Prof. Mohamed Medhat Gaber
Mohamed is a Professor in Data Analytics at the School of Computing and Digital Technology, Birmingham City University. Mohamed received his PhD from Monash University, Australia. He then held appointments with the University of Sydney, CSIRO, and Monash University, all in Australia. Prior to joining Birmingham City University, Mohamed worked for the Robert Gordon University as a Reader in Computer Science and at the University of Portsmouth as a Senior Lecturer in Computer Science, both in the UK. He has published over 200 papers, co-authored 3 monograph-style books, and edited/co-edited 6 books on data mining and knowledge discovery. His work has attracted well over four thousand citations, with an h-index of 35. Mohamed has served in the program committees of major conferences related to data mining, including ICDM, PAKDD, ECML/PKDD and ICML. He has also co-chaired numerous scientific events on various data mining topics. Professor Gaber is recognised as a Fellow of the British Higher Education Academy (HEA). He is also a member of the International Panel of Expert Advisers for the Australasian Data Mining Conferences. In 2007, he was awarded the CSIRO teamwork award.
Dr Jagdev Bhogal
Jagdev is an experienced lecturer whose main teaching area is Database Systems. She is the Course Leader for MSc Business Intelligence and the MSc Big Data Analytics courses. Jagdev has published conference and journal papers on relational/object/nosql database systems, ontologies, text mining and cloud computing.
Dr Atif Azad
Dr Azad is a Senior Fellow of Higher Education Academy. He specialises in the subject matter of Computer Science, Machine Learning, Evolutionary Computing (Genetic Programming, Genetic Algorithms, and Grammatical Evolution), Data Analytics, and Statistics. He received his PhD from Biocomputing and Developmental Systems Group at University of Limerick (UL). Since the year 2000, he has extensively worked on theory and applications of Machine Learning, and Nature Inspired Machine Learning (Evolutionary Computing), and has conducted internationally acclaimed work winning awards and honours from recognised international scientific fora. Dr Azad has received: Best Reviewer Award at the European Conference on Genetic Programming (EuroGP) 2015, Copenhagen, Denmark, and Silver HUMIES Award at Genetic and Evolutionary Computation Conference (GECCO) 2015 for his work on Automatic Parallel Programming.
Dr Yevgeniya Kovalchuk
Yevgeniya joined the School of Computing and Digital Technology in September 2016. She received an MSc in Economic Cybernetics from the National Technical University of Ukraine and PhD in Computer Science from the University of Essex. During her professional career, she worked in both pure industrial and academic environments, as well as on projects connecting the two. She has a strong track record of applying computer and data science to solve problems across a wide range of business areas, including banking, insurance, logistics, healthcare, sport and entertainment, among many others.
Dr Shadi Basurra
Shadi Basurra received the B.Sc. degree (Hons.) in computer science from Exeter University, U.K., the M.Sc. degree in distributed systems and networks from Kent University, Canterbury, U.K, and the Ph.D. degree from the University of Bath in collaboration with Bristol University. He is currently a Senior Lecturer in computer science with Birmingham City University, U.K. After his PhD degree, he was with Sony, where he was developing goal decision systems. He has taught postgraduate and undergraduate courses in computer science and networking. He has published a number of peer-reviewed scientific articles in international conferences and journals. His research interests include multi-agent systems, game theory, multi-objective optimization, machine learning in the Internet of Things, energy efficiency in smart buildings, emulation of mobile ad hoc networks, nature-inspired computing, and social networks. He received The Yemen President National Science Prize, in 2010, the Best Presentation at Meeting of Minds Bath, in 2012, the MEX Scholarship, in 2013, the Ph.D. Scholarship from Toshiba Ltd, Great Western Research, and Yemen Government, in 2009, and various academic grants.
Dr Shereen Fouad
Shereen has a PhD (2010–13) in the field of Machine Learning from the School of Computer Science at the University of Birmingham. Shereen’s PhD thesis developed novel Machine Learning algorithms for learning using privileged information in prototype based models, based on metric learning techniques. The proposed frameworks were all based on mathematical and algorithmic theories. Shereen also holds a BSc and MSc in Information Systems from the School of Computer and Information Sciences, Ain Shams University, Egypt. Shereen’s current research interest is primarily in the field of Machine Learning and Medical Imaging Analysis. Shereen is currently working as a Computer Science Lecturer in the Faculty of Computing, Engineering and the Built Environment. Shereen teaches the CMP4272 Data Structures and Algorithms module and also works as a support academic in a Knowledge Transfer Partnership project (Kadfire), providing specific expertise in machine learning and artificial intelligence. In addition, Shereen is an Honorary Research Fellow in the Institute of Clinical Sciences at the University of Birmingham. From 2015 to 2017, Shereen worked as a Research Fellow in an EPSRC research project (Novel context-based segmentation algorithms for intelligent microscopy) in the Institute of Clinical Sciences, University of Birmingham. The project looked at developing novel computational approaches for processing, understanding and quantifying histological information in digitised pathology images. The accurate interpretation of such information improves the detection, diagnosis, and prognosis of cancer diseases in digital microscopy. Shereen’s role was to advance the computer-assisted analysis and diagnosis of cancer by means of Computer Vision and Machine Learning techniques. This involves developing computational image analysis procedures particularly tailored to manipulate and analyse microscopic imagining data, as well as novel intelligent algorithms that automatically detect patterns in images that correspond to histological models. In 2013/14, Shereen worked as a Research Fellow in Machine Learning on a collaborative pilot project in the school of Computer Science at the University of Birmingham. The project aimed to develop Machine Learning approaches for mining complex high dimensional brain imaging data (FMRI) as well as complex behavioural and cognitive data. Shereen has a wide experience and great interest in teaching. Shereen worked as a Lecturer Assistant from 2004 to 2010 in two large academic computer science institutes in Cairo (Ain Shams University and German University in Cairo). During this period, Shereen taught undergraduate students in a wide range of modules. In the 2014/15 academic year, Shereen worked as a Teaching Fellow in the School of Computer Sciences at the University of Birmingham, for undergraduate and postgraduate levels. Shereen took the lead of two modules and a shared lead of two other modules. In addition, Shereen worked as a Java class tutor for first year students. Shereen’s research journey has expanded Shereen’s knowledge and expertise in many computer science areas including Machine Learning, Image Processing, Databases and Software Design/Development. Shereen is committed to publishing high quality research.
Dr Zahraa S. Abdallah
Zahraa is a lecturer in Data Analytics at Birmingham City University. She received her PhD in March 2015 from Monash University, Australia. Her thesis received the Mollie Holman Doctoral Medal, for best doctoral thesis completed in the faculty of information technology in 2015. Her research is broadly in the areas of Data Stream Mining, Adaptive Learning and Time Series Analysis, where her focus is on developing intelligent techniques that aim to service real-time information needs while having to function in highly dynamic and resource-constrained environments. She worked on different industrial and research projects in machine learning and applied data science. Her research/industry engagement has attracted various governmental and industrial grants targeting deep analytics of data in different sectors including spectroscopy, constructions and safety, energy and smart meters, and supply chain. Besides research, she is a lecturer in both the Masters of Big Data Analytics course and the Bachelor of Computer and Data Science course. She has contributed to the development of the online Graduate Diploma in Data Science (GDDS) and the on-campus Master of Data Science (MDS) at Monash University.
Dr Shuo Wang
Shuo Wang is a lecturer at School of Computing and Digital Technology, Birmingham City University (UK), and also an Honorary Research Fellow at the School of Computer Science, the University of Birmingham (UK). She received the Ph.D. degree in Computer Science from the University of Birmingham, U.K., in 2011, sponsored by the Overseas Research Students Award (ORSAS) from the British Government (2007). She has been involved in two EPSRC-funded projects and three EU-funded projects. Her research interests include class imbalance learning, ensemble learning, online learning and machine learning in software engineering and social media analysis. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, and International Joint Conference on Artificial Intelligence (IJCAI). Her leading research on multi-class imbalance problems and tackling class imbalance in software defect prediction published in 2012 and 2013 have received over 250 citations. Among other roles, she successfully organised and chaired the international workshop on learning in the presence of class imbalance and concept drift, IJCAI’17; she is the guest editor for the Neurocomputing on Learning in the Presence of Class Imbalance and Concept Drift, and the guest editor for the Connection Science on Learning from Data Streams and Class Imbalance. A tutorial on Learning Class Imbalanced Data Streams was given at IEEE World Congress on Computational Intelligence (WCCI), Rio de Janeiro, Brazil, 2018.
Dr Mariam Adedoyin-Olowe
Dr Mariam Adedoyin-Olowe is an Assistant Lecturer/Researcher in Big Data Analytics. She obtained her PhD degree in Computing Science from the School of Computing Science and Digital Media (IDEAS Research Institute) of Robert Gordon University (RGU), United Kingdom in 2015. She obtained a BSc (Hons) in Computing from the University of Portsmouth in 2011. She also holds a Certificate in Student Mentoring from the University of Portsmouth. Her PhD research applied data mining techniques to Twitter data to detect and track topic/event from real life occurrences for information and decision making. She was also a member of the Data Science and Information Retrieval/Case-based Reasoning Group at RGU. Prior to her appointment at BCU, she was a Lecturer in the Department of Computer Science and Information Technology, The Bells University of Technology, Ogun State, Nigeria, where she taught at undergraduate and graduate levels in areas related to Computer Science and Information Technology. She supervised students' academic work and projects at undergraduate and postgraduate level. She also engaged in committee activities within and outside the Department of Computer Science/Information Technology. In February 2017, Mariam was appointed as Chair/Convener of Bells University Advancement Centre. The Centre is charged with the responsibility of interfacing with industries and corporate organisations for collaboration and partnership. She is a member of Data Analytics and Artificial Intelligence Research Group (DAAI) at BCU. She is currently conducting research on Automated Information Retrieval from Twitter using Association Rule Mining and Visualisation Tool.