Monthly Archives: January 2016

A New Research Lab!

The Analytics Lab at OU has joined forces with Naiyu Wang and Amy Cerato (Civil Engineering and Environmental Science) to create a new joint research lab, the Community Resilience CORE Research Lab.  The new lab space will house up to 8 PhD students and Post-Docs (6 spots already filled!)   The lab space is on the 4th floor of the Carson Engineering Center on the OU campus.

Naiyu and I have been working on getting this CORE space up and running for our students for several months.  We both want to thank the Office of the Vice President for Research, Melany Dickens, and Michelle Wells for their tremendous support.  Michelle was (and always is) a pleasure to work with and is a great resource at the University of Oklahoma — she knows more color schemes, design features, and furniture styles than I can even dream of.

The NIST funded Center for Risk-Based Community Resilience is supporting several of the students and attached researchers.  The Office of the Vice President for Research is supporting others (through start-up funding).  The space will be used to provide students a great place to work and collaborate on resilience related issues.  From optimization modeling and machine learning to fragility curves and flood modeling, the students are working together to create something wonderful — a significant contribution to engineering science that ultimately can have a major impact on communities across the nation.

Here are some before, during, and after pictures of the lab space!

The first three are after the old furniture was cleared out and before the new paint and carpet.

CORE lab before!

CORE lab before!

Then we got new paint and carpet! 

And then new workstations and great new chairs!

And then the students moved in!

 

 

Six of eight spots are filled.  The seventh should be arriving this Summer.  And we are looking forward to growing together over the foreseeable future!

Spring 2016 Advanced Analytics and Metaheuristics!

a little more knowledge - Advanced Analytics ISE 5113

A little more knowledge lights the way: Master Yoda on Advanced Analytics ISE 5113

Welcome back to all the Data Science and Analytics and the ISE students for the Spring semester.  Hope the holiday break was enjoyable!  And I hope you are ready to get back to it — the Advanced Analytics course (ISE 5113) starts on January 19, 2016.   Class is from 12-1:15p on Tuesdays and Thursdays with lectures held in the Sarkeys Energy Center, room M0204.  Of course, if you are taking the course on-line, we will be videoing the lectures and making them available for you by the next day.

The course focus is on developing and employing problem solving strategies using advanced methods in the context of Data Science and Analytics.  We will tackle some traditional mathematical optimization problems with both traditional and modern techniques.  We will be using a new programming language (for most of you) to encode our mathematical formulations and solve some very interesting problems.  The language is AMPL and a student version of the software is available.  The textbook for the AMPL software is also freely available here.

While coding is a major part of what any Data Scientist must do, it is a means to an end — in this case, we very interested in learning how to think about problems and abstract them away to a point in which we can use a computer to help us solve them.  We will study how various forms of problems are amenable to different types of solution techniques; and how certain problems cannot be solved exactly before the Sun burns out.   This of course, will cause us to think about new approaches to hard problems.   In particular, the course focuses on developing mastery in three areas: 

  • Mathematical modeling of complex problems — objectives, constraints, decision variables, solution encoding, etc.
  • Collaborative development of solution strategies — e.g., feasibility violations, approximations, local and global searches
  • Implementation of advanced analytics method to solve complex problems — including Tabu Search, Path Relinking, Simulated Annealing, Genetic Algorithms, Swarm Optimization, Variable Neighborhood Search, and others

I love this course.  It will be a challenge.  But as Master Yoda says, a little more knowledge will help light the way.  Looking forward to seeing you in-class or online!