Organizers
Leman Akoglu, Carnegie Mellon University: Leman Akoglu is an associate professor of Information Systems at the Heinz College of Carnegie Mellon University, with courtesy appointments in the Computer Science and Machine Learning Departments of School of Computer Science. She received her PhD from the Computer Science Department at Carnegie Mellon University in 2012. Her research interests are algorithmic problems in data mining and applied machine learning, focusing on patterns and anomalies, with applications to fraud and event detection. Dr. Akoglu’s research has won 7 publication awards; Best Paper at SIAM SDM 2019, Best Student Machine Learning Paper Runner-up at ECML PKDD 2018, Best Paper Runner-up at SIAM SDM 2016, Best Paper at SIAM SDM 2015, Best Paper at ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML PKDD 2009. Dr. Akoglu is a recipient of the NSF CAREER award (2015) and Army Research Office Young Investigator award (2013). Her research has been supported by the NSF, US ARO, DARPA, Adobe, Facebook, Northrop Grumman, PNC Bank, and PwC.
Email: lakoglu@andrew.cmu.edu
Mihai Cucuringu, University of Oxford & The Alan Turing Institute: Dr. Cucuringu is an Associate Professor in the Department of Statistics, and an Affiliate Faculty in the Mathematical and Computational Finance group at the Mathematical Institute, at University of Oxford. He is also a Turing Fellow at The Alan Turing Institute in London. Previously, he was a CAM Assistant Adjunct Professor in the Department of Mathematics at UCLA. He finished his Ph.D. in Applied and Computational Mathematics at Princeton University in 2012. His research pertains to the development and mathematical analysis of algorithms that extract information from massive noisy data sets, network analysis and certain inverse problems on graphs, such as clustering and ranking, with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction purposes. His research interests in finance focus on statistical arbitrage, machine-learning for asset pricing, market microstructure in equity and crypto markets, synthetic data generation, and anomaly detection in financial transaction networks. He has previously organized a workshop on Network Science in Financial Services at The Alan Turing Institute.
Email: mihai.cucuringu@stats.ox.ac.uk
Xiaowen Dong, University of Oxford: Dr Xiaowen Dong is an Associate Professor in the Department of Engineering Science at the University of Oxford, where he is an academic member of both the Machine Learning Research Group and the Oxford-Man Institute. His main research interests concern signal processing and machine learning techniques for analysing network data, and their applications in studying questions across social and economic sciences.
Email: xdong@robots.ox.ac.uk
Saurabh Nagrecha, Google LLC: Saurabh Nagrecha, PhD leads ad-fraud fighting efforts at Google, LLC. Prior to this, he has been an Applied Research Lead at eBay and Capital One. He received his PhD from the Department of Computer Science and Engineering at the University of Notre Dame in 2017 specializing in machine learning. His research interests span across data science and machine learning with a focus on highly imbalanced cost-sensitive systems problems. He has 7 years of experience leading projects combating fraud and money laundering in retail trading platforms, financial institutions, auto insurance and travel & expense sectors. He also has a background in teaching as an adjunct faculty member at Capital One Tech College where he led the development of courses in the area of Network Science. Saurabh has organized multiple workshops including a series of workshops on Machine Learning in Finance at KDD, NeurIPS and ICML. Saurabh organized multiple workshops including a series of workshops on Machine Learning in Finance at Knowledge, Discovery and Data mining (KDD) in 2020 and 2021.
Email: snagrecha@google.com
Stefan Zohren, University of Oxford & Turing Institute: Dr. Stefan Zohren is an Associate Professorial Research Fellow at the Oxford-Man Institute of Quantitative Finance, an Associate at the Oxford Internet Institute and a Mentor in the FinTech stream at the Creative Destruction Lab at Said Business School, all at the University of Oxford. He is also a member of the European Laboratory for Learning and Intelligent Systems. Outside of academia, Stefan works as a Scientific Advisor at Man Group. His research is focused on machine learning in finance, including deep learning, reinforcement learning, network and NLP approaches, as well as early use cases of quantum computing. He is an Associate Editor of the Journal of Mathematical Finance. Stefan is also a frequent speaker on AI in finance at academic conferences, as well as industry panels and corporate events. He has organised a number of workshops on related areas, including one on Agent-based Modelling and Simulation in Finance at the Turing Institute and one on Machine Learning in Computational Finance at the SIAM conference on Financial Mathematics.
Email: stefan.zohren@eng.ox.ac.uk
Ivan Brugere, J.P. Morgan: Ivan Brugere is a research scientist within AI Research at J.P. Morgan. His work focuses on algorithmic fairness for financial applications, graph machine learning, and network science. He has worked in graph-structured fairness in recommendation and higher-order routing fairness for resource access equity. He received his PhD in 2020 from the University of Illinois at Chicago, focusing on graph representation inference from non-network data for biological and ecological applications. He has co-organized workshops and tutorials in graph machine learning at KDD, SDM, and ICDM. Ivan has been a co-organizer of the Broadening Participation in Data Mining Workshop at KDD, and a plenary speaker and Accessibility co-chair at the ACM Richard Tapia Conference.
Email: ivan.brugere@jpmchase.com
Lukasz Szpruch, University of Edinburgh: Lukasz Szpruch is a Professor at the School of Mathematics, and is the Programme Director for Finance and Economics at the The Alan Turing Institute, the National Institute for Data Science and AI. At Turing, he is providing academic leadership for partnerships with the National Office for Statistics, Accenture, Bill and Melinda Gates Foundation and HSBC. He is the Principle Investigator of the research programme FAIR on responsible adoption of AI in the financial services industry. He is also a co-Investigator of the UK Centre for Greening Finance & Investment (CGFI). He is also an affiliated member of the Oxford-Man Institute for Quantitative Finance. Before joining Edinburgh, he was a Nomura Junior Research Fellow at the Institute of Mathematics, University of Oxford.
Email: l.szpruch@ed.ac.uk
Mark Klaisoongnoen, EPCC at the University of Edinburgh: Mark is a final year PhD candidate at the University of Edinburgh and EPCC, the UK national supercomputing centre. His research interests include novel hardware architectures, low-latency and power-efficiency capabilities. During his PhD project Mark has been developing and optimising computational finance and AI workloads on recent generations of FPGAs, GPUs and other emerging hardware architectures. Based on his expertise in accelerating tentative finance workloads on FPGAs, Mark proposes to couple high-frequency data streams with FPGAs running AI inference models to significantly enhance the insight that can be delivered from real-time data in a short time window.
Email: Mark.klaisoongnoen@ed.ac.uk
Claire Barale, University of Edinburgh: Claire Barale is a PhD candidate in NLP at the University of Edinburgh and a Bloomberg Fellow. Her research interests include information extraction, indirectly supervised learning, domain-specific language models and fairness for NLP. She is currently working on legal NLP with a specific focus on designing and implementing NLP-based functionalities in the legal workflow to inform, speed up and improve the transparency of the refugee claim process. She is also affiliated with the Centre for Technomoral Futures, Edinburgh Futures Institute. Her research won a best student paper award at the international conference on AI and Law (ICAIL) 2023.
Email: claire.barale@ed.ac.uk