# Michael Elabd > Research @ Google DeepMind Location: San Francisco, California, United States Profile: https://flows.cv/michaelelabd Twitter: x.com/michaelelabd Website: michaelelabd.com ## Work Experience ### Research Engineer @ Google DeepMind Jan 2024 – Present | Mountain View, California, United States ### Research Engineer @ Google Jan 2023 – Jan 2024 | Mountain View, California, United States • Researched and developed on-device foundation model for the sensor modality, achieving 7-15% KPI improvement on downstream tasks (prod algos) with 30-50% less compute/memory. • Researched and developed Advanced Running Dynamics ML algorithms featured on Pixel and Android devices, demonstrating 10% better performance to Apple & Garmin by third-party testing. • Designed and implemented a modular, optimized training pipeline for motion algorithms, achieving 70-100x faster runtime. Enabled rapid hyperparameter search, resulting in the most widely adopted motion algorithm training pipeline in the Product Area. ### Student Researcher @ Google Jan 2023 – Jan 2023 | Mountain View, California, United States Building few-shot sequential models for gesture recognition. ### Machine Learning Research Intern (SWE) @ Google Jan 2022 – Jan 2022 | Mountain View, California, United States - Developing novel few-shot semi-supervised sequential learning algorithms for sensor-based data streams for wearable devices and achieving state-of-the-art performance with less than 50 examples - Devising comprehensive testing mechanism to ensure real-world replicability including developing real-time WearOS / Android app, sequential error modeling, false positive testing on numerous datasets - Working with key stakeholders to identify success metrics, leading data collection efforts, spearheading cross-team collaboration, managing tasks, and expectations with senior engineers ### Research Engineering Intern (SWE) @ Google Jan 2021 – Jan 2021 | Mountain View, California, United States Using unsupervised techniques such as Deep Generative Models, Reinforcement Learning, and Probabilistic Graphical Models to detect and remove erroneous data from large DBs at Google. I developed a novel generative adversarial imputation network designed to detect and recommend corrections to erroneous data fields with much better results than current industry standard solutions. These results were documented in an internal white paper where we discussed the problem, approach, and compared key metrics and results. This was done through a multi-team collaboration where we worked with key stakeholders across multiple teams to understand their problems, collaborate to find a solution that addresses their key concerns, and learn key insights about development at needed scale. ### Undergraduate Research Fellow @ Stanford Artificial Intelligence Laboratory (SAIL) Jan 2020 – Jan 2021 | Stanford, California, United States Developing AI systems (CNNs, GANs, RL, decision trees) with weak supervision to predict brick-kiln locations across South-Asia thus providing granular data to NGOs, nonprofits, and policymakers. ### Pear Garage Fellow @ Pear VC Jan 2019 – Jan 2021 | Palo Alto, California Pear Garage is a highly selective opportunity for 30 entrepreneurial Stanford students (undergraduate, graduate, and postdocs) to transform their ideas into iconic companies with the help of mentorship, peer networks, and outside resources. ### Software Engineering Intern (AWS) @ Ample Jan 2020 – Jan 2020 | San Francisco, California, United States Building the automotive cloud infrastructure to connect cars, charging stations, and autonomous robotic devices and using the data for efficient resource allocation, problem detection and diagnosis Designing and implementing the cloud infrastructure using Lambda, S3, Cloudwatch, EC2, EMR, IoT Core, Kenesis, VPC, CloudFormation, EKS, Elastic Search, Kibana, IAM ### Software Engineering Intern (SWE) @ Meta Jan 2020 – Jan 2020 | Menlo Park, California, United States Worked on the Connection Integrity team where we made sure that violence and disinformation are not perpetuated across the platform. I worked on the page transparency initiative where we provided touchpoints for users to be informed about Page actions and history. I also worked on redefining the page roles area to help admins understand their page and take action on misbehaving posts to ensure their page does not get any red flags. ### Research and Development Engineering Intern @ Swvl Jan 2020 – Jan 2020 | Cairo, Egypt I built a POC focused on providing captains with a more optimized route by developing a google maps alternative that was better suited to addressing our needs. It was more cost-effective and granular. This was done by combining open-source maps and company data to design an efficient map architecture. Then, processing and overlaying traffic data on top of the map to estimate the time of arrival using an optimized A* algorithm. ### Satellite Avionics Team Lead @ Stanford Student Space Initiative Jan 2019 – Jan 2020 Managing a team of computer science and electrical engineering student to develop the electronics for our satellite (launch for 2020). The satellite aims to send and receive cloud-free images back to home base. We are using Altium to design PCBs and Arduinos and Teensies to code the electronics. ### Machine Learning and Software Engineering Intern @ Samsung Electronics Jan 2019 – Jan 2019 | San Jose, CA ·Ideate an emotion detecting technology that revolutionizes the way you interact with your voice assistant and pitched to SSIC executives including CTO, VP of innovation, and VP of operation. ·Summer Internship where we developed a novel deep learning algorithm along with sound processing technology (Python) to allow voice assistants and chatbots in customer service to be able to interpret human emotion. We worked on developing an API for the end-to-end experience and filed a patent application under Samsung. The application could be used in any circumstance where a bot is interacting with a human as the developers can understand when the bot is not performing correctly. This could greatly accelerate digital assistants as developers can now understand which commands do the bots misinterpret or produce a subpar output. ·Pitch Deck: http://bit.ly/Emote-X ### Undergraduate Researcher - Stanford Integrated Biomedical System @ Stanford University Jan 2018 – Jan 2019 Working under Dr. Ada Poon to develop VAE and GANS algorithm to map the conductivity matrix of the brain to user-behavior, allowing users to input commands through EEG interface. We got a binary classification of user-behavior (eyes closed vs open/hungry vs full) and are currently working on multi-class classification. I also set-up and conducted experiments with Wireless EEG. ## Education ### Master's degree in Computer Science - Artificial Intelligence Stanford University ### Bachelor's degree in Computer Science + Mathematics Stanford University ### Visiting Student in International Development University of Oxford ### High School Diploma in Physics and Computer Science Misr Language School ### Computer Engineering College of the Canyons ### High School Diploma in Physics and Computer Science Valencia High School ## Contact & Social - LinkedIn: https://linkedin.com/in/michael-elabd - Website: https://www.michaelelabd.com - Website: https://x.com/MichaelElabd --- Source: https://flows.cv/michaelelabd JSON Resume: https://flows.cv/michaelelabd/resume.json Last updated: 2026-04-01