Andrew McIntyre, Ph.D.

Data Scientist, Acadia Institute for Data Analytics


Andy completed a Bachelor of Science degree at Mount Allison University in 2000, Andy continued his academic pursuits at Dalhousie University, enrolling in the Masters of Computer Science Program and later graduating with a Ph.D. in the field in 2007.  Andy’s focus has mainly been machine learning with specific interest in evolutionary computation, a population-based meta learning technique for model building. His work has most recently included a 10-year role as senior researcher with the Network Information Management and Security group at Dalhousie, working on computer gaming and behaviour mining applications. He was a previously a postdoc with the department of Ophthalmology and Visual Science, investigating shape-based, predictive GP / imaging models and had further postdoctoral collaborations with National Research Council Canada Institute for Biodiagnostics Atlantic at the Neuroimaging Research Laboratory, developing models of functional connectivity with clustering and classification algorithms applied to large-scale, resting-state functional MRI (brain-imaging) data.  Andy currently holds an adjunct professor designation at Dalhousie and an adjunct position with the Jodrey School of Computer Science at Acadia University with 19 publications and has been named on five US patents as of 2020. Andy has assumed the role of Data Scientist at the Acadia Institute for Data Analytics (AIDA) as of October 2018.

Daniel L. Silver, Ph.D

Director, Acadia Institute for Data Analytics

Professor, Jodrey School of Computer Science, Acadia University


Danny is a Professor in and a former Director of the Jodrey School of Computer Science at Acadia University. His areas of research and development are machine learning, data mining, user modeling, and adaptive systems. He has published over 60 scientific papers, edited special journal editions, and has been part of the organizing or program committee for a number of national and international conferences, seminars and workshops.  Most recently he was awarded a Harrison McCain Foundation Award for research into advanced machine learning methods. Since 1993, he has worked on machine learning and data mining projects in the private and public sector providing situation analysis and problem definition, project management, and guidance, and predictive analytic services.  In 2011, he received the Science Champion Award from the Nova Scotia Discovery Center for his work on youth robotics and the advancement of STEM education.

Darcy Benoit, Ph.D

Director, Jodrey School of Computer Science, Acadia University

Dr. Darcy Benoit is an Associate Professor in and Acting Director of Acadia’s Jodrey School of Computer Science. He earned a BSc in Computer Science from St. Francis Xavier University and his Ph.D. in Computing from Queen’s University. Dr. Benoit joined Acadia University in 2002. Since then, he has received several research grants from groups such as NSERC, inNovaCorp, a Nova Scotia Productivity and Innovation Voucher. He is the author or co-author of over 30 publications. Dr. Benoit’s research areas include large-scale data collection, database management systems, and mobile computing.

Greg Lee, Ph.D

Assistant Professor, Jodrey School of Computer Science, Acadia University

Greg Lee is the Lead Data Scientist at Fundmetric and Assistant Professor at Acadia University. Greg completed his Ph.D. in Computer Science at the University of Alberta. His research focus is on automated storytelling. This research includes work in human computer interaction and mobile computing. During his graduate studies, he collaborated with Minor League Baseball and CBC’s Hockey Night in Canada. He has published papers in numerous academic journals and conferences and has seen his work recognized by New Scientist magazine, CBC Radio’s Spark and NPR. At Fundmetric, Greg has researched methods to machine learn a donor’s charitable leanings, leading to orders of magnitude improvement in donations for various hospitals, universities, and charities.

Trevor Avery, Ph.D., P.Stat

Assistant Professor, Biology, Mathematics & Statistics, Acadia University

Trevor is an Associate Professor and Instructor at Acadia University in the Department of Biology and is an accredited P. Stat. of Canada. His research encompasses large-scale and long-term ecological projects including recreational fishing and fisheries, environmental interactions, and animal passage with a focus on using modeling to describe ecological relationships. Data from citizen scientists, historical records, and 'new' fieldwork are used in modeling. In 2012, he was awarded a Distinguished Visiting Lectureship to Rhodes University, South Africa to teach workshops on ecological modeling using R, and he will return to Rhodes in 2014 to teach advanced techniques. A Harrison McCain Emerging Scholar award will further his work on integrating Traditional/Ecological Knowledge and the use of citizen science and historical data into species conservation. He is/has been a member of international working groups investigating techniques to combine disparate historical data for long-term analysis of fisheries monitoring information. Data analytics is used often in the course of ecological modeling and the statistical techniques used by Trevor are transferable across disciplines.

Hugh Chipman, Ph.D.

Professor, Mathematics & Statistics Acadia University

Hugh Chipman is a Professor in the Department of Mathematics and Statistics.  His research interests include statistical learning, data mining, Bayesian methods, industrial applications, visualization, and statistical computing.  He has consulted with industry and government agencies.  He is a former editor of Technometrics, an international journal on industrial statistics.  He held a Canada Research Chair at Acadia from 2004 - 2014.

Richard Karsten, Ph.D.

Professor, Mathematics & Statistics, Acadia University

Dr. Karsten has been an Associate Professor in the Department of Mathematics and Statistics at Acadia University since 2001.  He has 25 years of experience in research focused on fluid dynamics and oceanography, Since 2007, Dr. Karsten and his colleagues at Acadia have been working on mathematical and numerical models of tides and tidal power in the Bay of Fundy. Their work resulted in the first published assessment of in-stream tidal power in Minas Passage based on a theoretical estimate of extractable energy and a numerical simulation of in-stream turbines. Dr. Karsten has examined the resource potential of the major tidal passages of Nova Scotia, including a detailed analysis of the Digby Neck passages.  His work has estimated not only power potential, but also the reduction in tides and currents that will result. His research has examined improving analytical and numerical models of power extraction from tidal systems, with extensive validation through comparison to field data. Dr. Karsten has also worked on examining the tidal resonance in Hudson Strait and the coupling of resonant shallow basins with the Atlantic Ocean.

Haiyi Zhang

Associate Professor, Jodrey School of Computer Science, Acadia University

Haiyi Zhang received his MS degree in 1990 from the Computer Science Department of New Jersey Institute of Technology of USA, and his Ph.D. in 1996 from the Computer Science department of Harbin Institute of Technology in China. He was a post-doctor in the information department of ABO, Finland in 2000. His research interests are machine learning and data mining. He has more than 50 academic papers published. He is working as an associate professor at Acadia University.

Pawan Lingras, Ph.D.

Professor, Computer Science, Saint Mary's University

Pawan Lingras is a graduate of IIT Bombay with graduate studies from the University of Regina. He is currently a professor at Saint Mary's University, Halifax. He has authored more than 190 research papers in various international journals and conferences. He has also co-authored three textbooks and co-edited two books and six volumes of research papers. His areas of interest include artificial intelligence, information retrieval, data mining, web intelligence, and intelligent transportation systems. The area of applications include engineering, retail, web and mobile analytics. He has served as the general co-chair, program co-chair, review committee chair, program committee member, and reviewer for various international conferences on artificial intelligence and data mining. He is also on editorial boards of a number of international journals.

Sean Myles, Ph.D.

Assistant Professor/Canada Research Chair, Agriculture Genetic Diversity

Dr. Sean Myles is from Fredericton, New Brunswick, where he received his BA at Saint Thomas University. He completed his Masters in Human Biology at Oxford University and his Ph.D. in Human Evolutionary Genetics at the Max Planck Institute for Evolutionary Anthropology in Germany. He turned to plants during his postdoc at Cornell where he worked on grape genomics. He also spent time as a postdoc at Stanford University before starting his current position in the Faculty of Agriculture at Dalhousie University as the Canada Research Chair in Agricultural Genetic Diversity in the summer of 2011. His research focusses on figuring out how to use genomics to more efficiently breed improved fruit that requires less chemical input to grow.

Ying Zhang, Ph.D.

Associate Professor, Mathematics & Statistics, Acadia University

Dr. Ying Zhang is an Associate Professor in the Department of Mathematics & Statistics and has been the Director of the Statistical Consulting Centre since coming to Acadia University in 2004.  Prior to this, Ying was the manager of the Statistical Laboratory at Western University (2001-2004) and has been an active statistical consultant since 1999. She has worked together with a variety of organizations, including the Society of Actuaries, the Canadian Institute of Actuaries, Brescia University College, the Institute for Catastrophic Loss Reduction, the London Health Sciences Centre, and the Real Estate and Land Use Institute at San Diego State University.  Recently Ying has participated in the policy and management studies for the Nova Scotia Department of Nature Resources, and the Nova Scotia Department of Health Wellness.  Ying has extensive experience with applied statistical methodology research and consulting, specializing in time series analysis, nonparametric statistics, experiment design, and survey sampling.  She has successfully produced numerous consulting project reports and some of her consulting work has been published in the top journals of applied statistics.

Suzanne (Suzie) Blatt, Ph.D.

Research Scientist, Agriculture, and Agri-Food Canada

Dr. Blatt studies the interaction between insect pests in tree fruit crops (apples/pears/cherries) and their host. Overall, the objective is to develop novel means to manage pest populations. Currently, she is examining the role of host volatiles in overall attraction and how this is influenced by micro-climate, cultivar, and rootstock. Anticipated work includes genetic expression of various traits and elucidation of heritability of attraction cues.