Computer science, machine learning, and data science researcher
Resume:
DR. GABRIELLE STRANDQUIST
www.elliestrandquist.com
************@*****.***
LinkedIn Github
I’m a computer science researcher, specializing in machine learning, neural nets, data science, and translating technologies to real-world environments. I am currently on the job market. Expertise: Machine Learning, AI, Deep Neural Networks, Data Science, System Integration, Neurotechnology Translation, Signal Processing
Education
Ph.D. in Computer Science - University of Washington, Seattle 2024 M.S. in Computer Science - University of Washington, Seattle 2022 B.S. in Computer Science - Virginia Commonwealth University 2019 Technical Skills
Programming Languages
· Python, Java, Bash, R, MATLAB, JavaScript, Perl, C Software Tools
· TensorFlow, Keras, Git, Apache Maven, LaTeX, Pandas, SciPy, Numpy, Matplotlib, Plotly, AWS, PyTorch, Android developer, UMAP, Altair, Jupyter Notebook, Rclone Platforms:
· Linux, Ubuntu, Windows
Research Experience
NSF Graduate Research Fellow - University of Washington, Seattle 2019 - 2024
· Engineered and deployed an at-home multimodal data collection platform ecosystem for op- timizing adaptive deep brain stimulation (aDBS) therapy for people with Parkinson’s disease.
· Utilized machine learning and data analysis to design continuous assessments of movement quality from video and wearable-sensor data for remote monitoring of symptom severity, which significantly correlated with expert clinician ratings.
· Remotely maintained the platform ecosystem for 2 years, collecting several hundred hours of multimodal data.
· Developed open-source software infrastructuring a scalable platform ecosystem for deploy- ment in multiple homes, including a custom Java/Maven application to automatically initiate home video recordings.
· Designed and conducted a semi-structured interview and reflexive thematic analysis to inves- tigate participant’s experience in an at-home aDBS research study, which demonstrated the value of co-researching for enhancing neurotechnology research. 1
Neuromodulation Research & Advanced Concepts - Boston Scientific 2023
· Algorithm development and data analysis for guiding surgical implantation of Deep Brain Stim- ulation therapy.
Paul G. Allen School First-Year Ph.D. Fellow - University of Washington 2019-2020
· Machine learning for decoding speech production and natural behaviours from human neural recordings.
Undergraduate Researcher - Virginia Commonwealth University 2016-2019
· Developed tools for genomics-based classification problems and pattern recognition through deep neural networks and natural language processing.
· Isolated novel bacteriophage from soil samples and annotated genomes for submission to GenBank.
Consultant and Research Assistant - University of Virginia 2014-2025
· Conducted investigative research and analyses in support of large intellectual property and patent litigations in several subareas of computer science. Publications
· Strandquist, G. (2024). Where is the Person in Personalized Medicine? The Missing Expert in Adaptive Neurotechnology (Doctoral dissertation, University of Washington). (pdf)
· Dixon, T. C., Strandquist, G., Zeng, A., Fraczek, T., Bechtold, R., Lawrence, D., ... & Little, S. (2024). Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely opti- mized neural decoder. medRxiv, 2024-08. (pdf)
· Strandquist, G., Frączek, T., Dixon, T., Ravi, S., Bechtold, R., Lawrence, D., ... Herron, J. (2023). Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation. Journal of visualized experiments: JoVE, (197). (pdf)
· Strandquist, G., Dixon, T., Frączek, T., Ravi, S., Zeng, A., Bechtold, R., ... Herron, J. (2023, April). In-Home Video and IMU Kinematics of Self Guided Tasks Correlate with Clinical Bradykinesia Scores. In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 1- 6). IEEE. (pdf)
· t Hart, B., Achakulvisut, T., Adeyemi, A., Akrami, A., Alicea, B., Alonso-Andres, A., ... Vishne, G. (2022). Neuromatch Academy: a 3-week, online summer school in computational neuro- science. Journal of Open Source Education, 5(49), 118. (pdf)
· Flounlacker, K., Miller, R., Marquez, D., Johnson, A. (2017). Complete genome sequences of Bacillus phages DirtyBetty and Kida. Genome Announcements, 5(10), 10-1128. (pdf) Conferences & Posters
· International Neuroethics Society, Baltimore MD: Volitional BCI: Designing for agency in inva- sive brain computer interface, 2024
· Computational Neuroscience Center’s CoNectome Symposium, SeattleWA:Designing a Scop- ing Review: How is Participant Experience Discussed in BCI Studies?, 2024 2
· 11th International IEEE/EMBS Conference on Neural Engineering, Baltimore MD, presented: In-Home Video and IMU Kinematics of Self Guided Tasks Correlate with Clinical Bradykinesia Scores, 2023
· Award-winner for the Dean’s Undergraduate Research Symposium, Richmond VA, presented: Species Classification Through Deep Learning, 2018
· Phage Lab Infographic, 2016
Talks and Presentations
PhD Defense: "Where is the Person in Personalized Medicine? The Missing Expert in Adaptive Neurotechnology" University of Washington, August 2024 Watch the talk
· I defended my PhD with a case study on delivering adaptive Deep Brain Stimulation (aDBS) therapy for Parkinson’s disease in the comfort of people’s homes. I engineered a platform to remotely collect multimodal data from a participant’s home over two years, developed novel methods to remotely evaluate aDBS outcomes, and qualitatively analysed the participant’s ex- perience which demonstrated the value of including their unique expertise as a co-researcher in neurotechnology teams.
PhD Thesis Proposal: "Translating Neurotechnology to the Home Through Naturalistic Neural Decoding and Participatory Co-Design" University of Washington, February 2024 Watch the talk
· Sharing my work in translating adaptive Deep Brain Stimulation therapy from research lab to the home and proposing next steps to continue advancement of this work. A Prototype Platform Demonstration: "Automated Deep Brain Stimulation for Parkinson’s Dis- ease – Exploring the Possibilities and Challenges of Home Monitoring" University of California San Francisco, July 2023
Watch the video
· Demonstrating an at-home multi-modal data collection platform that supports research opti- mizing adaptive deep brain stimulation therapy for people with neurological movement dis- orders, and sharing key findings from deploying the platform for over a year in the home of a person with Parkinson’s disease.
Guest Speaker: "Husky Brain Bytes Podcast: Interview with Gabrielle Strandquist" CoNECT Husky Brain Bytes Podcast, November 2021
Listen to the episode
· Discussing my non-traditional path to a Computer Science PhD as a first-generation graduate student and working on machine learning solutions to analyze neural activity during everyday human behaviours.
Teaching and Outreach
Graduate Teaching Assistant - University of Washington 2024
· Facilitated student learning in a semi-flipped System Software Tools class by providing in-class problem- solving assistance, conducting office hours, and monitoring online discussions. 3
Volunteer Programming Instructor - Seattle, Washington 2023
· Taught beginner computer programming for the Digital Stewards community-organized stipend- based re-entry and skills training program for formerly incarcerated women of color. Engage and Enable Blog Writer - Center for Neurotechnology 2021
· A series for aspiring engineers and scientists. Part I explores how scientific research works and Part II shares insights about the process.
Digital Media Coordinator - Center for Neurotechnology 2021
· Content writing and social media engagement for the Center for Neurotechnology’s Student Leadership Council to promote research opportunities to students. NeuroMatch Academy Course Developer - University of Washington 2020
· Developed course materials for Week 2 Day 2 “Linear dynamical systems”.
· Contributed to the design and creation of teaching materials with tutorial design and Python implementation.
Student Led Seminar - UW Computational Neuroscience Center 2020
· Started a neural engineering seminar series featuring junior faculty and post-docs, where speak- ers are selected by undergraduate and graduate students. Awards
· National Science Foundation Graduate Research Fellow, 2021
· Paul G. Allen School Dean’s First-Year Ph.D. Fellowship, 2019
· Winner of the Dean’s Undergraduate Research Symposium, 2nd place, 2018
· Dean’s Undergraduate Research Initiative (DURI) Fellow, VCU, 2018
· Goldwater Scholarship Honourable Mention, 2017
· Phi Kappa Phi – Life Sciences Undergraduate Scholarship, 2017
· Dean’s List, Virginia Commonwealth University, 2014 – 2019
· Academic Achievement Award NB, Virginia Commonwealth University, 2015 – 2019 Graduate Courses
· RegularizationMethodsforMachineLearning,Software Engineering, Neural Engineering, Com- putational Neuroscience, Data Visualization, AI and the Brain, Computing for Social Good, Re- search Design
Mentored Students
· Zeynep Toprakbasti, Bachelor of Science in Computer Science and Engineering, University of Washington, 2020 – 2023
· Jazlin Taylor, Master of Science in Electrical and Computer Engineering, University of Washing- ton, 2024
Hobbies and Interests
· Traveling, Argentine tango, ballet, salsa, west coast swing, and lyrical dancing, musical theatre, yoga, sky diving, snorkeling, baking, botanical gardens. 4
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