As CTO and co-founder of Pathways, I'm envisioning and building a platform for creating scalable, enterprise-grade, AI-era data experiences.

Advising HistoWiz on AI strategy as they continue to build out their platform and marketplace for digital pathology.

I'm working on a part-time basis to guide the machine learning strategy of Histowiz. This involves outlining a road map for how the company will apply modern computer vision and deep learning techniques to a variety of problems within histopathology. I'm also contributing to the development of some of these technologies and guiding the efforts of other machine learning researchers on the team.

I provide regular advice on strategies and thought capital regarding Pivan Interactive's machine learning architecture and service in the esports and gaming space.

I provide effective machine learning and computer vision solutions to hard, real-world problems across a wide range of domains. Whether the task involves finding objects in a vast collection of images, capturing trends in consumer data, predicting user interests from transactional data, or identifying acoustic patterns in heaps of audio recordings, it's likely that automated methods can be used to quickly extract intelligible information from an otherwise unmanageable amount of data. I help clients employ these methods to improve their services, better reach their customers, and reduce their overhead and expenses.

Olivecell is a healthcare startup focused on using artificial intelligence in the diagnosis and treatment of patients. Our initial products are built for neurologic disorders. In this role, I've led the company's initiative to develop computer vision and machine learning methods for identifying indicators of Parkinson's disease.

I'm involved with the tech developments at this early-stage start-up that delivers the social opinion on all topics under the sun. I manage the science behind extracting accurate sentiments from complex and widely varying sources, capturing trending social opinions, and analyzing domain specific sways in popularity.

Played an integral role in developing Sensory's cutting-edge face verification technology from the ground up. Researched and developed machine learning and computer vision algorithms for extracting useful content from face images. Performed in-depth analyses of system accuracy. Organized data collection efforts to build a very large face and voice dataset.

I taught machine learning for the computer science department at CU Boulder. The course provided a graduate-level presentation of the diverse methods that constitute modern machine learning.

Contract consulting work across a range of data analysis problems including predictive modeling, text analysis, classification, clustering, and visualization.

My dissertation research focused on building computational models of cognition. In explaining key behavioral traits of humans, I drew heavily on machine learning, in particular, Bayesian methods. Much of my work revolved around the study of short-term sequential effects---i.e., how individuals change their behavior as a function of recent experience. In the domain of human judgments, I proposed a novel machine learning technique that removes sequential biases from peoples' judgments to obtain more reliable estimates of their true sentiment.

Developed mapping algorithms for a mobile robot using computer vision to navigate an indoor environment.