Researchers

	Andrew McIntyre, Ph.D. Director and Data Scientist, Acadia Institute for Data Analytics E-mail: andrew.mcintyre@acadiau.ca Dr. Andrew McIntyre is an Assistant Professor in the Jodrey School of Computer Science at Acadia University and Director of the Acadia Institute for Data Analytics (AIDA). His research lies in evolutionary computation, with emphasis on cooperative–competitive frameworks, and the design of memory and archive models that enhance adaptability, modularity, and long-term knowledge retention in complex learning systems. His work seeks to advance scalable and explainable approaches to artificial intelligence, particularly in settings where dynamic environments and multi-objective trade-offs must be addressed. Dr. McIntyre has published over 20 peer-reviewed papers, supervised honours, master’s, and capstone students, and regularly integrates students into funded projects. Through his role as Director of AIDA, he connects academic research with applied collaborations in industry, government, and community sectors, while also fostering regional research capacity as co-founder of the AI Atlantic Consortium, a network of universities advancing AI research and training across Atlantic Canada.
	Daniel L. Silver, Ph.D Data Scientist, Acadia Institute for Data Analytics Professor Emeritus, Jodrey School of Computer Science, Acadia University E-mail: danny.silver@acadiau.ca Danny is a Professor Emeritus with and former Director of the Jodrey School of Computer Science, Acadia University, Wolfville, Nova Scotia. He is the founder and former Director of the Acadia Institute for Data Analytics and the Lifelong Machine Learning and Reasoning research group at Acadia. Danny’s research focuses on advanced methods of machine learning and their application in data analytics, intelligent agents and adaptive systems. He has authored over 75 refereed journal or conference papers, 40 industry project reports and delivered associated talks in Canada, USA, Mexico, Europe and Asia. Since 1993, he has worked on machine learning and data analytics projects in the private and public sector providing situation analysis and problem definition, project management, and guidance, and predictive analytic services. From 2007-09, he was the President of the Canadian AI Association (CAIAC), and was Past-President until 2013. In June 2016, he received the CAIAC Distinguished Service Award and was made a CAIAC Fellow. 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. And from 2014 to 2018 he was an Honorary Colonel in the RCAF attached to 415 Squadron of 14 Wing Greenwood, in Nova Scotia.
	Darcy Benoit, Ph.D Director, Jodrey School of Computer Science, Acadia University Dr. Darcy Benoit is a Professor in and 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 Associate Professor, Jodrey School of Computer Science, Acadia University Dr. Greg Lee is the Lead Data Scientist at Fundmetric and Associate Professor at Acadia University. Greg completed his Ph.D. in Computer Science at the University of Alberta. 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. His does research 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.
	Zoë Migicovsky. Ph.D. Assistant Professor/Canada Research Chair (Tier 2) in Agri-Food and Sustainable Agriculture Zoë works at the intersection of plant agriculture and data analytics, integrating plant biology with computational tools to investigate biological variation in crop species. In particular, their lab focuses on perennial fruit crops and their wild relatives, such as apples and grapes. Their research program leverages crop diversity, including genomic and trait data, to improve our understanding of plant biology and make data-driven decisions in both management practices and plant breeding.
	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 and Director, Master of Science in Computing and Data Analytics, Saint Mary's University Dr. Pawan Lingras is a Professor and Director of the Master of Science in Computing and Data Analytics at Saint Mary’s University, with an academic foundation from IIT Bombay and the University of Regina. His extensive research in artificial intelligence and machine learning has resulted in over 230 publications and more than $5 million in total research and development grants. He has provided significant leadership to the academic community through his service on the Vanier Scholarship, NFRF, and NSERC peer review committees. Beyond his research, he has supervised nearly 150 students and collaborated with over 40 industrial partners to implement practical AI solutions in sectors like healthcare and energy. His contributions have been recognized with numerous honours, including university-wide awards for excellence in both teaching and research.
	Ying Zhang, Ph.D. Associate Professor, Mathematics & Statistics, Acadia University Dr. Ying Zhang is a Professor of Mathematics and Statistics at Acadia University and has directed its Statistical Consulting Centre since 2004. With a consulting career beginning in 1999, including a prior role managing Western University’s Statistical Laboratory, she has partnered with a wide range of organizations. Her expertise focuses on applied statistical methodology, specifically time series analysis, nonparametric statistics, experiment design, and survey sampling.
	Suzanne (Suzie) Blatt, Ph.D. Research Scientist, Agriculture, and Agri-Food Canada Dr. Suzie 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.
	Dr. Lydia Bouzar-Benlabiod, Ph.D. Assistant Professor, Computer Science, Acadia University Dr. Lydia Bouzar-Benlabiod is an Assistant Professor at Acadia University. She holds a Ph.D. in Computer Science from Université d’Artois, France, and an M.Eng from ESI, Algiers. Her research focuses on hardware-based machine learning, privacy-preserving ML, and explainable AI. She is a member of the CILS research lab and actively works on advancing secure and interpretable AI technologies.
	Dr. Amir Eaman, Ph.D. Assistant Professor, Computer Science, Acadia University Dr. Amir Eaman is an Assistant Professor at Acadia University. He holds a Ph.D. in Computer Science from the University of Ottawa, as well as an M.Sc. and B.Sc. in Software Engineering. His research focuses on AI for software testing and verification, AI-enhanced computer security, applied data science, and the integration of formal methods in AI security.
	Dr. Esteve Hassan, Ph.D. Associate Professor, Computer Engineering, Acadia University Dr. Esteve Hassan is an Associate Professor at Acadia University. He earned his Ph.D. and M.Eng. in Electronic and Computer Engineering from the University of Limerick, as well as a B.Sc. and M.Sc. in the same field. His research interests include IoT and cyber-physical systems, industrial AI and predictive maintenance, low-power sensing modules with energy harvesting, big data analytics, and FPGA-based optimized digital prototyping.
	Dr. Sazia Mahfuz, Ph.D. Assistant Professor, Computer Science, Acadia University Dr. Sazia Mahfuz is an Assistant Professor at Acadia University. She earned her Ph.D. from Queen’s University, her M.Sc. from Acadia University, and her B.Sc. from the University of Dhaka. Her research focuses on streaming IoT data analytics, lifelong machine learning, and medical image analytics.