Group picture of faculty for Tackling the Challenges of Big Data


Twelve MIT experts to teach Tackling the Challenges of Big Data

The first MIT Professional Education Online X Programs course Tackling the Challenges of Big Data will be taught by 12 leading MIT faculty experts in the field of big data. The four week course begins March 4, 2014. At a cost of just USD$495, the online course offers people around the world the opportunity to learn from MIT faculty members without having to travel to its campus in Cambridge, Mass. The self-paced course is available to participants 24/7 in order to accommodate busy working professionals.

“The big data course offered by MIT should be required in any enterprise where business users interact with data. Business users crave big data and analytics tools, but without an understanding of what makes data good or bad they may make decisions based on insight that’s fallacious. MIT’s big data course is an important step for the industry,” said CIO Today online.

The course instructors are all members of the world-renowned Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Two years ago, CSAIL created the Big Data Initiative to develop, identify, and create new technologies that solve large data problems that organizations face today and in the future. Working with stakeholders in finance, government, high-tech, medicine, and computing, CSAIL’s goal is to help organizations from around the globe with the issues they face working with big data.

“Our instructors include inventors of key technologies that store, process, secure, and present data,” said course Co-Director Daniela Rus. Sam Madden, co-director of the course added, “The greatest value of this data comes from the ability to search, retrieve, interrelate, and synthesize the facts.”

About the faculty

Daniela Rus

Course Co-Director Daniela Rus, is a professor of Electrical Engineering and Computer Science at MIT, and is the Director of CSAIL. Her research involves robotics, computing, and programmable matter. Her work in the field of robotics centers on self-configuring robots, robots and sensors for first responders, underwater exploration, and transportation. She won the prestigious MacAuthur Fellowship in 2002. In this course, Rus will present a case study on transportation and coresets for global positioning systems.

Sam Madden

Sam Madden is a leader in the field of Big Data, and is co-director of Tackling the Challenges of Big Data, and director of the Big Data Initiative at CSAIL. Technology Review named him one of the “Top 35 Best Innovators under 35” in 2005 – one of the most prestigious awards given to innovators and entrepreneurs. Prior to coming to MIT, Madden was chief scientist at startup Cambridge Mobile Telematics. Madden will discuss a case study on Twitter and present ways to utilize MapD.

Regina Barzilay

Regina Barzilay, professor of electrical engineering and computer science at MIT, is currently Action Editor, Transactions of the Association for Computational Linguistics. In this course she will discuss a case study on Information Summarization.



John Guttag

Professor John Guttag co-heads the MIT Computer Science and Artificial Intelligence Laboratory’s Networks and Mobile Systems Group. This group studies issues related to computer networks, applications of networked and mobile systems, and advanced software-based medical instrumentation and decision systems. He has also conducted research, published, and lectured in the areas of software engineering, mechanical theorem proving, hardware verification, compilation, software radios, and medical computing. In Tackling the Challenges of Big Data, Guttag will discuss applications of Big Data in the field of medicine—looking for ways to improve efficiency and save organizations money.

Piotr Indyk

Piotr Indyk’s research revolves around computational geometry in high-dimensions, streaming algorithms, as well as computational learning theory. He won the Best Student Paper award at the Founders of Computer Science (FOCS) symposium. Indyk also received the Career Award from the National Science Foundation and in 2003 he received a Packard Fellowship from the Packard Foundation and a Sloan Fellowship from the Alfred P. Sloan Foundation. In 2012, he was the co-winner of the Kanellakis award for his work on Locality Sensitive Hashing. Indyk will cover fast algorithms and scale-up properties in this course.

Tommi Jaakkola

Tommi Jaakkola is a professor of electrical engineering and computer science at MIT. His research is in the areas of machine learning, computational biology, and information retrieval. He has won numerous awards for his research papers on uncertainty in artificial intelligence. He is currently researching the boundaries of hydrocarbon exploration, as they are often the places where oil is present. In this course, Jaakkola will cover machine learning tools as well as the challenges of scalability when using multicore processors in Big Data.

David Karger

David Karger leads the Haystack group at CSAIL. The Haystack Group develops tools to help people organize, manipulate, and retrieve the data that they encounter daily. His work involves facets of information management including: capture, organization, retrieval, and visualization. Read more about Karger’s work here. Karger will present future trends in user interfaces and applications to visualize Big Data in this course.


Andrew Lo

Andrew Lo is professor of Finance at the MIT Sloan School of Management. He is a leading authority on hedge funds and financial engineering and will teach the finance component of the course. He was named to Time magazine’s list of 100 most influential people in the world. Lo is the director of MIT’s Laboratory for Financial Engineering, a research associate of the National Bureau of Economic Research. His work includes researching aspects of financial engineering to find a cancer cure. Financial forecasting and risk management will be covered by Professor Lo in the course.

Ronitt Rubinfeld

MIT Professor Ronitt Rubinfeld teaches in the Department of Electrical Engineering and Computer Science. Her research includes what can be understood by looking at just small portions of data. Her research also includes studying the theory of computation. She has served on the board of several theoretical computer science conferences and is on the editorial board of The Theory of Computing Systems Journal. Efficiency in data analysis and fast algorithms will both be covered by Professor Rubinfeld.

Michael Stonebraker

Prior to joining MIT as a Professor, Michael Stonebraker founded or co-founded several companies. He has been a pioneer in database research for more than 25 years. Forbes magazine named him one of eight innovators creating the wealth explosion in the Silicon Valley in its anniversary edition in 1998. Stonebraker’s research and products are used in many relational database systems found in the marketplace today. In this course, Stonebraker will discuss modern databases, data cleaning, and integration.

Matei Zaharia

Matei Zaharia, recently joined MIT as an assistant professor. Prior, he was the Chief Technology Officer at Databricks, a startup based on Spark, a computing cluster project that he also created. In Tackling the Challenges of Big Data, Zaharia will present ideas on distributed computing platforms as well as hosted data platforms and the Cloud.


Nickolai Zeldovich

Associate Professor Nickolai Zeldovich developed HiStar, an operating system designed to minimize the amount of trusted code by controlling information flow. He is also the co-founder of MokaFive, a company focused on improving desktop management and mobility. His research includes work on cloud computing security. Last year, he won the Electrical Engineering and Computer Science Spira teaching award at MIT – an award that acknowledges high quality engineering education. Security and multicore scalability will be two areas that Professor Zeldovich will discuss during this course.

Tackling the Challenges of Big Data begins March 4, 2014. The course is USD$495. Professionals interested in taking the course should register by February 28.

For questions regarding this course, please visit: or email the Online X Programs team.