Emma Zohner and I teamed up to found the class, "Artificial Intelligence in Business"

The global advocacy organization dedicated to the discovery, development and delivery of better solutions to improve patient lives.

Leadership for fostering AI, Data, and Computing at Rice University. The Ken Kennedy Institute brings together more than 200 faculty to solve global challenges in partnership with diverse academic groups, industry, and government agencies. I coordinate collaborative efforts between scientific and multi industry-stakeholders to ensure that the latest data science innovations find their way in the world. This role requires the ability and knowledgebase to remain actively engaged with computing scientists, multi-sector executives, and city officials to ensure a united strategy for optimal effectiveness. In this capacity, I coordinate with:● Rice University – As part of Rice University, one of the country’s top-ranked institutions, I oversee daily operations for the school’s computing and data science research hub.● Healthcare – I coordinate efforts with hospitals to integrate machine learning capabilities to improve care, enhance discoveries and enable data-driven decisions.● Industries – I develop the institute’s relationships with external partners from various sectors like health care (Texas Medical Center) and energy, who will ultimately use innovations birthed from researchers. ● Government – I actively involve government officials to coordinate the local impact of our initiatives.

I helped founders build SynBio companies.

Now called OmniScience. Founding MDS was built around a keen focus on solving data science technology issues for businesses ranging from early phases to corporate stages. In two years I grew our data science team from one to 20 people, and together we created solutions for clients in health, life sciences, biotechnology, agriculture, consumer behavior, CpG, engineering, and energy. Projects we worked on included: ● Natural language processing to extract relationships from the scientific literature● Digital assessment platforms for behavioral health to assess patient depression and anxiety. ● Biometric eye tracing for assessment of cognitive performance in dementia, concussion, and intoxication● Training machine learning models to predict consumer purchase behavior to optimize CPG campaigns.● Machine learning algorithms to extract and organize food product ingredients from 2+ million product label images.

The goal of the organization was to bring together data scientists, neuroscientists, legal scholars, judges, and policymakers with the aim to use data science to make the justice system more efficient and fair. I led multiple data science projects to predict recidivism, gain insights from city and state data, and work with the city officials to use data to make better decisions.

As an Instructor, Research Faculty, and Postdoctoral Associate at the Baylor College of Medicine, I had the opportunity to expand my experience as a computational biologist while seeing the collective benefits of cross-institutional and commercial collaboration. I spearheaded collaborations like the SImplex Program (DARPA) with Stanford and Purdue University on biomedical science algorithms. This was instrumental in evolving my focus to artificial intelligence and natural language processing while laying the groundwork for my understanding of turning these applications into tools for commercial use. Notable milestones and achievements include:● IBM product, IBM Watson Drug Discovery - A three-year effort with IBM where I led collaborations between industry scientists, postdoctoral associates, and graduated students to automate the process to read, represent, and reason on biomedical literature. ● Simplex Program (DARPA) - I was responsible for leading the Baylor side of a large DARPA collaboration with Stanford and Purdue University with the goal of applying new AI technology to biomedical problems. ● My research led to four patents and 30 peer-reviewed articles, reviews, and book chapters in computational biology, disease, and drug discovery, physics, and machine learning, and artificial intelligence.● Research in involved integrating natural language processing and machine learning for automated discovery of the biomedical literature. This eventually expanded to better understand the functional role, structure, disease biomarkers, and re-engineering of proteins.● Developed algorithms to identify and model properties in proteins based on evolutionary information for use as tools in areas like new proteins and protein function. ● Developed a resource to provide consultation on computational and statistical problems in weekly data clinics. Acted as part of the core team for consultation.