Alumni

Sarah Bailey

Director of Football Research & Development- Tennessee Titans   

Sarah Bailey is currently the Director of Football Research & Development with the Tennessee Titans. In her role Sarah and her team work with Personnel, Coaches, and Medical staff to provide insight and strategy using data.

Prior to this role she spent 5 seasons with the Los Angeles Rams where they won the Super Bowl in the 2021 season and played in the Super Bowl in the 2018 season. Sarah earned her Masters Degree in Statistics from Simon Fraser University in late 2017 and her Bachelor of Science in Mathematics from the University of the Pacific.

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Dani Chu

Quantitative Analyst Seattle Kraken

Dani Chu recently graduated with his Masters in Statistics from Simon Fraser University. At SFU, he was the co-president of the SFU Sports Analytics Club with Lucas Wu and Matthew Reyers. Along with Lucas, Matt and James Thomson, he was the winner of the College Division of the 2019 NFL Big Data Bowl and the 2018 Sacramento Kings Case Competition. Dani has also interned as a statistician at the NBA, Best Buy Canada and Fraser Health Authority.

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Michael "Jack" Davis

Lecturer and Course Designer at University of Waterloo

Jack has worked in a variety of industry and academic placements, including as a machine learning postdoc for Sportlogiq, a statistician and survey designer for Big River Analytics, and a lecturer for SFU, UBC, and U of Waterloo.

Their research interests include statistics applications to gambling and games of chance, data ethics, polling, binned data analysis, statistical communications, and fraction-based computation. They also currently copyedit for the Canadian Journal of Statistics and have programmed a game for Xbox 360's Indie platform. 

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Kevin Floyd

Bear River Health Department

Kevin Floyd is a recent alumnus of the Master of Statistics program at Simon Fraser University. A lifelong sports fan from college basketball-crazy Utah State University, he interned with the Utah Runnin’ Utes men’s basketball team before coming to SFU. At SFU, Kevin managed to convince his supervisor, Dr. Tom Loughin, to let him write a Master’s Thesis called “Ain’t Played Nobody”, estimating the effect of a team’s non-conference schedule on their selection to March Madness.

While in school, he also participated in the VanSASH hackathon, did work with the SFU men’s basketball team, and moonlighted as a coxswain on the SFU rowing team. Kevin currently works on the COVID-19 pandemic response team for a local health department in Utah as a disease investigator and data visualization specialist.

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Brendan Kumagai

Data Scientist at Zelus Analytics

Brendan Kumagai is an alumni of the SFU M.Sc. in Statistics class of 2023 and a Data Scientist at Zelus Analytics. His research with supervisor Dr. Tim Swartz developed a novel approach of forecasting the outcome of the NHL draft.

While at SFU, he worked with several other graduate classmates and colleagues to place 1st in sports data competitions including the 2021 Stathletes' Big Data Cup, 2022 NFL Big Data Bowl and 2022 NYRA Big Data Derby. He has also interned with Stathletes and the Canadian Olympic team in Data Scientist and Analyst roles, respectively.

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Guilian Liu

Assistant Professor at the Chinese University of Hongkong (Shenzhen)

Guiliang Liu served as a Postdoctoral Fellow at the University of Waterloo and was affiliated with the Vector Institute, Canada. He completed his Ph.D. at SFU and earned his bachelor's degree from the South China University of Technology. Liu has been an integral part of various research projects, collaborating with esteemed organizations like the Machine Learning Group at the University of Toronto, the Cognitive Computing Lab of Baidu Research, the Autonomous Driving Group of Huawei Noah Lab, Canada, and the SLiQ Lab of Sportlogiq. His contributions have been recognized with a MITACS Research Training Award in Canada and a national scholarship from China.

Liu has published more than 20 papers at premier computer science conferences such as NeurIPS, ICLR, WWW, KDD, and IJCAI. Furthermore, he has served in reviewing roles or as a program committee member for highly regarded AI conferences and journals like NeurIPS, ICML, ICLR, AIJ, TMLR, and TPAMI.

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Yudong Luo

Ph.D. Student at University of Waterloo

Yudong developed an inverse reinforcement learning algorithm for ice hockey player evaluation during his master study at SFU with Professor Oliver Schulte. His work was published at an AI conference IJCAI in 2020. He continued his research on reinforcement learning (RL) at the University of Waterloo. His current research focuses on uncertainty estimation and risk optimization in RL.

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Saman Muthukumarana

Professor and Head of Department of Statistics 

Saman Muthukumarana completed his M.Sc. (2007) and PhD (2010) in Statistics under the supervision of Prof. Tim Swartz. He joined the Department of Statistics at the University of Manitoba (UM) as an Assistant Professor in 2010 where he was promoted to Associate Professor (with tenure) in 2016 and then to full Professor in 2022. He is the founding Director of Data Science Nexus at UM and he was appointed as the Head of the Department of Statistics at UM starting from July 2023.

His research interests lie broadly in Bayesian methods and computation for complex models which integrate both theoretical and computational aspects. Along with this main theme, he has developed methods to facilitate modelling and inference on non-standard complex data which lead to innovative analyses in the areas of social networks, health studies, sports, customer and user behaviour analytics, environmental and ecological studies.

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Robert Nguyen

Data Scientist at Sportsbet

Love sports, analytics in sports and cold beers. I was lucky enough to get a job in sports gambling in large part because of the work I did with Tim on Rugby League Live win probability. Host a podcast talking sports analytics called Chilling with Charlie and currently enjoying my time off work being a full time dad to Sierra. Would love to do more public work in cricket analytics a sport which has very rich data, but in my opinion analytically behind where it should be.

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Aaron Pearson

Ph.D. Student - Exercise Neurophysiology

Aaron Pearson graduated from SFU in 2020 with a BSc in Kinesiology and a minor in Statistics. While at SFU, he worked in Dr. Dave Clarke's lab as a research assistant where he utilized quantitative exercise physiology to model soccer players' athletic performance.

He won the 2018 Vancouver Sports Analytics Hackathon (VanSASH) and earned an honourable mention at the NFL’s Big Data Bowl (2020). Aaron is currently pursuing a PhD at the University of Calgary where is exploring the effects of neuromuscular fatigue on athletic performance.

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Matthew Reyers

Zelus Analytics

Matthew Reyers is a recent alumni of the Master of Statistics program at Simon Fraser University. During his time at SFU, he made strides in deriving insights from tracking data as a winner and honorable mention of the NFL Big Data Bowl. He furthered this work in his Master's Thesis on Quarterback Evaluation in the NFL.

He currently works with Zelus Analytics as a Data Scientist and fills his free time with College Football and Hockey side projects. His involvement with SAG goes back well into his undergraduate years and continues to be a highlight of his professional development.

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Peter Tea

Data Scientist

Peter Tea completed his Simon Fraser thesis in tennis, predicting Server actions with player-and-ball tracking data. Among the interesting insights he found include the tendency of players – including greats like Roger Federer – to be overtly predictable under high pressure situations. Currently, Peter is a Data Scientist at Sports Media Technology (SMT) generating statistical insights for broadcasters, fans and player coaches.

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Matthew Van Bommel

Senior Analyst Sacramento Kings

Matthew van Bommel is a Senior Analyst with the Sacramento Kings. He previously completed an analytics internship with the Philadelphia 76ers and graduated with an MSc Statistics from Simon Fraser University in 2017.

At SFU, his research focused on quantifying scorekeeper bias in recording box score statistics in basketball. He has presented the work in a research poster at the MIT Sloan Sports Analytics Conference and in a paper co-authored with his supervisor, Luke Bornn, published in a special issue on sports analytics for the journal Data Mining and Knowledge Discovery.

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Lucas Wu

Data Scientist

Lucas recently graduated with his PhD in Statistics from Simon Fraser University, where his thesis was on soccer analytics. He is currently working at Zelus Analytics as a Data Scientist. His team participated in the first three Big Data Bowl competitions, winning the college division in 2019, honorable mention in 2020, and finalists of the open division in 2021.

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Steven Wu

Shopify

As a passionate fan of basketball, Steven started his path in data science through sports analytics: having discovered play-by-play data that was being uploaded online but not consumed by anyone, his first hobby project was writing code to make this data useful to varsity basketball coaches. Developing statistical techniques to solve data integrity errors in the substitution logs of play-by-play became his MSc thesis (where this research was presented at MIT Sloan 2017). He spent a large chunk of his school time building this into a web application for basketball coaches to prepare for their next opponent.

Steven is now leading the People Analytics team at Shopify, a team of data scientists informing any organizational decisions that impact Shopify's employees (on topics like hiring, performance, retention, engagement, learning, wellness, etc.). He graduated with a MSc Statistics from SFU in 2017, supervised by Dr. Tim Swartz and getting a chance to play with SportVU tracking data working with Dr. Luke Bornn's lab.

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