Science Atlantic Conference 2022

Dr. Peter Selinger is a Professor of Mathematics and Computer Science at Dalhousie University.

He received his PhD from the University of Pennsylvania in 1997.

His research interests include the semantics of quantum programming languages, the theory of quantum circuits, and most recently, combinatorial game theory.

He is an editor of the journal Logical Methods in Computer Science and a founder of the workshop series Quantum Physics and Logic.

He has served on numerous program committees, and has given invited plenary lectures at international conferences including MFPS, TLCA, CTCS, FLOPS, TYPES, WoLLIC, FSCD, QPL, POPL, and LICS.

Hex as a combinatorial game

Hex is a strategy game for two players, invented in 1942 by Piet Hein and later rediscovered by John Nash. It is characterized by extremely simple rules that give rise to surprisingly complex strategies. Combinatorial game theory is a formalism for the study of sequential perfect information games, introduced by Conway, Berlekamp, and Guy in the 1970s and 1980s. In this talk, I will introduce the basic definitions and properties of combinatorial game theory, as it applies to the game of Hex. I'll give some examples of how this theory can be applied to play Hex better, and to solve some previously unsolved problems.

Dr. Amber Simpson is the Canada Research Chair in Biomedical Computing and Informatics, and Associate Professor jointly appointed in the Department of Biomedical and Molecular Sciences and School of Computing at Queen’s University.

She is an Affiliate of the Vector Institute for AI as well as a Senior Investigator at the Canadian Cancer Trials Group.

Dr. Simpson is the Director of the Centre for Health Innovation, a joint venture with Kingston Health Sciences Centre and Queen’s, which will launch in Spring 2022.

She received her PhD in Computer Science from Queen’s and was a postdoctoral fellow in the Department of Biomedical Engineering at Vanderbilt University.

Recently recruited from a faculty position at Memorial Sloan Kettering Cancer Center in New York, she holds research funding from the National Institutes of Health as well funding from all three Canadian research councils.

Dr. Simpson is an American Association of Cancer Research and Pancreatic Cancer Action Network award holder and a charter member of NIH study section, which recognizes her innovations in biomedical research.

She specializes in biomedical data science with a focus on developing novel computational strategies for improving human health.

Solving Fundamental Cancer Problems with AI

Precision medicine is an approach to patient care that considers individual differences in a patient’s genetic and molecular makeup to predict disease progression and optimize treatment response — and one of the greatest opportunities, and challenges, in modern cancer care. There is currently no way to predict how cancer may spread. We address this by creating a cancer digital twin, a digital replica of a cancer patient using state-of-the-art artificial intelligence (AI) techniques. Predicting metastatic progression at early stages would radically transform our approach to cancer treatment, but promises substantial implications for patients and society that must be considered. For example, would you want to know a dismal prognosis predicted by your digital twin? How would you act on this information — would you regard the choice as yours, or your fate as given by your twin? In a world where AI is increasingly biased, how do we ensure that AI doesn’t create further inequities? Dr. Simpson will present new work on the development of a cancer twin for predicting metastatic progression as well as provide some discussion of the social ramifications of such technology.

Dr. Jude Kong is a professor in the Mathematics & Statistics Department at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC).

Additionally, he is the Regional Node Liaison to the steering committee of the Canadian Black Scientist Network (CBSN), a member of the Scientific Advisory Committee of the Mathematics for Public Health Network, a member of the National COVID-19 Modelling Rapid Response Task Force and a member of the Canadian Centre for Disease Modelling.

He obtained his Ph.D. in Mathematics from the University of Alberta, his MSc. in Mathematical Modelling from the University of Hamburg-Germany and the University of L'Aquila-Italy.

His B.Sc. in Mathematics and Computer Science was acquired at the University of Buea-Cameroon and his Bachelor of Education (B.Ed) degree in Mathematics was earned at the University of Yaounde-Cameroon.

Before joining York University, he did 2-years of postdoc at Princeton University and Rutgers University.

Dr. Kong is an expert in mathematical modelling,  artificial intelligence, infectious disease modelling and mathematics education.

His principal research program focuses on the use of mathematical modelling and other quantitative methods to improve decision-making for epidemic and pandemic prevention, preparedness and response.

During the COVID-19 pandemic, he has been leading an interdisciplinary team of more than 52 researchers from key academic and government institutions in nine African countries that have been using Artificial intelligence to help government and local communities to contain and manage the spread of COVID-19.

In 2020, he won a York Research Leader Award. In 2021 he was spotlighted among Canadian Innovation Research Leaders 2021 for his work with ACADIC.

In 2022, he was spotlighted as a Change Maker by People of YU for his work in helping others learn mathematical concepts and encouraging them to find their passion and achieve more than they thought was possible.

He is an Area Editor of the Data & Policy Journal where he focuses on Data Technologies and Analytics for Policy and Governance.

Harnessing the power of mathematical models to inform disease outbreak policies.

The increasing need for informed policy and decision-making to address the unprecedented nature of the COVID-19 pandemic has brought mathematicians and infectious disease modelling to the centre stage of public health. 
 Mathematicians and mathematical models are playing a key role in real-time delivery of reliable and comprehensive information to predict the spread of COVID-19 and its impact, and in guiding governmental policies and best practice. So, HOW do we design a mathematical model of an infectious disease outbreak?  HOW can models be harnessed to inform public health measures at different stages of an outbreak? In this talk, I will provide answers to these questions.