Saturday, December 22, 2018

Is resilience the name of the game in academia? Part 3: Sophie is keeping both her kids, b!tches!

A year ago I wrote about the struggle to keep different projects going in my lab. I've been working on two separate and related lines of work that fit into the grander scheme of my lab. I like variety, and started with one scientific identity and then developed another. I kept both projects funded through my postdoc and at the beginning of my independent career, but it has been hard to keep them going lately.

Everyone was telling me to drop my original work and focus on the new one. Maybe it's silly, but it felt much like a failure. So, I threw a Hail Mary and pushed as hard as I could to sent out both R01 grants back to back...not quite twins in the same cycle as in my original plan to go for two R01s as a new investigator, but Irish twins. You see, if you submit as a New Investigator (NI), your grant remains in the NI pile for review even if the previous one was funded. I really wanted to make sure I sent something to the NIH in February because I heard that magical things happen in September/October when the fiscal year ends.

I almost didn't make it.

The grant just wasn't gelling and everyone's recommendations were going against my instincts. I decided to trust the coaching I was getting from colleagues in the field, and follow my PO's suggestions. If my PO had to choose to push for this grant, he needed to see that I listened to his advice. By the end, I was so exhausted, confused, and dejected waiting for the previous R01 to get reviewed on the same day the new R01 was due. One of my friends had to come to stay with me and watch me write. For a while, I didn't look at the grant after I submitted it. I wasn't sure if it was just word salad.

Then everything turned around. I found that the first R01 was going to be funded in the Spring. In July the second R01 was scored 1% from the payline! The NIH said that wasn't good enough, and I put on my big girl pants for the resubmission as an established investigator. But then, in an FY18 rebudgeting miracle the payline was changed! I didn't need to resubmit! And now everything will be funded again. I can't even express the joy at the idea of the science that we will get to do. The sense of wholeness and possibility.

I love and hate the emotional rollercoaster that comes with this job. But now I have 5 more years to keep going and do some very fun science. As we approach the holiday season I have been so thankful for this twist of fate, for my lab, for all others who have helped and supported me. It took a while to internalize this success after so many years of struggles, but there's a light at the end of the tunnel. And now I get to have fun at my job again!

I wish that 2019 will bring the same joy and awesome science to all of you.

Sunday, October 21, 2018

Time management, flexibility and communication during academic training

This week I half-jokingly tweeted something that got a lot of attention and some negative responses.

Some PI's sympathized, and others described the sentiment in the tweet was unfair to trainees and indicated poor time management. As all this was going on, I got my data, which was an Excel spreadsheet from which I was hoping to make a figure, and a friend was frantically waiting for a fellowship draft that was due that day from one of her people. To my defense, if I ask for data, I usually do so with at least 24-48hr notice and only ask for primary data I have already seen so that I can make the figure myself in my color scheme. This I expect to be perfectly reasonable.

In general, I find myself a lot more frequently in my friend's situation with trainees and students asking for things with very limited time to actually get them done. The struggle is always whether to turn this into a teachable moment and don't drop the other 10 things I need to do, or to deal with the last minute request and resign postponing my own work. Unless the problem is egregious and needs too much work (i.e. a grant so poorly written that cannot be submitted), many of the PIs I know will step in and take care of it. Often they will also try to provide some time-management training for the trainee.

I find that tweets out of context are usually interpreted by the reader based on their own experience and several responders replied with very valid concerns that are important to talk about in the context of the mentor-trainee relationship and any supervisor-employee situation. Students and postdocs mostly have to worry about their own projects, but PIs and managers, in general, respond to a variety of stakeholders: to name a few, their trainees in the lab, their students in the classroom, collaborators, administrators, funding agencies, and the greater scientific community requiring their service for reviewing papers and grants. Below are considerations trainees must have to become more empowered in their position.

1) Don't feel compelled to say "Yes" if you cannot deliver. Beside the fact of whether you already have a slide ready that you showed at lab meeting and you can retrieve in 5 minutes, my request above touches on a very critical issue which is the ownership of one's time and the ability to say "No". As a PI it took me a while to realize that sometimes people said "Yes" to please me, but are then too overwhelmed or stressed to follow through. For my own time-management and planning, I would 1,000 times prefer to be told "This cannot be done" or "I will not be able to meet that deadline for X and Y reasons". I always try to frame tasks based on the timeline of the trainee, but some PIs do not care or have no concept of how long it takes to do something. It is always better to have a clear discussion defining the timeline for deliverables and how long it's going to take to get things done. PIs may have expectations based on how long it takes them to do stuff and trainees may have no experience in planning. You think the PI is unreasonable and they think you are unreasonable, but many times this is a communication issue.

2) Don't underestimate the time it takes to do something. This is a corollary. Nowadays I can write an abstract in the 20 minutes before a deadline, I know it will take a trainee at least a week. When defining deadlines with your PI think of the time it will take you to do what you are asked to do and double it! If the deadline is tight and you are worried, ask for advice on how to handle it. If they realize you cannot do it and need it done, the PI can provide help and/or redirect resources.

3) This is your time to be selfish, but you are still in the real world. When I was applying for PhD positions, a PI told me this was going to be my time to be selfish, and I didn't really get it. My trainee years were the time when I could just follow my curiosity and go where my intellect would take me. Then I didn't have to respond to all the stakeholders listed above and jump from deadline to deadline imposed by multiple exterior forces on a weekly basis. In a non-academic job, you are requested to do the bidding of your managers, and your managers are compelled to do the bidding of their bosses and the company. An entire project may be dropped at a day's notice or you may be pulled as a pinch hitter to help complete someone else's task. You will sometimes not be given any reason for the change... Being change-agile and flexible are very important qualities in fast-paced environments like pharma, consulting, or publishing. So, you may be only want to work on your project, but still need to learn to function under pressure to respond to the needs of others while clearly communicating your needs and timeline.

4) Ask for training and mentorship. There are approaches that are obvious to me because I was trained to do them or they match my way of thinking, but these may not come naturally to other people. However, it is not immediately clear to me when someone wouldn't think the same way I do. There is nothing more disruptive to a project when someone says they have understood something and then they go off and do something else. Nobody knows everything, and you will always learn something new if you ask for an explanation and follow-up with questions or a summary of the conversation as you understood it. Turning a discussion into a teaching moment with a phrase like "Can you explain this to me? I am not familiar with the concept" can also help defuse conflict and lessen your boss' irritation if things are not getting done. Most academic scientists enjoy teaching and will switch to "teacher-mode" to make sure you understand.

5) You are ultimately responsible for your project and career transitions. To move forward in any career you need to advocate for yourself and be proactive. You must take ownership of your work and ask for the training you need. There is no one size fits all mentoring method and no boss (no matter how empathic) will be able to read your mind. By being open in your discussions about progression and feedback you can define the working relationship you want. Realize that this may not be the way your PI mentors and you may need to find a compromise and look for additional mentoring with senior postdocs or other PIs. If you need time, resources, and help, you must ask and clearly articulate why.

A PhD or a postdoc is a tricky job situation because trainees are treated like employees or students depending on the whim of the university or PI...and this is a whole can of worms I will not open right now. However, in both cases, they are temporary positions that are supposed to help you advance in your career, so learning transferable professional skills should always be in the back of your mind to make the most of your time.

Thursday, August 9, 2018

Deciding which university is the right fit for you

I have been spending a lot of time recently thinking about how universities differ and about how to
find the right place for you. Like any other job the work environment and the culture will determine how you fit and how happy you are. I have been talking to so many friends grappling with the same questions that I decided to jot down some ideas that may help others in their struggles or their choices.

In a way or another, universities are corporations for higher-education and that they would do anything in their power to protect their reputation and appeal for students, including throwing their faculty under the bus or swiping problems under the rug. They are companies like many others, with often the difference that their products are teaching and research, and that their administration may not run quite right no matter how fancy they are. Being a great scientist and being a manager are two different things, and while you may be good at both, not everyone is. In addition, once you see how the sausage is made and the money is distributed at the Hunger Games of the Deans, you realize that even the best intentions can be foiled by institutional politics and that academia is most certainly not a "family"...

So, how do you find the right place, or survive until you have another chance to find it? Being on the job market as a faculty or postdoc can be quite scary and very "impostor syndrome"-inducing. You are terrified that nobody will want you, and if someone says "yes" you may feel obliged to take the job you can, even if you are not quite sure it's the right one. You might also think it is the right one, and circumstances will change due to the aforementioned Hunger Game results or some other issue.

The most basic criterion to be met is operations: a smooth process in grant submission and management, hiring, ordering and facilities including animal handling if necessary. I haven't heard of a single place that gets a five-star rating in all these things. If you know if this unicorn university, please mention it in the comments...or keep it to yourself so that nobody else can apply. Nobody tells you, but the single greatest source of delays in your scientific progress is having to spend a large portion of your time dealing with admin issues, and these issues hindering the work of your trainees. Plus, the possible development of multiple ulcers. Admins are critical and while there may be no place where they are all efficient, the efficient ones will save your life and help navigate the difficulties of academic administration. Depending on the needs of your research program, you need to find out as much as you can about the different aspects of operations. Ask everyone you meet and look for a pattern of consistency or elusiveness in their answers. Also, figure out if anyone is doing something similar to what you want to do and talk to them, because other people may have no idea of obstacles you may encounter.

Then, as long as you can get work done, there is the scientific environment. Are there people thinking about the same questions you are thinking about? Or tangential questions you would like to explore? Or questions you have never thought about, but sound really cool? Is there scientific and technical support around for you and your trainees? Most new PIs will tell you that their job is very lonely. Often lab heads are hired to fill a specific hole in the departmental expertise. Sometimes they dovetail nicely with other people already in place, sometimes not. The loneliest situation will arise if you are in a place where your expertise is not present and not understood, and where you cannot speak to anyone in depth about your research. Conferences, collaborations, and publications will become the only source of information on advances in the field, and you may be the only expert to provide technical help for your lab, which may slow innovation. There are multiple ways to adapt, such as developing extensive collaborations or changing research direction, but again, asking in advance and knowing what you are getting yourself into always helps.

And then there's culture. I may be hypersensitive, but I find that the academic culture of an institution as a whole defines how happy you'll be as a scientist there and will also define the scientific environment. In a siloed system, the culture of the individual department may be enough. By culture I mean the university's spoken and unspoken value systems. Do they claim to value research? Do they think research increases their prestige, but would rather not pay for it? Do they claim to value diversity? Do they claim to value collaboration and interdisciplinary team science? Do they want to subvert the status quo and hire scientists doing high risk/high reward projects? Do they have a focused mission (cure cancer, solve state-specific problems, etc)? Do they want their names on high impact journals no matter what? Do they aim to be a global or national leader? All these questions are part of different cultures I encountered, and everyone may elicit a specific reaction in you. You may be inspired, or annoyed, but overall how they answer will define whether you would like to be there or not. Even if the answers are great, the next step is to figure out if they are actually embodied by the employees, i.e. the faculty and the administrators. Every single place has a mission and vision crafted by a media office with upper-level dean-types. This doesn't mean the rest of the institution has bought in, and changing institutional culture can take a decade. On a 6-9 year tenure clock, you don't have a decade. Again, look for consistency in answers, but not so much consistency that everyone is giving you a memorized version of the mission statement.

If you found your magic spot, enjoy it. If not, you are most likely wondering how much to compromise and whether the job will provide a launching pad for another job. People in academia move around all the time and you don't owe the university anything, especially if they cannot provide a suitable environment for research. But the grass is not always greener. A university can support your career, be neutral, or be toxic. I am discovering much in this job is about compromise and being pragmatic, so if you are in a neutral environment, you want to think things through. If it's toxic, leaving is a no-brainer.  The moment you get money, you have the option to search again and compare and contrast, but it's worth beginning to plot your escape the moment you decide you may want to leave. All information on operations, environment, and culture can be more easily obtained from friends and colleagues at other institutions. As your circle expands you can leverage these contacts for new opportunities. It may take a while for the right position at the right place to open up, and you want to be in the know. The more you go give seminars and meet other scientists at meetings, the clearer it will become what is a good fit...and also what is realistic, since that unicorn university doesn't exist.

Photo credit: By Diliff [CC BY-SA 3.0  ( or GFDL (], from Wikimedia Commons

Sunday, July 22, 2018

Networking for job transitions in academia (and out of it)

The #HiddenCurriculum hashtag has emerged on Twitter and reminded many of us who have been at this job a while how students and postdocs often are not taught the critical workplace skills beyond bench work.

One of the most important skills for any job is networking. While I have written before on how to network (here and here), in preparation for conference and job-search season, I want to focus this post on networking for job transitions. Any job transition within academia and from academia to other businesses can be made faster and more successful by networking to generate contacts and leads. Networking and reaching out to people out of the blue is often very scary for students, but there are multiple principles that can make it easier and even enjoyable.

1) Networking is a long game. Building a large network and identifying the people who will help you and support you over your career can take years. As you change universities and often countries, you will naturally build relationships with a variety of people. The expectation that you have to generate a large number of contacts in one sitting will only generate anxiety and a feeling of failure. While it may happen that you are in the "right place at the right time" to meet the person who is looking for someone with your skills and background, strong work relationships are mostly developed over time. So, relax, accept that you will have other chances and realize that even if you get to have 1 or 2 meaningful conversations at a conference or networking event, you have succeeded. Follow up with an email saying something like "Nice talking to you. Hope we can keep in touch/meet again", find them on LinkedIn, and build connections little by little.

2) Targeted networking requires planning. You are interested in a job with someone in particular and know they will be speaking at your university or at a meeting you are attending. Join the lunch or ask to be in the room if your PI is on their schedule, or email them in advance of the conference to set a time to speak. If they are in your PI's network, ask for an introduction, especially if this is someone outside of academia. Prepare a few questions: "Are they looking for postdocs?" "What is it like to work at their university/company?" "What are the characteristics of a successful applicant in their field?" "What is the faculty search committee looking for?" Have a short 2-5 min pitch about you and what you are passionate about that relates to their job (e.g. your research, your interest in drug development, your passion for science policy...) Remember to thank them for their time.

3) Network with your peers. It's perfectly fine to hang out with students and postdocs at conferences and create a network of "conference buddies". You will see the same people from labs with similar interests over and over again. You will be networking without even knowing you are doing it. My conference buddies from grad school are now professors in many different universities or executives in pharma and government. In addition, if you are scared of reaching out to the big professor or they are not coming to a particular meeting, you can go see their trainees' posters and chat with them about their work, the lab, and how much you would like to join. You will get insider information about the lab environment and possibly a ringing endorsement "Hey, I met this awesome student at the meeting and they want to apply!". And voila', your application is at the top of the pile.

4) Sit at the table. One of the biggest benefits of a network is information, and information can be obtained even if you are sitting quietly or asking just one question at lunch. Table networking events where experts in different topics sit at thematic tables are now a staple of many meetings and university career development events. Go sit at the tables you care about, ask questions, or listen to the conversation. You can always reach out to the speakers afterward because that's what they're there for. Similarly, go to lunches with seminar speakers, and sit at those terrifying tables filled with big-wigs at conferences. Protip: those bigwigs have probably been conference buddies since grad school and they may be shooting the breeze about what's going on in their current labs, universities, or reminiscing. Everything they say is information about them, their current and past institutions. Those of them with a mentoring bone in their body will ask you about yourself and your work. If they don't, that's information also about who cares for trainees. A caveat is if they are having a working lunch to discuss a shared grant or paper and are too stressed to interact with the young'uns.

5) Mi network es su network. Make use of other people's networks to expand your reach. This tends to happen organically in academia, but it's particularly important if you are interested in a job outside the ivory tower and want to gather information and contacts. Homo sapiens is a social species and an introduction to show that you are "good people" goes a long way to get you a response. In any job, internal recommendations have a higher success rate in getting interviews and positions. Many companies give employees a bonus upward of $1,000 for recommending someone who ends up being hired. If someone on the inside can vouch that you are a good fit with the organizational culture and have the right skills, it will make things easier for you. Use your friends and colleagues who have moved into the university or industry you care about and ask them to put you in touch with others. If you are just starting and do not know anyone, ask your PI or other senior scientists. Remember, only 15% of science PhDs stay in academia, so we all know A LOT of people doing different things.

6) Find the other loners. This is all nice and dandy, but you hate small talk and tend to blend with the wallpaper at parties. You're in luck! A lot of scientists feel the same way. Scan the networking event for other wallflowers or the lunch room for that almost empty table. Go over: "Hi, I'm [name]" If you feel like it, commiserate about how hard it is to network, connect over your shared hatred of small talk, or just talk science. You have immediate access to that person's entire network. It is entirely possible they are a lonely journal editor and if you treat them as a human being, you will receive insider information on their journal and perpetual invitations to review. Repeat over and over again, until after a decade you find yourself with a network of hundreds of people and are confused because you're such a loner.

Going to meetings will soon become a joy and creating new contacts will go a lot faster. I sometimes have turned it into a game like a scavenger hunt: I have to talk to 20 new people and follow-up with 5 contacts that can provide job leads/collaborations. Set the numbers with your friends and who gets more business cards/emails wins!

Wednesday, July 11, 2018

Project Management in Academia 101: Getting people invested into the project

The thing about managing a project, any type of project, is that you cannot do it without the people who are actually doing the work. You can plan every step in detail and assign specific tasks and deadlines, but everything can come tumbling down because of lack of interest or miscommunication. Even when you're running an independent project, unless you work in a separate room with your own equipment focusing techniques you perform alone, you will need other people to help or provide services. In academic research, like in any business, personnel will always be the variable that makes or breaks your lab.

I have written extensively on how to hire a few years ago: some of those pieces are still timely and I wish I had taken the time to re-read them recently (herehere and here). Briefly, in an ideal world, you want lab members who are smart, engaged, passionate, and self-directed. Drive and intellectual curiosity are qualities that beat expertise in a job candidate, and in a training environment, independence and passion for a specific topic can be cultivated. But as I like to put it, if there is no wood, you cannot light a fire!

The issue of motivation fascinated me since I was a grad student. One night a new hotshot PI showed up in our lab, while a postdoc and I were working away and asked us "Why are you here? Your boss has been gone for hours and you're here. Everyone in my lab is gone and I'm here..." Our simple answer was "I don't know....we have experiments to do?" There are multiple reasons why someone would burn the midnight oil. Some have to do with carrots or sticks, but what would make someone WANT to work and do it happily?

Before I even started my lab I read "Drive" by Daniel Pink which I think is a necessary read for anyone interested in motivation. To foster workplace happiness and engagement, Pink proposes a motivation paradigm based on three principles: Autonomy, Mastery, and Purpose. People want Autonomy: to be in control of their lives and of how they do their job. They also want Mastery: to be really good at something and to keep trying to achieve our goals. And finally, Purpose (a greater ideal to aspire to) brings it all together. Studies have shown that once the basic economic needs are met, Autonomy, Mastery, and Purpose always trump financial gain.*

As a scientist, this made sense to me, since this is exactly why I do what I do. I am (mostly) in charge of my work and of my schedule, I love solving complex scientific problems with the final goal to help mankind better understand and treat neurodevelopmental disorders. Purpose is the hook and I find it's the way to get lab members engaged in a project starting with undergraduates and interns. If you do not give them the bigger reason, sometimes the slog of troubleshooting is too much to bear. Also, a lot of science tasks are boring, but still need to get done, and knowing why they are important helps get through them. Mastery gives a sense of accomplishments, and I tend to match projects to the techniques that someone is good at or would love to learn.

Sure, but in a small starting lab, how do you get Autonomy? What if you have someone picking up or joining an existing project? Or you need someone to focus away from what they are doing for the own project and help with something else? Again Mastery and Purpose come into play where clearly explaining the reasons why this is important and why a certain expertise is needed can get employees to buy in. "Ownership" of the research question and of the experiments is one of the most important aspects of getting students and trainees invested in the research.**

Finally, in a great piece on research motivation that touches some of the same topics, Uri Alon also discusses "social connectedness" as a motivation tool. We all know how much better it is to be in a work environment where everyone is invested in your success. While I don't buy the "My lab is a family" argument, I think a manager should strive to obtain a harmonious work environment by keeping conflicts in check, setting clear rules, and make employees feel listened to and appreciated. The right "vibe" in the lab will make people want to come to work.

* Daniel Pink's newest book is When: The Scientific Secrets of Perfect Timing and talks a lot about when you should perform certain tasks depending on your circadian rhythm. I'll review when I'm done.

** As a disclaimer, even as a small lab I give everyone their own project which makes me much slower than I should be in publishing. I always had complete autonomy in my work, and I cannot bear to force someone else to do otherwise, but this may not be the best solution for everyone and many trainees may like working together and benefit from it.

Long time no see...

Three months have probably been the longest time away from this blog since I started it. It's not that I
didn't want to write, but life has been so busy that time has gone by quickly. I barely had time to deal with social media, and my limited free time had to be devoted to other projects. I've been lurking on Twitter, catching only a whiff of the most recent scientific and political controversies. It's time to regroup and recenter.

There is a lot to talk about that I cannot disclose, yet. So many awesome and exciting things have been happening after the horrible couple of years I just had, and I've been buzzing around. I promise I'll start writing posts that will be released soon. I have been meaning to finish the Project Management series and I want to do that first. As people are posting about their new faculty jobs it seems like a good idea to provide some insight into managing a group. There is still a lot to say about resilience, grant writing, fighting impostor syndrome, and establishing yourself. Stay tuned, since more time to write is opening up at the horizon.

Thursday, April 12, 2018

Project Management in Academia 101: Managing your own project vs. Managing a lab

After discussing why you should care about Project Management (PM) in academia and that there are multiple PM techniques available, we can start getting down to business talking about possible applications. There is a big leap between managing an individual project and managing a lab. If you start using PM techniques early, it will make it easier to juggle managing multiple projects later, or just changing your role and transitioning into a different type of PM role in pharma or the private sector.

Managing Yourself

Let's start with managing your own project. If you have read the previous posts, you already should have some ideas on how to apply PM techniques to your daily work. If you haven't read them, click on the links above and come back in a bit.

If you are a student or postdoc, managing your project is one of the most difficult things you need to learn, often through trial and error. While PIs must have some elements of PM to hit all the deadlines necessary to submit papers, grants, and job applications for getting and keeping their jobs, most are not trained PMs and just expect you to figure stuff out like they did. Unless your boss is a total control freak that needs to double check everything, they will be ecstatic if you come up with a clear question and experimental plan (which includes deadlines for deliverables on their calendar). They will discuss ideas with you, then get off your back, tell you to ask for advice if you have any problems, and wait for the data to flow in. In some labs, especially in the US, the boss may just let you do whatever you want, and there you really need to know how to manage your time and experiments!

In the first post I outlined how to Initiate and Plan a project leading to a research paper and what are the necessary questions to ask yourself, but here I would like to delve a bit more in the mechanics of Executing and Monitoring. Because science is a recursive loop of experimenting, troubleshooting, and analyzing data leading to more experiments, the Agile PM approaches are a lot more adaptable as my guest blogger Duc Phan presented. Kanban, which is Duc's method of choice is a great place to start. Kanban meaning "card" in Japanese uses visual cues such as cards, diagrams or flowcharts to outline the project. Having tasks clearly ordered in "to be completed", "in process" and "done" is a great way to start and he discusses a great way to use the Trello software to do this here.

As you perform experiments, you need to think at two levels: 1) questions/hypotheses and 2) actual experiments. Anyone with some training can do tons of experiments, but one of the most important skills in science is to identify important questions and design the best experiments to answer them with appropriate controls. You will save yourself a lot of time and grief if you remain grounded in the big picture. So I recommend that you title cards with the question the experiments are going to answer, e.g. "What is the developmental expression profile of gene X?" "Does transcription factor Y repress activity of gene Z?". At the end you should have an image or a figure with data that answers the question, leading to other questions. Asana is another software that will allow you to organize experiments under specific headings. To get started you can also just print out an empty monthly calendar and plot out your month, or do a thorough job on Google Calendar in planning your weekly experiments. Blocking time for specific experiments and scheduling things around seminars, meeting and lectures, is one of the ways I figured out how to organize my day in grad school. What else can you do during that 2hr incubation?

This leads easily to a process we use in my lab to write papers which is also based on cards, but borrowed from screenwriting, which is storyboarding on index cards. Each piece of data/answer (graph or images) is pasted on an index card or printed out on a sheet of paper, then attached on a white board where we look at all the data to decide how to present the story. Experiments are rarely presented in chronological order in an article, and are often organized to fit a narrative. Some supporting data will end in the supplemental information and other data will need to be showcased in the first figure. Some data will be set aside because it may belong into a different paper. (Daily reminder on rigor: no data should be set aside because it contradicts the main hypothesis). By rearranging the cards in front of you, you will see how the data fits together in figures and where there are holes that need more work, leading to another round of traditional Plan/Execute/Monitor or agile Speculate/Explore/Adapt.
From Matt van dar Meer (U Waterloo)

Managing a lab

Once one moves into managing a lab, the requirements of the job often change dramatically. After the first few years time to do experiments declines, and you are balancing teaching, admin work, travel, writing grants and papers with tons of other random things you never thought could be part of your job. Deadlines come at you without interruption, and sometime without much warning. So there are two requirements now: managing your own job and managing everyone involved in the research which is people in the lab, collaborators and research administrators. I was reminded recently on Twitter that having a 5 year plan really comes in handy and I wrote about it a while ago. Briefly, plot out month by month your next 5 years: conferences, grant deadlines, desired paper submission deadlines, beginning/end of grants with progress reports, and everything else you need to get to the next level.

Then, your management style really depends on you and on your lab culture. In general, as a new investigator you will almost never be able to get postdocs that were just like you, so you will have to also budget substantial amounts of time for training and budget time for trainees to learn how to think on their own. While you may need to be patient at the beginning when you just want to tell them what to do and they ask for your opinion on everything, teaching to trust themselves and develop their own ideas, will pay you back in spades in the future when they decide to ignore you for a month because they need to get s--t done. As an advanced PM, it will be really up to you to move between traditional sequential Waterfall methods and recursive Agile methods depending on the project that needs to be completed and the stage it is at. One simple approach may be to have a separate project outline for each grant and then one for each manuscript that would fit into it.

I want to spend some time talking about Scrum. Scrum is a PM method that combines both traditional and agile concepts to get a project done in small parcels. The advantage is that there are both a long term goal, but also short term goals that give the group continuous sense of accomplishment. Each task is completed within a 2-4 week sprint which is managed sequentially like a traditional project. I find this particularly suitable when you rapidly need to generate preliminary data or when finishing a paper, because it adds a sense of urgency and focus. Responsibilities are divided between the Project Owner, Scrum Master, and Team. The Project Owner inititates and plans the project, but the Scrum Master is in charge of the sprints and of getting the Team going. You can easily imaging your Scrum Master to be a senior grad student or postdoc. I'm still working on really understanding how to adapt Scrum to the lab, but here is a nice little guide that can get you thinking.

Overall, having a plan and a general sense of whether the "lab machine" is humming or stalled will hopefully reduce your stress level and free time for other things you need to do as a leader, including going on vacation.

Monday, April 2, 2018

Project Management for Academia 101: an Introduction to Traditional Project Management and Agile Project Management

Guest post from Duc Phan, University of California, Irvine

I am currently a PhD candidate in biomedical sciences. I developed a keen interest in project management (PM) while working on my thesis project and have been an advocate for teaching/learning PM in academia. I’m planning to become certified as a project manager after I defend. These are some ideas that stemmed from my readings about PM.  

“What is a project?” 

“A project is a temporary endeavor with a beginning and an end and it must be used to create a unique product, service or result.” 

“Temporary” and “unique” are 2 important concepts here that affect how a project is managed. A project is temporary because it does not run forever, but has a beginning and an end. It is unique because it is not a routine work but set out to accomplish some specific goals. Once these goals are achieved, the project is completed. These 2 concepts are kind of confusing in academia, because we rarely think this way. We do our research, and we follow where the results lead. We keep chasing the lead, get excited about it, and tend to forget about everything else. We blur the line between a project and an operation (i.e. a routine thing), which makes it harder to manage. It just feels like a never-ending run, and everything is chaotic. 

If we think a project is temporary and unique such as one manuscript or an aim in a grant proposal, we will approach it differently. Because it is unique with specific goals, we need to define the scope, the outcomes, and the benchmark criteria (i.e. milestones). With that in mind, we can look at resources (i.e. budget) in hand to meet these requirements. And because a project has a beginning and an end, we need to plan a timeline accordingly. 

Traditional project management vs. Agile project management:  

Each project is unique, so there is no one-size-fit-all approach. However, there are formalized systems (with knowledge, skills, and tools) that have been tried and tested by professionals in different industries. It is generally accepted that they fall into 2 groups: 

Traditional project management (TPM), as in its name, is the basic and traditional method to manage a project. It’s also called Waterfall Model (or Waterfall Project Management), because it is structured in a linear, sequential order. TPM system and methodology stemmed from the heavy industries (e.g. manufacturing, construction) in the mid 50-60’s of the 20th century, and it is still a popular system today. 

TPM emphasizes on a clearly defined management plan to deliver project outcomes on-time and within a stringent budget. In the ideal TPM world, everything has to be planned out, documented, and followed according to the plan. As introduced in our first post, TPM breaks a project into 5 phases: Initiating, Planning, Executing, Monitoring/Controlling, and Closing. In TPM ideal world, one phase must be completed before the next phase can start (i.e. You can’t plan a project without initiating it, you can’t execute a project without planning it, so on and so forth). In addition, any changes during execution (scope creep, schedule change, budget change) must be reviewed and approved by higher level management and related parties.

Let’s translate TPM concept to academic research. A good example of a straightforward waterfall project could be generating preliminary data for a grant and writing the grant. 

You decide to write an R01 proposal for the NIH (Initiating). This entails outlining 5 years of research in a large project with 2-3 Specific Aims/Hypotheses. In the Planning phase, you outline your aims and define whether you have all preliminary data to support your hypotheses and feasibility. You gather your lab personnel and assign one or more pieces of missing data to each person, giving them a specific deadline. You sort out details with the grants office to define which documents are needed when and who is going to do what, including documents needed from collaborators. You outline the project budget for all groups involved. You have a set of tasks spread across multiple people each with deadlines. Your lab starts working on the project (Executing), you oversee the process (Monitoring/Controlling). In a real waterfall scenario, you start writing the grant when the necessary data is obtained (more Executing). In reality, it’s probably in parallel as the data starts coming in. You prepare all documents needed and your grants office checks them (more Monitoring/Controlling). The grant is completed and submitted by the NIH deadline (Closing). 

The strength of TPM (and its skills/tools/techniques) is to keep a project on track. With clearly defined objectives and milestones to hit, you are less likely to wander around and waste your time/resources. TPM can work for PIs or heads of a research group to have a global view of a project and its related aspects (scope, time, budget/cost, human resources, risk, stakeholders, etc.). However, the weakness of TPM is that it takes a lot of advance planning and it is not as flexible to changes, which happens so much and so fast while doing exploratory research. 

Let’s explore the other end of the spectrum.       

Agile project management, as its name suggests, focuses on rapid and adaptive management. Agile itself is rather a concept/strategy, and there are several management systems that fall under this banner, including Scrum, Kanban, Lean, to name a few (here and here). Agile began in the software development industry, but has since becoming popular and overshadowing TPM. The motivation behind Agile is that TPM is too bulky and slow, thus cannot keep up with rapid changes of a project, especially in digital age.  

Instead of breaking down a project into 5 sequential stages where one phase has to finish before the next one starts in TPM, Agile management de-emphasizes rigid management structure, strict timeline, and documentations. Instead, it splits the project into small work packages (or features) that can be independently addressed, and deliver each one as steps toward the final project objectives. The goal is to carve out these work packages so you don’t necessarily have to do them in sequential order, nor they are hard to change. Agile divides and conquers small work package according to level of priority, available resources in hand (manpower, expertise, budget, facility…), and feedback from stakeholders (project sponsor, higher management, public…). Each package has a specific deadline and deliverables so that overall working in short bursts increases the feeling of accomplishment in the group. 

That doesn’t mean Agile is unorganized. Agile still have a framework with 5 phases: Envision, Speculate, Explore, Adapt, and Close. The project will iterate through the Speculate, Explore, and Adapt phases until all objectives are reached. Results and feedbacks will be evaluated after each cycle.  

Let’s translate Agile concept to academic research with a moonshot idea:  

Drug development is too slow right now (Fact: It could go up to 10 years) and the pre-clinical screening process is ineffective (Fact: 9 of 10 drugs fail Phase 2 trials or after, and about 2/3 is due to efficacy and toxicity issues), so I want to develop a new drug screening platform. I set out 6 months for a pilot study.

I envision that if I have something more similar to a human body to screen compounds in lab. It will be better than testing with cells on a petri dish.  So how should this look like? 

I start to speculate the features for this new assay based on prior knowledge, educational guessing, and imagination. It should mimic human vital organ structures and functions to certain level. It should have blood vessel and flow because I want to look at systemic drug delivery. At the same time, I want it to be cheaper than animal models and easy to handle, so I can screen a good number of compounds in lab. I am an expert in a few things here, and I know some colleagues on campus who might be able to help me out. I will need expertise in bio-engineering, anatomy, physiology, pharmacology, and medicinal chemistry. Let’s gather everyone and cook up something!

We hold meetings and explore what it takes to get the features we sketched out. We come up with a list of experiments and their priorities. Some are doable within our expertise and in-house resources, while some are pretty challenging. But whatever, we will start doing stuff because it’s now or never. We decide what needs to be done to decide whether this is even feasible, divide the workload, and set out 4-week block for this round. 

After 4 weeks, we come up with the first iteration. It was much harder than we think, but we have something to test, and a list of issues/incomplete features. We start some testing to adapt our cooked-up Frankenstein to drug treatment. People mostly hate it (ouch, that hurts) but they give us a list of things they like/hate. We also realize that some original features that we thought are just overkilled and not practical. We repeat the Speculate-Explore-Adapt cycle again and again. 

After 6 months, we close the pilot study with version 6 of our Frankenstein and lessons learned. It’s obviously nowhere near what we envisioned, but we have come up with new ideas for developing different tissues in vitro and for promoting vascularization. 

As mentioned above, there are many Agile management systems like Scrum, Lean, Kanban, etc. Each has pros and cons depending on your project, but you can click here to explore the differences. 

My favorite is Kanban and it has been my go-to strategy. Kanban, "card" in Japanese, is a PM system implemented by Toyota in the 1950s, and has been widely adopted in the tech industry. The core of Kanban management strategy is using visual cues (i.e. cards) to keep track of tasks at different stages of a whole project. Kanban focuses on statuses rather than due dates to create a continuous workflow. I have written 2 blog posts on why I chose Kanban and how I use this method to manage my research project.

Recommended reading and resources:
The gold standard for TPM is the Project Management Body of Knowledge PMBOK Guide developed by the Project Management Institute (PMI).

PMI is the globally recognized organization that represents PM professionals and provides formal training/certification to project managers. It is not the only professional organization in the world, but it’s the most recognized, and its Project Management Professional (PMP) certification remains a standard for senior-level PM positions in the industry.

You can also read The Fast Forward MBA in Project Management (Fast Forward MBA Series) by Eric Verzuh and Agile Project Management: QuickStart Guide - The Simplified Beginners Guide To Agile Project Management (Agile Project Management, Agile Software Development, Agile Development, Scrum) by Ed Stark

Sunday, April 1, 2018

5 years on the tenure track

Sometimes I wonder what made me think that April 1 was a good start date for my faculty job! But here it is, the April Fool's PI once again. I just completed year 5.

As customary in this occasion I go back to my past lab-birthday posts which reflect on the year that passed and on plans for the new year (Y1, Y2, Y3, Y4). The historical memory of this blog is one of the things I love the most (after my readers) because it give me prospective on my efforts and feelings. I am very glad that everything I put in place last year allowed me to survive this year. It was touch and go there for a minute, but the lab is good and back on its footing again. The new hires are great and after taking the time to get everyone up and going in 2017, things are moving along, papers are getting out and being published or in revision, we have cool new data, and we finally got our much needed R01 funding!

I have reflected a lot about what resilience means in academia during this process. The first month after getting the R01, when people were coming into my office to hug me or high-five me, I was stunned. I felt I had just come back from war. I was injured and psychologically devastated and there was no reason to cheer. A feeling I found so many of my friends who had been in the same boat shared. I don't know if it's true that the NIH throws you a bone at the very last second before drowning, but I have definitely heard of single-digit percentile R01s born out of extreme desperation.

Then a friend who is more senior mentioned that when you get your first R01 is when you realize you will survive, when you know that people trust you. And this is true. I still remember the joy of getting my very first grant as a postdoc, the sense of accomplishment and belonging "I can do this!" The disconnect in the past couple of years was that I knew I could do the job, but study sections didn't believe me, so I had to figure out how to convince them.

I feel like I am still recovering, but the outlook is a lot more optimistic. I made sure I had vacations planned to clear my head in case of both good or bad outcomes, and I'm ready to get back in the ring. This year I want to close this first chapter and really think about the future and what I can do with this lab now that there is some security for the next 5 years. There are still 2 more R01s in the pipeline...and I want to recapture the wonder and excitement I had on Day 1 of my faculty job.
I can also spend more time to focus on my other passion which is professional development for young scientists. After the Project Management series is done there will be more things in the pipeline.

Wednesday, March 21, 2018

Project Management for Academia 101: learn the basics

Project Management (PM) is a full-fledged discipline which focuses on the planning, execution, and completion of projects. In large corporations, projects like the launch of a new product can take a year and involve dozens or hundreds of people with different expertise in different departments (R&D, marketing, sales, production, distribution). The Project Manager's role is to plan and coordinate the project so that it is successfully completed by a defined date.

I started looking at these practices just through conversations with friends in other industries and quickly realized that many academics are already expert project managers and other academics would really benefit by learning some of the PM rules. I have always thought that your PhD is supposed to teach you how to conduct and complete a project, your postdoc is to identify and plan a project that will get you a job while you learn how to write grants and manage a few people, and your faculty position steps everything up to next level where you manage projects, people, budgets, and several other responsibilities. PM is a critical skill to develop early as it will sustain you throughout your career, and is readily translatable to any other industry.

A good place to start is the Harvard Business School Project Management Manual. It's 20 years old, but it's short and simple and a good introduction to basic practices. The major steps for managing a project are: Initiating, Planning, Monitoring, Executing and Close Out.

Initiating involves defining and organizing the project and it’s one of the most critical aspects of PM. If you don't know what the project is about, what is needed, and who is doing what, you are setting yourself up for chaos from the very beginning. You need to define objectives and the expertise needed to achieve them, including bringing people on board and giving them clear instructions on what their responsibilities are.

Then you Plan: set intermediate objectives, deadlines, and deliverables, assign specific tasks to people and define how the different parts of the process fit together, for example, whether assignments are sequential or parallel and which steps depend on completion of previous assignments. My guest blogger Duc Phan will expand on this in the next post.

Only after you define objectives and set out a plan, you start Monitoring how the different moving parts proceed and get into the Execution/Management portion. Depending on the size of the project this is where things can get hairy due to planning errors, unforeseen obstacles, or personnel issues. This is where the manager may step in to troubleshoot or find someone else with the expertise to do it, reallocate resources, adjust deadlines, etc, until "Tada!" the project is complete and the final deliverable is produced. As part of the Close-Out process, it will be important to go through the flow of the project and see where things went wrong and whether a different design is needed for the future.

I'm sure you can see how these principles could be applied in the lab, but I will give you a concrete example. You can think of projects in terms of published articles, and this type of project management is a good primer for both trainees and faculty. Managing a large project like the one outlined in an R01 application will include multiple publications.

GOAL: I want to publish a paper!
Great! How?

* What is the paper about? Define the title of your project/paper, the main hypothesis that you want to prove.
* What was known about this before? Read the literature to decide what are important questions left to answer and where the knowledge gaps lie.
* What do you need to do? What are the necessary steps to prove your hypothesis and what are the techniques/approaches involved? Are there techniques/reagents you need to acquire?
* How many people do you have and how many do you need? Do you need collaborators to bring different expertise? Do you need to recruit a few junior trainees to take on data collection/analysis?
* Do all the steps need to progress in an IF/THEN fashion (each step dependent on the previous) or are there parallel steps where multiple techniques can be used at the same time (e.g. biochemistry and histology on the same tissue to answer parallel questions)?
* What if my hypothesis is wrong? Develop a risk management plan: alternative approaches and when to abandon the project.

* Lay out the steps you have identified and outline experiments.
* Assign experiments (to yourself or different people) and determine how long it would take to complete them.
* Factor experimental failure, broken equipment, illness, and other factors that could delay the process.
* Prioritize experiments and organize them based on time available, personnel, reagent generation and troubleshooting, animal breeding, equipment scheduling and other factors intrinsic to your research design.
* Define clearly who is responsible for what, who reports to whom, and how participants are supposed to communicate.
* Set deadlines for each component of the project and make everyone aware of them. This is critical when hand-offs are needed, e.g. animals to be shipped to a collaborator, summer student leaving, etc...
* Remember that most likely the manuscript will go to several rounds of review and that you also need to factor time for revisions. If you plan to graduate or leave the lab, who is going to finish it?

Note: Some people like to lay out figure plans when starting to develop a paper: they organize the outline of each figure and which data is needed to complete it. I sometimes find this difficult as the story can change a bit as you go along. I use a hybrid system where I write out the questions we are asking (e.g. Does loss of gene X alter protein levels of Y and Z?) followed by bulleted experimental outline (e.g. 1. Generate 5 animals per genotype/sex, 2. Prepare protein lysates, 3. Run Western blot for Y and Z). Then when we have enough data, we print things out and start organizing figures at the white board, then find the holes and plug them. Which brings me to...

* If the plan is laid out well, the tracking becomes easier during regular staff meetings, as you can just go through the experimental outline and see what is done and what is not done.
* There are ways to monitor without meeting such as Asana which I have discussed in a previous post or the team messaging app Slack and its ToDo option. I will further discuss these strategies in a post dedicated to getting lab members on invested into projects.
* The simpler and more annoying part of managing the project will be dealing with troubleshooting and unforeseen delays leading to changes in deadlines.
* The hardest part of managing a scientific project will be reframing and readjusting your goals as you go along. Scientific discovery is not like launching a new iPhone. As your results come in, you may find your hypothesis is not true or you may find your beloved biological mechanism does something amazing you had not anticipated. The thing is that once you have this PM loop ingrained in your day to day planning, you can shift very quickly and use the same framework I outlined to redesign your workflow and continue based on new goals. Instead of single loop, doing research is more like going through these steps over and over again in a spiral.
* Overall the hardest part of managing in general is managing people, which is particularly challenging with smart and strong-willed scientists, but as I said above, this merits an entire post.

Close out:
* Once you have a collected all the data, you set up a mini-project to write the manuscript and submit, while you plan for revisions do to on the holes that you willingly left for the reviewers to find. 
* The reviewers find a few other holes and you plan for revisions. The manuscript goes back in and is accepted!! 
* Remember to celebrate and thank all the contributors for their valuable contribution. 
* The proofs of course come back while you're on vacation without internet access (I don't know how journals know that, but they do), so remember to plan for a back-up proofreader as your final Close-Out item. 
* Review your process, adjust, and repeat roughly 100-200 times throughout your career.

Now, think of how else you could apply these strategies!