Course Detail

June 7-9, 2017

Location: Columbia University Campus, New York City, New York USA


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Lead Faculty: Prof. Venkat Venkatasubramanian

is Samuel Ruben-Peter G. Viele Professor of Engineering in the Department of Chemical Engineering, and a Professor of Computer Science (affiliated) and a Professor of Industrial Engineering and Operations Research (affiliated), at Columbia University in the City of New York. Venkat worked as a Research Associate in Artificial Intelligence in the School of Computer Science at Carnegie-Mellon University, taught at Purdue University for 23 years, where he was Reilly Professor of Chemical Engineering, before returning to Columbia in 2011. At Columbia, Venkat directs the research efforts of several graduate students and co-workers in the Complex Resilient Intelligent Systems Laboratory. He is also the founding Co-Director of the Center for Systemic Risks Management, a transdisciplinary center with faculty from a number of departments at Columbia University.

Prof. Garud Iyengar

Professor Garud Iyengar joined Columbia University’s Industrial Engineering and Operations Research Department in 1998. Professor Iyengar teaches courses in simulation and optimization.

Professor Garud Iyengar’s research interests include convex optimization, robust optimization, queuing networks, combinatorial optimization, mathematical and computational finance, communication and information theory. He has published in numerous journals including IEEE Transactions on Information TheoryMathematics of Operations ResearchMathematical ProgrammingIEEE Transactions on Signal Processing, and IEEE Transactions on Communication Theory.

He was elected as chairman of the IEOR Department on July 2013.

Prof. Rengaswamy

Prof. Raghunathan Rengaswamy

Professor Rengaswamy is a Professor at the Department of Chemical Engineering, IIT-Madras. Prior to this, he was a Professor and co-director of the Process Control and Optimization Consortium at Texas Tech University, Lubbock, TX USA. He has also held appointments as Professor at Clarkson University (USA), Assistant Professor at IIT-Bombay (India), and Visiting Assistant Professor at Purdue University (USA). He earned his undergraduate degree in chemical engineering at IIT-Madras and PhD at Purdue. He has 25+ years of research experience in process systems engineering, particularly on the topics of design, control, optimization, data analytics, and risk analysis. Rengaswamy was the recipient of the Young Engineer Award for the year 2000 awarded by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32. His paper on fault diagnosis was awarded the CAST Directors’ Award for the Best Poster Presentation at the AIChE Annual meeting (2000). A paper that he co- authored was chosen by the International Federation of Automatic Control (IFAC) for the Best Paper Prize for the Engineering Applications of Artificial Intelligence Journal (2002-05). His research has been funded by federal and state agencies such as the NSF, DOE, ACS-PRF, NYSERDA and companies such as Honeywell, Nanodynamics and KBR. 3.

Prof. Narasimhan

Prof. Shankar Narasimhan

Professor Narasimhan is the M. S. Ananth Institute Chair Professor in the Department of Chemical Engineering at IIT Madras. He earned his undergraduate degree in chemical engineering at IIT-Madras and PhD at Northwestern. He taught at IIT-Kanpur prior to joining IIT-Madras. His major research interests are in the areas of Data Mining, Process Design and Optimization, Fault Detection and Diagnosis and Fault Tolerant Control. He has co-authored several important papers and a book titled Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data which has widely received critical appreciation.

Course Content and Structure

This short course introduces the participants to data science -- concepts, tools, and techniques -- and its applications in chemical, petrochemical, consumer care, pharmaceutical and allied engineering industries. This course, through a case study approach, will provide an overview of how to pose meaningful problems in such situations. Structured thinking to transition from data to business problem definitions will be emphasized. An open source tool for data analytics will be introduced -- more from the viewpoint of familiarization and appreciation rather than from a purely coding perspective.

Engineers starting out in their careers can also benefit from this course through a gentle introduction to what is considered to be a field with immense potential for career advancement.

Who Should Attend

The course is targeted at management and mid-career individuals who are generally called upon to either conceptualize or lead new data analytics initiatives within their organizations.