In 2011, I joined Rutgers University as a PhD student. At Rutgers, I started my career in data science by working with my advisor, Prof. Hui Xiong, who is a Dean’s Research Professor at Rutgers Business School and an ACM Distinguished Scientist. I received rigorous academic training in the foundations, algorithms, and applications of data science. I also work on several data science projects including mobile recommender systems, in-App behavior analysis, and retail return and refund analysis.
What Do You Feel Are Some Common Misconceptions About Data Science Or Your Work In General?
Big Data is often mistakenly identified as the exponential volume of data. However, Big Data is not just about “big.” Big Data should refer to the capability of exploring data that are large-volume, real-time, and heterogeneous (e.g., multi-domain, multi-source, multi-format, multi-dimension). Urban and mobile data are heterogeneous since such data are usually crowd-sourced, large-scale, geo-tagged, time-stamped, and collectively-related. It is important to develop the algorithms and tools to address the challenges of data heterogeneity in urban computing.
What Inspired You To Learn More About Urban Computing?
Cities are growing faster than at any time in history. However, the cities have been facing many challenges, such as traffic congestions, air and water pollution, city noises, increased gas consumption. Consequently, the problems in the cities, as they grow, are only going to be worse. With the advent of mobile, sensing, and internet technologies, large-scale urban and mobile data, such as Point of Interest data, mobile check-in data, and taxi GPS data, have been collected from buildings, vehicles, mobile devices, and human beings. These bring up the question: can we use data mining techniques to create win-win solutions that improve urban environments, human life quality, and city operations? In summer 2012, I joined the Urban Computing Group of Microsoft Research Asia to work as a research intern. The internship experience brought my research attention to urban computing, which is the main focus of my research.
What Value Do You Hope Your Research Into Urban Computing Will Eventually Provide?
First, I hope my research can systematically contribute to the theory, algorithms, and applications of urban intelligence. Second, I hope my research can provide an in-depth and unique understanding of the nature and mechanism of urban phenomena, and ultimately make our cities traceable and predictable. And I believe in what I do.
What Are Some Tools That You Use To Conduct Your Research?
I use Python and PhP to crawl and preprocess spatio-temporal socio-textual data, Python and R to develop analytic and statistical approaches, PyTorch and TensorFlow to build deep learning models, MATLAB to visualize analysis results, RShiny to create systems to demonstration our preliminary results, and Latex and Atom to write papers.
What Do You Look For When Hiring Teaching Assistants And Research Assistants?
I like students that have strong programming skills and are hard-working, self-motivated, and passionate.
What Advice Would You Give To Students Who Aspire To Be Data Scientists?
First, learn solid statistical and algorithmic foundations of data science. Second, learn how to formulate real-world problems into data mining tasks. Lastly, learn to deal with large-scale noisy data using strong programming skills and a variety of analytic tools.
Featured Schools for Data Science
The Master of Information and Data Science (MIDS) is an online degree program for professionals looking to advance in the field of data science. Drawing upon the social sciences, computer science, statistics, management and law, the programs prepare students to solve real-world problems by deriving insights from complex and unstructured data. Students in the program benefit from UC Berkeley’s strong ties to the Bay Area and Silicon Valley.
Syracuse University’s online Master of Science in Applied Data Science is designed for professionals who want to gain specialization skills to excel in their evolving field. Featuring live online classes, interactive coursework and opportunities to network, the online learning format facilitates collaboration, problem-solving and in-depth analysis within an interdisciplinary curriculum. Students focus on pulling insights to drive business decision-making and operational processes using applications of data.
Another program offered by Syracuse is the Master of Science in Business Analytics. Delivered online by the Martin J. Whitman School of Management at Syracuse University, BusinessAnalytics@Syracuse helps data-driven thinkers develop or sharpen their ability to interpret complex data and guide their organizations in making more informed and actionable decisions. Through an action-oriented online learning format, students develop and hone their expertise in areas such as predictive analytics, data modeling, and information systems.
Designed for working professionals looking to advance their careers, DataScience@SMU is an online Master of Science in Data Science from Southern Methodist University. DataScience@SMU’s interdisciplinary curriculum draws from SMU’s Dedman College of Humanities and Sciences, Lyle School of Engineering and Meadows School of the Arts. Classes and coursework focus on statistics, computer science, strategic behavior and data visualization skills so students can drive decision-making and advance in careers across industries. The program blends live online classes, self-paced coursework and in-person learning experiences with classmates and faculty.
Analytics@American is the online Master of Science in Analytics (MSAn) from American University’s Kogod School of Business. Through a combination of collaborative online classes, self-paced coursework, and hands-on learning experiences, students become experts in evidence-based data gathering, data modeling, and quantitative analysis. Analytics@American classes are designed by Kogod faculty members who take a student-centered, team-based approach to delivering a world-class business education.