Tag Archives: analytics

Tenure-Track Position in Big Data Analytics, University of Miami, School of Business

I’m very happy to announce that the School of Business at the University of Miami is hiring in my department! Details below. This is an exciting time to be involved in Business Analytics!

Tenure-Track Faculty Position in Management Science (Big Data Analytics)

The Management Science Department at the University of Miami’s School of Business Administration invites applications for a tenure-track faculty position at the junior or advanced Assistant Professor level to begin in the Fall of 2015. Exceptional candidates at higher ranks will be considered subject to additional approval from the administration. Salaries are extremely competitive and commensurate with background and experience. This is a nine-month appointment but generous summer research support is anticipated from the School of Business.

Applicants with research interests in all areas of Analytics will be considered, although primary consideration will be given to those with expertise in Big Data Analytics and the computational challenges of dealing with large data sets. Expertise in, or experience with, one or more of the following is particularly welcome: MapReduce/Hadoop, Mahout, Cassandra, cloud computing, mobile/wearable technologies, social media analytics, recommendation systems, data mining and machine learning, and text mining. The Management Science Department is a diverse group of faculty with expertise in several areas within Operations Research and Analytics, including statistics and machine learning, optimization, simulation, and quality management. Duties will include research and teaching at the graduate and undergraduate levels.

Applicants should possess, or be close to completing, a PhD in computer science, operations research, statistics, or a related discipline by the start date of employment. Applications should be submitted by e-mail to facultyaffairs@bus.miami.edu, and should include the following: a curriculum vitae, up to three representative publications, brief research and teaching statements, an official graduate transcript (for the junior Assistant Professor level), information about teaching experience and performance evaluations, and three letters of recommendation. All applications completed by December 1, 2014 will receive full consideration, but candidates are urged to submit all required material as soon as possible. Applications will be accepted until the position is filled.

The University of Miami offers a comprehensive benefits package including medical and dental benefits, tuition remission, vacation, paid holidays, and much more. The University of Miami is an Equal Opportunity/Affirmative Action Employer.

Leave a comment

Filed under Analytics

The First Sentence of the Great Analytics Novel

Thedarktower7 I’ve written many times before about the importance of promoting O.R. to the general public. One of the ideas that’s been suggested by several people is the possibility of writing a work of fiction whose main character (our hero) is an O.R./Analytics person. I still believe this is a great idea, if executed properly.

Today, my wife brought to my attention The Bulwer-Lytton Fiction Contest, which, according to their web page, consists of the following:

Since 1982 the English Department at San Jose State University has sponsored the Bulwer-Lytton Fiction Contest, a whimsical literary competition that challenges entrants to compose the opening sentence to the worst of all possible novels. The contest (hereafter referred to as the BLFC) was the brainchild (or Rosemary’s baby) of Professor Scott Rice, whose graduate school excavations unearthed the source of the line “It was a dark and stormy night.” Sentenced to write a seminar paper on a minor Victorian novelist, he chose the man with the funny hyphenated name, Edward George Bulwer-Lytton, who was best known for perpetrating The Last Days of PompeiiEugene AramRienziThe CaxtonsThe Coming Race, and – not least – Paul Clifford, whose famous opener has been plagiarized repeatedly by the cartoon beagle Snoopy. No less impressively, Lytton coined phrases that have become common parlance in our language: “the pen is mightier than the sword,” “the great unwashed,” and “the almighty dollar” (the latter from The Coming Race, now available from Broadview Press).

Just like an awful first sentence can be a good indicator of a terrible book, the converse can also be true. Take, for example, the first sentence of Stephen King’s The Dark Tower series, which I happen to be reading (and loving) as we speak:

The man in black fled across the desert, and the gunslinger followed.

It’s such a strong, mysterious, and captivating sentence…

…which brings me to the point of this post. If it’s going to be difficult to write The Great Analytics Novel, what if we start by thinking about what would be the perfect, most compelling sentence to start such a novel? Yes, I propose a contest. Let’s use our artistic abilities and suggest starting sentences. Feel free to add them as comments to this post. Who knows? Maybe someone will get inspired and start writing the novel.

Here’s mine:

Upon using the word “mathematical” he knew he had lost the battle for, despite the dramatic cost savings, their logical reasoning was instantly halted, like a snowshoe hare frozen in fear of its chief predator: the Canada lynx.

I can’t wait to read your submissions!


Filed under Analytics, Books, Challenge, INFORMS Public Information Committee, Motivation, Promoting OR

Should You Hire Security When Tenting Your House?

Last week I had my house tented because of termites. For those of you who don’t know what “tenting” is (I didn’t until about a year ago), it amounts to wrapping an entire house inside a huge tent and filling the tent with a poisonous gas that kills everything inside (and by everything I really do mean everything). Those who have been through this experience know what a hassle it is. We received a to-do list of pre-tenting tasks, which included:

  • Remove or discard all food that isn’t canned or packaged in tightly-sealed, never-opened containers
  • Turn off all A/C units and open one window in each room of the house
  • Open all closet and cabinet doors
  • Turn off all internal and external lights (including those operating on a timer)
  • Prune/move all outdoor plants away from the house to have a clearance of at least 18 inches
  • Soak the soil around the house (up to a foot away from the structure) on the day of the tenting
  • Warn your neighbors about the tenting (so that they can keep their pets away from the house)
  • etc.

We had to sleep two nights in a hotel, with two dogs, one of which had just had knee surgery. What an adventure!

The main point of concern was that the house would stay vulnerable (open windows) and unattended during the process. On top of that, one of our neighbors told us that he knew of a house that had been robbed during tenting a couple of months ago. So we started to consider hiring a security guard to sit outside the house for 48 hours. Would that be a good idea? Let’s think about this.

Our insurance’s deductible is $2500. I assume that if thieves are willing to risk their lives (wearing gas masks; oh yeah! they do that!) to enter a tented house, they’d steal more than $2500 worth of stuff. Therefore, being robbed would cost us $2500. This doesn’t take into account that one might have irreplaceable items in the house. However, most of the time those can be taken with you (unless they are too big or inconvenient to carry). In my case, I took the external hard drive to which I back up my data, and the mechanical pencil I’ve owned and used since 1991 (yes, you guessed right, the eraser at the end doesn’t exist any more). The security company we called would charge $15 per hour for an unarmed guard to be outside our house. Multiplying that by 48 hours brings the cost of hiring security to $720.

Let’s say that the likelihood (a.k.a. probability) of being robbed while your house is tented without a security guard is p_1 (in percentage terms; for example, p_1 for the White House is pretty close to 0%), and when a security guard is on duty that likelihood is p_2. Unless p_1 > p_2, there’s no point in having this entire discussion, so I’ll assume that is true. Here’s a pretty neat rule of thumb that you can use: divide the cost of hiring security by your deductible to get a number n between zero and one (of course, if hiring a guard costs more than your deductible, don’t do it!). Unless the presence of the guard reduces your chance of being robbed (p_1) by more than n, you should not hire security! (Later on, I’ll explain where this rule comes from.) For example, in my case 720/2500 is approximately equal to 29%. If the chance of being robbed without security is 30%, unless hiring a guard brings that chance down to 1% or less, it’s better not to do it. If the value of p_1 is less than or equal to 29% to begin with (I live in a reasonably safe neighborhood), the answer is also not to hire security (probabilities cannot be negative). This rule works regardless of the value of p_1; what matters is how great the improvement to p_1 is.

In addition to looking at the numbers, we also took into account the following clause from the security company’s contract:

…the Agency makes no warranty or guarantee, including any implied warranty of merchantability or fitness, that the service supplied will avert or prevent occurrences or the losses there from which the service is designed to detect or avert.

In other words, if you hire us (the security company) and still get robbed, we have nothing to lose!

So what did we do? We chose not to hire security and, fortunately, our house was not robbed. However, even though the tenting instructions  say that you don’t have to wash your glasses and plates after returning home, we decided to do so anyway (as they say in Brazil: “seguro morreu de velho”).

Disclaimer: The advice contained herein does not guarantee that your house will not be robbed. Use it at your own risk!

Details of the Analysis

So where does that rule of thumb come from? We can look at this problem from the point of view of a decision tree, as pictured below.

In node 0, we make one of two decisions: hire a security guard (payoff = -$720, i.e. a cost), or not (payoff = -$0). For each of those decisions (branches), we create event nodes (1 and 2) to take into account the possibility of being robbed. At the top branch of the tree (node 2), the house will be robbed with probability p_2, in which case we incur an additional cost of $2500, and the house will be safe with probability (1-p_2), in which case we incur no additional expense. Therefore, the expected monetary value of hiring security, which we call EMV_2, is to spend $720+$2500 with probability p_2, and to spend $720 with probability (1-p_2). Hence

EMV_2 = - 3220p_2 - 720(1-p_2) = - 2500p_2 - 720

Through a similar analysis of the bottom branch (node 1), we conclude that the expected monetary value of not hiring security, which we call EMV_1, is to spend $2500 with probability p_1 and to spend $0 with probability (1-p_1). Therefore

EMV_1 = -2500p_1 - 0(1-p_1) = - 2500p_1

Hiring security will be the best choice when it has greater expected monetary value than not hiring security, that is when EMV_2 > EMV_1, which yields

-2500p_2 - 720 > -2500p_1


2500(p_1 - p_2) > 720


p_1 - p_2 > \frac{720}{2500}

which is the result we talked about earlier (recall that p_1 > p_2).

How Does Analytics Fit In?

The Analytics process is composed of three main phases: descriptive (what does the data tell you about what has happened?), predictive (what does the data tell you about what’s likely to happen?), and prescriptive (what should you do given what you learned from the data?). In this problem we can identify a descriptive phase in which we try to obtain probabilities p_1 and p_2. This could be accomplished by looking at police or insurance company records of robberies in your area. It’s not always possible to get a hold of those records, of course, so one might need to get a little creative in estimating those numbers. Having knowledge of the probabilities, the calculation described above could be classified as a prescriptive phase: what’s the course of action? Hire security if (cost of security)/(insurance deductible) < p_1 - p_2. There is no predictive phase here because our analysis does not require the knowledge of any future event (only how likely it is to occur). Operations Research can be used in some or all of these phases. Most of what I do in my research and consulting projects lies in the prescriptive phase (optimization). Recently, however, I’ve decided to broaden my horizons and learn more about the other two phases as well, starting with some self-teaching of data mining.


Filed under Analytics, Applications, Decision Trees, INFORMS Monthly Blog Challenge, Security

INFORMS 2010 Wrap-Up

I had a very productive and fun time at the INFORMS Annual Meeting in Austin, Texas. So I thought I’d share with you some of my observations about the meeting:

  • Analytics initiative by INFORMS: great idea! I believe that we (INFORMS members) have to jump onto the Analytics bandwagon and let everyone know that we can do Analytics too! It’s all about the power of words these days (see the country’s political arena for a perfect example). Starting today, I’ll tell everyone that I can do “Advanced Prescriptive Analytics” (optimization).
  • Panel on Social Networks: it was nice to hear from some of the most popular OR bloggers and learn about their motivations, fun stories, and blogging strategies. Great job Mike and Laura!
  • John Birge‘s plenary (Omega Rho Distinguished Lecture): very informative and entertaining. The simple and powerful take-home message was: align incentives. What’s good for the employees has to be good for the company as well.
  • RAS Problem Solving Competition: Michael Trick and I (a.k.a. Team MATHY) received an honorable mention at the Railway Applications Section (RAS) 2010 Challenge (certificate + photo :-). After watching the three finalists’ presentations, we were happy to see that our solution value of $11,399,670.88 was equal to the best solution found by the winning team, and better than the solution found by the other two finalists. Nobody managed to prove optimality, though. Interestingly, none of the finalists thought about scaling the problem (thinking in terms of tanks of gasoline instead of gallons of gasoline), which made a huge difference in the performance of our model. Overall, it was a lot of fun to participate in this competition and I want to thank the organizers for putting it together. Here’s a picture of team MATHY with Juan Morales from BNSF Railway.

    And here’s a picture of the railroad network we had to deal with:

  • Technical sessions: I watched many interesting and inspiring talks (as a matter of fact, I had some great ideas for my own research while watching a number of presentations). It’s nice to see that *a lot* of people are using Latex Beamer these days. Let’s aim for a Powerpoint-free INFORMS by 2020!
  • Idea for an iPhone App? I applaud the going-green initiative of reducing the number of conference program booklets that have to be printed out. However, for this to work it requires a lot of organization from each of us: we have to go over the program in advance, select the talks we want, and print the appropriate pages. I don’t know about you, but I never manage to get this done. So I propose we create an iPhone (mobile) app to allow participants to browse the program on-the-go. It’s not convenient to browse the program PDF on a phone. We need an app. We need to be able to filter by author, by chair, by topic/keyword, etc. We want a time-sensitive app that tells you what’s next. We want an app that sends notifications to your phone reminding you that a talk/event is coming up so that you ask for the check in time. If we had something like that, I think that a lot fewer people would ask for a printed program (myself included).
  • Thumbs up for all the vegetarian food: being a vegetarian myself, I was impressed with the generous availability of vegetarian food (i.e. not only salad) at both the Sunday and Tuesday receptions. Well done!
  • Austin’s Convention Center: in addition to being huge, the convention center’s numerous under-construction areas made it very hard to navigate from session to session. I always felt like I was taking the longest path from point A to point B.
  • Meeting old and new friends: it was great to make many new friends and to meet old friends from my PhD days at Carnegie Mellon at the Tepper Alumni reception. I also had some very productive research meetings with several colleagues.

Last but not least, I’d like to thank John Hooker, Christopher Beck, and Willem-Jan van Hoeve for agreeing to give a talk in my session, and Willem for inviting me to present in his session.

Time to say bye-bye to weird Austin and fly back to Miami! Hence, I had to put on my “U” shirt:


Filed under Analytics, Challenge, INFORMS, iPhone, Travel

Cited by Competing on Analytics, by mistake…

I just found out that one of my papers (Building Efficient Product Portfolios at John Deere and Company, co-authored with D. Napolitano, A. Scheller-Wolf and Sridhar Tayur), has been cited in chapter 2 of the book “Competing on Analytics: The New Science of Winning“, by Thomas Davenport and Jeanne Harris. The chapter is entitled “What Makes an Analytical Competitor?” (link to PDF). Another interesting coincidence was that today I listened to the Science of Better Podcast featuring Thomas Davenport! I was excited to see the context of the citation, but it all turned out to be a mistake :-( The authors refer to the work on “direct derivative estimation of non-stationary inventory” (which was also done by SmartOps, saving Deere 1.2 billion dollars over 5 years) and cite our paper as a reference for it. Could it have been because our paper has the words “John” and “Deere” in the title? On a positive note, however, this may drive some readers to our paper, which is, IMHO, a great read anyway.

Leave a comment

Filed under Analytics, Mistakes

The Name Race: is O.R. Analytics?

Through a link in the latest INFORMS eNews email, I ended up watching IBM’s video on Analytics. That made me wonder whether we (O.R.ers) are losing the name recognition race. Broadly speaking, I think that O.R. and Analytics are the same thing. Nevertheless, I greatly prefer the name Analytics because of its more intuitive interpretation. It seems to me that with companies like IBM pushing for the term, I may have to say something like this in the near future: “I work in the field of Operations Research”, “What?”, “I mean, Analytics”, “Ahhh…”

What do you think? Let me know in the comments.


Filed under Analytics, INFORMS