I have been wanting to do this for several years: create an anti-CV that documents the major academic/career failures that I had. I used to have a folder for rejection letters, but it soon ran out of space, and I stopped collecting them a long time ago…
Time to find a different way to remember my failures! So here it is, an incomplete version of my anti-CV. I will keep it updated from now on.
My goal has always been to receive no less than 10 rejections a year.
It looks easy. All you have to do is to pretend as someone who is better than yourself for half an hour (on the phone/Skype/Zoom), and then a day (on campus), to get that dream faulty job offer.
I tried that a few times as a candidate but was not very successful. I didn’t know what the problems were until years later after serving as the chair or a member on several faculty search committees.
Faculty interview is like speed dating. By the time you made to the interview, we (the search committee) have been impressed by your achievements (on paper), but we haven’t got to know you as a person. We are afraid of picking someone that we may regret and be stuck with for years… That scary thought motivates us to do a careful job.
So what do we care about? First and foremost, we like to know if you are a person we want to work with as a colleague. Are you an open and frank person? Would you see our institution as your future home? Are you the type that can make the people around you better? Do you hold a balanced view of different matters?
Second, we try to predict if you would become a star (not just meeting the tenure requirements) with our yet-to-be-proven fortune telling skills. Do you have a solid grasp of the fundamentals in your area? Are you passionate about something? Can you think critically and independently? Are you aware of ongoing trends in research and in the society? Are you an ambitious person with big dreams and a strong vision? Can you communicate well with different audience, make sound arguments, and be persuasive? Can you be an effective teacher? Can you handle pressure, stress, and setbacks?
Finally, we also want to know if your success would matter to other people. Are you bringing complementary skills (teaching, research) to the institution? Are you a team player? Are you more interested in yourself, the community, or the society? Can you lead a team to build something bigger than us individuals can do?
In a nutshell, we are looking for someone who is way closer to perfection than ourselves …
Of course, we don’t know how to evaluate all that… In robotics terms, we are facing a decision making under uncertainty problem, an active perception problem, and a bounded rationality problem (e.g., making decisions with incomplete information and limited time). Each of us on the committee tries to observe and to probe you with questions. We fall victims to our cognitive biases, jump into conclusions with insufficient data, while trying hard not to fill the missing pieces with stereotypes, imagination, and random thoughts/mood of the day.
So how to survive this complicated, stressful, inherently stochastic, and often biased process? There are a lot you can do before coming to an interview. Preparation and experience help. Iterate on the answers to common questions leads to better, more focused answers. Known what may come helps you to prepare and know when to relax.
However, a skillful search committee can/may see through some of the facade. There are things that can be couched by a good advisor in a matter of hours (e.g., which funding programs to target), but we are not hiring your advisor. Interview experience can be learned (someone had many interviews in the past is not necessarily preferred to someone on the first trip). Always done you homework is a good quality; but is only one of many that we are looking for. Worked on a cool project, like a NASA mission, only means you were on a large team. There are also things that any intelligent person can learn on the job later, without much risks.
What we really want to get to know better is you. We try to focus on things that would take real effort and experience to understand. For example, someone who has never taught a class before would likely not understand the true challenges in teaching and learning. Someone who only did what the advisor told him/her to do may not have deep insights on what is the next step, the step after that, and why. You are unlikely to be a good leader without appreciating the meaning of compromise and sacrifice. Your strong desire to help the community needs to be backed up with a purpose and a track record. You may pretend well for a minute, but if you didn’t have the real experience, this may not survive a few rounds of probing.
Faculty candidates often don’t know why they failed (or succeeded). Almost no search committee can provide frank and detailed feedback due to a variety of reasons. We won’t/can’t tell you that you’ve been acting like a teenage; your accent was not a problem; that name dropping/talking down other researchers did not serve your interest; your honesty and willingness to expose your vulnerability was appreciated; you didn’t seem to be prepared to write a proposal/teach a class/run a lab; etc. I have seen candidates apparently interviewed at many different places didn’t quite understand why they haven’t landed a job. I was in that boat for a few years as well.
So, pretending to be a better, more desirable version of yourself during the interview may not work out. I think a better strategy is to act like that version now, to identify and build up these experiences, and to collect honest feedback. It is never too late for doing that. After you have started on this path, you can follow what many people suggested to do during interviews: “just be yourself”. You no longer have to pretend to be someone better; you are a better faculty candidate.
There are 7.8 billion people living on this planet and everyone’s brain is running, fast or slow. Most of the time, people are thinking about more or less the same things: sports, weather, girls/guys/kids, shopping, promotion, money, politics, to name a few. Imagine how many times the same thought on “which phone to buy” goes around the globe? (sounds like a lot of redundancy and waste here, but that’s for a different topic…)
Occasionally, unique ideas pop up in the mind of a person, any person, often because she/he is in the right place at the right time (e.g., what if the blanket can fold itself after I pick up the baby?). These ideas could be trivial or infeasible. We might feel good about our creativity for a few seconds, then it would just slip away out of the memory. Not known what to do with the ideas, we are throwing away an enormous number of intellectual products each day (hint: at least write them down like what I am doing with this blog…).
At the same time, our world is in a desperate shortage of creative ideas (just watch a few recent movies or see the design of all the new cell phones…). Once someone is in need of a solution (e.g., stop the pandemic), good ideas don’t come by on schedule. Beaming a lot of brainpower by a few smart people is not necessarily the answer.
So what can we do? How can we involve everyone in the creative process everyday on solving the world’s everything problems? I think we can benefit from something like a “Wikipedia for ideas”, where millions of diverse ideas are shared, debated on, grow and connect, and found by people who need them. How to discover incentives for everyday people to join this collaborative effort would be an important question to answer. Any ideas?
When I was a kid, my dream was to become a scientist. I was fascinated with reading early discoveries in chemistry, physics, and biology. Those scientists were my heroes.
Following a series of random and not so random events in life, I end up being an engineer, which I am equally happy about.
For a long time, I didn’t see much difference between science and engineering. We are all researchers. That was until I had my first proposal rejected by NSF, the National Science Foundation.
Scientists and engineers have different goals. Scientists discover and engineers create. Scientists observe something already exists (e.g., nature, universe, human society) and try to explain it. Engineers dream up something new (e.g., a bridge, a rocket, a material) and try to make it real.
For these reasons, scientists and engineers think and work in nearly opposite ways. Scientists observe, ask questions, form hypothesis, and then design experiments to test them. Engineers conceive designs, build prototypes, integrate parts into a system, and then perform evaluation.
If you ask an engineer to tackle a science problem, say why migrant birds often fly in formation; she/he may say let’s make airplanes fly in formation first, take measurements, and see what the data tell us. That was the kind of the mistake I made in writing my first NSF proposal.
Can someone function with both scientist’s and engineer’s minds? It’s very difficult. If you have sat in a meeting with both scientists and engineers, you would know that they don’t really speak the same language or live on the same planet (imaging adding a few artists into the mix!). Heck, they don’t even look the same. But the ability to handle difficulty is what sets one apart from the rest. I don’t have enough knowledge to comment on the importance of engineering to science, but in my opinion, it’s unlikely someone can be a great engineering researcher without sometimes thinking like a scientist.
Take robotics for example, we can always dream up robots that are more refined and algorithms that can squeeze out a few percentages of performance gain. In fact, we always want to do that because as engineers we feel itchy about flaws we can see, and improvements not made. Most of us live comfortably (or not so comfortably) in the cocoons we carefully engineered for ourselves. Every piece of silk we lay makes our world smaller. We are occupied and always so busy; while in the meantime, we ask, why innovation is so hard?
If only we could use some of our silk to explore, to take us to the next tree, and help us see a different world! What about taking a break from solving problems; spend some time to observe, ask why instead of how? We would be thinking like a scientist with the creativity and hands of engineers.
Of course, it would take a risky and painful transition to break the cocoon. But there is also no reason this cannot be done. Someone clearly had the wisdom at WVU a long time ago. After all, my office is in the Engineering Science Building (ESB).
The current-generation Amazon robot assisted warehouses are painfully boring to watch, once you realized how time, energy, and space inefficient they are. Each piece of merchandise has to travel on racks maybe thousands times heavier than itself, at a slow speed, through heavy traffic, while the majority of the warehouse volume (> 80%) is left unused.
Vision: I think most goods can be simply tossed up in the air by robots and be caught by other robots at distances. The future warehouses, which I would like to call them Monkey Houses, should be highly dynamic and densely filled with flying objects. It will improve the throughput of a same size warehouse by more than an order of magnitude and drastically reduce the energy consumption compared to the current systems.
Justification: while humans occasionally use throwing and catching for object handoff (e.g., sports), it is not generally considered as a reliable method, especially when there are multiple objects flying simultaneously. Robots, on the other hand (no pun intended), can be particularly good at this. This includes estimating object motion, performing fast and precise control actions for object catching, simultaneously tracking the trajectory of multiple flying objects, as well as communicating and coordinating with thousands other robots in making plans. With these super-human abilities (i.e., speed, precision, reliability, memory, and communication) of future robots, the engineering trades of future systems design often shift toward counter-(human)-intuitive directions.
More Detailed Vision: each rack in the warehouse will be a stationary robot that can throw and catch objects. Each type of object will have a g-loading rating, dictating how far it can fly in one hop (the packaging of some future goods may have to be redesigned to be better suitable for flying). The goods may go through multiple hops (i.e., catch and throw by robots in between) before reaching final destinations. All object information is shared and an air traffic management system will ensure objects flying pass each other with safe clearances. Like goods, small robots can also be tossed up in the air. They can intersect other flying objects to improve the flexibility of stationary rack robots…
Thinking beyond the warehouse settings, it is conceivable that the main mode of object handoff for robots in the future would be throwing and catching, once the reliability of such systems exceeds human’s capabilities. Compared to the continuous-contact object handoff between two robots, throwing and catching involves much less complex robot-robot interactions and thus is far simpler and robust for robots to perform. This would have many implications to the design of other future systems. For example drone delivery can be performed by throwing packages to balconies equipped with catching robots (or just baskets with nets). Battery changes for drones could be done by simply tossing batteries up and down. Exchange of cargos (and passengers) between two self-driving vehicles on the highway could be accomplished through the air. What other cool applications of robot throwing and catching can you think of?
After a long winter, it’s about the time to start something new!
