I am a Ph.D. candidate in the Department of Mathematics and Statistics, working under the supervision of Dr. Paul McNicholas. My research focuses on developing unsupervised learning techniques, specifically model-based clustering for mixed-type data. During this fellowship, I plan to implement and develop clustering and classification methods for analyzing multiplexed tissue images in pathology. This fellowship project will be co-supervised by Dr. Jonathan Bramson from the Department of Pathology and Molecular Medicine and Dr. Paul McNicholas from the Department of Mathematics and Statistics.

The aim of this project is to develop a robust and computationally efficient framework for
the statistical analyses of genetic variants using quantile regression. The proposed framework
aims to achieve similar performances with respect to power and computational efficiency as
OLS models under the correct model specification, while outperforming OLS models under
true complex interactions with unknown/unmeasured variables. The project will involve
empirical data analysis in the discovery of novel genetic variants for Body Mass Index (BMI)
as a complex phenotype with gene-environment components. This project will utilize the
publically data base of genotypes and phenotypes (dbGAP) on the NIH servers, and aims
to publish and disseminate any knowledge as a result of this work.

How Prescription Drug Coverage affects Health Services Utilization among Immigrants in Canada using Linked Data

I am a PhD student in the Health Policy Program, working under the supervision of Dr. Lisa Schwartz. My thesis explores how health insurance coverage impacts health service utilization and outcomes of vulnerable populations, such as refugees. I completed my Honours BSc in Human Biology (Health and Disease) at the University of Toronto and MSc in Global Health at McMaster University. My MacDATA fellowship is being co-supervised by Dr. Emmanuel Guindon and Dr. Arthur Sweetman. This research will examine how supplementary drug insurance coverage affects health care utilization by immigrants in Canada, combining health research methods, economics, political science and global health disciplines. Given the current federal government’s interest to increase the intake of immigrants into the nation, and the steady rise of pharmacare options onto the agenda, provincially and nationally, this research aims to inform these key policy decisions by presenting the effects of health policies pertaining to the health and well-being of future Canadians.

Fatemeh Armanfard is a Master’s student at the Department of Electrical and Computer Engineering, co-supervised by Profs. Jim Reilly and John Connolly. The focus of her thesis is on developing supervised machine learning techniques for analyzing electroencephalogram (EEG) signals recorded from comatose patients.

I am a PhD student in the Department of Economics here at McMaster. I completed my MA in Economics at Carleton University and my BA in International Economics at Toronto Metropolitan University (formerly Ryerson University). My research interests primarily lie in the fields of Labour Economics, the Economics of Education and Public Economics. Currently, I am investigating the factors that underlie the dramatic lack of applications to undergraduate-level engineering, mathematics and computer science programs (EMCS) compared to other STEM programs among female applicants. Employing data on all applicants to Ontario undergraduate programs, I incorporate factors including high school courses, peer effects and student socio-economic characteristics into various binary outcome models to assess application likelihood. This work has further implications for broader research on the gender earnings gap, as an absence of women from certain STEM professions (namely those involving engineering) has been identified as a factor in exacerbating the earnings disparity between men and women.

Automated Brain Injury Assessment- Machine Learning EEG/ERP Analysis

Trained both as a computer scientist (B.Sc., Dalhousie University) and a neuroscientist (M.Sc., McMaster University), my PhD work in biomedical engineering targets the development of automated assessment tools for brain function. Specifically, I utilize machine learning techniques to analyze brain signals, recorded using electroencephalography (EEG), to assess brain injury and its effects on different aspects of cognition.

Physical activity, sedentary time, sleep duration and health outcomes in preschoolers:
exploring the relationships with compositional analyses

The objective of the proposed project is to establish the relationships between
movement behaviours (physical activity, sedentary time and sleep duration) and health
outcomes in preschoolers (i.e., less than 5 years old) using compositional analysis.
Compositional analysis uses data that are proportional of a finite whole (e.g., 24 hours) and
can be used when all parts or just some parts of the whole have been measured (1,2). This
method considers the effects of a behaviour as a proportion relative to the other behaviours
instead of an independent behaviour. This is a novel approach to analyzing movement
behaviour data and it aligns with Canada’s newest Physical Activity Guidelines for Children
and Youth (i.e., 5 years and older). In 2016, the Canadian 24-Hour Movement Guidelines for
Children and Youth were published and this new version focuses on the interplay between
sleep, physical activity and sedentary time, rather than focusing on these movement
behaviours independently.

Mohammad is a first-year PhD student at Dr. Sue Becker’s lab. He obtained his BSc in Physics from Amirkabir University of Technology (Tehran’s Polytechnic) and his MSc in Cognitive Science jointly from the University of Trento in Italy and Osnabrueck University in Germany. His primary research interests are computational modelling of learning and memory and the brain’s dynamics. Specifically, he is working on computational modelling of traumatic memories in patients with post-traumatic stress disorder (PTSD) using artificial neural networks. His MacDATA project involves analyzing functional magnetic resonance imaging (fMRI) data, previously obtained from PTSD patients and healthy individuals, to investigate whether the patterns of hippocampal connectivity are different between PTSD patients and healthy individuals. Hopefully, the result of his MacDATA project will lead to a better understanding of which neural circuits specifically drive memory retrieval and the direction of information between these circuits in PTSD patients. In his non-academic life, Mohammad is a photography enthusiast and loves taking shots in various genres, such as nature photography and street photography.

Machine Learning and Bayesian Inference in Finance

I am a Masters student at the School of Computational Science and Engineering, with a focus on Machine learning, finance, and economics. Currently, my supervisor is Matheus Grasselli; the professor of financial mathematics and my co-supervisor is Dr. Anand for this fellowship. My main research is focused on how to apply machine learning techniques into finance due to the specialty of the capital markets. In other words, how to grasp the complexity of machine learning applications into investments. For this project, my supervisors will guide me using the Bayesian method to calibrate the work for option pricing.

I am an MSc student at McMaster University, studying statistics. I also completed my BSc (Hons.) at McMaster. My research focuses on machine learning, specifically outlier detection in Gaussian mixture modelling. This project will use machine learning algorithms to investigate the relationship between brain structure and psychosocial functioning and the link between changes in sleep and circadian rhythms and risk of postpartum depression. This project will be co-supervised by Dr. Benicio Frey from the Department of Psychiatry and Behavioural Neurosciences and Dr. Paul McNicholas from the Department of Mathematics and Statistics.

I am a third year PhD student in Cognitive Science of Language Department. I am also a researcher at the Reading-Lab in The Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL). My interests include newly emerging words and word learning. My research focuses on the dynamics behind the spread of newly popular words and the question of how these words are learnt by adult speakers. I have been conducting eye-tracking, EEG, and computational linguistics studies to answer these questions.

Project: Twitter data has been a popular way to examine newly popular words. My project aims to answer two questions by looking at the United States and Canada geo-tagged tweets: (1) what makes a word popular? (2) how have been these words spreading?

Cassandra D’Amore is a PhD candidate, supervised by Dr. Marla Beauchamp, in the School of Rehabilitation Science. Her research interests focus on aging, and include physical activity, mobility, participation, and end-user involvement. Cassandra’s thesis is using the Canadian Longitudinal Study on Aging (CLSA) to describe usual physical activity levels and behaviours in middle-aged and older adults. Her MacDATA fellowship work, supervised by Dr. Marla Beauchamp and Dr. Paul McNicholas, will expand this project by adding two additional analyses under this purpose. The specific aims of Cassandra’s project include 1) identifying latent physical activity behaviour patterns among middle-aged and older Canadians; and 2) exploring the relative importance of health-related factors in predicting these behaviours. The CLSA is nationally representative cohort study of approximately 51,000 middle-aged and older Canadians and contains 100’s of health-related factors. In her first aim, Cassandra will identify latent physical activity behaviour patterns that include not only moderate- and vigorous- but light-intensity activities, creating a more complete picture of physical activity patterns that may better inform physical activity promotion efforts. She will then use machine learning to identify the health-related factors that appear to be driving the prediction of physical activity behaviour patterns. This work will expand the current literature on determinants of physical activity by providing novel information using machine learning to identify the relative importance of predictors of physical activity.

Michael De Coste is a Ph.D. candidate in the Department of Civil Engineering. His graduate research focuses on the analysis and prediction of extreme ice events on Canadian rivers, applying machine learning and statistical analysis techniques. For his MacDATA project, he is co-supervised by Dr. Ridha Khedri from the Department of Computing and Software and his Ph. D. supervisor Dr. Zoe Li from the Department of Civil Engineering. This project aims to develop a data-driven model to predict the timing of the spring breakup of river ice in Canada on a national scale. This model will allow the strongest predictors of these events to be identified well ahead of the breakup, allowing forecasts with significant lead times to be made. This will provide decision-making support for communities directly affected by spring breakup while also providing insight on the impacts of climate change on river ice, both on a regional and national scale.

Big Data Genomics Infectious Disease Biosurveillance

I am a PhD Candidate in the Department of Anthropology, and I study a disease which has had a long and turbulent history with humans: The Plague. My thesis explores how ancient pandemics of plague are connected to modern outbreaks, and what interplay of factors leads to its re-emergence and dispersal. To examine this connection between the past and present, I work with a novel form of evidence: ancient DNA. My work in the McMaster Ancient DNA Centre involves extracting DNA from archaeological plague victims to capture, sequence, and reconstruct the genome of this ancient and deadly bacterium. However, to interpret this ancient genetic evidence, I need access to an extensive comparative dataset of genomes to robustly test the relationships between my ancient samples and modern disease outbreaks. With recent advances in sequencing technology, comparative datasets are plentiful but remain largely inaccessible and underutilized due to the challenges inherent in working with Big Data. In response, my project in collaboration with the MacDATA Institute, is to develop software tools to assist in querying online repositories for infectious disease metadata, and to streamline the data transformation steps needed to acquire and integrate raw data into comparative genomic projects. By improving data accessibility in the realm of infectious disease biosurveillance, this project seeks to advance current models of disease emergence and spread by giving researchers the tools needed to acquire crucial comparative data, particularly from currently underrepresented areas of the globe.

Arman is a first-year master’s student working under Dr. Andrew G. McArthur’s supervision in the Department of Biochemistry and Biomedical Sciences. His master’s thesis aims to contextualize and better understand antimicrobial resistance genes in bacteria by mining biomedical literature using natural language processing techniques. For his MacData fellowship, he will be examining the titles, abstracts, and metadata of papers discussing antimicrobial resistance to identify biases in the field and give insight into areas needing further exploration. With this opportunity, his fellowship may facilitate interdisciplinary training across antimicrobial resistance, natural language processing, and data sciences to understand the gaps in antimicrobial resistance and guide future research.

I am a Ph.D. Students at the Department of Economics at McMaster University. I am interested in Health Economics, Economics of Education, and Applied Econometrics. I also hold MSc in Financial Economics. Currently, I am working on the application of the minimax regret criterion to inform decision-making using data from actual clinical trials with multiple treatments and outcomes. In particular, my work includes assessing the performance of minimax regret decision rules compared to the conventional statistical inference-based approaches such as hypothesis testing, which are not ideally suited for decision-making problems. Additionally, I am interested in optimal trial design using the minimax criterion. Therefore, the ultimate goal is to improve the ex-ante and ex-post decision-making process under uncertainty. I am working under the supervision of two eminent professors, namely, Professor Arthur Sweetman from the Department of Economics and Professor Jean-Eric Tarride from the Department of Health Research Methods, Evidence, and Impact (HEI).

Anne Fuller is a PhD student in Health Research Methodology under the supervision of Dr. Kathy Georgiades. She is also a general paediatrician at the Hospital for Sick Children. Following her paediatric residency at the Hospital for Sick Children, she pursued formal research training through a fellowship in Academic General Pediatrics and a Master of Science in Clinical Research Methods at the Children’s Hospital at Montefiore/Albert Einstein College of Medicine in Bronx, New York. Anne’s research program is focused on understanding poverty-related risks among children with chronic health conditions, and exploring factors that promote resilient health outcomes in the setting of low income. Her MacDATA fellowship will use survey and linked administrative data to model how neighborhood characteristics contribute to resilient mental health outcomes for children and youth facing poverty and poverty-related risks.

Analysis of Social Inequalities in Child and Youth Mental Health

I am a second year PhD student and Vanier Scholar in the Department of Mathematics and Statistics working under the supervision of Dr. Paul McNicholas. I completed my Honours B.Sc. and M.Sc. at McMaster. My research focuses on model-based clustering and classification which generally makes use of finite mixture models. Recently, I have been looking at model-based clustering and classification for matrix variate data examples of which include image data and multivariate longitudinal data. During of the course of this fellowship, I plan to apply clustering and classification techniques to better understand social inequalities in child and youth mental health and associated academic outcomes, specifically in migrant groups. Due to social and political unrest in many countries throughout the world, there has been a recent influx of immigrants and refugees to Canada, and it will therefore be important to determine which factors might affect the mental health of some of these migrants and help them to more easily settle into Canadian life

Sophiya is a PhD candidate in the Health Policy program, specializing in health economics. Sophiya obtained her Master’s degree in Public Health from the University of Guelph and her Honours Bachelor of Science degree from McMaster University. She has previous professional and research experience in epidemiology and health communications. Sophiya is interested in areas of health equity and data analytics, and in working at the intersection of health and social sciences to improve the health of under-served populations in Canada. Her current thesis under the supervision of Emmanuel Guindon, Arthur Sweetman and Kathy Georgiades, focuses on the health and health care experiences of immigrant and refugee populations in Canada.

With a background in both humanities and cognitive science, my graduate research focuses on the psychology of migratory populations. By combining millions of demographic and psychological data points, I will adopt machine learning techniques to identify the psychological and demographic predictors of human migration. Further, I will analyze the attitudes towards migration phenomena by considering language from online sources (i.e., Reddit) and use computational methods to detect linguistic properties -e.g., emotional valence and arousal – when referring to immigration issues. This work will enable testing of current social-psychological models of personality using novel data sets that comprise many responders from multiple countries.

Identifying Risk Factors for Severe Influenza Infection

I’m a 2nd year MSc. student in the Department of Health Research, Evidence and Impact studying within the Health Research Methodology program. My research for the fellowship is focused on 2 projects; firstly, identifying potential risk factors for severe influenza infection using both conventional statistical techniques (i.e. multi-levelling modelling of independent patient data) and modern machine learning methods. The second half of my research project involves identifying phenotypes or subgroups of patients at higher risk for severe influenza infection using cluster analysis methods. The proposed MacDATA fellowship project will be co-supervised by my MSc. thesis supervisor, Dr. Domnik Mertz from the Department of Medicine, and Dr. Shikrant Bangdiwala from the Department of Health Research Methods, Evidence and Impact.

Somayeh is a 2nd-year doctoral student in Health Management in the area of Health Policy and Management. She is interested in Machine Learning, Big Data, Knowledge Discovery, Personalize Medicine, and Medical Decision Making. Coming with a mathematical background, Somayeh became more interested in Mathematical Programming and pursued her master’s degree in industrial engineering focused on healthcare optimization. Before starting her graduate studies at the DeGroote School of Business, she worked in different healthcare R&D centers for more than six years. She has contributed to several data analytics projects in healthcare.

Robert is a first year Medical Sciences MSc. Student. He is supervised by Dr. Alison Holloway where his research focuses on the metabolic reproductive impacts of Oil-Sands related contaminants. In addition to the biochemical pathways by which specific contaminants alter tissue function, he is interested in how the measured concentrations and distribution of these contaminants relate to the reproductive health of animals in the wild. Robert’s MacDATA project will use a several datasets from the Athabasca Oil Sands Region collected in collaboration with Environment and Climate Change Canada to analyze spatial patterns in contaminant distribution and markers of ecosystem health, including the reproductive rate of female otters.

Yujiao Hao is a second-year Ph.D. student in the Computer and Software department of McMaster whose current research field is activity recognition and mobile computing. Yujiao received both MA.Sc. and B.Sc. degrees in a Software Engineering major from Northeastern University, China. For the master’s phase, her research direction was multimedia medical information technology, such as the medical image registration algorithm. After graduation, she served the mobile phone industry for 5 years, accumulating rich experience in software testing and the at-hand skill of Android application development.
Her current research topic is related to the wellness of senior people, analyzing their mobility status with inertial measurement unit (IMU) sensory data.

My name is Jinfeng Huang and I am a Ph.D. student at Dr. Melacini’s lab in the Department of Chemistry and Chemical Biology. My research focuses on understanding protein allostery from the perspective of structural biology with NMR. I will be working with Dr. Giuseppe Melacini and Dr. Paul W. Ayers to build a new model for protein allostery prediction that is compatible with missing data. I also use molecular dynamics simulations to understand the protein structure-function relationships.

Data Optimization with Applications to Assemble-to-Order Systems

Ning Huang is a Master’s student of the Department of Computing and Software, supervised by Dr. Antoine Deza. The focus of her research is on Data Optimization with Applications to Assemble-to-order Systems.The fellowship project aims to optimize the base stocks to achieve a higher reward for a period-review ATO system based on an independent base policy and an FCFS allocation rule.

Yixin (Elliott) Huangfu is currently a Ph.D. candidate in Mechanical Engineering at McMaster University. His research topic is data-driven fault detection and classification for mechatronics and software systems. The goal of his MacDATA fellowship project is to explore image data processing for autonomous driving applications. Previously he had worked as a vehicle control engineer at Ford China for three years. Before that, he obtained his M.Sc. in Mechanical Engineering at Beijing Institute of Technology. His previous research includes software validation test of vehicle control module, transmission shifting control, and mode changing strategy of hybrid electric vehicles.

A Weighted Estimation Method for Improved Precision and Efficiency: Applications in Health Service

Rabiul Islam is an international PhD student from Department of Economics at McMaster University. He is currently working on a weighted method to improve the efficiency and precision of estimation in the presence of self-selection bias in survey data. Pursuing his interest in Health Economics, he is attempting to utilize this estimation method for health data under the supervision of two eminent professors, namely, Professor Arthur Sweetman from Department of Economics and Professor Emmanuel Guindon from Department of Health Research Methods, Evidence, and Impact (HEI). Additionally, Islam is interested in research related to Labour Economics, and Applied Econometrics.

Zhe (Betty) Ji is a Ph.D. Candidate in the area of Marketing at DeGroote School of Business. She is under the supervision of Dr. Ruhai Wu. Her research mainly focuses on firms’ or business units’ strategic marketing decisions. In her research, the mathematical model is used to investigate why firms make certain strategic decisions, how these strategic decisions affect market outcomes and what the optimal decisions are under different market conditions. In line with her current research, MacData Project will be a great empirical extension. The goal of the MacData Project is to develop an effective analytical solution for lead generation and transaction outcome forecast in the real estate industry. As digital transformation is an inevitable trend, real estate brokers lack the knowledge and analytical tools to improve data-driven decisions on lead generation and transaction outcome forecast. Therefore, this project aims to develop the econometric model, empirically estimate the model and predict the transaction outcomes. The predictive model can be used by real estate brokers to better target sellers and improve selling strategies.

Mahdis is a PhD student in the Health Research Methodology program, under the supervisor of Dr. Kathy Georgiades. During her time at McMaster, she plans to establish a strong research foundation in the mental health of immigrant and refugee children in Canada by understanding the social and economic factors that contribute to inequities in their mental health service use. Her MacDATA fellowship will utilize machine learning techniques to identify the specific factors that predict levels of mental health service use among youth in Ontario.

Analyzing Astrophysics Data using Machine Learning Techniques

Currently, I am a PhD student in the School of Computational Science and Engineering at McMaster University under the supervision of Dr. Paul McNicholas and Dr. Sharon McNicholas. Prior to coming to McMaster University for my degree in MSc Statistics, I obtained my Honours BSc in Mathematics and Statistics from University of Ghana. My PhD is focused on developing evolutionary algorithms, where some elements of the biological theory of evolution are incorporated into computer algorithms. Specifically, since missing data is inevitable when dealing with multivariate data, our interest lies in applying our developed algorithms to combining the problems of clustering and missing data

Jianpeng is a Master’s student at the department of Computing and
Software, with a focus on mobile crowdsourcing. He also completed his
Bachelor’s Degree in Mechatronics Engineering at McMaster University
in 2019. Driven by his passion in applying the data science
technologies to real life, the proposed MacDATA fellowship project
will focus on utilizing the semi-supervised learning techniques to
develop a WiFi fingerprint based indoor localization model and
deploying the model to the MacQuest McMaster Campus Navigation Mobile
App to further provide a better indoor-navigation service for the
users.

I am currently a PhD candidate in the Department of Chemistry & Chemical Biology and my thesis explores developing analytical separation methodologies to expand metabolomic and lipidomic coverage in complex biological samples. Specifically, my thesis is involved in applying these methods to uncover novel biomarkers that will improve screening of cystic fibrosis in neonates using dried blood spots that were collected at the Children’s Hospital of Eastern Ontario. Data pre-processing in biomarker discovery based projects is typically plagued with several thousands of spurious signals, and redundancies that are time consuming and prone to error. Thus, in this co-supervised project with Dr. Philip Britz-McKibbin and Dr. Andrew McArthur, I will be exploring advanced software tools for rapid, reproducible data processing and visualization of large metabolomics datasets that are generated using a high-throughput platform our group recently developed. This project aims to deliver an accelerated data pipeline for ongoing research studies conducted in our group as well as other metabolomics-based researchers who are interested in comprehensive profiling in large scale-epidemiological studies.

I am a PhD student in Finance at DeGroote School of Business. My research interests are in areas of algorithmic trading and high-frequency asset pricing. My MacData fellowship will be co-supervised by Dr. John Maheu and Dr. Noah Forman. In this research project, I will explore the viable ways of a low-risk investment in cryptocurrency markets. Specifically, I want to devise a market-neutral investment strategy as a low-risk approach for investing in cryptocurrencies via machine learning techniques as well as asset pricing models.

I am is a PhD candidate in the Department of Classics at McMaster University. I completed my BA at the University of Alberta in Anthropology and Classical Studies, and my MA in Classical Archaeology at Brock University. I have been doing archaeological field work in Greece since 2013, working mostly with the Kastro Kallithea Archaeological Project (KKAP) and the Central Achaia Pthiotis Regional Survey Project (CAPS).

My personal research interests focus on understanding the development of urbanism in the Mediterranean using archaeological evidence, mostly between the Archaic and Hellenistic periods (~800 – 200 BCE). My dissertation focuses on how the interactions of Hellenistic rulers and local civic governments interacted and changed the urban landscape in Thessaly, a region of Northern Greece.

My MacData Research project aims to map examples of archaeologically attested urban infrastructure across Thessaly using GIS technology to better understand the spatial relationships within and between cities, and how they met the needs of their populations in the Antiquity.

I am currently a MSc. student in the Computational Science and Engineering program at McMaster University and have completed my bachelor degree in Electrical Engineering at Amirkabir University of Technology, Tehran, Iran. Working under the supervision of Dr. Sue Becker and Dr. John Connolly, my MSc thesis aims to design and implement a Brain-Computer Interface (BCI) for communication with coma patients (speechless communication).
I have focused my academic pathway on machine learning and data science since the third year of my undergraduate studies and due to my profound interest in brain and neuroscience, I am currently applying these techniques in analysing brain signals (esp. EEG) and designing BCIs. Thus, it brought me to MacDATA institute to propose a project at the intersection of several paradigms: neuroscience, psychology, electrical engineering, machine learning and data science.

Predicting Adverse Health Events Among Older Emergency Department Patients

I am a PhD student in the department of Health Research Methods, Evidence, and Impact at McMaster University. I received both my Bachelor (2014) and Master of Science in Nursing (2017) from the University of Windsor. Clinically, I work as an emergency department nurse at the Detroit Medical Center, with previous experience in critical care and pediatrics. For my doctoral research I am interested in using big data to evaluate and inform geriatric models of care in the emergency department, and creating decisions support systems for emergency clinicians. My MacData fellowship will be co-supervised by Dr. Manaf Zargoush and Dr. Andrew Costa where we will utilize supervised machine learning methods to predict poor patient outcomes for older adults who present to the emergency department for care.

Clustering Analysis of Infant General Movements for Neurodevelopmental Evaluation

My name is Omar Nassif and I am a Master’s student in the department of electrical engineering. My research interests include high dimensional data analysis, deep learning networks, and computer vision. My research project involves characterization of infant motor movement for assessment of potential neurological deficit. The proposed methodology is to extract movement primitives from video sequences for analysis by machine learning methods. My hobbies include badminton, jogging, and piano/classical music.

I am a PhD student at the Cognitive Science of Language program, and a research fellow at the ARiEAL (Centre for Advanced Research in Experimental and Applied Linguistics). I did my BA in Teaching Chinese as a Foreign Language at Beijing Language and Culture University, and an MA in Chinese Linguistics and Pedagogy at the University of Alberta. My research interests include mechanisms of visual word processing under natural reading conditions, which I study with the help of concurrent recording of electroencephalography and eye-tracking.

I’m a PhD student studying Statistics at McMaster University. My research focuses primarily on machine learning, clustering, and processes. Specifically I’m interested in the applications of functional data analysis methods in the context of environmental, health, and sports data. This fellowship will be co-supervised by Dr. Dawn Bowdish from the Department of Pathology and Molecular Medicine and my PhD supervisor Dr. Paul McNicholas. This fellowship is aimed at developing and implementing statistical methods for problems related to monocyte function and disease.

Unsupervised Machine Learning: Robust Model-Based Clustering for High-Dimensional Data

I am a first year PhD student at the School of Computational Science and Engineering, with a focus in computational statistics. I completed my HBSc and MSc degrees through the Mathematics and Statistics Department. My research is focused on unsupervised machine learning methods for very high-dimensional or “big” data. Specifically, I have developed methods for clustering high-dimensional data containing outliers, which has shown a drastic improvement in image recovery of the Martian surface. I am now working on extending this approach for cluster analysis of skewed, asymmetrical data and looking to extend this method to areas other than astrophysics. These clustering approaches are very flexible and can be applied to data originating from many different research areas such as health, technology, etc. Given the recent influx of data, machine learning has become very popular and extending the literature in this area of statistics is crucial to understanding and utilizing all the incoming information.

Clustering Glioblastoma Multiforme Subtypes Using Variable Selection

I am a PhD student in the School of Computational Science and Engineering at McMaster University under the supervision of Dr. Paul McNicholas. I completed both my Honours BSc and MSc in statistics at McMaster University. The focus of my PhD is on computational statistics as my research interests lie in clustering and high-performance computing. My current research has been in developing new mixture model-based approaches for clustering discrete valued time series. I hope to apply my research to real world applications in areas such as healthcare, economics, and behavioural psychology.

I am a marketing Ph.D. student at DeGroote School of Business. My MacData fellowship will be co-supervised by Dr. Sourav Ray and Dr. Matheus Grasselli. In this research project, I will explore the impact of the sharing economy rental market, such as Airbnb, on the housing prices. In particular, I want to study how different aspects of the short-term rental markets of the sharing economy impact the utilization and prices of the traditional rental market. I will use a series of statistical and machine learning techniques to achieve my goals.

I am currently pursuing my PhD in the Marketing Department at the DeGroote School of Business, McMaster under the supervision of Dr Ruhai Wu. Before coming to DeGroote I spent some time working in the marketing research and management consulting industries. My current research involves examining the market of a homogenous good and estimating how much players in the market can gain by exploiting available information.

Bhanu is a PhD student under the supervision of Dr. Brian Timmons. His background is in concussion and more severe forms of brain injury. In working with Dr. Timmons and the Child Health and Exercise Medicine Program, Bhanu hopes to bridge his background in brain injury with the field of pediatric exercise medicine. By doing so, Bhanu hopes to better understand the complex relationship between exercise and concussion recovery, and thus inform the rest vs. activity debate that remains active in the field of brain injury and exercise medicine.

I am a third year Industrial Ph.D. Candidate in Electrical and Computer Engineering at McMaster. This MacDATA project includes an interdisciplinary collaboration between supervisors Dr. Michael D. Noseworthy (expert in neuroimaging and statistical analysis) and Dr. Brian Timmons (subject matter expert in clinical concussion). Our team will utilize MRI and Big Data approaches to develop objective concussion assessment tools. The project will mainly focus on implementing techniques such as skewness, kurtosis, Mahalanobis distance, and outlier detection schemes to develop more accurate concussion patient assessments. I look forward to an exciting project and I can’t wait to present my research to the MacDATA community!

1 am a first year MSc. Candidate in the Chemical Biology Graduate Program at McMaster University, working under the supervision of Dr. Jennifer Stearns at the Farncombe Family Digestive Health Research Institute. My MSc thesis explores the impact of solid food introduction on bacterial abundance and function in the infant gut microbiome. Specifically, my project aims to uncover bacterial carbohydrate metabolism genes and map their abundance in relation to the introduction of solid food in a cohort of exclusively breastfed infants. While metagenomic assembly is a powerful tool to analyze the infant gut microbiome, the accuracy of metagenomic assemblers is heavily impacted by the relative proportions of genera in these samples, strain diversity of the species, and the size of their pangenome. The proposed MacDATA project, co-supervised by Dr. Jennifer Stearns and Dr. Paul McNicholas, is an investigation into metagenomic assembly in order to improve taxonomic and gene prediction. This project specifically aims to identify the reasons for metagenomic assembly errors pertaining to the infant gut microbiome, understand whether these errors are impacted by relative abundance and strain diversity, and improve assembly with novel clustering methods.

Understanding How Physicians Read Ultrasound Images

I am a MSc candidate in the Department of Psychology, Neuroscience and Behaviour working under the supervision of Dr. Ranil Sonnadara and Dr. Sue Becker. The purpose of my graduate research is to examine how physicians interpret medical images and to provide a tool to help physicians make better diagnostic decisions and improve training for new physicians. This investigation will be broken into four major phases: creating a machine learning classifier, interviewing physicians to understand how they inspect medical images, tracking physician’s eye movements during image interpretation, and using these findings to develop an educational tool for medical trainees. These four tasks involved in each phase will use 2D ultrasound images from patients with prenatal hydronephrosis (PH), a condition where blockages cause urine to accumulate in the kidneys of fetal infants. The current study will use PH as a model in each phase, however, our long-term goal is to generalize the findings and concepts from these studies to other types of medical images and diseases. We hope that this work will lead to the development of data analytic tools which will help reduce diagnostic error associated with medical image interpretation.

Massive Multilingual Semantic Norm Extrapolation

Bryor Snefjella is PhD student at McMaster University, in the Cognitive Science of Language program, with interests in corpus linguistics, quantitative linguistics, psychology, and psycholinguistics. He uses corpora – including representative academic copora, social media, and other sources, to examine how the sensorimotor and affective connotations of words affect language processing, and reflect how we mentally represent people, events, and places.

Imputing Missing Matrix Variate Data: Applied to Missing Accelerometer Data in Children

I am a first year PhD student at the School of Computational Science and Engineering, working under the supervision of Dr. Paul McNicholas. My research focus is multivariate and computational statistics. I completed my Hons BSc in Statistics at the University of Toronto and after a number of years working in the software industry, completed my MSc in Statistics at McMaster University. My MacData fellowship is being co-supervised by Dr. Joyce Obeid in the Child Health & Exercise Medicine Program (CHEMP) group in the Department of Pediatrics at McMaster University and Dr. Paul McNicholas in the Department of Mathematics and Statistics. The research I will be conducting is directed towards developing an effective and reliable means of imputing missing accelerometer data, a common problem in studies measuring physical activity and something that has never been done before. I plan on implementing our methodology in easy to use software that will be accessible to clinician scientists. This will improve the quality of accelerometer studies across all health science disciplines.

Yasaman is a direct Ph.D. student in Electrical and Computer Engineering. She obtained her BSc in Electrical Engineering from the Sharif University of Technology. Her primary research interests are biomedical signal and image processing, biosensors, artificial intelligence, and data science.

Specifically, she is working on the analysis of biomedical signals using machine learning. Her thesis project involves analyzing heart and lung sounds, obtained from patients using wireless sensors, to investigate whether the patterns of signals are different between patients and healthy individuals. Hopefully, the result of her project will lead to a better diagnosis of heart and lung diseases.

Artificial Intelligence Algorithms for in-Silico Discovery of Novel Therapeutics for Acute Myeloid Leukemia

I am a 1st year MSc. candidate in the Faculty of Health Sciences at McMaster University, working in the Hope Cancer Lab at the Stem Cell and Cancer Research Institute (SCC-RI). My research focuses on the development of artificial intelligence (AI) pipelines for clinical next-generation sequencing (NGS) data analysis in acute myeloid leukemia (AML), as well as the real-world validation of resulting analytics in animal models of human AML. My research efforts have culminated thus far in the development of an AI program that has been able to discover promising weaknesses in the gene expression program of leukemic stem cells (LSCs), the speculated driver cells of the leukemia disease. The AI applies genetic algorithms to perform multifactorial analyses across multiple transcriptome NGS datasets, looking out for genes that best fit factors of uniqueness to the LSC expression program and association to disease aggressiveness and post-therapy relapse, but also safety as clinical targets. It has yielded new potential targets for pharmaceutical and gene therapies, some of which have only very recently been discovered in classical ways and have only just become actively pursued by other international research groups. My work in collaboration with the MacDATA Institute endeavors to expand upon this AI’s abilities to allow it to intelligently sift through the volumes of literature in fields of medicine, biochemistry and pharmacology published online in order to create self-updating data repositories for pharmaceutical repurposing. These datasets would allow it to “recycle” drugs that have already passed approval and completed clinical trials for other diseases, but that nobody ever imagined could be used against AML. The successful completion of this project and its coupling with robotic high-throughput screening facilities could lend possibility to an unthinkable concept: fully autonomous and precise drug discovery for AML, and perhaps many other types of human cancer.

My name is Kara Tsang, I am a first year Masters of Science candidate in the Department
of Biochemistry and Biomedical Sciences, performing my graduate research with Dr. Andrew
McArthur (Cisco Research Chair in Bioinformatics). I am applying to the MacDATA Graduate
Fellowship Program because my multidisciplinary project aligns to the aims of the MacDATA
Institute, the Biochemistry and Biomedical Sciences Graduate program, as well as my
professional learning goals.

Jing Wang is a PhD candidate in the School of Computational Science and Engineering. His research topic is data analytics in supply chain optimization. The goal of his Fellowship project is to investigate the application of reinforcement learning to the optimal decision making in supply chain systems.

Yue Wang is a Ph.D. candidate in the Department of Biology. Her overall Ph.D. thesis project aims to understand the patterns of mitochondrial genome variation in human fungal pathogens. She has chosen the four dominant species to investigate: Aspergillus fumigatus, Cryptococcus neoformans, Candida albicans, and Candida auris. Together, these four pathogens account for over 80% of the global invasive fungal infections. The goal of her Fellowship project is to investigate the use of data analytics in interpreting the relationships between the nuclear and mitochondrial genomes in the emerging human fungal pathogen Candida auris. Due to the intrinsic differences in inheritance between nuclear and mitochondrial genomes, such comparisons should provide novel insights into the evolution of fungal pathogen Candida auris.

Using objective data and machine learning to establish more objective diagnostics approaches in psychiatry.

I am a PhD student supervised by Prof. An-Chang Shi in the Department of
Physics & Astronomy. My PhD research project mainly concerns the phase be-
haviour of various polymeric systems containing block copolymers. Block copoly-
mers are composed of two or more chemically distinct sub-blocks, tethered together
by covalent bonds. Depending on the molecular details, block copolymers can form
different ordered mesoscopic structures. The main goal of my research is to use theo-
retical and computational tools to understand the mechanisms of the formation and
stabilization of different structures in these systems. This fellowship, co-supervised
by Prof. An-Chang Shi and Prof. Li Xi, focuses on the application of data science and
machine learning techniques to the prediction of the diffusion rate of different plasti-
cizers in a polymer matrix. The outcome of this project will facilitate and accelerate
the study of the migration of plasticizers in host polymers and thus contribute to
the selection of better plasticizer candidates that are beneficial to the environment
and human health.

Qianqian Zhang is a Ph.D. candidate in the Department of Civil Engineering. Her graduate research is developing robust optimization models for water quality management to address nonlinearity and various uncertainties in the water system. The MacDATA project is being co-supervised by Dr. Emil Sekerinski from the Department of Computing and Software and her Ph.D. supervisor Dr. Zoe Li. This project is aiming at developing robust and reliable machine learning models for real-time and continuous water quality forecasting using surrogate indicators collected from water monitoring sensors. The predictions can provide temporal variations of water quality and save the cost for laboratory works. The proposed method can be extended to other water systems to supplement their water quality monitoring programs, and further provide decision support for dynamic water quality management.

Data Optimization with Applications to Shared Electric Vehicles

I am a Master’s student in the department of Computing and Software at McMaster University. My Master’s project is entered on general computational method for combinatorial optimization, specifically the optimization formulations and algorithms. This fellowship will be co-supervised by Dr. Kai Huang from DeGroote School of Business and my Master’s supervisor Dr. Antoine Deza. This fellowship project is data optimization with applications to shared electric vehicles, which focuses on analyzing and exploiting the features of traffic and geographic information system location data used to optimize shared electric vehicle charging stations.

I am a first year PhD student in the Department of Civil Engineering working under the supervision of Dr. Zoe Li. My research focuses on implementing uncertainty analysis for wastewater simulation models. This MacDATA project is being co-supervised by Dr. Wenbo He and Dr. Zoe Li. It will establish an integrated toolkit for the prediction of influent and effluent emerging contaminants at wastewater treatment plants. In the short-term, the research program will focus on the development of data preprocessing, feature selection, predictive model development, and uncertainty analysis approaches for the real-time influent and effluent emerging contaminant control. In the long-term, the generated knowledge might be extended to various environmental engineering fields and multiple regions internationally.