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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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.
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.
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.
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.
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.