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Academic Credentials
  • Ph.D., Mechanical Engineering, Stanford University, 2024
  • M.S., Mechanical Engineering, Stanford University, 2019
  • B.S., Mechanical Engineering, Massachusetts Institute of Technology (MIT), 2017
Professional Honors
  • Stanford Center for Diabetes Research Center Pilot Grant Awardee, 2023-2024
  • Stanford Center for Digital Health Pilot Grant Awardee, 2023-2024
  • Stanford Precision Health and Integrated Diagnostics Center Pilot Grant Awardee, 2022-2024
  • National Science Foundation Graduate Research Fellow 2019-2022
  • Stanford Graduate Fellow (Medtronic Foundation Fellow) 2017-2020
  • Stanford Enhancing Diversity in Graduate Education Fellow 2019
  • MIT Lincoln Labs Undergraduate Research and Innovations Scholar 2015-2016
Professional Affiliations
  • Diabetes Technology Society, Community Member, 2024-Present
  • IEEE Engineering in Medicine and Biology Society, Member, 2022-Present
  • IEEE Robotics and Automation Society, Member, 2022-Present

Dr. Adenekan is a research engineer with over seven years of experience in developing novel digital and wearable health technologies. She has extensive experience in physiological sensing, signal processing, data analysis and visualization (using MATLAB and Simulink, R, Python, etc.), user study design and implementation (in both healthy and clinical populations), and leading interdisciplinary collaborations between engineers and clinicians. She is proficient in making sense of the often-noisy datasets that result from collecting data on humans

During her doctoral program at Stanford, Dr. Adenekan developed and deployed a high-resolution, reproducible, and accessible smartphone-based platform that can be used for early identification and monitoring of individuals who are at risk of developing complications from diabetes. This involved reverse-engineering smartphones, characterizing them and tuning governing parameters to measure clinically relevant sensory response. She independently built and led collaborations between endocrinologists, neurologists, and primary care physicians at Stanford Hospital, designed and conducted user studies in over 150 adults with varying diabetic peripheral neuropathy risk, and established the foundation for predictive metrics. This entailed performing statistical tests and data visualizations to analyze the relationship between relevant electronic health record (EHR), health survey, and smartphone-based sensory perception data. She also won various grants to fund the projects and presented the project findings at various peer-reviewed conferences and journals.

In addition to developing platforms for monitoring people with diabetes, Dr. Adenekan also developed balance-enhancing controllers for wearable robots. This involved developing real-time software-based methods of controlling wearable robotic devices (exoskeletons) to enhance balance ability in older adults. She designed and conducted human subject pilot experiments using biomechanics tools (EMG, Respirometry, Motion Capture, Force plates), custom signal processing and visualization scripts, and simulation platforms (OpenSim) to study human response to exoskeletons. Additionally, Dr. Adenekan has also applied machine learning to various fields including exercise feedback, infection prediction, and biofilm identification.

At Ä¢¹½tv, Dr. Adenekan aims to collaborate with clients who are committed to developing accessible technologies and interventions that improve health.