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

Specialties: Business process reengineering, knowledge management, enterprise systems, analytics
Conferences: Keynote Speaker, 2014 Informs Business Analytics & Operations Research, Keynote Speaker, 2014 Vertica Big Data Conference
Books: Competing on Analytics: The New Science of Winning, Strategic Management in the Innovation Economy: Strategic Approache..., Thinking for a Living: How to Get Better Performances and Results f..., Working Knowledge: How Organizations Manage What They Know

Cornell University

Hod Lipson

Associate Professor, Mechanical & Aerospace Engineering

Specialties: Evolutionary robotics, design automation, rapid prototyping, artificial life, self-assembly
Notoriety: Named one of Forbes’ World’s Most Powerful Data Scientists
Books: Fabricated: The New World of 3D Printing

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

Specialties: Computational social science, organizational engineering, mobile computing
Notoriety: Named one of Forbes’ World’s Most Powerful Data Scientists in 2014
Projects: Social Physics
Books: Social Physics: How Good Ideas Spread — The Lessons from a New Science, Honest Signals: How They Shape Our World

Matei Zaharia

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

Specialties: Tools for large-scale data-intensive computing
Conferences: Strata 2014 Faster and Smarter Big Data Analysis with BlinkDB, MLbase, GraphX, ...
Projects: Spark, Shark, Mesos, other systems for big-data scheduling and coordination

Stanford University

Hector Garcia-Molina

Professor, Computer Science and Electrical Engineering Departments

Specialties: Distributed computing systems, digital libraries, database systems
Conference: IEEE BigData 2013, Using Crowdsourcing for Data Analytics
Projects: Stanford Digital Library Project
Books: Database Systems: The Complete Book, Database System Implementation

James Matheson

Consulting Professor, Stanford School of Engineering

Specialties: Management science and engineering
Conferences: 2014 Informs Conference on Business Analytics & Operations Rese...
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

Specialties: Theoretical and practical problems in data management
Conferences: Strata 2014, Thorn in the Side of Big Data: Too Few Artists
Projects: Hazy

Sebastian Thrun

Research Professor, Google Fellow, co-founder of Udacity

Specialties: Robotics, AI
Conferences: Strata 2014, Closing the Job Skills Gap: One Class at a Time; DataBeat 2014
Projects: Google’s Self-Driving Car, Google Glass,Udacity
Books: Probabilistic Robots, Robots: Science and Systems I
Recommendation for learning about big data: “Udacity has a data science track built by industry. Leading companies like Cloudera, Facebook, and MongoDB have contributed courses. Learn from the leading experts in the world. All content is accessible for free, and every student can sign up for our classes.”

University of California-Berkeley

Joshua Bloom

Professor of Astronomy

Specialties: Astrophysics, Python for data science
Conferences: Strata 2014, Overcoming the Barriers to Production-Ready Machine-Learning Workflows
Projects: wise.io

Michael J. Franklin

Professor of Computer Science

Specialties: Large-scale data management infrastructure and applications
Conferences: IEEE BigData 2013, The Berkeley Data Analysis Stack: Present and Future
Projects: AMPLab, MLbase, CrowdDB

Read full list at http://www.mastersindatascience.org/blog/21-thought-leader-professo...

0 members like this

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Dean Abbott on April 2, 2016 at 2:07pm: Just looking back at this now...I originally thought it said "professional" rather than "professor". Given my misread. I concur, Bob: one must first be a professor to ultimately be a thought-leader professor!

Comment by Bob Vanderheyden on April 20, 2015 at 7:19am: Dean, it would seem to me, that to be a "Thought-Leader Professor", one must first be a professor.....

Comment by Dean Abbott on March 27, 2014 at 9:06am: I conclude, therefore, that to be a thought leader in data science you much be a university professor? The mastesrindatascience.org site should have made their title, based on their own criterion that everyone on the list be a university professor, to "Academic Thought Leaders in Data Science". :)