Math and Computing Research Discovery Days

The Math & Computing Research Discovery Days is a new multi-day showcase featuring research from the University of Waterloo Faculty of Mathematics. The inaugural iteration of this annual event will bring together current and prospective industry partners to engage in discussion about leading research topics and the impacts this research will have. Fostering and supporting student engagement in mathematical innovation and entrepreneurship will be at the forefront of this initiative. 

This in-person event will feature an impressive lineup of keynote speakers, research demonstrations, lightning rounds, panel discussions, networking opportunities, and more. 

Join us April 17-18, 2023 to: 

  • Learn about leading-edge math research and applications from world-class researchers
  • Meet talented graduate and undergraduate students and be inspired by their passion for mathematics and its ability to change the world
  • Connect and share insights with thought leaders including industry experts, government policymakers, technical experts, and an ever-growing family of successful entrepreneurs with roots in the Faculty of Mathematics
  • Tour state-of-the-art research labs and take part in research demonstrations
  • Have fun with the math community during socials and more!


Research Themes

Health and Medicine

The Faculty of Mathematics is poised to support the University of Waterloo’s long-term planning for Health Futures, as presented in the ‘Waterloo at 100’ vision. The institution aims to be a national and global leader at the interface of health, society, and technology. The Faculty of Mathematics is home to several prominent researchers studying topics at this interface:

  • Mathematical modeling to better understand aspects of health and disease, including cancer treatment, kidney physiology, diabetes, antibiotic resistance, hypertension, epilepsy, hydrocephalus and syringomyelia, as well as modelling of public health and One Health processes
  • Development and application of statistical methods for public health: analysis of life history data, longitudinal data, incomplete data, sequential methods, multivariate analysis, clinical trial design, and the assessment of diagnostic tests
  • Computational health informatics: computational mechanisms for enabling more efficient and easy-to-use healthcare delivery, including large-scale data analytics problems in healthcare
  • Mathematical biology: understanding cellular and biomolecular networks driving gene expression, signal transduction, and metabolism

Anita Layton

Anita Layton picture

Health and Medicine Keynote: Anita Layton

Anita Layton is the Canada 150 Research Chair in Mathematical Biology and Medicine, and Professor of Applied Mathematics, Computer Science, Pharmacy and Biology at the University of Waterloo. She leads a diverse and interdisciplinary team of researchers who use computational modeling tools to better understand aspects of health and disease. Mathematics is their microscope! The Layton group collaborate with physiologists, biomedical engineers, and clinicians to formulate detailed models of cellular and organ function. Model simulations and predictions are used to answer questions such as: How should anti-hypertensive drugs be prescribed differently for men and women? Is it better to take one's medication in the morning or at night (or does it matter), given the interactions between the drug and our body's circadian rhythms?

Professor Layton is a Fellow of the Association for Women in Mathematics (2022), winner of the Krieger-Nelson Prize (2021), and winner of Canada’s Most Powerful Women: Top 100 Award (2021). She is an Associate Editor of SIAM Review Book Section, an Associate Editor of SIAM Journal on Applied Dynamical Systems, a Section Editor (AI/Machine Learning) of Hypertension, and an Associate Editor of Maple Transactions. Additionally, she serves as the Associate Dean, Research and International, for the Faculty of Mathematics, and chairs the Research Equity, Diversity and Inclusion Council at the University of Waterloo.

Yue Lyu

Yue Lyu Research spotlight

Student Research Spotlight: Yue Lyu

Yue Lyu is a Master’s student in Human-Computer Interaction at the WatVis Lab in the School of Computer Science, University of Waterloo, under the supervision of Drs. Jian Zhao and Keiko Katsuragawa. Her research focuses on building new user experiences and systems for autistic children by using advanced computer technologies to offer various knowledge and skills presentations as well as create opportunities for children to apply the skills in both the digital and physical worlds. Leveraging her background in computer science and business administration, her work aims to create solutions that are not only innovative but also practical and beneficial to the community. Yue has extensive experience in system design, database design, machine learning engineering, and mobile application development with multiple programming languages and platforms.

Security and Privacy

The Faculty of Mathematics at the University of Waterloo is world-renowned for its research excellence in data science, with faculty members and students exploring foundational and emerging questions in cryptography, privacy, security, machine learning, and the quantum environment. Examples of recent work in this area include:

  • Security and privacy in machine learning: Analyzing and preventing the leakage of private information from machine learning models, enabling provenance verification of machine learning models, analyzing model theft and security-relevant failure modes of machine learning models
  • Traffic classification in an increasingly encrypted web: Service detection, classification, and analysis from encrypted communication using Machine Learning
  • Methods and tools for mapping and optimizing the implementation of quantum circuits to realistic architectures

Michele Mosca

Michele Mosca picture

Security and Privacy Keynote: Michele Mosca

Michele Mosca is co-founder of the Institute for Quantum Computing at the University of Waterloo, a Professor in the Department of Combinatorics & Optimization of the Faculty of Mathematics, and a founding member of Waterloo's Perimeter Institute for Theoretical Physics. He was the founding Director of CryptoWorks21, a training program in quantum-safe cryptography. He co-founded the ETSI-IQC workshop series in Quantum-Safe Cryptography. He co-founded evolutionQ Inc. to support organizations as they evolve their quantum-vulnerable systems to quantum-safe ones and softwareQ Inc. to provide quantum software tools and services.   

He obtained his doctorate in Mathematics in 1999 from Oxford on the topic of Quantum Computer Algorithms, an MSc in Mathematics and the Foundations of Computer Science in 1996 from Oxford. In 1995 he earned a BMath in Combinatorics & Optimization and Pure Mathematics from Waterloo. His research interests include quantum computation and cryptographic tools designed to be safe against quantum technologies. 

Elham Akbari Azirani

Elham Akbari research spotlight

Student Research Spotlight: Elham Akbari Azirani

Elham Akbari received her B.Sc. and M.Sc. degrees from Sharif University of Technology. She worked as a web developer and a project manager prior to starting her Ph.D. at the University of Waterloo where she is currently studying. Her work focuses on the classification encrypted network traffic. More specifically, she works on applying few-shot and meta-learning models to encrypted network traffic to provide some visibility into the traffic at the internet service provider (ISP) level. Such models could be used to carry out attacks on encrypted network traffic using increasingly smaller labeled datasets.

Data and integrity trust

In a world of ‘Big Data’, integrity and trust are critical aspects of data analytics and management for decision-making. With the data life cycle, data needs to be correct, truthful, complete, valid, and reliable.

  • Leveraging the AI-people partnership: how to enhance and make sense of intelligent systems, addressing transparency, engagement, trust and collaboration
  • Constrained optimization using interior point and trust region approaches analysis of theoretical algorithmic properties and development of efficient and robust software for application in financial services
  • AI-powered meta-analyses: Building systems using AI and neural networks to help users gain insights from large amounts of varied data types

Ihab Ilyas

Ihab Ilyas

Data Integrity and Trust Keynote: Ihab Ilyas

Ihab F. Ilyas is a professor at Cheriton School of Computer Science at the University of Waterloo, and he is NSERC-Thomson Reuters Research Chair on data quality. Professor Ilyas’ main research interest lies in database systems, with focus on probabilistic and uncertain data management, machine learning for data quality and enrichment, and information extraction. He co-founded Tamr Inc. in 2013, a startup focusing on large-scale data integration and data cleaning. He also co-founded Inductiv in 2019 (acquired by Apple), a Waterloo-based startup using AI for structured data cleaning. Professor Ilyas’s research is premised on the belief that poor data quality is a main hurdle to adoption of AI technologies, and machine learning can be leveraged to clean datasets and train effective models accurately and timely at scale, significantly reducing time and labour costs. 

Professor Ilyas is an ACM Fellow and IEEE Fellow, a recipient of the Ontario Early Researcher Award, a Cheriton Faculty Fellowship, an NSERC Discovery Accelerator Award, and a Google Faculty Award. In 2014, Professor Ilyas was a winner of ACM Distinguished Scientist. Additionally, he was an elected member of the VLDB Endowment board of trustees (2016-2021), elected SIGMOD vice chair (2016-2021), an associate editor of the ACM Transactions of Database Systems (2014-2020), and an associate editor of Foundations of Database Systems. Professor Ilyas is currently on leave from the university, leading the Apple knowledge platform at Apple. 

Aida Sheshbolouki

Research Spotlight Data integrity

Student Research Spotlight: Aida Sheshbolouki

Aida Sheshbolouki is a Ph.D. candidate and a member of the Data Systems Group at the University of Waterloo under the supervision of Prof. M. Tamer Ozsu. Her research involves steps towards data-driven algorithm design for explainable and interpretable analytics over streaming graphs with a particular focus on (2,2)-bicliques (known as butterflies) in bipartite structures.

Founding Innovation

As the only dedicated Faculty of Mathematics in North America, our faculty make world-leading contributions to technology and innovation, while our students are trained and supported to become industry leaders and disrupters. The entrepreneurial spirit within the Faculty of Mathematics is strong, and many of our alumni have founded and exited successful businesses that translate foundational math into innovations for real-world application.

Steven Woods

Steven Woods

Founding Innovation Keynote: Steven Woods

Steven has worked in software development, research and technical leadership for more than 30 years. As co-founder of three software and services companies based in the USA (, Kinitos, NeoEdge Networks), he built strong, high-performing teams of product and technical experts based in both Silicon Valley and the Waterloo-Toronto tech corridor.  One of these companies - - pioneered the world's first interactive voice assistant and was acquired by Netscape/America Online in 2000.

Steven holds a BSc in Computer Science from the University of Saskatchewan and a Masters and PhD in Computer Science (Mathematics) from the University of Waterloo. Among numerous awards, Steven received the prestigious JW Graham Medal in Computing and Innovation from the University of Waterloo in 2010 and was recognized with a Distinguished Lifetime Achievement Award from the University of Saskatchewan in 2021.

Enamul Haque

Enamul Haque

Research Abstract: Lightning Round

Reference in Structured Data Integration for Data Science

For any enterprise whether it is a business or research organization, making better decisions is extremely important for success but that requires handling huge amounts of data analytics from diverse sources which is again a big technical challenge. Data integration helps in this regard by bringing data from disparate sources together to provide users with a unified view where they can run queries easily to obtain the required information. In this research, we discuss semantic data modelling and reference issues in structured data integration. Our work will explore and establish the theoretical foundations for related data models, reference issues, query language grammar, semantics, and constructs for the integration mechanisms as well.

Enamul Haque's Biography

At the University of Waterloo (Data Systems Group), Enamul's research area falls broadly under AI, specifically at the cross-section of Knowledge Representation and Databases. He is supervised by Dr. Grant Weddell and Dr. David Toman. Before that, he completed his MSc. In Computer Science (Data Science Lab) under the supervision of Dr. Fei Chiang from McMaster University. At McMaster, his research topic was data cleaning from an ontological perspective. Also, he has an MBA in Management and Marketing from North South University and an undergrad degree in Computer Science and Engineering from Chittagong University of Engineering and Technology, and his undergrad research project was on Machine Learning.

Zitao He

Zitao He

Research Abstract: Lightning Round

Predicting Measles Outbreaks from Vaccine Sentiments on Social Media

Vaccinating decisions for childhood diseases such as measles have been studied using coupled
models of disease dynamics and individual behaviors. Such models applied evolutionary game
theory to model people’s vaccination strategies as they are affected by social norms.

The popularity of social media has provided convenience for the spread of pro- and anti-vaccine
sentiments. High-frequency social media data also brings new ideas to study population dynamics
of infectious diseases.

This project aims to build a bridge directly from social media data to potential disease outbreaks
through deep learning models and provide novel methods for disease outbreak prediction
near thresholds. We improved existing coupled behavior-disease models by considering different
social groups and investigated the effect of homophily on disease dynamics near tipping points. We
generated realizations from our developed stochastic model. After being trained with enough theoretical
data, we showed that a deep learning model (e.g. a CNN-LSTM model) could capture EWS
directly from real-world social media data and predict a potential disease outbreak in the future.

Zitao He's Biography

Zitao He is a PhD candidate in Applied Mathematics at the University of Waterloo under the supervision of Dr. Chris Bauch. Zitao's research interest lies at the intersection of population dynamics in epidemiology, data-driven dynamical systems, and applications of evolutionary game theory. His current research focuses on critical slowing down in online social media and social networks in coupled behavior-disease systems. Specifically, he is investigating how vaccine strategy change in social networks affects the spread of infectious diseases, such as measles, and the possibility of using machine learning models to predict future disease prevalence from vaccine sentiments on social media.


Amandeep Kaur

Amandeep Kaur

Research Abstract: Lightning Round

GradXcepUNet: Explainable AI Based Medical Image Segmentation 

Medical images segmentation is an important research area. Physicians and radiologists can diagnose diseases in their patients by observing the visual features using various imaging methods like CT, MRI, X-ray, and Ultrasound. AI-based medical image segmentation models can help radiologists and experts analyze various ailments. However, the prediction results are only trustworthy when the results can be interpreted by a doctor. Our work utilizes “Explainable AI(XAI).” We propose GradXcepUNet, an XAI-based medical image segmentation model, that  couples the segmentation power of U-Net and explainability features of the Xception  classification network by Grad-CAM. The Grad-CAM trained images highlight the critical regions for the Xception classification network. Then, as the guidance, the visualized results for critical regions are combined with an existing segmentation model (U-Net) to produce the final segmentation results. With the assistance of XAI analysis and visualization, our GradXcepUNet outperforms the original U-Net and many state-of-the-art methods. The evaluation results show that we can reach a Dice coefficient of 97.73% and an Intersection over Union (IoU) score of 78.86% on the 3D-IRCADb-01 database.

Amandeep Kaur's Biography

Amandeep Kaur is a Master’s student in the Computational Physiology Team in the School of Computer Science, University of Waterloo, under the supervision of Prof. Anita Layton. Her research focuses on building domain adaptive models to improve healthcare outcomes for people using Artificial Intelligence that may help lower costs and increase efficiency and accuracy. Advancement in technology brings a massive and scalable change in community health outcomes. This idea of creating the most positive impact for the most number of people is what motivates, inspires, and propels her to pursue her passion for using computing technologies for the greater good. 

She was a MITACS Globalink Research intern at the University of Alberta, Canada, under the guidance of Prof. Anup Basu, and an awardee of the MITACS Graduate Fellowship worth $15,000 to provide financial support for her master’s.

Melissa Stadt

Melissa Stadt

Research Abstract: Lightning Round

Impact of pregnancy and lactation on maternal calcium homeostasis: Mathematical model and analysis

During pregnancy and lactation there are significant changes in the maternal body. In fact, physiological adaptations occur in almost all tissues and organs. In particular, calcium homeostasis is impacted due to the large demand for calcium by the fetus and breastmilk. Despite having a similar additional calcium demand, maternal adaptations in pregnancy and lactation are different. During pregnancy, the mother’s body increases intestinal absorption of calcium. However, during lactation, intestinal absorption returns to normal levels and the calcium needs of breastmilk are instead met by increased bone resorption and calcium reabsorption from the kidneys. The multitude of physiological processes involved in calcium homeostasis along with the complex and dynamic adaptations involved in lactation and pregnancy make this problem well-suited for investigation using mathematical modeling. In this project, we explore the differential impacts of pregnancy and lactation on calcium demand in the mother’s body. Understanding these mechanisms are key for understanding the complicated physiologies of pregnancy and lactation.

Melissa Stadt's Biography

Melissa Stadt is a PhD student in applied mathematics under the supervision of Prof. Anita Layton.  She also received her MMath degree from the University of Waterloo under Prof. Layton’s supervision. Her research focuses on using mathematical models to investigate problems in physiology related to women’s health. Currently, she is developing mathematical models of calcium homeostasis to study the effects of sex differences as well as the multi-organ adaptations that occur during pregnancy and lactation. These models can be used to unravel how maternal adaptations sustain the calcium additional demands of the fetus and milk in pregnancy and lactation, respectively. 

Lena Podina

Lena Podina

Research Abstract: Lightning Round

A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data

Lena Podina, Brydon Eastman, Mohammad Kohandel

Given ample experimental data from a system governed by differential equations, it is possible to use deep learning techniques to construct the underlying differential operators.This small data regime in machine learning can be made tractable by providing our algorithms with prior information about the underlying dynamics. Physics Informed Neural Networks (PINNs) have been very successful in this regime (reconstructing entire ODE solutions using only a single point or entire PDE solutions with very few measurements of the initial condition). We modify the PINN approach by adding a neural network that learns a representation of unknown hidden terms in the differential equation. The algorithm yields both a surrogate solution to the differential equation and a black-box representation of the hidden terms. These hidden term neural networks can then be converted into symbolic equations using symbolic regression techniques like AI Feynman. In order to achieve convergence of these neural networks, we provide our algorithms with (noisy) measurements of both the initial condition as well as (synthetic) experimental data obtained at later times.

Lena Podina's Biography

Lena is completing her Master of Mathematics with a focus on machine learning applications in biology. She grew up in Vancouver and did her undergrad at UBC. She is a bird enthusiast, and in her free time, she enjoys reading, doing yoga, and ice skating.

Stéphanie Abo

Stephanie Abo

Research Abstract: Lightning Round

Investigating the role of circadian rhythm-based exercise and diet in type 2 diabetes

Justification of the importance of the work being presented 

We develop mathematical models of metabolism and its interactions with our body's internal clock, also known as the circadian clock. Circadian rhythm disruption can contribute to metabolic dysregulation (diabetes, hypertension) and memory issues. Although the brain centrally controls circadian rhythms, peripheral tissues like the liver and muscles impose their own rhythms. It is therefore important to conduct research that directly compares exercise at various times versus meal ingestion. We present a whole-body exercise and diet model that incorporates controls from the liver and skeletal muscle peripheral clocks. Our model predicts that post-meal exercise improves glycemia in patients with type 2 diabetes mellitus (T2DM), and that the benefits of morning or afternoon exercise vary according to chronotype. The model could be used to guide future experimental studies aimed at determining the effect of circadian rhythms on the efficacy of exercise and diet for T2DM. 

Stéphanie Abo's Biography

My name is Stéphanie Abo, and I'm a fourth-year PhD student in applied mathematics. My research is devoted to understanding biological clocks, also called circadian clocks. I specialize in the development of mathematical models of the immune system and metabolism and their interactions with our body's clock, both in healthy conditions and diseases like diabetes or immune infections. To understand biological timekeeping, I use techniques from a variety of fields, including computer simulations, detailed mathematical modeling, and mathematical analysis. My research aims to make predictions that can be verified experimentally.

Yuzhe You

Yuzhe You

Research Abstract: Lightning Round

Adversarial machine learning focuses on studying attacks that target machine learning algorithms and the
defenses against worst-case attacks to strengthen the adversarial robustness of existing models.
Specifically for image classification tasks, it is often difficult to comprehend the underlying logic behind
adversarial examples even for machine learning practitioners due to key challenges such as 1) the attacks
exploiting ``non-robust" features that are not human-interpretable and 2) the perturbations applied being
almost imperceptible to human eyes. In this study, we propose an interactive web-based application that
aims to visualize the properties and consequences of evasion attacks for adversarial machine learning
learners and provide data and model performance analytics for more experienced practitioners. In
addition, we design the visualization to be informative in terms of robustness comparison evaluation
between different models and show how prediction results may alter when varying levels of perturbations
are applied to the data instances.

Yuzhe You's Biography

Yuzhe is a Master of Mathematics student from the Cheriton School of Computer Science, University of Waterloo. She is under the supervision of Dr. Jian Zhao and has a background in Information Visualization, Machine Learning, Transportation Analysis, Data Analytics & more. Yuzhe is a member of both WatVis research group and iTSS lab, where she conducts research in the area of InfoVis for AI Explainability with a focus on visualizing Adversarial Machine Learning and traffic analysis. Specifically, she designs interactive visualization tools that enhance AI interpretability & help improve the adversarial robustness of machine learning models.

Mackenzie Cameron

Mackenzie Cameron

Research Abstract: Lightning Round

Spatial and Temporal Discounting in a Coupled Social-Climate Model
This research discovers the effects on mitigation and projected temperature anomalies through applying spatial and temporal discounting in human decision making in a coupled social – climate model. While past climate models have implemented a static human contribution to climate change through Representative Concentration Pathways and Shared Socioeconomic Pathways, this research develops and implements a dynamic social system coupled to the climate system. This idealizes a two-way feedback between human decision making, social norms, and human behaviour with changes in the climate. The spatial and temporal discounting addressed in this research is guided by the definition of judgemental discounting, such that individuals consider climate change conditions to be worse in other locations and further in time. We begin by implementing a coupled system of differential equations where a two-patch model for the human subsystem includes spatial discounting in the form of updated utility functions. The utilities determine the impacts of individuals in different populations taking up mitigative or non-mitigative strategies. Then temporal discounting is applied through percentage discounting per year within the system rather than a varied parameter. Results will be obtained through implementing the coupled feedback model with climate, social, and economic data. We hypothesize that the results will directly address the fundamental problem in dealing with climate change: where the negative effects of climate change are often portrayed as “there and later”, thus reducing the motivation to mitigate “here and now”.

Mackenzie Cameron's Biography

Mackenzie is a second year Master's student in Applied Math studying coupled human environment systems. Her research focuses on the interactions between human behaviour and climate change, stiving to model key barriers to mitigation. Apart from research, Mackenzie is an active member of the math faculty community through her role on the Math Graduate Student Association. 

Amrita Punnavajhala

Amrita Punnavajhala

Research Abstract: Lightning Round

Climate change mitigation efforts across the world are informed by results from mathematical models of climate change. These models, while immensely useful, have been criticized for assuming that anthropogenic carbon emissions are driven solely by demographic and economic forces, thus neglecting descriptions of human behaviour. Recently, social and behavioural frameworks have started making their way into models of climate change, resulting in ‘coupled social-climate models’. Results from these models show that the inclusion of human behaviour, driven by social and psychological forces, can significantly alter climate change projections.

We build on existing work by constructing a coupled social-climate model with country-level structure and parameterization. This allows us to introduce the element of diverse, dynamic human behaviour, which we model using tools from evolutionary game theory. Ultimately, if we are to leverage individual behavioural change to aid climate change mitigation, we need to develop mathematical models to understand the complex interactions between human behaviour and climate change. This effort could also benefit us by predicting outcomes, yet unforeseen, which may prove crucial in helping us prepare our response to climate change.

Amrita Punnavajhala's Biography

Amrita is a third-year PhD candidate in Applied Mathematics. She works in the area of coupled social-climate models, focusing on the mathematical representation of diverse and dynamic human behaviour. She hopes that the outcomes of her research will be useful to climate change mitigation efforts.


Shervin Hakimi

Shervin's bio

Research Abstract: Lightning Round

Beyond the Bones: Calcium Transport and Regulation in the Kidney
Calcium homeostasis is essential for the proper functioning of various cells and the kidney plays a vital role in this balance. The goal of this study is to expand our previous mathematical model of transepithelial transport along the male rat nephron to include the transport of calcium and to investigate the effects of inhibiting transepithelial sodium transport on urinary calcium excretion and its reabsorption along different nephron
segments. We simulated the effects of inhibiting the Na-K-2Cl transporter (NKCC2), reflecting the effects of the hypertensive drug, furosemide, the inhibition of the Na+/H+ exchanger (NHE3), the Na-Cl cotransporter (NCC), and the Epithelial Na channel (ENaC). Our results suggest that disruption in sodium transport significantly impacts renal calcium transport: NKCC2 inhibition significantly decreased calcium renal reabsorption along the thick ascending limb (TAL) and substantially increased calcium excretion by 8.7 folds in the case of complete inhibition. Our simulations of NHE3 inhibitions predicted reduced calcium reabsorption along the proximal tubule (PT) by ~46% and slightly increased calcium urine excretion. NCC inhibitions had slight effects on calcium reabsorption. The most notable effect of ENaC inhibition was the increased calcium reabsorption along the connecting tubule (CNT), resulting in a considerably reduced calcium excretion.

Shervin Hakimi's Biography

I'm currently pursuing a MMath degree in Applied Mathematics under the supervision of Dr. Anita Layton with a focus on calcium transport and regulation in the kidney. I'm particularly interested in studying the effects of drugs and investigating sex differences in these processes.

Before starting my Master's, I worked as a data scientist at Mindpax, a digital health company based in Prague that's committed to improving mental health outcomes through innovative technology. I received my Bachelor's degree in Electrical Engineering from Sharif University of Technology, where I focused on control theory. My thesis project involved investigating EEG signal applications to control wheelchair driving. In my free time, I enjoy learning new languages (currently fluent in English, Persian, German and a bit of French), reading, or being outdoors.

Cameron Meaney

Cameron Meaney

Research Abstract: Lightning Round

A longstanding problem in mathematical oncology is to accurately estimate the mathematical parameters which govern the growth of glioblastomas, a particularly aggressive form of brain tumour. While a method exists to derive these parameter estimates, it is plagued by problems including a high sensitivity to measurement error, a reliance on unrealistic mathematical assumptions, and a dependence on precise human measurements. Using deep learning techniques such as PINNs, it is possible to develop an algorithm that is capable of producing more accurate estimates of the key parameters while eliminating the shortcomings of the existing method. As part of my PhD research, I created a deep learning algorithm capable of making patient-specific predictions more accurately than the existing method. The data used in the research was collected by my collaborators at St. Michael’s Hospital and the University of Toronto. My computational approach allows for improved insight into the fundamental biological mechanisms that govern
tissue heterogeneity. This work has numerous implications in treatment planning, drug development, and patient survival.

Cameron Meaney's Biography

Cameron Meaney is a 4th year PhD candidate in the University of Waterloo Department of Applied Mathematics whose research focus is in mathematical oncology. His specializes in using mathematical models and machine learning approaches to better our understanding and treatment of cancer. In his previous work, he has developed improved procedures for radiation, chemo-, and nanoparticle therapies, as well as developed methods for furthering precision cancer medicine.

David Thomas Radke

David Thomas Radke

Research Abstract: Lightning Round

Individual artificial intelligence (AI) agents learning to work together is an important problem in multiagent systems (MAS). We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i.e., teams). Our model of credo regulates how agents optimize their behavior for the groups they belong to. We evaluate credo in the context of challenging social dilemmas with reinforcement learning agents. Our results indicate that the interests of teammates, or the entire system, are not required to be fully aligned for achieving globally beneficial outcomes. We identify two scenarios without full common interest that achieve high equality and significantly higher mean population rewards compared to when the interests of all agents are aligned.

David Thomas Radke's Biography

I am in my 5th year of my PhD at the University of Waterloo studying Artificial Intelligence with Dr. Kate Larson and Dr. Tim Brecht. My research focuses on understanding how teams and mixed incentives influence the behavior of learning agents. This includes research in multiagent reinforcement learning, multiagent systems, and team structures.


Pritha Dutta

Pritha Dutta

Research Abstract: Lightning Round

Kidneys: regulator of magnesium homeostasis

Magnesium plays an important role in several physiological functions and metabolic and biochemical processes within the cell and its deficiency has been associated with neurological disorders, heart disorders, hypertension, and type 2 diabetes. The kidneys play a central role in maintaining serum magnesium within a narrow range (0.70–1.10 mmol/L). In this study, an epithelial cell-based computational model of solute transport in rat kidneys was developed to understand the mechanism of magnesium transport along different nephron segments. Since magnesium transport is tightly coupled to sodium transport, the model was used to simulate the effects of different antihypertensive diuretics on renal magnesium reabsorption. The simulations showed that loop and thiazide diuretics result in increased magnesium excretion, whereas potassium-sparing diuretics favor magnesium retention.

Pritha Dutta's Biography

Pritha Dutta is a Postdoctoral Fellow in the Department of Applied Mathematics, University of Waterloo, Canada. She is currently working on sex-specific computational models of calcium and magnesium transport in the kidney and whole-body magnesium homeostasis in Professor Anita Layton’s lab. She completed her Ph.D. from Nanyang Technological University, Singapore in 2022. During her Ph.D., she worked on mathematical models of pancreatic beta-cell dysfunction and insulin resistance in the presence of hyperglycemia. She is interested in Computational Biology and Complex Systems.

Mojtaba Valipour

Mojtaba Valipour

Research Abstract: Lightning Round

Daneshbaz: Open Research, Open Problem, Open Ideas.

Publishing a paper can take a long time, and collaborating with someone you don't know can be challenging. Throughout their careers, researchers encounter these issues, but it is particularly crucial for those in their early stages and newcomers, such as graduate students. Their small network limits their ability to facilitate such collaborations due to their inexperience and lack of publications. In addition, because the status quo usually prevents publication updates, papers will no longer be subject to revisions. This can create a unique challenge for those just entering the research field, as they may not have the resources or experience to navigate these obstacles. Moreover, without periodic revisions, the integrity of the published work is put at risk. supports current practices while improving the whole process, advocating for a transparent, fast, and hassle-free publication system. Our proposed approach creates a more efficient publishing ecosystem, facilitating collaboration among researchers, introducing an alternative to dead papers, and helping shape research's future. There is no doubt that Daneshbaz is a revolution in research.

Mojtaba Valipour's Biography

Mojtaba Valipour is a Ph.D. candidate studying computer science at the University of Waterloo. He is passionate about Artificial General Intelligence, Machine Learning, and Causal Inference. His research focuses on devising novel approaches to make large language models more efficient and controllable. In addition, he views the current scholarly publication system as outdated and inefficient. He aims to develop a platform that removes barriers for researchers, allowing them to focus on their research and publish scientific results without hassles.

Kanav Mehra

Kanav Mehra

Research Abstract: Lightning Round

Citizen-focused democratic processes where participants deliberate on alternatives and then vote to make the final decision are increasingly popular today. While the computational social choice literature has extensively investigated voting rules, there is limited work that explicitly looks at the interplay of the deliberative process and voting. In this paper, we build a deliberation model using established models from the opinion-dynamics literature and study the effect of different deliberation mechanisms on voting outcomes achieved when using well-studied voting rules. Our results show that deliberation generally improves welfare and representation guarantees, but the results are sensitive to how the deliberation process is organized. We also show, experimentally, that simple voting rules, such as approval voting, perform as well as more sophisticated rules such as proportional approval voting or method of equal shares if deliberation is properly supported. This has ramifications on the practical use of such voting rules in citizen-focused democratic processes.

Kanav Mehra's Biography

Kanav Mehra is a Master’s student in computer science at the University of Waterloo. His broad research interests lie in the domain of artificial intelligence, computational social science, and multiagent systems, especially where these methods can be applied to derive insights for governance, policy-making, and human-system interactions. His current research is focused on designing fair and proportional deliberation and voting mechanisms for elections in participatory democratic processes, with the objective of improving minority representation in public decision-making and local governance. Furthermore, he is also studying how well-studied notions of fairness and proportionality developed for social choice literature (voting systems) can be appropriately modified to develop fair and proportional search and recommender systems. Previously, his research focused on building novel, scalable methods that leverage social media data to assist relief operations during disasters and emergencies. In this capacity, he also volunteered as a Data Scientist for the Data Science for Social Good foundation (DSSG Solve) and the International Water Management Institute (IWMI) to help his local community impacted by a devastating cyclone. 


Research Discovery Days Schedule

April 17, 2023     April 18, 2023  
Time Activity   Time Activity
7:30 AM Registration, Continental Breakfast and poster set-up   7:30 AM Registration, Continental Breakfast, and poster set-up
8:30 AM Opening Remarks and the Dean's welcome   8:30 AM Opening remarks and Dean's welcome
8:45 AM Theme 1: Address from Invited Partner   8:45 AM Founding Innovation: Steve Woods's Keynote and Q&A
8:50 AM Theme 1: Health and Medicine, Anita Layton (faculty) plus Q& A    10:00 AM Snack break and poster session 
9:45 AM Theme 1: Health and Medicine, Research spotlight (grad student) Yue Lyu, plus Q&A    10:30 AM Lightning talks presented by students
10:00 AM Lightning talks presented by students   11:30 AM Lunch Break
10:30 AM Snack break and poster session    12:40 PM Director of Advancement remarks
11:00 AM Theme 2: Address from Invited Partner   12:45 PM Entrepreneurship 101: Workshop by Zero Experience
11:05 AM Theme 2: Security and Privacy, Michele Mosca (faculty) plus Q& A    3:00 PM Closing Remarks
12:00 PM Lunch and poster session       
1:00 PM Theme 2: Security and Privacy, Research spotlight (grad student) Elham Akbari Azirani, plus Q&A    Poster sessions during the snack break and latter half of lunch will be staffed by students
1:45 PM Lightning talks presented by students       
2:15 PM Theme 3: Address from Invited Partner      
2:20 PM Theme 3: Data and Integrity Trust, Ihab Ilyas (faculty) plus Q& A       
3:15 PM Break      
3:30 PM Theme 3: Data and Integrity Trust, Research spotlight (grad student) Aida Sheshbolouki, plus Q&A       
3:45 PM Closing Remarks        
4:30 PM Cocktail Hour      
5:30 PM Dinner      

Poster sessions during the snack breaks and latter half of lunch will be staffed by students


Recommended Hotels

Delta Hotel by Marriott Waterloo

110 Erb Street West

Waterloo, Ontario, Canada, N2L 0C6


Courtyard by Marriott Waterloo St. Jacobs

50 Benjamin Road East

Waterloo, Ontario N2V 2J9

Event Dates

April 17-April 18, 2023

Event Location

Federation Hall, University of Waterloo

200 University Ave. W,
Waterloo ON
N2L 3G1