Moove.ai enables easy use of the largest Connected Vehicle database in the world, helping local governments and enterprises leverage Connected Vehicle data to fully realize transportation safety and efficiency.

Founded this successful startup, leading all initial business formation, product development, and fundraising efforts in addition to recruiting a cross-functional team of A+ talent to support all corporate engineering, sales, product management, fundraising, data science, and HR needs. Hold co-leadership role in all ongoing Sales, Product, Marketing, and Data Science activities, in addition to serving as chief technologist driving the hands-on development of predictive models, and machine learning architecture, and algorithms for real-time statistics-based navigation. During the company's early stages, led the design, development, and implementation of ML applications and provided technical leadership to Agile teams focused on productionizing machine learning applications and systems at scale. Served as a technical domain expert in machine learning, guided key architectural design decisions, developed/reviewed model and application code, and ensured high availability and performance of all machine learning applications. Additionally drove the development, negotiation, and closure of over $10M in key licensing deals with blue-chip enterprises such as BMW and Deutsche Telekom.Key technologies utilized to date in development of the company's systems include Scala, Java, Python, REST, JSON, Protocol Buffers, gRPC, NoSQL, Relational Databases, Spark, ML Notebooks, Hadoop, Kubernetes, Docker, Maven, Kubeflow Pipelines, Argo Workflows, and AWS/Cloud Infrastructure—to name a few.Awards received by ParkNav (and its Ai Incube predecessor) include 1st place in the Data & Enterprise category of the 2016 SXSW competition, selection by Gartner in 2020 as a "Top 3 Cool Product" in mobility, and selection by Investor Place as a top investment on NASDAQ.com.

Pivoted prior full-time tenured role into a less-demanding Adjunct role, in parallel with industry leadership responsibilities, advising Computer Science PhD students in machine-learning and artificial intelligence topics and helping advance the frontiers of the industry by writing/publishing academic research papers (over 100 papers published to date), delivering presentations to top data science/AI/ML industry conferences, and serving in various committee leadership roles for industry bodies and organizations.

Recruited into initial Assistant Professorship on the faculty of this top 5 global CompSci institution and elevated into a fully tenured position in 2009—with an emphasis on teaching machine learning and artificial intelligence concepts to PhD students, leading R&D initiatives, writing/publishing academic research papers, and serving on various institutional committees, federal government committees, and journal review boards. Additionally have played a critical role in fundraising, raising over $3M in research funds, managing over 30 research assistants and postdocs, and publishing over 100 research advances.Key research areas of focus include Artificial Intelligence and Machine Learning. Co-developed Lifted Relational Probablistic Inference, Bayesian Inverse Reinforcement Learning, Logical Particle Filters, Factored Planning, Partitioned Reasoning, and Probabilistic Shortest-Path algorithms.

Recruited to lead innovation and AI/ML integration for this groundbreaking B2C/B2B software firm, managing a team of data scientists in the development of technologies to support Adobe's Display Advertising product. Key contributions included developing models for real-time prediction of customer conversion, crafting algorithms for offline and real-time optimization of display advertising, and leading the technical management and deployment of new capabilities related to machine learning, big data, optimization, statistical analysis, and reporting. Collaborated extensively with other stakeholders within Adobe, co-led SCRUM development cycles, and held ownership for ensuring the technical superiority of Adobe’s Display business vs. other competitive offerings.

Supplied data science SME to this marketing prediction company, creating a series of machine learning models (using Java/Weka and Python/Scikit-learn) and machine-learning engineering solutions (in Java, Python, and Unix Shell scripts) to support the company's product suite. Additionally provided expertise in feature engineering, data pipelining, data science team leadership, and strategic planning with respect to the company's data science direction and future product map.

See my blog entry at http://www.builtinchicago.org/blog/startup-leadership-program-perspective

Created algorithms for factored planning (together with Barbara Engelhardt), created algorithms and theories for partitioned reasoning (With Sheila McIlraith, Tomas Uribe, and Bill MacCartney), created theories about craig interpolation for nonmonotonic reasoning, and co-created algorithms for balanced vertex cuts (with Robi Krauthgamer and Satish Rao).