Data Sciences Institute Research Day 2023

Please note: the event has ended. Watch the video recording here.
The info is being left up for reference. Stay tuned for next year’s event

On September 27, 2023, join us for the Data Sciences Institute Research Day, featuring leading
voices in data science.  

The DSI Research Day #DataSciencesDay showcases the work of the DSI community and
provides a forum for collaborators in academia, industry and government to connect. 
The day includes a poster session to highlight the innovative research taking place in DSI-funded projects.

Join us for engaging lightning talks and discussions, networking activities and interactive panels.
Discover cutting-edge research through presentations and posters presented by DSI researchers.  

9:00 – 9:30 am
Registration Opens
9:30 – 9:40 am
Welcome and Opening Remarks

Prof. Lisa Strug, Director, Data Sciences Institute
9:40 am – 10:45 am
Frontiers of Data Technologies as a force for change
Challenges and opportunities for applied data sciences in humanitarian and development - Keynote

Dr. Manuel Garcia-Herranz, Data Principal Researcher, UNICEF
10:45 am – 11:00 am
11:00 am – 12:00 pm
Data for Social Good - Lightning Talks

Equitable prioritization of active transportation infrastructure in Canadian cities – Madeline Bosma-Fisher

Urban parks for people: anonymized movement data to determine access and equity – Marie-Josee Fortin

Social and Behavioural Determinants of Health in Prognostic Machine Learning Models for Patient Outcome Prediction – Zahra Shakeri

Information geographies of water supply schedules in Coimbatore, India: Implications for equitable water access – Nidhi Subramanyam

Moderator – Bree McEwan, Associate Director, University of Toronto Mississauga, Data Sciences Institute
12:00 – 1:30 pm
Lunch and Poster Presentations

Posters will be displayed during the lunch and trainees will be available to discuss their work
1:30 – 2:30 pm
Methodologies in Novel Applications - Lightning Talks

Inequality in Childcare: The Case of Nannies in Canada – Monica Alexander

Reliably auditing fairness in health applications with semi-supervised inference – Jessica Gronsbell

The rise of social media and the transformation of influence: Joining foundational sociological theory and data science to rethink influence in social systems - Peter Marbach

Machine Learning for Dynamic and Short-Term Falls Risk Assessment in People with Dementia - Babak Taati

Moderator – Ethan Fosse, Associate Director, University of Toronto Scarborough, Data Sciences Institute
2:30 – 2:45 pm
2:45 – 4:00 pm
Data Science for an Effective Workforce – Panel

Yves Jaques, Chief, Frontier Data & Tech Unit, UNICEF

Ann Meyer, Director, BioInnovation Scientist Program, adMare BioInnovations

Dana Ohab, Associate Partner, Digital & Emerging Technology, EY

Mark Fiume, Co-Founder & CEO, DNA Stack

Moderator – Lisa Strug, Director, Data Sciences Institute
4:00 – 4:10 pm
Closing Remarks

Prof. Lisa Strug, Director, Data Sciences Institute


Manuel García-Herranz 

Data Principal Researcher, UNICEF

Dr. García-Herranz is the Principal Researcher of the newly created Frontier Data Technology Unit of UNICEF´s Chief Data Officer. He joined UNICEF in 2014 and has focused on bringing the value of Big Data, AI and other Frontier Data Technologies to UNICEF. Within UNICEF, he has served as Chief Scientist of its AI and Frontier Data Initiative and has led THE Frontier Data research at the Office of Innovation, the Information and Communication Technologies Division and the Data and Analytics Division. He is strongly focused on creating research, tools, partnerships and internal capacity that can transform AI, Big Data and other Frontier Data Technologies into equitable tools to better respond to the problems of the most vulnerable children.

Dr. García-Herranz is deeply interested in human behavior and dynamics, particularly in computational social sciences, network analysis and complex systems and in how new types of data and analysis can be used for human development and humanitarian response. His work also focuses on the future of digital vulnerabilities, working on data ethics and privacy and in identifying emerging computational inequalities from data bias to algorithmic unfairness.

He holds a PhD on Computer Science from the Universidad Autonoma de Madrid where he was an assistant professor in the Department of Computer Science.

Data for Social Good - Lightning Talks

Madeleine Bonsma-Fisher 

Data Sciences Institute Postdoctoral Fellow  

Dr. Madeleine Bonsma-Fisher is studying how safe cycling networks influence the equitability of destinations people can access by bicycle in Canadian cities. She completed her MSc and PhD in biophysics at the University of Toronto and holds a bachelor’s degree in Honours Co-operative Physics from the University of Waterloo. She is a co-founder of UofT Coders, a group for graduate students to teach each other programming skills in a supportive peer environment. She is a volunteer and board member with the advocacy group Bike Ottawa and has appeared on CBC News and several podcasts to discuss cycling infrastructure and advocacy. You can find her on Twitter at @mbonsma or riding around Toronto and Ottawa on her beloved cargo bike. 

Marie-Josee Fortin  

University Professor, FRSC 
Canada Research Chair in Spatial Ecology 
Department of Ecology & Evolutionary Biology, Faculty of Arts & Science 
University of Toronto 

An ecologist by training, Prof. Fortin has four main research areas: spatial ecology, disturbance ecology, conservation, and spatial statistics. Her research program studies the effects of global change (land-use and climate) on species spatial dynamics at the landscape and geographical range levels both in multiuse forested ecosystems and aquatic networks to maintain biodiversity and species conservation.

Zahra Shakeri  

Assistant Professor 
Dalla Lana School of Public Health 

Dr. Zahra Shakeri is an Assistant Professor of Health Informatics and Information Visualization and the director of the Health Informatics, Visualization, and Equity (HIVE) Lab at the Institute of Health Policy, Management and Evaluation at the Dalla Lana School of Public Health, University of Toronto. She received her PhD in Computer Science from the University of Calgary and was a Postdoctoral Fellow at Gehlenborg Lab in the Department of Biomedical Informatics at Harvard Medical School. Dr. Shakeri’s research centers on health informatics, machine learning, natural language processing, information visualization, precision (public) health, digital health, and social media analysis. 

Nidhi Subramanyam  

Department of Geography & Planning, Faculty of Arts & Science 
University of Toronto  

Dr. Nidhi Subramanyam’s research investigates just approaches to plan and govern water and wastewater infrastructures in the face of rapid urbanization and the growing impacts of climate change. She also investigates questions on urban governance and rural-urban transitions in cities of the global South. At their core, all her projects interrogate how planning processes reflect and reinforce the status quo in moments of transition and how different social groups contest inequalities and transform planning at such moments to create just and sustainable futures. 

Dr. Subramanyam is a faculty affiliate with the School of Cities, the Data Sciences Institute, and the Centre for South Asian Studies at University of Toronto. She welcomes inquiries from undergraduate and graduate students interested in pursuing water-sanitation infrastructure planning, planning and governance in cities of the Global South, rural-urban transitions, just adaptation, environmental dispossessions, and critical data studies as it applies to environmental planning. 

Methodologies in Novel Applications - Lightning Talks

Monica Alexander 

Assistant Professor 
Departments of Statistics and Sociology, Faculty of Arts & Science 
University of Toronto 

Prof. Monica Alexander’s research focuses on statistical demography, mortality and health inequalities, and computational social science. She has worked on research projects with organizations such as UNICEF, the World Health Organization and the Bill and Melinda Gates Foundation. She has served as a Fellow at Data Science for Social Good, and has worked at the ANU’s Centre for Aboriginal Economic Policy Research. She received a PhD in Demography and a Masters in Statistics from the University of California, Berkeley. Prior to that she received a Masters of Social Research from the ANU and a Bachelor of Science at the University of Tasmania. 

Jessica Gronsbell 

Assistant Professor 
Department of Statistics, Faculty of Arts & Science 
University of Toronto 

Professor Gronsbell specializes in machine learning and statistical inference methods for electronic health records and mobile health data. 

She received her bachelor of arts in applied mathematics at the University of California, Berkeley and her PhD in biostatistics at Harvard University under the direction of Tianxi Cai. Gronsbell completed her postdoctoral work in the Department of Biomedical Data Science at the Stanford School of Medicine with Lu Tian and spent a few years working on the Mental Health Research and Development team at Alphabet’s Verily Life Sciences. 

Peter Marbach 

Department of Computer Science, Faculty of Arts & Science 
University of Toronto 

Prof. Peter Marbach’s research interests are in the theory of social networks, user behavior and interactions in social networks, game theory and social choice, information networks and machine learning, algorithms for social networks analysis, online auctions and bidding algorithms, optimal design of online social networks, social networks and economic theory. He  received the Eidg Dipl El-Ing from the ETH Zurich, Switzerland, MSc in electrical engineering from the Columbia University, NY, and PhD in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts. 

Babak Taati 

Senior Scientist, KITE Toronto Rehabilitation Institute, University Health Network 

Babak Taati, PhD PEng, is a Senior Scientist at KITE | Toronto Rehab – UHN and an Associate Professor at the University of Toronto. His research applies computer vision technologies to continuous health monitoring and the management of chronic conditions. A major focus of his work is to move away from the laboratory and contrived situations, and to develop devices that work reliably in the home or in long-term care. Examples include affordable pain assessment technology to continually monitor people with advanced dementia who cannot verbally express their pain and automated fall risk assessment based on changes in gait. 

Data Science for an Effective Workforce - Panel

Mark Fiume

Co-Founder & CEO, DNA Stack

Dr. Fiume is the co-founder and CEO of DNAstack, Toronto-based genomics software company with a mandate of making genomics and clinical information globally accessible and useful, supported by major cloud providers, and academic and clinical research partners around the world. an entrepreneur, researcher, and thought-leader in genomics. He has a strong presence supporting Toronto- and Canadian-led genomics initiatives and sequencing centres, and is also the co-founder of the Canadian Genomics Cloud and Autism Sharing Initiative.

He serves as Chair of the Beacon Project for the Global Alliance for Genomics & Health, defining global technical, ethical, and security standards for genomic mutation sharing. He is the founder and Lead of the Beacon Network, the world’s largest search engine for public sharing of genomic mutations, which has facilitated millions of searches and has led to disease-gene discoveries and clinical translation. He chairs the Discovery Work Stream for the Global Alliance for Genomics & Health, with the mandate of building the Internet of Genomics, i.e. digital network infrastructure to connect genomics and clinical information systems to accelerate genomics and biomedical research and discovery worldwide.

He earned his doctoral degree at the University of Toronto and the Hospital for Sick Children by exploring the intersection of biology and computer science, inventing novel software algorithms and applications for the detection of genetic mutations, particularly structural and copy number variations.. He is committed to developing transformational technologies to better quality and longevity of human life.

Yves Jaques 

Chief of the Frontier Data and Tech Unit 

Yves Jaques has led data and tech teams in the private and public sector across domains as diverse as epidemiology, agriculture, and retail analytics. Following several years as the technical director for big data and machine learning at WGSN, a New York based fashion forecasting company, he is currently Chief of the Frontier Data and Tech Unit delivering on data science, geospatial data, and open data solutions for UNICEF. 

Ann Meyer 

Director, BioInnovation Scientist Program 
adMare BioInnovations 

Dr. Ann Meyer is the Director, BioInnovation Scientist Program at adMare BioInnovations. The program aims to provide early-career scientists with the foundational drug development knowledge and professional skills to succeed in the Canadian life sciences industry. Previously, Ann was Manager, Sector Innovation and Programs at Ontario Genomics supporting the implementation of strategic initiatives to bring new genomic-derived solutions to Ontario, and the Manager, Knowledge and Research Exchange at the Ontario Institute for Cancer Research overseeing the Canadian Bioinformatics Workshops series. Ann is a University of Guelph alumni with a PhD in Plant Agriculture, Crop Breeding and Genetics, and a BSc Honours in Biological Science.  

Dana Ohab 

Associate Partner, Digital and Emerging Technology practice 

Dana Ohab is passionate about connecting data, digital innovation and business strategies to drive transformation in technology, processes and people. She joined EY in 2021 after working 15+ in industry. She helps clients to create a data-enabled culture that they will thrive in, Dana leverages a “human-at-the-center” approach to explore business-focused issues, and create technology-enabled solutions. She holds an HBSc in Psychology and Neuroscience from University of Toronto, an MBA from Ivey School of Business, University of Western Ontario. 

Lisa Strug 

Director, Data Sciences Institute  

Director, Canadian Statistical Sciences Institute (CANSSI), Ontario Region  

Professor, Departments, Computer Science and Statistical Sciences, University of Toronto  

Senior Scientist, Program in Genetics and Genome Biology, The Hospital for Sick Children  

Dr. Lisa Strug is the inaugural Director of the Data Sciences Institute, a tri-campus, multi-institutional, multi-disciplinary hub for data science activity at the University of Toronto and affiliated Research Institutes. Dr. Strug holds several other leadership positions at the University of Toronto including the Director of the Canadian Statistical Sciences Institute Ontario Region (CANSSI Ontario), and at the Hospital for Sick Children as Associate Director of the Centre for Applied Genomics and the Lead of the Canadian Cystic Fibrosis Gene Modifier Consortium and the Biology of Juvenile Myoclonic Epilepsy International Consortium. She is a statistical geneticist, and her research focuses on the development of novel statistical approaches to analyze and integrate multi-omics data to identify genetic contributors to complex human disease. She has received several honours including the Tier 1 Canada Research Chair in Genome Data Science. 


Poster Presenter

Poster Title

Tara Henechowicz

DSI Doctoral Student Fellow
A comprehensive investigation of the shared genetic architecture of motor traits, neuromotor traits, and music engagement.
Peiyu Liao

DSI SUDS Scholar 2023
Analysis of risk factors of cardiovascular diseases using machine learning from pathobiological and behavioral perspectives
Mavra Ahmed

DSI Trainee
Application of a machine learning approach to predict the free sugars content of foods and beverages in the Canadian food supply
York Xu

DSI Trainee
AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery
Caitlin Harrigan

DSI Doctoral Fellow
Automated registration of cell and tissue images
Lindsay Katz & Callandra Moore

DSI Trainees
Challenges of detecting donors' gender based on names
Soumita Ghosh

Schmidt Science Fellow
CleVER-LG (Cell-Free DNA Methylation with Clinical Variables) - Machine learning based early recognition of liver graft pathology
Mohammad Rashidujjaman Rifat

DSI Trainee
Compassionately: A Keyboard Interface to Highlight and Justity Harmful Posts Online
Sangwook Kim

DSI Doctoral Fellow
Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications
Fatema Tuz Zohora

Schmidt Science Fellow
Delineate cell-cell communication in anti-cancer drug resistance using multi-modal deep learning model on single-cell data
Mariangela Castra Arteaga

DSI Trainee
Developing a Transgender Women-Led HIV Data Justice Guide for Health Researchers.
Gengming He

DSI Doctoral Fellow
Detecting compound heterozygous effect with long-read sequencing data
Lynn Li

DSI SUDS Scholar 2022
Diversification and evolution of ubiquitin across the Kingdom Fungi
Tianyi Liu

DSI Doctoral Fellow
Energy-based Modelling for Single-cell Data Annotation
Qin Liu

DSI Trainee
Exploring Student Data Analytics in Engineering Education Research and Practice
Viki Prasad

DSI Trainee
From benchmarking to development of quantum algorithms: Towards the application of quantum computing
Vedant Choudhary

DSI Trainee
FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs
Marcus Barnes & Rogan Gutwillinger

DSI Trainees
Gender Inference: Can ChatGPT Outperform Common Commerical Tools?
Sasha Mitina

DSI Trainee
Genome-wide characteristics of tandem repeat expansions in cardiomyopathy
Henry Quach

DSI Doctoral Student Fellow
Heterogenous cell types and developmental dynamics form the early human fetal lungs
Tselot Tessema

DSI Trainee
Improving Public Health Decision-making using Geospatial Uncertainty Visualizations
Kevin Kuriakose Joseph

DSI Trainee
Inequality During Intermittent Water Supply Improvement Projects: Evidence and Mitigation Opportunities in Coimbatore, India
Spencer Farrell

DSI Trainee
Inferring cell fate dynamics in fetal lung development
Deleram Pouyabahar

DSI Doctoral Fellow
Interpretable single-cell transcriptomics data analysis using factor decomposition
Jingcheng Shao

DSI SUDS Scholar 2022
Investigating the Relationship between Air Pollution and Crohn’s disease, Gastrointestinal Biomarkers and Microbiome in Canada
David Dayi Li

DSI Doctoral Fellow
Learning how to count again: Inferring Globular Cluster Counts in Ultra-Diffuse Galaxies with Bayesian Marked-Dependently Thinned Poisson Point Process
Jiesse Tie

DSI Trainee
LLMs in Scientific Programming Tasks
Grace Yu

DSI SUDS Scholar 2022
Mapping the Milky Way Halo with Blue Horizontal Branch Stars
Miranda Doris

DSI Trainee
Modelling spatial & temporal variability of air pollution in an area of unconventional natural gas operations
Guanlan Hu

DSI Trainee
Natural Language Processing and Machine Learning Approaches for Food Composition Database
Matthew Boccalon

DSI Trainee
ORCESTRA for transparent and reproducible cancer datasets to strengthen life science data collaborations
Coreen Daley

DSI Trainee
Phase-specific density and proximity metrics for oil and gas wells: Exposure estimates in British Columbia
Preet Mistry

DSI SUDS Scholar 2023
Predicting Chemical Bond Strengths with Graph Neural Networks and Higher Quality Dataset Generation
Anton Sugolov

DSI SUDS Scholar 2023
Quantum circuit architecture selection via local optimization towards quantum machine learning of chemical bond dissociation energy
David Veitch

DSI Doctoral Fellow
Rank-adaptive covariance changepoint detection for estimating dynamic functional connectivity from fMRI data
Madeleine Bonsma Fisher

DSI Postdoctoral Fellow
Revealed preferences for low-stress cycling routes in Strava data
Rongqian Zhang

DSI Doctoral Fellow
SAN: Mitigating inter-scanner biases in high-dimensional neuroimaging data via spatial Gaussian process
Xiao Shang

DSI Trainee
Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework
Dingke Tang

DSI Trainee
The Synthetic Instrument: From sparse association to sparse causation
Henry Leung

DSI Doctoral Fellow
Towards an Astronomical Foundation Model for Stars with a Transformer-based Model
Kaushar Mahetaji

DSI Trainee
Tracking global data flows in the app ecosystem
Xiaoning Wang

DSI SUDS Scholar 2023
Using Natural Language Processing to Understand Naturalistic Speech Perception
Garland Xie

DSI Trainee
Using smartphone GPS data to learn how people engage in urban parks
Julia Watson

DSI Trainee
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics

September 27, 2023
9:30 a.m. to 5:00 p.m.
MaRS Auditorium, 101 College Street
Toronto, ON

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