NRC-Fields Mathematical Sciences Collaboration Centre

Dr. Sajjad Ghaemi

Dr. Ghaemi is a research officer and site lead for the NRC–Fields Mathematical Sciences Collaboration Centre and an adjunct professor at the Department of Mathematics and Statistics, York University. He also serves as a mentor for the Artificial Intelligence for Public Health (AI4PH) program, sponsored by the Dalla Lana School of Public Health at the University of Toronto, and is an affiliate member of the Acceleration Consortium. His BSc and MSc studies were in theoretical computer science and machine learning at the University of Tehran and the Sharif University of Technology, respectively. His master's thesis was focused on graph-based semi‑supervised learning algorithms, which was inspired by scarcity of labelled information. His subsequent PhD research at Polytechnique Montréal concentrated on identifying structures in large datasets where the labels were completely unknown. Prior to joining the NRC, he was a postdoctoral fellow at Stanford University's School of Medicine, where he developed generalized linear models for learning across multiple high‑throughput biological assays that were successfully implemented for a variety of bioinformatics projects.

At the NRC–Fields Collaboration Centre, Dr. Ghaemi is working on interdisciplinary research projects, such as the design of algorithms to develop applied methodologies for solving various cutting-edge and high-tech problems in science and industry, leveraging machine learning and artificial intelligence.

Dr. Ashkan Ebadi

Dr. Ebadi is a multidisciplinary applied data science researcher specializing in AI, machine learning, deep learning and graph analytics. He holds a PhD in information systems engineering with a focus on AI-based decision support systems from Concordia University and completed a two-year postdoctoral fellowship in health informatics at the University of Florida. Currently, he serves as a senior research officer at the NRC and is an adjunct assistant professor at the University of Waterloo, an affiliate assistant professor at Concordia University and a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Ebadi has extensive experience in designing and implementing intelligent data-driven solutions across the entire data science pipeline, from problem definition to scalable big data analytics applications. His research interests include medical informatics, healthcare analytics, scalable and intelligent decision support systems, hybrid recommender systems and hypergraph analytics and evolution. His work has significantly contributed to real-world health applications, leveraging advanced analytics and machine learning to solve complex problems. Dr. Ebadi actively collaborates with national and international researchers, which has resulted in several high-impact journal publications.

Dr. Aaron Goldberg

Dr. Goldberg is a research officer in the quantum theory group at the NRC's Quantum and Nanotechnologies Research Centre (formerly Security and Disruptive Technologies). He has expertise in quantum optics and quantum information theory, with focuses on finding quantum-enhanced sensing protocols, investigating entanglement and nonclassicality in photonic quantum information processing and researching light–matter interactions to find new quantum advantages. He received his PhD in quantum optics from the University of Toronto and was an NSERC postdoctoral fellow at the University of Ottawa.

Current and planned projects include developing protocols for enhanced magnetometry and spectroscopy, generation and manipulation of quantum resource states, analyses of photon-number-resolving detectors and their uses, quantum machine learning for data encoding and neural network design, photonic implementations of quantum reservoir computing, machine learning for quantum experimental designs and data analyses, and quantum statistics and estimation theory.

Dr. Nava Leibovich

Dr. Leibovich has been a research officer at the NRC–Fields Collaboration Centre since 2023. She holds a BSc in mathematics and physics as well as an MSc and a PhD in physics from Bar-Ilan University. Her master's research focused on single-file diffusion, while her doctoral studies concentrated on non-stationary power spectra.

As a postdoctoral fellow in the Department of Physics at the University of Toronto, Dr. Leibovich joined the biophysics group, where she conducted research on complex interaction networks, including multispecies ecological interactions and biochemical reactions.

Dr. Leibovich's research primarily explores stochastic processes through a combination of analytical tools, computational methods and machine learning approaches. While she acknowledges that theoretical studies often employ simplified models, her work aims to provide a framework for interpreting real-world phenomena and predicting their behaviour. Her research applications span physical, biological and ecological systems, striving to bridge the gap between theoretical models and practical insights.

Dr. Junan Lin

Dr. Lin is a research associate at the NRC–Fields Collaboration Centre. He obtained his bachelor's degree from McGill University in 2016 in the honours physics and chemistry program. He obtained his PhD from the University of Waterloo in May 2023, under the supervision of Professor Raymond Laflamme. His thesis focused on noise characterization and mitigation on quantum computers. His current research at the NRC focuses on combining quantum chemistry and machine learning methods for de novo drug discovery. He is also interested in developing quantum algorithms for quantum chemistry problems, an area he has been working on with Professor Artur Izmaylov at the University of Toronto.

The research at the NRC involves applying methods from quantum chemistry and machine learning to develop better algorithms for drug discovery. These efforts are dedicated to delivering highly controllable, high throughput methods that can target specific diseases. in the future, Dr. Lin plans to explore various state preparation techniques for quantum algorithms, which can speed up the drug development pipeline.

Dr. James Ooi

Dr. Ooi is a research officer and team lead for the data science for complex systems group and an adjunct professor at the Department of Mathematics and Statistics, York University. His research interest is in mathematical modelling and simulations of dynamical systems. He obtained his Master of Science in Electrical Engineering (MSEE) and PhD in biomedical engineering from the University of Texas at Dallas, followed by postdoctoral training at the University of Ottawa. He then joined IBM Canada and worked on high-performance computing research before joining the NRC.

At the NRC–Fields Collaboration Centre, James is pursuing interdisciplinary research that applies techniques from mathematical modelling, machine learning and quantum computing to disease modelling and material and drug discoveries.