Matthew Woods
Verified Expert in Engineering
Machine Learning Developer
San Jose, CA, United States
Toptal member since December 21, 2020
For the past 12 years, Matthew has been creating applied machine learning and data-driven engineering projects in multiple industrial sectors, including agriculture, biotechnology, cybersecurity, and automotive. He is passionate about developing technologies that exploit his knowledge and experience with machine learning.
Portfolio
Experience
- Artificial Intelligence (AI) - 20 years
- Machine Learning - 20 years
- Algorithms - 20 years
- AI Research - 20 years
- Software - 18 years
- Python - 12 years
- Computer Vision - 7 years
- Sensor Fusion - 6 years
Work Experience
Machine Learning Engineer and Scientist
Self-employed
- Developed a computer vision system for automotive damage assessment.
- Created a machine learning-based system to diagnose disease states from a biological sample.
- Built a system to answer questions specific to published scientific literature in a given domain.
Principal Software Development Engineer, Artificial Intelligence
Traptic
- Led the development of the robot's visual system software using deep learning and computer vision, including coding in Python and Bash for training, tuning, and deploying to the production of customized convolutional neural network architectures.
- Contributed to the architectural modifications and custom loss functions for the neural networks that allowed the inference of multiple targets from images with semi-supervised training.
- Championed the delivery of a mathematical model of harvest yield that allowed tuning aspects of the visual and robotics systems to maximize the robot's strawberry picking rate.
Senior Machine Learning Engineer
SAIC Innovation Center
- Developed a driver monitoring system fusing video and biometric streams.
- Built a system to predict a driver's intended destination.
- Developed a system to anticipate drivers' environmental control preferences.
Senior Data Scientist
Venafi
- Developed an anomaly detection system for PKI certificates using Spark and Python.
- Developed software to organize customers' internal PKI certificates into functionally meaningful groups with hierarchical clustering and a customized domain name similarity metric. Built a stand-alone REST API for this back end using Flask.
- Developed a system to assign a score to certificates on the basis of revocation likelihood as estimated with machine learning.
Senior Research Scientist I
Pfizer
- Developed a machine learning system for the prediction of antibody thermal and acidic stability on the basis of primary sequence with the aim of identifying stability improving inducible mutations.
- Identified common biological activity among a large panel of compounds with unsupervised learning and computer vision applied to digital microscopy.
- Performed text mining and natural language processing of a large corpus of miRNA-related publications.
Project History
Neural Network and Bioinformatic Designs for Predicting HIV-1 Protease Inhibitor Resistance
Education
PhD in Cognitive and Neural Systems
Boston University - Boston, MA, USA
Bachelor's Degree in Physics and Mathematics
University of Michigan - Ann Arbor, MI, USA
Skills
Libraries/APIs
OpenCV, Scikit-learn, Pandas, TensorFlow, Keras, MLlib, PyTorch
Tools
PyCharm, Git, Trello, MATLAB, You Only Look Once (YOLO), ChatGPT, Amazon SageMaker
Languages
Python, R, Snowflake
Platforms
Amazon Web Services (AWS), Ubuntu, MacOS, Unix, Amazon EC2, Linux, AWS Cloud Computing Services, Google Cloud Platform (GCP), Docker
Frameworks
Spark, Flask, RStudio Shiny, gRPC
Storage
Amazon S3 (AWS S3), JSON
Other
Machine Learning, Science, Statistics, Simulations, Computer Vision, Supervised Learning, Unsupervised Learning, Time Series, Signal Processing, Mathematics, Algorithms, Modeling, Artificial Intelligence (AI), Research, Custom Models, Data Science, Supervised Machine Learning, Object Detection, Neural Networks, Predictive Modeling, AI Research, Outlier Detection, Software, Decision Trees, Dimensionality Reduction, Gradient Boosting, K-means Clustering, Linear Regression, Logistic Regression, Random Forests, Support Vector Machines (SVM), Time Series Analysis, ML Pipelines, Differential Equations, Software Development, Sensor Fusion, Sensor Data, Deep Learning, Convolutional Neural Networks (CNNs), Data Engineering, Image Processing, Large Language Models (LLMs), API Integration, APIs, LangChain, OpenAI GPT-3 API, OpenAI, Generative Pre-trained Transformers (GPT), OpenAI GPT-4 API, Natural Language Processing (NLP), Language Models, Containerization
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