Big Data, Machine Learning, and their Real World Applications

Minwoo Song is an accomplished educator and researcher in the field of economics and finance. He currently works as an adjunct professor at several universities, including New York University, Fordham University, and Baruch College, where he teaches a range of subjects including economics, finance, and statistics. His exceptional teaching skills have earned him high praise from both students and colleagues.

Minwoo Song has an impressive educational background, holding a bachelor's degree in electrical engineering and a master's degree in economics. He is currently a Ph.D. candidate in financial economics at the City University of New York, the Graduate Center. His research focuses on the application of machine learning techniques to financial markets. In particular, he has conducted research on the herd instinct in the digital currency market. In his paper, he utilized textual analysis to forecast cryptocurrency returns and constructed a novel data set using machine learning techniques. With this data, he developed a long/short strategy that generated significant profits. Overall, Minwoo Song's expertise in economics and finance, coupled with his innovative use of machine learning, has made him a respected and accomplished academic.

Joshua Young is an Assistant Professor in the Otto H. York Department of Chemical and Materials Engineering at the New Jersey Institute of Technology. He received his PhD in Materials Science and Engineering at Drexel University and BS in Chemistry from Case Western Reserve University. His research focuses on the use of machine learning and other computational methods to design new materials for cutting edge applications such as batteries, water treatment, catalysis, and novel electronic; this involves the analysis of large databases of material structures and properties using advanced data science techniques. He has also been actively involved in designing, implementing, and teaching data science at all levels from elementary school to post-college, especially as it relates to solving engineering problems.

Marcela Mendoza is a researcher and educator in the field of Data Science. She is currently an Adjunct Associate Professor of Data Science at Beyond Campus Innovations as well as a Data Science tutor at Practicum. She received her PhD in Bioengineering (2018) and an MA in Applied Mathematics (2017) from the University of California, San Diego where her thesis focused on developing the theory and application of novel machine learning algorithms to biomedical data. She also received a BS in Biomedical Engineering (2012) from the University of Texas at Austin. Her experience in data science expands both academia and industry. She has postdoctoral studies in the field of genomics from Scripps Research Institute and the field of citizen science from El Colegio de la Frontera Sur with a focus on Machine Learning algorithms. She has also worked as a data scientist at several companies and continues to do consulting for industry.