Journel is a resourceful, creative and self-motivated data scientist, entrepreneur and author. He is passionate about leveraging data and technology to solve problems, enhance efficiency and drive meaningful decisions. Journel brings a unique blend of experience in corporate research, technology, healthcare and education data. He has expertise in data visualization, predictive analytics, data sourcing/collection, Machine Learning, data integration, data preparation, quantitative and qualitative research methods, behavioral analytics and data privacy. He is seasoned in designing and implementing cost-efficient software solutions.
Journel has supported and provided data and analytics solutions to several federal contracting projects, including the Department of Defense Education Activity (DoDEA); the Department of Education’s Office of Elementary and Secondary Education, the Office of English Language Acquisition (OELA); the National Clearinghouse for English Language Acquisition (NCELA); the Substance Abuse and Mental Health Administration (SAMHSA)—the Center for Substance Abuse Prevention (CSAP). Over the years, he has worked within academic settings, consulting, for-profit and non-profit organizations, including New England Research Institutes (NERI) acquired by HealthCore, Tufts University, Education Development Center (EDC), The Executive Leadership Council (ELC), and has collaborated on research projects with Deloitte and the Alliance for Board Diversity (ABD). Journel has experience with commonly used data science tools, technologies and big data solutions, such as Python, AWS, TensorFlow.js, Tableau, ArcGIS, MySQL, SQL, R, visualization, SPSS, STATA, MATLAB, Salesforce, Hadoop, NoSQL, Hive, AWS, etc. He has strong interest in virtual, augmented and mixed reality, and video game simulations for learning.
Journel has a proven ability to make complex technical and scientific concepts comprehensible and engaging for diverse audiences. He has conceived, proposed and led new business development initiatives from inception to execution. Journel has a keen understanding of commonly used machine learning techniques and algorithms. His work explores the unintended consequences and potential solutions that address the bias and lack of diversity in datasets used to train and test AI systems. He currently focuses on designing frameworks to identify and build inclusive and representative datasets leveraging empirical research methods and data science techniques.
Journel is the author of Data & Analytics 4.0: The Future of Work, Privacy and Trust in the Age of Artificial Intelligence. He is also a co-author of various peer-reviewed publications. As a passionate,multi-disciplinary, multilingual data and analytics professional, he brings over a decade of experience to the industry and consulting arena. He is the founder of Analytics for Lunch: a masterclass series on the business of data & analytics and implications of AI systems, and a co-founder of Insert Analytics.
Journel is an engaging and inspiring speaker with a keen interest in subjects that are related to the implications and opportunities of advances in artificial intelligence (AI), big data and analytics, digital strategy, automation and the future of the work. His most recent speaking engagement was as part of a distinguished keynote panel at the 2019 National Science Foundation's Innovative Technology Experiences for Students and Teachers (ITEST) STEM Summit. His well-received talk covered the subject of “Diversity and Ethical Implications of Advances in Artificial Intelligence: Unintended Consequences on Steroids.”
Journel is a graduate of the Fletcher School of Law and Diplomacy at Tufts University with a Master of International Affairs degree where he specialized in Economics, and International Trade. In addition, he has a Master of Public Policy degree from the Department of Urban and Environmental Planning and Policy (UEP) at Tufts University. He holds a bachelor’s degree in Technology and Community Media from the University of Massachusetts and he has completed a 2-year NSF-funded training in Program Evaluation and Applied Research. He holds a Certificate of Advanced Graduate Study (CAGS) in Program Evaluation from Tufts University.
Exploring the unintended consequences and potential solutions that address the bias and lack of diversity in data sets used to train and test AI systems. Focusing on designing frameworks to identify and build inclusive and representative data sets leveraging empirical research methods and data science techniques.
AI-assisted healthcare systems will ultimately play an essential role in helping to transform medicine and liberate the full potential of healthcare professionals. AI systems should not be designed to take away the intuition, experience and heart of the doctor — the intangibles that separate her from machines. Medicine must always remain human.
“Data privacy has become an absolute global concern. In addition to implementing data protection and privacy principles to protect their citizens, the biggest challenge for developing nations would be the high cost and resources to enforce and fine the violators.” —Journel Joseph
Cities have become the battle ground for interconnected systems and data collection, but policy initiatives remain so far behind as governments continue the traditional way of playing catch up...
“Contrary to the past when various companies failed due to the delay in adopting technology, the future’s disruption might come from underrating humans, overusing robots and failing to build a symbiotic workforce for the future.” Journel Joseph