Fundamental Paradoxes and Biases in Epidemic Research

Talk Abstract: Faced with a rapidly evolving virus, inventors must seek to narrow the intellectual gaps that exist between two intimately intertwined communities: namely, bio-medical researchers driven by hypotheses and technologists informed by clinical trials, experiments, and data. Supported by empirical model-driven analysis, this paper delves into fundamental paradoxes and biases in the context of epidemic research, and provides necessary antidotes at every stage of the clinical trial; ranging from hypothesizing to sampling, and analyses to fake data detection. Critically, the paper also provides original research that demonstrates how these play into technology development and deployment to combat the surging pandemic, e.g. COVID-19. Speaker Bio: NYU Courant Institute Professor Bhubaneswar "Bud" Mishra is a mentor, a teacher and a thinker, helping students, entrepreneurs and collaborators, solving problems in statistics, machine learning, and data science with applications to AdTech, BioTech, FinTech, InfoTech, RegTech, etc. He was named a Fellow of the National Academy of Inventors (NAI) for his seminal work in these technologies. Mishra holds 21 issued and 23 pending patents in areas ranging over robotics, model checking, intrusion detection, cyber security, emergency response, disaster management, data analysis, biotechnology, nanotechnology, genome mapping and sequencing, mutation calling, cancer biology, financial technology, advertising technology, Internet architecture, and linguistics. He has industrial experience in computer and data science, finance, robotics and bio- and nanotechnologies, and is the author of a textbook on algorithmic algebra and more than 200 archived publications. Disclaimer: All views, thoughts, & opinions expressed in the webinar belong solely to the panelists, & not to the panelists’ employer, organization, committee, other group or individual.