My name is Sacha Davis, I'm a recent Computing Science M.Sc. graduate from the University of Alberta. Under Dr. Russell Greiner (and many others) I have investigated the application of machine learning techniques to medical and biological problems, focusing on the overlap between survival prediction, natural language processing, deep learning, and large, unruly datasets. Beyond my technical expertise, I contribute to the teams I'm on with my experience in project management, passion for teaching, and ability to contribute to a vibrant and engaged workplace culture.
Feel free to stay on this page to see the highlights of my professional history, or explore the different pages linked above for more details.
September 2020 – July 2023
University of Alberta | Edmonton, AB
September 2016 – June 2020
University of Alberta | Edmonton, AB
Faculty of Medicine and Dentistry, Department of Medicine
Working with Dr. Ross Mitchell in the Precision Health AI Research lab.
Faculty of Graduate Studies and Research, Department of Computing Science
Developing course material, hosting office hours, and grading quizzes and assignments to aid the delivery of CMPUT 497/501: Introduction to Natural Language Processing
Alberta School of Business, Department of Accounting and Business Analytics
Improving 30-day all-purpose hospital readmission prediction by 26% compared to baseline by integrating 571 million rows from Alberta Health Services datasets using Natural Language Processing and Statistical Machine Learning
Working with computing scientists, administrative professionals, and physicians to see the completion of an interpretable and scalable unplanned hospital readmission prognosis system
Faculty of Science, Department of Biological Sciences
Developing two novel, data-driven techniques for estimating the heritability of complex diseases, which contributed compelling insight into the “missing heritability problem”
Proposing and prototyping machine learning solutions tailored to the needs of the Provincial Population and Public Health (PPPH) Division
Designing and delivering educational materials, including lectures, workshops, and terminology glossaries, to equip Alberta Health Services employees with foundational knowledge and best practices for implementing machine learning in healthcare settings
Crafting a comprehensive curriculum outline for the upcoming AI+Genomics Massive Open Online Course (MOOC), outlining key learning objectives, course materials, and instructional strategies to teach how one can enhance genomic research through artificial intelligence
Educating 20-80 industry clients per month about Artificial Intelligence by delivering Machine Learning Foundations 1 and 2; promoting concrete steps for the AI-based transformation of small-to-medium sized enterprises within and beyond Alberta
Furthering the field of plant genomics by employing deep-transfer learning techniques to predict DNA expression, proposing a simple model that out-performs recent methods, and contributing five notable insights into the behavior of plant gene-enhancers
Implementing a modular machine learning pipeline (which supports Cloud-based data preprocessing, model definition, and training) for the National Research Council’s use in proofing Canadian agriculture against climate change
Implementing techniques that compress genetic datasets by over 99.5% with no significant information loss, allowing for the computational feasibility of transcriptome-based survival prediction for cancer patients
Motivating and demonstrating novel metric that evaluates the quality of individual survival distributions with applications in the treatment of terminally-ill hospital patients
Capturing, condensing, perfecting, and expanding upon over 300 important takeaways from twelve Thought Leadership: Managing AI Risk roundtable discussions; supporting the execution of this project through event planning and communicating with stakeholders
Leading four teams of 3-5 through two iterations of Vector’s three-day Time Series Forecasting Bootcamp as project management facilitator
Kumar N, Skubleny D, Parkes M, Verma R, Davis S, Kumar L, Aissiou A, Greiner R. Learning Individual Survival Models from PanCancer Whole Transcriptome Data. Clinical Cancer Research. 2023 Jul 18.
Davis S, Greiner R. Improving Hospital Readmission Prediction with Longitudinal Medical Histories and Survival Targets. Poster Presented at: Upper Bound Conference, Alberta Machine Intelligence Institute. 2023 May 24; Edmonton AB.
Davis S, Zhang J, Lee I, Rezaei M, Greiner R, McAlister FA, Padwal R. Reading You Like a Book: Aggregated Medical Code Embeddings for Predicting Hospital Readmissions. Poster Presented at: Reverse Expo, University of Alberta. 2023 Feb 24; Edmonton AB.
Davis S, Zhang J, Lee I, Rezaei M, Greiner R, McAlister FA, Padwal R. Effective hospital readmission prediction models using machine-learned features. BMC health services research. 2022 Dec;22(1):1-0.
Davis S, Aissiou A, Kumar L, Lee L, Greiner R. Medical Topic Modeling using dLDA on mRNA-Seq Multi-Cancer Datasets. Poster Presented at: The Global Women in Data Science (WiDS) Conference at the Centre for Health Informatics, Cumming School of Medicine. 2020 Mar 3; Calgary AB.
Haider H, Hoehn B, Davis S, Greiner R. Effective Ways to Build and Evaluate Individual Survival Distributions. Journal of Machine Learning Research. 2020 Jan 1;21(85):1-63.
Aissiou A, Davis S, Kumar L, Lee L, Greiner R. Finding Gene Expression Patterns and Making Predictions for Cancers using Machine Learning. Poster Presented at: Excellence in Medical Student Research, A Collection of 2019 Medical Student Research from the University of Alberta. 2019 Nov 26; Edmonton AB.
Leading teams of 9-12 executives and 27 volunteers in the planning and execution of more than 25 day-to-day student group activities and five internationally reaching events/initiatives (including the Artificial Intelligence in Healthcare Symposia, which have cumulatively attracted over 550 attendees, engagement from 20+ industry partners, and more than $50,000 in sponsorship funding)
Creating and delivering course content (e.g. lectures, workshops) for 130+ participants as part of the AIMSS AI in Medicine Curriculum
Managing all internal communication within the club (e.g., sending biweekly newsletters, responding to inquiries) plus assisting with the planning and execution of the club's monthly general events.
Enhancing the computing science graduate student experience by proposing and leading three social and academic events, taking on undesirable tasks to lessen the burden on other executive members
Assessing the novelty of undergraduate student-submitted work and the appropriateness of the methodologies used, giving constructive feedback for manuscript improvement
Carefully evaluating interdisciplinary talks and posters during the inaugural Eureka Undergraduate Research Symposium
Teaching 2-3 students weekly about concepts to from Intro to the Foundations of Computation, Formal Systems and Logic in Computing Science, and Algorithms I.
Meeting weekly with a cohort of young women in high school interested in STEM fields, offering career, education, and life advice. In collaboration with WISEST UAlberta.
Python + Modules for Data Science and Machine Learning (NumPy, pandas, Matplotlib, scikit-learn, Keras, TensorFlow, NTLK, etc.)
Tableau
Snowflake
SQL / SQLite
BASH and Linux Environments
Git / GitHub (Including Project Boards for Project Management)
Google Cloud, Google Drive + Google Colaboratory, Compute Canada
Machine Learning and Deep Learning
Prompt Engineering
Data Visualization and Data Storytelling
Natural Language Processing / Natural Language Understanding
Medical and Agricultural Applications, Bioinformatics
Big Data Processing and Management (Sample-wise, Feature-wise, and Longitudinal)
Cloud Computing
Leading Small-to-Medium Sized Teams in both Organizational and Technical Settings
Project Management
Interdisciplinary Communication and Mediation, Engaging Effectively with Clients
Creating and Delivering Presentations, Public Speaking
Teaching and Mentorship