(Microsoft)

Darren Edge is Director of Human Rights Technology in Microsoft Research Special Projects, where he builds technologies and partnerships to tackle some of the most challenging problems affecting people and society. He represents Microsoft in the Tech Against Trafficking (TAT) coalition working to combat human trafficking with technology, and represents Microsoft Research in the Microsoft ACTS program aiming to accelerate government transparency in the fight against corruption. 

Darren’s research focus is creating interactive software that empowers domain experts who are not data scientists to view, explore, and make sense of data in ways that inform evidence-based action. Using an activity-based design approach, he takes technologies with the potential to transform real-world data work (including methods from generative AI, graph statistics, differential privacy, and causal inference) and makes them available for real-world use. 

Previously, Darren’s research has aimed to transform a wide range of human activities, including achieving Societal Resilience, making sense of media and organizations, combating cybercrime and misinformation, preparing for presentations and second language conversations, and managing work tasks through peripheral, tangible, and embodied forms of interaction. He has published broadly in these areas and contributed to a variety of Microsoft products. 

Darren holds a BA and PhD from the University of Cambridge. He returned to Cambridge in 2016 following eight years as an HCI researcher at Microsoft Research Asia. 

(UCD)

Nial Friel is a Full Professor of Statistics in the School of Mathematics and Statistics at University College Dublin and a Principal Investigator in the Insight Centre for Data Analytics. His research interests lie broadly in the statistical analysis of network data; Bayesian statistics; Monte Carlo methods. He is also interested in applied statistics problems which motivate new methodology. He has served on ISBA board of directors from 2020-22 and was the joint editor of the Journal of the Royal Statistical Society, Series C from 2019-2022.

(Securonix)

Dr Joshua Neil is a Chief Data Scientist who leads the Securonix data science team. He is primarily responsible for driving the machine learning initiatives needed to power content-as-a-service and ultimately bolster the defenses of Securonix customers. Joshua Neil is a PhD statistician with over 20 years of data science experience. Neil most recently served as Principal Data Science Manager at Microsoft, where he helped design detection technology as part of Microsoft Defender for Endpoint and Microsoft365 Defender products. He also produced solutions for the Microsoft Threat Experts Hunting team. Prior to Microsoft, Neil worked at Ernst &Young, where he built their security data science capabilities, delivering cutting edge anomaly detection to Fortune 500 clients globally. Preceding Ernst & Young, Neil spent 15 years working as a principal investigator at Los Alamos National Laboratory, building a reputation as a high-quality researcher with over 1,000 citations, 8 patents, and an R&D 100 award for his development of PathScan, a network anomaly detection tool that was licensed and commercialized by EY. See source 

(McGill University)

Eric Kolaczyk is a Professor of Statistics in the Department of Mathematics and Statistics at McGill University, where he also serves as the inaugural director of the new Computational and Data Systems Initiative (CDSI). He was previously on faculty at Boston University, during which time he served as director of the Program in Statistics, the founding director of the department’s MS in Statistical Practice (MSSP) program, and the director of the Hariri Institute for Computing.

Eric Kolaczyk has more than 20 years of experience in research, teaching, and scientific collaboration. He has taught students at all levels, ranging from“mathophobes” to doctoral students, and has developed or redesigned more than a half dozen courses. Kolaczyk’sresearch interests over the years have facilitated interdisciplinary work with colleagues in areas as diverse as astrophysics, computational biology, computer network traffic analysis, geography and remote sensing, neuroscience, and social work. Currently, his research focuses on the statistical analysis of network data.

(EPFL) – ISOC Chair

Sofia is a professor of Statistics at EPF Lausanne. She joined UCL prior to this in 2007, before which she was a senior lecturer of statistics (associate professor) at Imperial College London (2006-2007), a lecturer of statistics (assistant professor) (2002-2006), where she also completed her PhD in 2003 and MSci in 2000. She has held three research fellowships while at UCL: a UK Engineering and Physical Sciences Springboard fellowship as well as a five-year Leadership fellowship, and a European Research Council Consolidator fellowship. Sofia has contributed to the study of stochastic processes; time series, random fields and networks. Sofia was also a member of the Royal Society and British Academy Data Governance Working Group, and the Royal Society working group on machine learning. Most recently she was one of 3 commissioners on a law society commission on the usage of algorithms in the justice system.

(KAUST)

Hernando Ombao is Professor of Statistics at KAUST and former Chair of the Statistics Program. He is the PI of the Biostatistics Group. His research is on statistical models for time series with dynamic and complex structures. At KAUST, he is also an adjunct Professor in the Bioengineering Program and the Applied Mathematics Program. He is the Chair of the Institutional Biosafety and Bioethics Committee. Prior to coming to KAUST, he was a faculty member at UC Irvine, Brown University, University of Illinois and the University of Pittsburgh. At UC-Irvine, he was a recipient of the Mid-Career Distinguished Research Award. He is Co-Editor of the Handbook of Statistical Methods for Neuroimaging. He was PI of several US NSF awards. He is a Fellow of the American Statistical Association (ASA). He serves the community as AE of Statistics journals (Metron, JASA, JRSS-B, CSDA). He was a member of the Biostatistics grant panels at NIH. He is a founding member and previous chair of the Statistics in Imaging Section of the ASA.

(Johns Hopkins)

Carey Priebe is a professor in the Department of Applied Mathematics and Statistics, with research interests primarily concerned with computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, dimensionality reduction, model selection, and statistical inference for high-dimensional and graph data. 

Priebe received a BS degree in mathematics from Purdue University in 1984, an MS degree in computer science from San Diego State University in 1988, and a Ph.D. in information technology (computational statistics) from George Mason University in 1993. He has been a professor at the Whiting School of Engineering since 1994. 

He is a senior member of IEEE, a lifetime member of the International Statistical Institute,  and Fellow of the American Statistical Association and the Institute of Mathematical Statistics. 

Priebe has received many awards and honors, including the 2010 American Statistical Association Distinguished Achievement Award, the 2011 McDonald Award for Excellence in Mentoring and Advising, and in 2008 he was named one of six inaugural National Security Science and Engineering Faculty Fellows. 

(EPSRC UKRI)

Dr Marianne Rolph joined EPSRC in February 2018 as a portfolio manager in the Physical Sciences theme, looking after the Materials for Energy Applications, Electrochemical Sciences, and Plasma and Lasers portfolios. Since November 2019, Marianne has been a senior portfolio manager in the Mathematical Sciences theme. Marianne leads the team strategies around the Mathematical Sciences Institutes (the INI and ICMS), Discipline Hopping and Uncertainty. 

(ICL)

Guy Nason is Chair in Statistics at Imperial College London. His research interests are in time series, statistical learning, modelling, fair and ethical algorithms.