I am a PhD student in the Computational Science program in the School of Computer Science who is advised by Dr Stefan Kremer (Computer Science) and Dr Steffen Graether (Molecular and Cellular Biology). As a Computational Science student, I get to apply Computer techniques to the field of biology. My research focuses on determining the best methods for classifying protein structural data using Machine Learning techniques. My goal during my PhD is to develop and optimize an initial framework of a mathematical model that can characterize similar structures. The main idea is to obtain 2-dimensional representations of the protein structures that can be used to train a deep neural network for the classification of these structural patterns. This new framework can be then implemented as a tool to classify protein structures in a standard fashion, while also giving users the flexibility to define classification categories.

I obtained a Bachelor of Chemistry with Honours and an MSc in Chemistry at Western University. During my third year of undergraduate studies, I was introduced to the field of Computational Chemistry. In my first research position, I learned about the applications of Quantum Chemistry where I was exposed to the complexity around the chemical interactions. During my Master's, I explored various theories in Computational Biochemistry and Biophysics and developed a new method to show that Adenosine Triphosphate (ATP) Synthase, a protein responsible for producing ATP (an energy storage molecule), rotates within a cell membrane at an atomic level.

After my Master's degree, I quickly realized that I wanted to learn more about other Computer Science techniques that can be applied to the fields of Chemistry and Biology. Having researched graduate programs in Ontario, I concluded that the PhD in Computational Sciences program at the University of Guelph aligned with my goals.

I am a 2^nd year MSc graduate student studying artificial intelligence (MSc.CS+AI). Before Guelph, I completed a Bachelor of Engineering Science, which did well to prepare me for my current studies. AI is an incredibly interesting field that finds applications in virtually every industry. No matter what excites you, I'd wager you can apply AI to it.

Being here has given me the opportunity to explore and research what I am most passionate about - the intersection of finance and state of the art deep learning. Two forms of analysis in finance lend themselves well to using supervised learning: technical and fundamental analysis. Currently, I am exploring the use of modern transformer networks, often used in natural language processing applications, to consider features of technical and fundamental analysis for portfolio optimization. Beyond this, I have interests in developing intelligent agents for financial applications using reinforcement learning.

I am a PhD student in Computational Sciences with a Collaborative Specialization in One Health working under the supervision of Dr. Deborah Stacey (School of Computer Science) and Dr. Theresa Bernardo (Population Medicine). I am also a Technical Manager for the Global Burden of Animal Diseases (GBADs). My research area will look at the operationalization of data governance principles to improve the quality, discoverability, and reusability of animal health and production data in the GBADs knowledge engine. I received an Ontario Graduate Scholarship to pursue this research.

A PhD wasn’t always my goal. When I finished my BSc in Biological Sciences, I had an interest in data science and machine learning and knew I really hated lab work. I was lucky enough to sit beside a computer science professor (Dr. Andrew Hamilton-Wright) on the bus one day. Feeling brave, I expressed my interest in data science and my concern for not knowing how to make the leap into tech. After completing some exercises to demonstrate my programming abilities, I started an MSc in Bioinformatics under the supervision of Dr. Hamilton-Wright. My Master’s research focused on exploring the use of unsupervised machine learning to analyze electrodermal activity data (i.e., data that measure activation of sweat glands on the skin, which can be used to indicate someone’s physiological response to stress).

The opportunity to do an MSc allowed me to develop personally and professionally and provided me with opportunities to develop and explore my interests outside of my direct research area. Because I didn’t have a formal computing background, it was difficult at first to get funding (and TA positions), so I jumped on contract positions to make extra money and gain experience. One of these positions was Technical Manager for GBADs. This position sparked my interest in data governance and designing data-centric systems to unleash the potential of data and led to my role as PhD student on the project. I am very excited to see where this opportunity leads!

I graduated with my PhD in Physics from the University of Windsor, where I worked on non-destructive testing of modern materials such as composites, metals and plastics, with the aid of thermography; a method based on the study of temperature patterns developing on the surface of the studied objects. Although it is known as an industrial method, thermography was demonstrated to be highly suitable for such delicate applications as inspection of canvas and wood-based paintings.

After several years of post-doctoral fellowship at the University of Windsor, I worked as a research fellow at the Institute of Physics of the Czech Academy of Sciences in Prague, Czech Republic (Fyzikální ústav Akademie věd ČR), where I participated in a range of projects related to development of microcontroller-based devices for industrial telemetry and medical projects.

I am currently enrolled in the PhD in Computational Sciences at the School of Computer Science, where I am interested in developing AI methods for aerial images processing, as well as developing novel methods for analysis of works of art with the aid of machine learning.

In the PhD program, I will engage in comprehensive and in-depth research in blockchain security and privacy for financial services (such as online syndicated lending and trade finance). More concretely, I will focus on investigating and exploring the security requirements of different financial services, and propose novel and comprehensive solutions that simultaneously address low cost-effectiveness, privacy leakage and unfriendly regulation issues based on blockchain and cryptography theories.