Congratulations to 2021 graduates!

I created a video to congratulate 2021 graduates that I posted on YouTube and am including on my blog. I am looking forward to congratulating graduates in person in the future.


On codes of conduct for conferences and workshops

Years ago I helped edit a draft of the first INFORMS code of conduct for meetings. I was also on the INFORMS Board, where I argued in favor of a motion to approve the first INFORMS code of conduct. I am thrilled to say that the motion passed [see the latest version of the code of conduct here]. I am proud of this work.

I am knowledgeable on this topic, and I am a mandatory reporter at the University of Wisconsin-Madison. I am aware that much of the sexual harassment occurs off campus at conferences and while doing field work, where codes of conduct often have not been established. Sexual harassment in field work is highlighted in the documentary Picture a Scientist. When there are no mechanisms for reporting incidents, incidents aren’t reported. Sexual harassment does significant damage to our disciplines and leads to many scientists leaving the field entirely [Read the National Academies’ report [here]. It’s critical that we make our discipline a welcoming place where everyone can flourish.

As of now, 22 people have come forward with allegations of sexual harassment against the University of Michigan Computer Science Professor Walter Lasecki, and many of these allegations occurred at conferences. This has caused me to reflect on the importance of keeping conferences free from harassment, bias, and discrimination. There are so many structural changes we need to make to stamp out harassment, and creating a code of conduct is one of these important structural changes.

I am passionate about diversity, equity, and inclusion in operations research, engineering, and academia, and as a result, allies often ask me for suggestions on what they can to do help. For years, I have kept a running list of what allies can do to advocate for underrepresented groups in academia [See the list here]. One of the items on my list is:

Ask conference organizers if there is a code of conduct for meetings to convey the expectation that the conference is to be a welcoming and inclusive space where all attendees feel safe. If not, ask them to create one.

I am happy to say that at least two people (that I know of) have taken me up on this suggestion and created a code of conduct for a workshop or conference. I want to reiterate this request today. This is important for equity, since those from marginalized groups are most affected by harassment, bias, and discrimination.

A code of conduct sets expectations regarding what behavior will not be tolerated and what the consequences will be for violating the policy. Second, it enables an organization to take swift action if it is violated. It’s worth noting that the ACM barred Lasecki from its events and meetings for at least five years for violating its Policy Against Harassment. Third, a code of conduct creates a mechanism to report incidents. Not having a mechanism has been noted in the literature as a major barrier to those who want to report an incident. I realize that a code of conduct will not prevent all incidents or sexual harassment from ever occurring, and therefore, it is equally important to take each allegation seriously and have a process for addressing allegations.

If you want to create a code of conduct, there are many guidelines to help you get started.

* Clancy, K. B., Nelson, R. G., Rutherford, J. N., & Hinde, K. (2014). Survey of academic field experiences (SAFE): Trainees report harassment and assaultPloS one9(7), e102172.


On academic burnout and time management during a pandemic

Like many of my colleagues, I have been struggling with burnout. I’ve been ruminating about why this is the case, why I have been feeling more burned out lately, and what are some strategies for preventing burnout. I ended up writing a tweet thread about burnout a couple of weeks ago that I wanted to include on the blog. Here is the thread. At the end, I also offer some insights into what I am doing to manage time and stay on top of research. I also encourage academic leaders to consider flexibility as a potential tool to prevent burnout.

A major challenge has balancing care responsibilities with academic responsibilities. This is the context of my thread. My three kids attend three different schools, and all have returned at three different times. The changes to my children’s routines and their K12 instructional models have required a lot from me in terms of time and emotional energy. I wrote the tweet thread after hearing that the school district was once again changing their instructional model for two of my three children. The good news is that my children are doing well at school and I felt a lot better after writing this tweet thread. Feedback is welcome.

The tweet thread

I am a solo parent of 3 and a professor, I am experiencing massive role conflict and am burned out. A thread. #AcademicChatter

Star Wars R2D2 GIF

Since the winter break, there has been a major instructional change at one of my daughters' 3 schools almost every week. I am the sole parent who handles their education with a full time job, my roles are increasingly in conflict. It is getting much, much worse.

Not Interested GIF

Every week I have to adapt to a new schedule or instructional change at one of my daughters' schools. I have to constantly change my workflow and routine.

Multitasking GIF

I shouldn't use the term routine to refer to this year. This year hasn't been routine, and I've been operating in a state of flux. It is wearing me down.

Arrested Development Deflated GIF

I have to be responsive to new K12 school requirements and new requirements at work. Each change or new requirement slowly chips away at any remaining flexibility in my schedule.

Star Wars Fail GIF

I am trying to find ways to restore my energy, but it's impossible when I am adapting to new changes and requirements.

Iod Iodiod GIF

Years ago, I learned to function at a high level as a single parent by setting routines & exploiting flexibility in my schedule to maximize performance and achieve work-life integration. But without a steady routine & little flexibility, this is no longer a tool at my disposal

Spinning Plates GIF

Meanwhile, my university has implemented many new procedures that have inflexible requirements and deadlines. This was necessary (I know it's a tough year!), but flexibility did not appear elsewhere.

David Rose Schitts Creek GIF

If this resonates with you, know that I see you and that you are not alone. And I get it.

princess leia GIF by Star Wars

Solutions at work:
Fewer new demands ✅
Flexible deadlines & requirements✅
More support ✅
Fewer & shorter emails✅
Cancel/sunset ✅

The solution does not involve setting up an additional hour-long zoom meeting to address 🚫

https://www.theatlantic.com/politics/archive/2021/03/how-tell-if-you-have-burnout/618250/

Solutions I've been trying:
Replace hour-long 1-on-1 zoom calls with a 15 min phone call ✅
Replace some meetings with asynchronous work/email check ins ✅
Reduced cmte meetings to 1 hour (from 90 min)✅
Saying no more ✅
Blocking off times for research ✅

Other tips are very welcome! I know I'm missing some good strategies. I want to learn from this and also to not be the source of burnout to my colleagues.

And I want reiterate that flexibility is an underutilized tool for better work-life integration. Have a great day.

Also: I now have a wonderful partner in my life who provides much-appreciated support. But I'm still the solo/primary parent b/c my kids' dad lives out of state, and that's been tough during the pandemic.

Originally tweeted by 𝕃 𝔸 𝕌 ℝ 𝔸 🍀 𝔸 𝕃 𝔹 𝔼 ℝ 𝕋 (@lauraalbertphd) on March 26, 2021.

A template for time management

After writing the above tweet thread, I thought about what I was doing well. I have a pretty decent weekly routine and have been working in some self-care in the form of a daily walk and some exercise.


The Packers should have gone for it on 4th and goal

The Green Bay Packers were defeated by the Tampa Bay Buccaneers last night. The Packers trailed 31-23 when it was fourth down and goal with 2:22 to go in the fourth quarter. The Packers decided to kick a field goal instead of trying for a touchdown. The decision was universally criticized. Without crunching the numbers, I knew it would be better to go for it and attempt to get a touchdown, even though either decision was a longshot. The Packers lost 31-26.

Since the game ended, I crunched the numbers.

Here is how I approached the decision. First, the Packers needed a series of events to occur, with all or nearly all events working in their favor to win. Computing the probability of the intersection of multiple events occurring is likely to be a small number. I examined the pathways to winning below. There were some fluke ways to win that I left out because those probabilities were negligible. My calculations are in this spreadsheet.

Decision #1: Go for it on fourth down. There are two ways to win in this scenario.

  1. Score a touchdown.
  2. Make the two point conversion to tie the game.
  3. Stop the Buccaneers defensively (a TB field goal means the Packers lose).
  4. Win by scoring within regulation or in overtime if time expires.

I estimate that the Packers had a probability of 0.6 of scoring a touchdown based on Aaron Rodgers’s pass completion numbers. Teams have a probability of 0.48 of getting the two point conversion. Teams have a probability of 0.68 of stopping their opponent from scoring on a possession. There was not much time on the clock, so this may have been an underestimate. However, both teams had multiple time out to stop the clock, and there had not yet been the two minute warning. Winning in overtime for two evenly matched teams is 50-50. Winning within regulation with very little time left has a small probability (say, 0.03). Putting this together, I estimate that the Packers had a win probability of 0.104.

Decision #2: Make a field goal attempt. There are also two ways to win in this scenario:

  1. Make the field goal.
  2. Stop the Buccaneers defensively while leaving enough time on the clock to score.
  3. Win by scoring a touchdown within regulation.

or

  1. Miss the field goal.
  2. Stop the Buccaneers defensively while leaving enough time on the clock to score.
  3. Score a touchdown within regulation, make the two point conversion to tie, and win in overtime (see Decision #1).

I estimate that the Packers had a probability of 0.96 of scoring a field goal. Teams normally have a probability of 0.68 of stopping their opponent from scoring, but I lowered that to 0.5 here because it needed to happen in such a way that the Packers had enough time for one last drive. That is likely an optimistic estimate. I estimate that the Packers could score a touchdown with a probability of 0.15 with the remaining time (Rodgers had an MVP worthy season). The second way to win involved missing the field goal and tying the game in regulation with a last second touchdown and later winning in overtime. Putting this together, I estimate that the Packers had a probability of 0.076. I believe this is optimistic.

Takeaways

  1. Going for a touchdown increasing the win probability by about 3% compared to kicking a field goal. It’s not a huge different, but it’s also not insignificant.
  2. Either way, the Packers were unlikely to win. So while the decision was bad, it wasn’t a decision that likely cost the Packers the game.
  3. Kicking the field goal (Decision #2) could make sense with high confidence in a defensive stop or scoring a TD with time expiring. For the best defensive team in the NFL, decision #2 might be the better option. If Tampa Bay had, say, the worst defense in the country, especially if their secondary was weak, Decision #2 would be more attractive.
  4. The Packers had two bad choices.

Reflections on 2020 and New Year’s resolutions for 2021

A new year begins tomorrow. I’m taking the opportunity to reflect upon the past year. 2020 was a historic and terrible year in many ways. The COVID-19 pandemic changed life as we know it and demanded many sacrifices. I lost my sabbatical (read my sabbatical posts here).

But 2020 was not entirely a bad year. I took on new hobbies, habits, and challenges. As 2020 comes to an end, I reflected upon what I was able to achieve in 2020.

  • I started new research related to the pandemic and critical infrastructure resilience. It has been a creative year.
  • I did more media outreach to improve public understanding of risk management.
  • I wrote my first op-ed. Actually, I wrote four.
  • I was selected as a IISE Fellow and a AAAS Fellow.
  • I learned about best practices for inclusive teaching in online environments and updated my teaching materials and improved my pedagogy. I am a better teacher now than I was a year ago.
  • I developed a new routine at home that helped my productivity.
  • Virtual K12 school at home is not easy for my three kids, but they are doing about as well as anyone can.
  • I started new hobbies, including jigsaw puzzles and tennis. I even went to the driving range and (sort of) golfed for the first time.
  • I expanded my vegetable garden and was able to grow a lot more than in the past.
  • I love being able to cook and bake. Working from home means I can knead bread dough between meetings and cook elaborate and healthy dinners. I have been eating very well.
  • Extra quality time with my family has been wonderful.
  • I have been able to appreciate the small things all year long.

New Year’s resolutions in 2021

  1. Less doom scrolling.
  2. Create more, consume less.
  3. Continue high levels of public outreach through media appearances and public lectures.
  4. Fewer zoom meetings. I often did not meet my goal of 4 hours or or less of meetings in 2020.
  5. Replace one-on-one zoom meetings with phone calls, where I can go on a walk and stretch my legs during the call.
  6. Write and edit my writing every day, even if only for a few minutes.
  7. Become a better vegetable gardener. I’m good at growing tomatoes and herbs. I want to learn how to grow more vegetables, including the cool weather vegetables like greens and root vegetables.
  8. Go on vacation.

For more reading, check out my New Year’s resolutions in 2018 and 2019. Dijkstra’s 10 commandments of academic research also serve as potential New Year’s resolutions.


How to use the title “Dr.” in academia: possible best practices

I was upset to read the Wall Street Journal op/ed entitled, “Is There a Doctor in the White House? Not if You Need an M.D. Jill Biden should think about dropping the honorific, which feels fraudulent, even comic.” The op/ed was upsetting, because it suggested that anyone who has earned a degree that comes with the title of “Dr., such as those with a PhD or Ed.D., should not use their titles for degrees they earned.

This is concerning because research has shown that women doctors are less likely to be called by their titles then men, almost half of Black and Latina professors report having been mistaken for janitorial staff, and women and BIPOC professors routinely have their credentials ignored. Women over-invest in credentials, in part because research has shown that women need more credentials than men to be considered for awards and promotions.

The problem is not with Dr. Biden, it is with the cultural construct of expertise, who is presumed to have it, and who is given permission to wield the terms of power that signify it. In dominant culture, the construct of “expert” is based on false hierarchies – crafted to exclude the vast majority of the world’s knowledge (including the expertise of women and people of color).

Katie Orenstein from the twitter thread below about the WSJ article.

Mis-titling and de-titling professors is an equity issue. I gave some thought as to how to address this issue. I have a few suggestions below that are based on my experiences.

Here is some background. I used to ask students in my research group to use my title and last name. Students in other research groups often called me by my first name without my permission, and I found it strange that they addressed me in a casual way even after hearing the students in my research group address me in a formal way. There seemed to be two causes. (1) Students on a first name basis with their advisors and possibly other professors incorrectly assume that all professors let students call them by their first name. (2) Other professors, with whom I am on a first name basis, refer to me using only my first name in front of other students, which gives the students “permission” to call me by my first name. But I did not given permission. The students’ advisors in these situations have almost entirely been male, which possibly reflects societal constructs of power. Men inadvertently signal to students when it is acceptable to de-title and mis-title others, and these signals carry a lot of weight, especially if the person in question is a woman and/or is BIPOC. It seems that is was worth explicitly addressing these two mechanisms to reduce the chances that other professors are not mis-titled or de-titled.

I now ask students in my research group to call me by my first name. I wanted to make sure that all students knew what to call me while also not de-titling other professors, since new students have joined my group. In this conversation, I was surprised that not everyone knew about this rule, so I was glad we revisited this so I could make corrections and make sure that no one feels singled out.

I discussed the article with the students in my lab and this is what I suggest.

  1. On a regular basis, remind all students how you would like to be addressed in a group meeting , such as when new students join the lab. This can also be included in a lab compact.
  2. Use professors’ titles (Professor or Dr.) in informal settings unless they say otherwise. If they have given you permission to call them by their first name, it is still appropriate to sometimes use their titles, such as when there are other professors or students in a conversation.
  3. Use professors’ titles in formal settings, such as when introducing a speaker or in a committee meeting.
  4. When in doubt, ask someone what they want to be called.

What else is missing from this list?

In full disclosure, I have not always followed these rules in practice, and I will make a conscious effort to do better. I am a work in progress. I try to learn and make adjustments on a regular basis for continuous improvement.

For more reading, read my post about changing my name:


PhD development seminar: Time management and work-life balance

I am teaching a PhD development seminar for first year PhD students in industrial engineering and related disciplines. The purpose of this course is to prepare students for the dissertation research in industrial and systems engineering. The course helps set expectations, introduces campus resources to students, and creates a cohort of student to connect students with their peers.

Last week, a student panel composed of three senior PhD students discussed time management and work-life balance. The panelists were fantastic. Below are some highlights from the panel.

I am creating a series of blog posts featuring some of the classes from the semester. Those, along with previous PhD related posts, are tagged with the “PhD support” tag.

Other posts in this series:


Time management and work-life balance for (new) academics

I was on a panel about time management for the 2020 INFORMS New Faculty Colloquium (NFC). I recorded a video sharing my tips for time management with assistant professors in mind. I posted my video on YouTube below.

The live Q&A was fantastic, and I learned a lot from my fellow panelists Professors Tom Sharkey and Jonathan Helm. I want to give a big thank you to Professor Siqian Shen, who organized the NFC.


Presidential election forecasting: a case study

I am sharing several of the case studies I developed for my courses. This example is a spreadsheet model that forecasts outcomes of an election using data from the 2012 Presidential election.

Presidential Election Forecasting

There are a number of mathematical models for predicting who will win the Presidential Election. Many popular forecasting models use simulation to forecast the state-level outcomes based on state polls. The most sophisticated models (like 538) incorporate phenomena such as poll biases, economic data, and momentum. However, even the most sophisticated models are often modeled using spreadsheets.

For this case study, we will look at state-level poll data from the 2012 Presidential election when Barack Obama ran against Mitt Romney. The spreadsheet contains realistic polling numbers from before the election. Simulation is a useful tool for translating the uncertainty in the polls to potential election outcomes.  There are 538 electoral votes: whoever gets 270 or more votes wins.

Assumptions:

  1. Everyone votes for one of two candidates (i.e., no third party candidates – every vote that is not for Obama is for Romney).
  2. The proportion of votes that go to a candidate is normally distributed according to a known mean and standard deviation in every state. We will track Obama’s proportion of the votes since he was the incumbent in 2012.
  3. Whoever gets more than 50% of the votes in a state wins all of the state’s electoral votes. [Note: most but not all states do this].
  4. The votes cast in each state are independent, i.e., the outcome in one state does not affect the outcomes in another.

It is well known that the polls are biased, and that these biases are correlated. This means that there is dependence between state outcomes (lifting assumption #4 above). Let’s assume four of the key swing states have polling bias (Florida, Pennsylvania, Virginia, Wisconsin). A bias here means that the poll average for Obama is too high. Let’s consider biases of 0%, 0.5%, 1%, 1.5%, and 2%. For example, the mean fraction of votes for Obama in Wisconsin is 52%. This mean would change to 50% – 52% depending on the amount of bias.

Using the spreadsheet, simulate the proportion of votes in each state that are for Obama for these 5 scenarios. Run 200 iterations for each simulation. For each iteration, determine the number of electoral votes in each state that go to Obama and Romney and who won.

Outputs:

  1. The total number of electoral votes for Obama
  2. An indicator variable to capture whether Obama won the election.

Tasks:

(1) Create a figure showing the distribution of the total number of electoral votes that go to Obama. Report the probability that he gets 270 or more electoral votes.

(2) Paste the model outputs (the electoral vote average, min, max) and the probability that Obama wins for each of the five bias scenarios.

(3) What is the probability of a tie (exactly 269 votes)? 

Modeling questions to think about:

  1. Obama took 332 electoral votes compared to Romney’s 206. Do you think that this outcome was well-characterized in the model or was it an expected outcome?
  2. Look at the frequency plot of the number of electoral votes for Obama (choose any of the simulations). Why do some electoral vote totals like 307, 313, and 332 occur more frequently than the others?
  3. Why do you think a small bias in 4 states would disproportionately affect the election outcomes?
  4. How do you think the simplifying assumptions affected the model outputs?
  5. No model is perfect, but an imperfect model can still be useful. Do you think this simulation model was useful?

More reading from Punk Rock Operations Research:

How FiveThirtyEight’s forecasting model works: https://fivethirtyeight.com/features/how-fivethirtyeights-2020-presidential-forecast-works-and-whats-different-because-of-covid-19/

Files

  1. The assignment
  2. A shell spreadsheet with basic data to share with students
  3. A spreadsheet with the solutions

More teaching case studies


SIR models: A teaching case study to use in a course about probability models

This past summer, I created a few examples about COVID-19 to use in my course on probability models. I’ll post those materials here as I teach with them. Here is the first case study that introduces SIR models for modeling the spread of infectious disease. SIR models are widely used in epidemiology.

Infectious disease modeling: framing and modeling

Assume we have a constant population with N individuals. We can partition the population into three groups:

  1. Those who are susceptible to disease (S[n], i.e., not infected).
  2. Those who are infected (I[n])
  3. Those who are recovered (R[n]).

We assume a discrete time model, where we are interested in how the number of susceptible, infected, and recovered individuals vary according to time. Therefore, we start at time n=0 and index these values by n. The time between time n and n+1 could represent, say, a week.

A new strain of influenza or a novel coronavirus emerges. Susceptible individuals can become infected after exposure, and infected individuals can recover. Recovered individuals have immunity from reinfection.

New infecteds, result from contact between the susceptibles, and infecteds, with contact rate beta/N, which represents the proportion of contacts an infected individual has. Infecteds are cured at a rate (gamma) proportional to the number of infecteds, which become recovered.

Question #1: Come up with an expression to relate N to S[n], I[n], and R[n].

Question #2: Develop recursive expressions for S[n+1] based on S[n] and perhaps other variables.

Question #3: Then, do the same for I[n+1] and R[n+1].

Question #4: What are the boundary conditions?

Question #5: How would you estimate the total number who become infected by time n? 

Discussion questions:

  1. What other diseases fit this model?
  2. What are some possible ways to reduce the infection rate?
  3. What are some possible ways to increase the recovery rate?
  4. How does a vaccine effect this model?
  5. There is an interruption in the production of the vaccine, and your state will only receive 20% of the vaccines that you need before influenza season begins. Vaccines will slowly be released after this level. What are some criteria we could use to decide how to distribute these vaccines? What else can you do?

The second part performs computation in a spreadsheet. The assignment is here. We use the CDC 2004-5 data from a population of 157,759 samples taken from individuals with flu-like symptoms and 3 initial infections. Let n=0 represent the last week in September, the beginning of influenza season. Then, we compute these numbers in a spreadsheet to see how the disease may evolve. Next, we fit the model parameters (beta and gamma) using data that was collected by minimizing the sum squared error (SSE). Finally, we assess the impact of a vaccine. 

Files:

  1. The assignment.
  2. The solution.
  3. The assignment for the computational part.
  4. A google spreadsheet with the calculations (create a copy or download)

More examples