The PI as COO


June 3, 2022

Faculty at a research university wear many different hats. One analogy might be to the executive roles in a company. Now a company tries to make profit, which is usually not the goal in academia (quite the contrary). But still, one can think of the mapping like

Title Tasks for Faculty Member
CEO Strategy, overall planning, team management
CFO Budgeting, managing funds, making “payroll”
CIO Figuring out IT equipment, choosing between cloud, govt infrastructure, managing equipment
CTO Looking at trends and new tools (e.g., Google Colab, VS Code, Qualtrics)
CMO Marketing the team and PI to the world, creating a need for the lab’s products (papers)

The one role not listed here is the one that I think in many ways is the most important: COO. Now, I don’t know much about business roles, but to my mind the Chief Operating Officer is sort of similar to the executive officer of a submarine. They are the person who makes the business work: ensuring inventory is at the right level, planning for new space as the company grows, managing supply chains, etc. Tim Cook, now CEO at Apple, made his name growing Apple’s manufacturing to the vast network it is now. That meant ensuring secrecy, getting supplies to factories, scaling to manage millions of devices being released at the same time, etc. Apple isn’t profitable and gigantic if the day-to-day operations aren’t running efficiently.

But the same is true in academic life!

I think of the analogy as requiring thought and attention to the day to day management of productive work in the university. You need to make sure the ‘operations’ are smooth. One small component of this is the paper funnel: we need to ensure we have a lot of ideas in our funnel, that they get turned into data collection and analysis, and that the analysis gets written up and submitted, and eventually published. This is Arvind’s statement below about “getting sh*t done”, because it can be frustrating to think of oneself as moving papers from idea to publication. We want to pretend we are supposed to be thinking about ideas, noodling on the whiteboard, and being inspired by genius. And we are! But that’s not the COO part of the job.


We could look, like I’m sure Apple does, at operational metrics and efficiencies. For example:

  • Number of papers in draft/being reviewed/published (WIP)
  • Time between idea and paper publication (lead time)
  • Students graduated in expected time
  • Number of important unanswered emails in Inbox
  • To the nearest thousand, how much money is in various accounts, and what the projected “burn rate” is for those accounts.
  • How long has each student been in the program, what milestones have they finished, and when they should graduate
  • Grant money received
  • Grants used efficiently
  • Reviews conducted within deadline
  • Talks invited/given/follow up
  • Size of industry collaboration address book
  • Travel reimbursements received within 30 days of trip
  • Time between equipment being needed and equipment being purchased and equipment delivered

Now for some of these you might say “but someone else is blocking that!” Which is of course true of EVERYTHING and also not an excuse Steve Jobs was likely to accept. That’s all part of being efficient and operating smoothly. If you know the university takes forever to process room bookings, you need to factor that in to the operational goals.

Why Care About Operations

I think operational efficiency is what separates average researchers from those seen as impactful. Sure, in some cases it might be a brilliant one-off paper, but often we reward output volume: “quantity has its own quality”. Did the project lead to a single paper, or did you harvest 3-4 papers from it? That’s an operational detail that has to do with a PI’s ability to direct students, target appropriate venues, manage meetings to keep the papers on schedule, and so on.

I think the importance of the COO view of one’s career is that for better or worse these outputs are the easiest to turn into data, and subsequently evaluate you, and your institution. So ignoring number of students graduated, or number of publications, or grant values, will result in poor scores on these data metrics. It doesn’t matter how many brilliant ideas you have if no one gets to read them.

The question for this COO view of a research career is to figure out which metrics one truly cares about, and when to stop focusing on operations and think more about strategy and trajectory. Metrics, because the metrics you choose reflect your priorities (e.g., papers published vs industry collaborations nurtured), and strategy, because (hopefully) the research you pursue should reflect some higher level of understanding about what problems are important to be spending time on.

My approach

For me personally, it can be hard to remember to manage the operational details. The easiest way for me to see this concretely is when papers fail to meet a venue deadline. That’s an operational failure: we didn’t move fast enough on data analysis, the meetings were not productive and the project spun its wheels, I didn’t kill the project or value the cost of delay, I answered emails about committee work rather than spending 2 hrs editing.

My current management approach is to check in on each project (I have about 9-10 in various stages) weekly, using a dedicated card (using a note in Apple’s Notes tool). A Kanban board with stickies can be really helpful here too, but the important thing is not the particular system but that you use it and check it regularly.

Another idea I have just started to implement is reflecting on lessons learned from a project (e.g., after a paper is published). Not just the research problems, but the operational challenges. What would I do differently for project management? What worked well in moving the project along? Was this a productive collaboration? Why did it get delayed (it’s always delayed)?