the aim is not to replace economic theory or corporate finance, but rather enhance them, deploying models that can learn from non linear, discontinuous or hierarchical data structures and that can foster the engineering of features that could not be observed directly.
  This research explores an area at the intersection of finance and computer science, investigating machine learning techniques for equity markets prediction, processing signals from different set of features (statistical, technical, fundamental and sentiment). The aim is to research the development and application of techniques that can have an impact in the financial industry.
  This research aims to propose solutions to tackle the problems of equity market forecasting given the fact that securities prices undergo different regimes, and price formation is affected by several factors of different nature (micro, macro, sentiment).
- DILARA UYSAL
  
  Nature of supervision:
  Principal Supervisor, Jan 2021 onwards
- GASSO MWALUSEKE
  
  Nature of supervision:
  Principal Supervisor, Oct 2020 onwards
- GURMENDER SINGH ATWAL
  
  Nature of supervision:
  Principal Supervisor, Jan 2020 onwards
- LAWRENCE OLUSANYA
  
  Nature of supervision:
  Principal Supervisor, Oct 2020 onwards
- MARIO RODRIGUEZ URETA
  Nature of supervision:
  Principal Supervisor, Oct 2023 onwards
  
  Title of thesis:
  Digital Twins of Macroeconomic Models
  
  Description:
  The objective of this research is to introduce an innovative macroeconomic digital twin framework intended for policymakers. By constructing a twin digital  of real-world economic phenomena, we can  analyse the potential impact of macroeconomic policies within a digital environment before their actual implementation in reality. Our contingency  is based on the recognition that forecasting in social sciences, particularly in economics, often needs a reasonable degree of accuracy in predicting events. The lack of accuracy in forecasting in economics is primarily due to the underutilization of appropriate statistical and computational techniques. Instead, policymakers frequently rely on methods that often yield impractical outcomes. Leveraging twin technologies can be a valuable tool, empowering policymakers to explore and assess various scenarios effectively before implementation.
  The aim of the research is to: 
  “Create a twin digital of an economic system (society, country, or industry) that allow us to simulate and reproduce different economic policies implementation and outcomes”.
  The objectives of the research are: 
  To review twin technologies methodologies, policy making theories, forecasting methods in economics, and behavioural economics. 
  To create a twin model of an economic system.
  To create an agent based model in Python  that will be the foundation  of the twin model.
  To validate our model using historical data and traditional statistical tools such r square, standard error and mean average performance error (MAPE). 
  Agent Based Model (ABM) will be the technology chosen to create the digital twin simulation. ABMs are able to create intricate social systems, incorporating different agents and allowing these agents to dynamically change. This in turn, allow us to simulate different social contexts
  After reviewing existing literature, we define the components of our ABM, 
  The agent/s. This can be one ore more agents. For instance, if the aim is to replicate a entire society, the agents could  be the public sector, the private sector, households, and international relations.  
  The agent/s rules. These rules must be based on behavioural economics and general economic principles.
  The environment.  The environment will be the economic ecosystem. For instance, if the model aims to replicate a specific country then variables like  the base level of pricing, debt, interest rates and other macroeconomic variables must be set prior to run the simulation. 
  The environment rules. Regulatory policies, regulations and economic principles that dictate the how these variables act.  Decision rules. Once the agent and the environment is defined, then the decision rules that trigger behaviour and the magnitude of the effect must be defined. Here we also define loops and reactions. 
  Once the ABM is created and in working conditions, we validate our model by using historical data. For instance, we can validate our simulation by forecasting  GDP growth and running the model under different scenarios (actual vs forecasted) and compare the outcome and the performance against historical models. 
- NEIL MACKINNON
  
  Nature of supervision:
  Principal Supervisor, Oct 2020 onwards
- OMAR ALHAWI
  
  Nature of supervision:
  Principal Supervisor, Oct 2023 onwards
  
  Title of thesis:
  Enhancing Mobile Security with ML-Driven Anomaly Detection in Network Traffic
Doctoral alumni since 2013-14
- SEONGIL HAN
  
  Nature of supervision:
  Joint Principal Supervisor, Jun 2020
  
  Title of thesis:
  Explainable Credit Scoring through Generative Adversarial Networks
  
  Awarded:
  2021
Teaching

Teaching modules
- Foundations of Data Science II (BUCI070H5)
- Foundations of Data Science II (BUCI070H5)
- Applied Machine Learning (BUCI077H7)
- Applied Machine Learning (BUCI077H7)
Publications
Publications
Article
- Taha, K. and Yoo, Paul and Al-Hammadi, Y. and Muhaidat, S. and Yeun, C.Y. (2023) Learning a deep-feature clustering model for gait-based individual identification. Computers and Security ISSN 0167-4048.
- Almahmoud, Zaid and Yoo, Paul and Alhussein, O. and Farhat, I. and Damiani, E. (2023) A holistic and proactive approach to forecasting cyber threats. Scientific Reports 13 (8049), ISSN 2045-2322.
- Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro (2023) A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions. Expert Systems with Applications ISSN 0957-4174.
- Uysal, Dilara and Yoo, Paul and Kamal, T. (2022) Data-driven malware detection for 6G networks: a survey from the perspective of continuous learning and explainability via visualisation. IEEE Open Journal of Vehicular Technology ISSN 2644-1330.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. (2022) Detecting implicit cross-communities to which an active user belongs. PLoS One ISSN 1932-6203.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. and Nedunkulathil, S. (2022) Inferring the densest multi-profiled cross-community for a user. Knowledge-Based Systems 237 (107681), ISSN 0950-7051.
- Al Hamadi, A. and Yeun, C.Y. and Damiani, E. and Yoo, Paul D. and Hu, J. and Yeun, H.K. and Yim, M.-S. (2021) Explainable Artificial Intelligence to evaluate industrial internal security using EEG signals in IoT Framework. Ad Hoc Networks 123 (102641), ISSN 1570-8705.
- Al Alkeem, E. and Yeun, C.Y. and Yun, J. and Yoo, Paul D. and Chae, M. and Rahman, A. and Asyhari, A.T. (2021) Robust deep identification using ECG and multimodal biometrics for Industrial Internet of Things. Ad Hoc Networks 121 (102581), ISSN 1570-8705.
- Taha, K. and Davuluri, R. and Yoo, Paul D. and Spencer, J. (2021) Personizing the prediction of future susceptibility to a specific disease. PLoS One ISSN 1932-6203.
- Al Hammadi, A. and Lee, D. and Yeun, C.Y. and Damiani, E. and Kim, S.-k. and Yoo, Paul D. and Choi, H.-j. (2020) Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure. IEEE Access 8, pp. 206222-206234. ISSN 2169-3536.
- Taha, K. and Yoo, Paul D. (2020) An effective disease risk indicator tool. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ISSN 2694-0604.
- Lee, S.J. and Yoo, Paul D. and Asyhari, T.A. and Jhi, Y. and Chermak, L. and Yeun, C.Y. and Taha, K. (2020) IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8, pp. 65520-65529. ISSN 2169-3536.
- Kim, S.-k. and Yeun, C.Y. and Yoo, Paul D. (2019) An enhanced machine learning-based biometric authentication system using RR- Interval Framed Electrocardiograms. IEEE Access 7, pp. 168669 -168674. ISSN 2169-3536.
- Li, M. and Selim, B. and Muhaidat, S. and Sofotasios, P. and Dianati, M. and Yoo, Paul D. and Liang, J. and Wang, A. (2019) Effects of residual hardware impairments on secure NOMA-based cooperative systems. IEEE Access 4, ISSN 2169-3536.
- Al Alkeem, E. and Kim, S.-K. and Yeun, C.Y. and Zemerly, J. and Poon, K. and Yoo, Paul D. (2019) An Enhanced Electrocardiogram biometric authentication system using machine learning. IEEE Access 7, pp. 123069-123075. ISSN 2169-3536.
- Yoo, Paul D. (2019) Popularity-based video caching techniques for cache-enabled networks: a survey. IEEE Access 7, pp. 27699-27719. ISSN 2169-3536.
- Yoo, Paul D. (2019) Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels. IEEE Transactions on Sustainable Computing 5 (1), pp. 48-60. ISSN 2377-3782.
- Yoo, Paul D. (2019) Shortlisting the influential members of criminal organizations and identifying their important communication channels. IEEE Transactions on Information Forensics and Security ISSN 1556-6013.
Book Section
- Kim, S.-K. and Yeun, C.Y. and Yoo, Paul and Lo, N.-W. and Damiani, E. (2022) Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms. In: The 8th International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems. Springer. (In Press)
- Parker, L. and Yoo, Paul D. and Asyhari, T. and Chermak, L. and Jhi, Y. and Taha, K. (2019) DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection. In: ARES '19 Proceedings of the 14th International Conference on Availability, Reliability and Security. ACM. ISBN 9781450371643.
- Yoo, Paul and Zhou, B.B. and Zomaya, A.Y. (2007) Machine intelligence in protein sequence analysis and structure prediction. In: Arabnia, H.R. and Yang, M.Q. and Yang, J.Y. (eds.) BIOCOMP 2007: International Conference on Bioinformatics & Computational Biology. CSREA Press. pp. 370-377.
- Yoo, Paul and Kim, M.H. and Jan, T. (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: CIMCA 2005: International Conference on Computational Intelligence for Modelling Control and Automation. IEEE Computer Society. pp. 835-841. ISBN 9780769525040.
Conference Item
- Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro and Kim, Maria (2022) Machine learning methods for Equity Time Series forecasting: a compendium. 31st ACM International Conference on Information and Knowledge Management, 2022, Atlanta, U.S.
Editorial
- Yoo, Paul and Tari, Z. (2021) Sustainable information security and forensic computing. IEEE Transactions on Sustainable Computing 6 (1), pp. 2-3. IEEE Computer Society. ISSN 2377-3782.

Dr Paul Yoo

Overview

Biography

Qualifications

Web profiles

Administrative responsibilities

Professional memberships

Honours and awards

ORCID

Supervision and teaching

Supervision

Current doctoral researchers

ZAID ALMAHMOUD

JOHN OMOKORE

THIAGO SUZUKI

ALBERTO MATUOZZO

DILARA UYSAL

GASSO MWALUSEKE

GURMENDER SINGH ATWAL

LAWRENCE OLUSANYA

MARIO RODRIGUEZ URETA

NEIL MACKINNON

OMAR ALHAWI

Doctoral alumni since 2013-14

SEONGIL HAN

Teaching

Teaching modules

Publications

Article

Book Section

Conference Item

Editorial