21 Thought-Leader Professors in Data Science

The field of data science continues to grow, and with it come thought leaders who contribute to the industry through outreach and education. Many of the data science professors teaching today are leaders in the big-data field, speaking at conferences, writing books, and even creating groundbreaking big-data developments themselves. Find out which schools boast the most influential leaders in the data science industry.

Babson College

Tom Davenport


President’s Distinguished Professor of Information Technology & Management

Cornell University

Hod Lipson


Associate Professor, Mechanical & Aerospace Engineering

Massachusetts Institute of Technology (MIT)

Alex Pentland


Toshiba Professor of Media Arts and Sciences, Director of Human Dynamics Lab, Director of MIT Media Lab Entrepreneurship Program

Stanford University

Hector Garcia-Molina


Professor, Computer Science and Electrical Engineering Departments

James Matheson


Consulting Professor, Stanford School of Engineering
  • Specialties: Management science and engineering
  • Conferences: 2014 Informs Conference on Business Analytics & Operations Research
  • Projects: SmartOrg
  • Books: The Smart Organization: Creating Value Through Strategic R&D
  • On big data’s impact on our lives: “Exploring data can be revealing. However, big data is not so good for decision-making. We have asked executives to look back at important decisions to see how much more data about the past would have helped, versus better judgements about the future. We get about 30% from past data and 70% from better judgements. Also, for big decisions it may be more important to adapt well and quickly as the future unfolds. So good data about the present and near past may loom in importance. Analysis of decision can direct data searches to the most beneficial areas. Of course, sometimes just playing with the data can produce valuable insights, but that is serendipity.”

Chris Re


Assistant Professor, Computer Science

Sebastian Thrun


Research Professor, Google Fellow, co-founder of Udacity

University of California-Berkeley

Joshua Bloom


Professor of Astronomy

Michael J. Franklin


Professor of Computer Science

AnnaLee Saxenian


Dean and Professor, School of Information
  • Specialties: Economics, international communities and migration of talent
  • Conferences: DataEDGE
  • Books: The New Argonauts: Regional Advantage in a Global Economy, Regional Advantage: Culture and Competition in Silicon Valley and Route 128
  • On big data and how it touches our lives: “The impacts of big data are currently visible in the worlds of social media, technology, advertising and marketing, and finance. Big data is also many science and engineering fields like physics, biology, and astronomy. It will increasingly be visible in in health care, schools, government, and in a wide range of older industries, from autos to aerospace. Virtually every organization will want to be able to work with their data.Big data is working behind the scenes when we surf the web, use social media, and even email–whether on our mobile devices or computers. Big data is being used in our financial transactions and in our cars. It is really widespread–and soon will become ubiquitous.”

Ion Stoica


Professor, Computer Science Division

Bin Yu


Chancellor’s Professor, Department of Statistics, Department of Electrical Engineering & Computer Science

Matei Zaharia


Founder of Databricks, Assistant Professor in EECS (in 2014 academic year)

University of Massachusetts

Jeffrey M. Keisler


Professor of Management Information Systems
  • Specialties: Decision and risk analysis, analytics, spreadsheet modeling, project/portfolio management
  • Conferences: 2014 Informs Conference on Business Analytics & Operations Research
  • On big data’s largest impact: “In recent years, big data was finding a lot of small uses, such as figuring out which pop-up ad to show you on a web page. More recently, it has been used to find efficiencies in business processes, which has a lot of impact in the economy. Big data also plays a role in national security, of course. I don’t think it has yet had tremendous impact on the most important decisions companies and our society makes, but it has the potential to and it should. For example, the debate on healthcare reform involved a lot of conjecture on a wide range of issues about what the likely impacts would be from various changes to the system. Answers to a lot of the questions that were asked or should have been asked might have been found in the existing data covering the experience of many millions of Americans. This would have been possible if enough of the circumstances of each individual case were encoded and analysts were able to extract and compare all the micro experiments of policy variations that happen every day. I would like to see the methods of decision analysis in particular used as a front end to large policy and strategic decisions that would provide a framework for identifying and incorporating the most valuable information to extract from the sea of data.”

University of Virginia

John Elder


Adjunct Professor, Data Mining Consultant at Elder Research Incorporated

Yael Grushka-Cockayne


Assistant Professor of Business Administration

University of Washington

Cecilia Aragon


Associate Professor, Department of Human Centered Design & Engineering
  • Specialties: Human factors in computer interaction, data science, collaborative games
  • Projects: Scientific Collaboration and Creativity Lab, Sunfall
  • Recommendation for learning more about big data: “Some good resources for learning about big data can be found here in a proposed data science curriculum that I developed along with the eScience Institute. These are the key skills that market research and scientific experience have taught us are critical to data-intensive science. We are also currently developing big data PhD tracks across multiple departments in the University of Washington.”

Magdalena Balazinska


Associate Professor, Computer Science and Engineering

Carlos Guestrin


Amazon Professor of Machine Learning, Associate Professor in Computer Science and Engineering, Adjunct Professor in Statistics

Jeffrey Heer


Associate Professor, Computer Science and Engineering