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time management tips for assistant professors (and everyone)

I recently saw a short list of advice for new assistant professors by Chris Blattman [Link]. Chris is an Assistant Professor of Political Science & International and Public Affairs at Columbia University (soon to be tenured). His list is summarized below. Go to his blog post for the full discussion:

  1. Learn to say no to new projects.
  2. Have a higher bar for projects with big exit costs.
  3. Book chapters and reviews are a waste of time.
  4. Get your dissertation papers or book out.
  5. Seek out mentorship.

This list is not comprehensive. This is not a weakness, but rather an opportunity to add to the list. The above list solely focuses on research and publication for assistant professors at research intensive universities.

I have a few things to add to the list. My tips, however, narrowly focus on time management. Most aren’t just for academics.

I was on a panel at the 2012 INFORMS Annual Meeting about time management for assistant professors hosted by the Junior Faculty Interest Group. Other panelists included Mark Lewis (Cornell), Kathy Stecke (UT-Dallas), and Brian Denton (now at Michigan). The panel discussion was chaired by Shengfan Zhang at the University of Arkansas. I had recently been awarded tenure from my previous university, and Shengfan asked me to talk about how I managed to get tenure with three kids. I only had a few minutes, so I focused on four general principals of time management that I use:

  1. There are two basic rules for time management: Do less or do it faster. There are no real tricks. I recommend learning how to say no to superfluous department, college and university service that will not add value to a tenure case and to be careful with collaborations.
  2. 80% of success is just showing up.  Academics of both genders with kids married to working non-academics tend to be the “flexible” ones, who attend to children’s doctors appointments, sick days, and days off of school. It can add up unless a fair plan is in place.
  3. Be careful with how you work on the weekends and evening. This seemingly contradicts #2, but it really takes a different angle. It’s important to work “enough” but to also put in quality time. Parkinson’s Law: Work expands to fill the time available for completion, so set deadlines and give tasks less time.
  4. Make time for the important but not urgent things. I do this by getting things done before deadlines, staying on top of email (I’m learning this is much harder to do as a tenured professor), getting sleep so that I can focus on important tasks and every day. Yes, sleep is important. If I didn’t follow this advice, I would be constantly putting out fires for looming deadlines rather than working toward my research portfolio. You won’t magically have more time in the future—now is a great time to cross something off your to do list.  This idea is consistent with Stephen Covey’s “Big Rocks First” management strategy
    • Corollary: Never be so busy that you are not a good colleague—it matters for tenure (and for life).

If you want a comprehensive list, the best I have seen is Shane Henderson’s paper “Staying Sane ont he Tenure Track” published in the 2008 Winter Simulation Conference [Link]. My favorite part:

Do not work 14 hours a day, assuming life gets calmer after tenure. It doesn’t. It gets busier

What would you add?

 

 

 


Bill Cook’s TSP talk at the University of Wisconsin-Madison

On Monday, Professor Bill Cook, Professor in Combinatorics and Optimization at the University of Waterloo gave the Hilldale Lecture at the University of Wisconsin. See my earlier blog post about this here.

Bill’s talk on the Traveling Salesman Problem (“One NP-hard problem to rule them all“) was terrific! Bill is a master multi-tasker as evidence by how he effortlessly controlled his talk on three separate screens by three iPhones that he carried around his talk (one was strapped to his wrist).

Bill gave a great overview talk about the TSP. He talked about the history of the TSP and the main contributions, the intuition problem behind proving tours are optimal using linear programming duality and why cutting planes are helpful, and why heuristics work so well, and challenges for parallelizing the algorithms. I highly encourage reading Bill Cook’s book In Pursuit of the Traveling Salesman to learn more.

Fun TSP fact: One of the early mentions of the TSP was made by a report by Julia Robinson of the RAND Corporation in 1949, who published On the Hamiltonian game (a traveling salesman problem) [Link].

Another fun TSP fact: More recently, a woman started a 50 first dates quest via Kickstarter, where she would have a date in each of the lower 48 states plus DC and Canada [Link]. She will tour the cities and make a documentary about it. (Side note: She will probably talk about her dates more than about math in the documentary – I bet she won’t even mention cutting planes once!)

In 2012 Bill ran a TSP challenge that boasted a winning prize of $500 for finding the best tour in a 115,475-city challenge through (nearly) all cities, towns, and villages in the contiguous 48 states. [Link]. The three best tours all independently found different tours of the same length. A good approach for finding a near-optimal tour in a big TSP instance is to use a combination of local search and genetic algorithms. Local search tweaks part of a tour to make it slightly better. Genetic algorithms mix different tours to take good partial tours from two potential solutions to (potentially) find a better tour that combines their best parts. The combination is powerful. Bill has a few new TSP challenges and it sounds like he’ll buy you a beer if you solve them. Another fun fact: If you want a bigger prize, The Clay Institute offers a $1 million prize for finding a polynomial time algorithm for solving TSP.

Bill mentioned that fast computers helped to solve hard TSP instances but not for the reason that you might think. The fast computers and processors we have now are more accessible than the supercomputers that were used in early TSP research. Now we can tinker with computer algorithms on a laptop (or phone), and this helps to reduce the feedback loop in research, and the faster feedback is helpful for driving good research (more so than the fast computing itself).  I am young enough that I was surprised by one  of Bill’s comments about a 1987 TSP algorithm that took 23 hours on a supercomputer, “We were all surprised they were allowed to use that much computing time.” Time has certainly changed.

Bill ended the talk by hinting at future research directions including time travel and parallelization. Recently, there have been some publications that suggest that monkeys [Link] and bees [Link] solve TSP instances. But Bill was skeptical that monkeys will assist us with the next TSP algorithm breakthrough.

 

A tour from Newsweek published on July 26, 1954


land O links

  1. An except from the forthcoming book Why we all love numbers by Alex Bellos (HT @wjcook)
  2. Life was not boring before the Internet by Jordan Ellenberg (@JSEllenberg)
  3. Delta Airlines is using data analytics to predict which parts will fail to schedule maintenance (HT @scianalytics)
  4. The art of live tweeting.
  5. A new paper by Brett Green and Jeffrey Zwiebel find evidence of the hot hand effect in ten statistical categories in baseball. The idea here is that the hot hand effect could exist in other sports such as basketball, but the defense can react and counter the hot hand effect. In baseball, that isn’t so easy. (HT @seanjtaylor).
  6. The billion dollar bracket challenge revisited.
  7. Floating sheep has a new post on beer and data analytics.

Aggregated Geographies of Tweets referencing Regional ‘Cheap’ Beers via Floating Sheep


the traveling salesman problem challenge for cheeseheads

bucky_tsp

Bucky the Badger is ready to tour Wisconsin!

This TSP blog post is in honor of Bill Cook’s lecture at the University of Wisconsin on Monday, April 7:

HF Deluca Forum

The Discovery Building.

12 – 1 pm.

More info: go.wisc.edu/salesman.

We are very excited to host Bill on Monday. Bill informally launched an information Dairyland TSP challenge on twitter, a tour through all 165 stops in America’s Dairyland [link to pdf]. What is the shortest tour? Hint: try using Bill’s TSP iPhone/iPad app!

For more reading:

Here are a few other TSP links.

xkcd on the TSP

A TSP genetic algorithm that finds a tour of the lower 48 state capitals. Courtesy of MathGifs

This video shows a visualization of Greedy, Local Search, and Simulated Annealing strategies for solving the Traveling Salesman problem.


my talk at the University of Michigan IOE department

On Friday I gave a seminar at the University of Michigan’s Industrial and Operations Engineering Department. I was invited by the INFORMS student chapter and the Michigan IOE Diversity Initiative headed by Mariel Lavieri. I owe a special thank you to the INFORMS Speakers Program that subsidized the cost of my trip.

Women, minority, and international students attended the lunch. I was overjoyed to speak to such a large, diverse group including some students not from under-represented groups (white men). As I’ve written about before, you don’t need to be from an underrepresented group to want everyone to feel welcome at the table. Some of the best advocates of researchers from underrepresented groups that I know happen to be white men. I was delighted that all attended and asked great questions and shared a variety of experiences.

Questions ranged from raising three children to biases to biases that I’ve internalized to INFORMS WORMS initiatives. I tried to maintain a good balance of positive things with things that could use some improvement. The students were surprised to hear that I only had one women engineering professor in college and had been the only women in some of courses I took in graduate school. I’m glad that some of these things have changed! I especially liked hearing the students talk about their experiences and impressions.

I was impressed with how many students attended and participated in the program. Kudos to Mariel Lavieri, who has done a fantastic job with the diversity initiative and encouraging students to attend my talk. After the seminar, the students and faculty gathered in a lounge to enjoy snacks and each other’s company.

 

Mediterranean lunch buffet

Mediterranean lunch buffet (falafel!!)

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Me talking at the diversity initiative luncheon

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Answering questions at the diversity initiative luncheon

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Luncheon attendees.

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The thank you note and USB stick from the diversity initiative.

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A yarn gift from IOE student and fellow knitter Zohar Strinka. Zohar is also one of the WORMS student liaisons this year.


land O links

  1. Why it’s important to talk math with your children. “With practice, parents and children alike will find that math makes a very satisfying second language”
  2. On a related note, this The Atlantic article entitled “5 year olds can learn calculus” is going around and is worth reading.
  3. Can Bayesian statistics help find the missing Malaysian Airlines airplane? A story by Carl Bialik (@carlbialik) at fivethirtyeight. Arnie Barnett from MIT is quoted!
  4. A Popular Science article called “The Garbage Man“about what happens to our throwaway electronics (HT @ForecastWatch)
  5. An interesting article on academic writers in Aljazeera America. “Academics write for the public more than ever before but are hampered by precariousness of their profession”
  6. There is so much to say about this figure. Men are unphased by Bs in introductory courses, but it causes women to switch majors! There is much to say about this. I’ll just say that there are some real challenges to retain women in STEM disciplines unless we give out all As in introductory courses (which I do not advocate).
  1. A comic strip on the etiquette of sharing comic strips. This is a great tool for citing all kinds of electronic media in presentations and blogs. The comic strip is by John Kovalic of Dork Tower (@muskrat_john) (HT Stuart Ciske)


roundup of march madness sports analytics articles

  1. Michael Lopz (@StatsByLopez) uses analytics to identify which teams and over- and under-valued in the tournament.
  2. Evelyn Lamb at Scientific American blogs about the math behind a perfect bracket.
  3. Carl Bialik at FiveThirtyEight writes about the odds of getting a perfect bracket using analytical methods. It depends on how good those analytical methods are. Nate Silver claims it might be as high as 1 in 7.4 billion. Interesting.
  4. Will a 16 seed ever beat a 1 seed?” by Ed Feng (@thepowerrank) Ed also has a bracket tool that visualizes different game outcomes.
  5. My advisor Sheldon Jacobson who maintains BracketOdds was interviewed in the News-Gazette, the local paper in Champaign-Urbana, IL.
  6. The Huffington Post has a “Predict-O-Tron” that helps you fill out your bracket using a probabilistic tool that lets you set the importance of different attributes (like seed, offensive efficiency, and even tuition) using moving sliders. It looks interesting but reeks of overfitting.
  7. I was on the local NBC 15 affiliate in Madison on March 18 to discuss the odds of a perfect bracket (video included).

Good luck perfecting your bracket!


creating a March Madness bracket using integer programming

An Associated Press article on ESPN outlines how the Division I men’s basketball committee wants to make bracket construction to be more fair [Link]. At present, there are 68 teams with no plans to expand the field. However, the committee has many decisions to make when it comes to who makes it in and who doesn’t as well as the seed and the region. All of this together determines potential matches. Previously, the committee tried to entirely avoid rematches in the first few rounds of the tournament. Given the large number of potential match-ups depending on who wins and loses, this constrained the bracket (possibly too much).

“There have been years where we’ve had to drop a team or promote a team; there was even a year where teams dropped two seed lines. We don’t feel that’s appropriate.” – Ron Wellman, the athletic director at Wake Forest

The article doesn’t exactly hint that integer programming could be used to solve this problem, but that’s the next logical step. In fact, there is a paper on this! Cole Smith, Barbara Fraticelli, and Chase Rainwater developed a mixed integer programming model (published in 2006, back when there were 65 teams) to assign teams to seeds, regions, and pods (locations). The last issue is important: constructing the bracket is intertwined with assigning the bracket to locations for play. For example, four teams in a region in the field of 64 (e.g., a 1, 8, 9, and 16 seeds) must all play at the same location to produce a single team in the Sweet 16.

The Smith et al. model minimizes the sum of the (then) first-round travel costs (the round of 64), the (then) expected second-round travel costs  (the round of 32), and the reseeding penalty costs while considering typical assignment constraints as well as several side constraints, including:

  • no team plays on its home court (except in the Final Four – that location is selected before the tournament),
  • no intra-conference match-ups  occur before the regional finals (what was the fourth round). This is the constraint that may be relaxed somewhat in the new system. Therefore, this existing model can be used to make brackets in the proposed new system.
  • the top-seeded team from each conference must be assigned to a different region as the second- and third-highest seeded teams from that conference.
  • the best-seeded teams should be assigned to nearby pods (locations) in the first weekend (a reward for a good season!), and
  • certain universities with religious restrictions must be obeyed (e.g., Brigham Young University cannot play on Sundays).

It is worth pointing out that this model assigns the seeds to teams. A team that could be considered as an 11-13 seed would be assigned its seed (11, 12, or 13) based on the total cost of the system. That may seem like it’s unfair on some level, but it might be better for a team to be a 13 seed and play nearby than a 12 seed but have to travel an extra 1000 miles. (Note: Nate Silver and the 538 team use travel distance in their NCAA basketball tournament prediction model because distance matters). Flexible seeds allows for a bracket that gives more teams a fair shot at winning their games, but too much flexibility would be unfair to teams. The Smith et al. model allows for some flexibility for 6-11 seeds.

The mixed integer programming model by Cole Smith, Barbara Fraticelli, and Chase Rainwater already addresses the committee’s concerns, which begs the question: why isn’t the committee using integer programming??  

OK, it’s probably pretty easy to think of a few reasons, and none of them involve math. One concern is that the general public seems to distrust models of any kind. This may be because models are black boxes to non-experts. This lack of transparency makes it hard to generate any kind of public support (Exhibit A: the debate about the model for the BCS football rankings). Perhaps marketing could improve buy in (“The average team traveled 500 miles fewer this year than last” or “Five teams had to travel across all four US time zones last year, and none had to do so this year.”) A better suggestion may be to give a few of the top integer programming solutions to the committee, who can then use and adapt (or ignore) the solutions as they see fit. Currently, the committee looks at several rankings (including the LRMC method, last time I heard), so they are already using math models to influence the decisions ultimately made by humans.

How would you use operations research and math modeling to improve the tournament selection and seeding process?

Reference:

Smith, J.C., Fraticelli, B.M.P, and Rainwater, C., “A Bracket Assignment Problem for the NCAA Men’s Basketball Tournament,” International Transactions in Operational Research, 13 (3), 253-271, 2006. [Link to journal site]


will someone create a perfect bracket this year?

Warren Buffett is offering $1B to whomever produces a perfect bracket [Link]. Here is my take.

There are about 9.2 quintillion ways to fill out a perfect bracket. This is often mistakenly used to predict the odds of filling out a perfect bracket – it is not 9-quintillion-to-1 because:

(a) the tournament isn’t like the lottery where every outcome is equally likely, and

(b) monkeys are not randomly selecting game outcomes. Instead, people are purposefully selecting outcomes.

Outcomes for “good” brackets made by people who play the odds and, for example, choose 1 seeds to beat 16 seeds in the second round. These brackets have a much better chance of reaching perfection, somewhere in the range of 128 billion-to-1 or 150 million-to-1 (See here and here).

The limitation here is that these odds give an individual likelihood of getting a perfect bracket; they give no insight into how to construct a pool of brackets that collectively has a high degree of likelihood for producing a perfect bracket.

Just like in the lottery, there is a difference between you willing the lottery and someone winning the lottery (just like in the classic Birthday Problem). Let’s say we have the perfect methodology that gives us the 150 million-to-1 odds. If 150M people filled out brackets, would we expect to see a perfect bracket? Probably not. If everyone used the same methodology that maximized our individual chance of getting a perfect bracket, this wouldn’t necessarily lead to a pool of brackets that collectively guarantee that someone gets a perfect bracket. The problem is, many of the brackets will be identical or almost-identical if they use the same methodology (meaning that they are all perfect or they are all not perfect). There needs to be enough variation between the entries to probabilistically “cover” the possible brackets with a certain reliability level. We would expect to see more variation between entries in the lottery, where many people purchase lottery tickets with randomly generated numbers (and we can more easily estimate the odds that someone will win a lottery based on the number of tickets sold). Recall: randomly generated brackets aren’t the answer! In a nutshell: what is good for the goose isn’t necessarily good for the gander.

The probability of a perfect bracket depends on the tournament. Let’s look at brackets in the last 3 years on ESPN. Let’s only look at how many people correctly select all Final Four teams:
- 47 of 8.15 million brackets correctly picked all Final Four teams in 2013
- 23,304 of 6.45 million brackets correctly picked all Final Four teams in 2012
- 2 of 5.9 million brackets correctly picked all Final Four teams in 2011

Both 2011 and 2013 had “Cinderella stories” of VCU and Wichita State, respectively. A single surprise can drastically affect the number of outcomes and make it less likely for someone to have a perfect bracket. On the other hand, when a 1 seed wins the tournament, brackets have more correct picks, on average. Certain tournaments therefore provide the right atmosphere that could lead to perfect brackets than others.

While having a good methodology for filling out a bracket is key to maximizing your chances, chance plays a much larger role. However, while you cannot control the randomness of the tournament, you can control how you fill out a bracket. In terms of strategy, a person should use statistics, analytical methods, and expert opinions to fill out a bracket to maximize the chance of picking a perfect bracket.

It would be a mistake to look at the two best brackets in 2011 and use the methodology that went into creating those brackets in other tournaments. Basing your bracket methodology on a single tournament is not a good idea (a single tournament is a small sample, no statistically significant conclusions can be drawn from it). If we applied the 2011 methodology to other years, we would quickly see that in the long run, we would do very poorly in March Madness office pools.

If we are acting in our own self-interests (and we are if we want that $1 billion prize!) then we should use the best models to maximize our personal odds and then hope for the best. Luckily, my colleagues have used analytics, operations research, and math to create some pretty good methods we can use to fill out brackets. This is a terrific place to start.

For my tips on filling out a bracket based on analytical methods: read my post here.

Are you participating in the Warren Buffett contest?


the umbrella problem in my office

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Once you learn about certain models used in operations research and industrial engineering, you start seeing them everywhere. I see the umbrella problem and its variants everywhere (read about the umbrella problem in this post).

I try to keep a few pens in my laptop bag at all times. These pens will drift in and out of my bag when I work. Every now and then, the pens entirely move out of my bag and I am left without a much needed pen. This happened earlier in March when I was on a trip and had planned to review a paper on my flight.

It has been a very cold winter in Wisconsin. I learned the hard way that it’s useful to keep a spare sweater or two in my office. I often forget to wear them home, and so sweaters and scarves have been accumulating in my office.

The city of Madison tries to be pedestrian friendly. They have these pedestrian flags (see picture below) that are supposed to help you cross the street safely. Each intersection has a bunch of flags in two bins with on either side of the street. I drive by a few of these intersections on the way home, and sometimes I see an intersection where all the flags are in one of the two bins. It always makes me smile.

This isn’t exactly the umbrella problem since the same pedestrian doesn’t go from one side of the street to the other indefinitely as in the umbrella problem, but the umbrella problem only needs to be slightly modified to capture the real problem here and to provide insight into how many flags should be stocked to maintain a certain reliability level.

 

Look for a few posts about March Madness next week!


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