About Joanne

I am a computer scientist, data scientist, and interdisciplinary technical leader whose career has centered on a recurring problem: how to turn complex data, scientific knowledge, and emerging technologies into something people can actually use. My work has crossed software engineering, artificial intelligence, predictive modeling, biomedical informatics, data architecture and integration, knowledge representation, semantic technologies, systems engineering, and scientific program development.

I began my career in software development and systems programming, working on word processing, process-control systems, user interfaces, and software engineering. I later founded a consulting company serving technology and life-sciences organizations, and my interests increasingly moved toward computational approaches to difficult biomedical problems. My doctoral research at Boston University used neural-network models to study patterns of recovery from major depressive disorder and predict treatment outcomes. That work ultimately led to two U.S. patents covering automated treatment selection and prediction of therapeutic outcome.

Much of my subsequent career has been at the intersection of computer science and biomedical research. I was a core member of BioPAX, an international effort to create a standard for exchanging biological pathway data, where I helped advance the technology, build a worldwide stakeholder community, and secure funding. At MITRE, I led development of an influenza ontology supporting research and surveillance and established an ontology-evaluation research program. My work has also included appointments and collaborations with Harvard Medical School and Massachusetts General Hospital, Rensselaer Polytechnic Institute, GE Global Research, the University of Manchester, and organizations across the pharmaceutical and biotechnology industries.

Through Predictive Medicine, Inc., I worked with clients on data science, predictive analytics, statistical analysis, biomedical informatics, data integration, knowledge representation, and emerging technologies. Projects ranged from drug discovery and pharmacogenomics to recommender systems, healthcare applications, cybersecurity, and the integration of heterogeneous biomedical data.

Education and program building have become another important part of my work. At Indiana University, I developed and taught graduate data science courses organized around the complete data science workflow. At the University of the Virgin Islands, I conceived, designed, and implemented an interdisciplinary undergraduate Data Science Minor and Certificate, working across the university to make the curriculum accessible to students from many disciplines. The program combined technical skills with data ethics, governance, privacy, stewardship, analysis, visualization, machine learning, databases, cloud computing, and the ability to reason critically about the full data lifecycle.

Today, I continue to work across research, education, program leadership, and applied technology. At MIT, I administer the EECS SuperUROP advanced undergraduate research program, working with students, faculty, industry partners, donors, and university leadership. I also serve as a scientific research officer supporting peer review of biomedical research and continue to advise organizations on data, technology, and program development.

Across all of these roles, the common thread has been the same: bringing together people, disciplines, data, and technology to solve problems that do not fit neatly inside a single field.

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