Vivian Zhang
Data Scientist/Instructor
Vivian is the founder of NYC Data Science Academy and co-founder of SupStat. She is an adjunct professor at Stony Brook University and founded the NYC Open Data Meetup, which is 4000 strong. She has many years of practical experience in data technologies and the analytics, and has expertise in multiple programming languages including R, Python, Hadoop, and Spark.
Vivian was ranked in "9 Women Leading The Pack In Data Analytics" by Forbes in August 2016. She enjoys meeting people and enjoys sharing her experiences with young professionals and students.
Aiko Liu
Data Science Instructor
Aiko grew up in Taiwan where he studied Mathematics and Physics in college. He then moved to the United States to obtain his PhD in Mathematics at Harvard. After finishing his degree, Aiko conducted research and taught at M.I.T and U.C Berkeley for nine years before moving into the world of finance. He worked in the hedge fund industry on quantitative trading for a decade before diving into Data Science full time. Aiko enjoys programming and using machine learning algorithms for industrial research. When at home he enjoys reading books on a really, really wide variety of topics.
Luke Lin
Data Scientist
Luke holds a PhD in Mathematics at Stony Brook University, specialized in partial differential equations. As a lifelong learner of mathematics, he is extremely efficient in quantitative analysis and also skilled at communicating abstract concepts. With proficiency in R and Python, Luke is primed to be a major asset to any analytic force. Being extremely passionate to share the insight of the data from variety of industries, Luke looks forward to meeting talented students from all kinds of background here in NYC Data Science Academy.
Thomas Laetsch
Data Science Instructor
Thomas Laetsch is an experienced instructor and researcher with various teaching excellence awards and faculty recognitions during his years as a doctoral student and later as an assistant professor. His publications appear in top journals, such as the
Journal of Functional Analysis and
Transactions of the American Mathematical Society amongst others. In 2012, he received his Ph.D. in mathematics from the University of California, San Diego, specializing in probability, differential geometry, and functional analysis. He was a Visiting Assistant Professor at the University of Connecticut, working on central tendency theorems for random walks in degenerate spaces. From January 2016 until joining the New York City Data Science Academy, he was a Moore-Sloan Postdoctoral Research Associate in the Center for Data Science at New York University working in statistical criminology. On his own time, he enjoys music composition, dancing Argentine tango, and feeling over-confident while attempting to speak Spanish.
Zeyu Zhang
Data Scientist
Zeyu obtained his master degree of Electrical Engineering from New York University. With a strong background in object oriented programming and a solid understanding of machine learning algorithms, he helps virtual and physical machines to evolve. Known for doing many difficult things well at the same time, or one simple thing very slowly, Zeyu thrives on problems that require multiple skills. Throw him into a pool of Python, C++, R, SQL, C#, HTML/CSS, JavaScript or find him actually swimming since retiring from his short-lived very-amateur basketball career.
Drace Zhan
Data Science Instructor
Drace Zhan has honed the bulk of his communication skills by teaching math and reading skills to high school and college graduates since 2007. A whiz at translating abstruse concepts to easily understood terms, he entered the field of data science around the year end of 2016 and now joined NYC Data Science Academy. He is a lover of code, cats, and quests for questions.
Alexander Baransky
Data Science Instructor
Alex received his degree in Environmental Biology from Columbia University. He has experience with multiple computer languages including Python, R, and SQL. As an engineer at heart and biologist through training, Alex is passionate about animal behavior and finding innovative ways to use data science in the field of biology.
Helen Zhang
Data Scientist/Instructor
Helen is an experienced instructor who obtained two Master’s degrees, one in Statistical Science from Indiana University, and another in Education from Purdue University. She specializes in Statistics with experience in Exploratory Data Analysis, Machine Learning, and other statistical methods, such as hypothesis testing. She is proficient in multiple programming languages, including Python, R, SAS, and SQL. As a data scientist, she enjoys working on various analytical projects using statistical and machine learning models.
