Keegan Hines's Email Addresses & Phone Numbers

Data Science Intern @ Civitas Learning During an internship with the Data Science team, I developed predictive models for improving student success in higher education. From July 2013 to September 2013 (3 months) Vice President of Machine Learning @ ArthurAI We're building the first enterprise platform for Responsible AI, come join us in NY or DC!As ML...

Data Science Intern @ Civitas Learning During an internship with the Data Science team, I developed predictive models for improving student success in higher education. From July 2013 to September 2013 (3 months) Vice President of Machine Learning @ ArthurAI We're building the first enterprise platform for Responsible AI, come join us in NY or DC!As ML is deployed in core applications across all industries, it must be done in a way that is safe, reliable, and fair. Arthur's model monitoring platform provides real-time observability into critical functionality of ML systems. The ML team at Arthur develops core capabilities around model monitoring, data monitoring, algorithmic fairness, and model explainability. Check out our careers page or send me a message!https://www.arthur.ai/careers Co-Founder & Chair @ CAMLIS The Conference on Applied Machine Learning for Information Security is an annual conference for discussing new developments in machine learning as applied to problems in cybersecurity and defense.More info at https://www.camlis.org/ PhD Candidate @ The University of Texas at Austin PhD in Neuroscience in the lab of Rick Aldrich. My focus was applying powerful methods from statistics and machine learning to systems in molecular biophysics. From August 2009 to July 2014 (5 years) Associate @ Texas Venture Labs Worked collaboratively in a team of MBA students, Law students, and Natural Science PhD students to conduct market validation, competitive analysis, and financial modeling for Austin startups. From January 2014 to May 2014 (5 months) Machine Learning Engineer, Senior Manager @ Capital One Led technical projects and provided modeling guidance in domains including time series anomaly detection, malware detection, and digital marketing using techniques such as Bayesian Multi-arm Bandits, Gaussian Process Regression, and convolutional networks.Worked with leadership team on the development and strategy of Capital One’s Center for Machine Learning. Helped grow the Center from 10 to over 150 through recruiting, interviewing, and mentoring engineers and data scientists. From February 2017 to September 2018 (1 year 8 months) Data Scientist @ IronNet Cybersecurity • Use math to catch bad guys.• Worked with a team of exceptional data scientists and developers to build powerful algorithms for anomaly detection in computer networks.• Used Spark to create new approaches for large-scale outlier detection and alerting in cyber data. Full lifecycle development of novel algorithms through R&D, testing, and deploying across the enterprise. From October 2015 to February 2017 (1 year 5 months) Adjunct Assistant Professor @ Georgetown University Develop and teach graduate level course material in Georgetown’s Masters Program in Data Science (https://analytics.georgetown.edu/). ANLY-590: Neural Networks and Deep Learning. Topics include artificial neural networks, optimization and gradient descent, backpropagation, convolutional neural networks, recurrent neural networks, autoencoders, embeddings, generative methods.ANLY-512: Statistical Learning Theory. Topics include classification and regression, model evaluation, parametric and nonparametric methods, regularization, and unsupervised methods. Director, Machine Learning Research @ Capital One Built and led the ML Applied Research team within Capital One's Center for Machine Learning. Worked with a phenomenal team to develop novel applications of ML in critical financial services areas such as Explainable AI, Computer Vision, and Graph Representation Learning. From September 2018 to January 2020 (1 year 5 months) Data Scientist @ L-3 Data Tactics • Worked on DARPA program to build large-scale machine learning applications forcyber defense. Used Spark to develop algorithms for modeling terabyte data sets. • Developed data science and predictive analytics for government customers in domainsincluding time series forecasting, social media analytics, and cyber defense. From August 2014 to October 2015 (1 year 3 months)