Category: Students

On Time and Research Productivity

by Yu Gu, posted in Ideas, Research, Students

What is a good heuristic linking the time spent and the research progress made?

If we use bean counting as an example, the progress made would be a function of talent, skill, and effective time spent. This is not as simple a relationship as it may look like at first glance. The talent (e.g., good hand-eye coordination and dexterity) separates us to some degree, but skill can make up for most of the differences. Skill is also developed over time, as a function of effective time spent on bean counting in the past. Notice the word effective here. Three people (A, B, and C) can be spending the same amount of time counting beans; but,

  1. A spends 30% of the time wondering: B seems to be more talented than I am?
  2. B spends 20% of the time managing the group.
  3. C is not thinking at all, just counting.

Who may be the one that has more beans counted and has more bean counting skills gained over the time?

However, research is not bean counting. If instead, we use mountain climbing as an example, we may consider progress as a function of talent, skill, time spent, and the direction we take. If we are making poor choices by going on the longer route, we could be climbing fast and hard but still be late.

However, research is not mountain climbing. If instead, we use mushroom foraging as an example, we may consider progress as a function of talent, skill, time spent, direction, and luck. If we are lucky, we would find many (and good) mushrooms on the way with less time and effort. But luck is not something we have direct control of. The only part that we can do is to increase the number of trials, e.g., explore more, which is also a function of the time spent.  Also, it’s assuring to think that no one can be always unlucky on a time scale of, say, 40 years.

However, research is not mushroom foraging by one person. That would be ignoring the bigger picture, e.g., the role played by others on one’s progress. If instead, we use group mushroom foraging as an example, we may consider progress as a function of talent, skill, time spent, direction, luck, and interaction. The time spend on teaching, making friends, brainstorming, and cooperation may payoff in ways we can’t anticipate.

How do we know if we have been managing our time productively? We probably won’t know for sure. It usually takes years to develop a skill (e.g., language, playing musical instrument, writing) and the process is not linear. Your skill could be stagnating for a long time before making the next jump. I would suggest following a simple heuristic: the research progress, along with many of its contributing factors, have a positive correlation with the effective time spent. There is also a simple test. Just think about in the last week (while still can remember), how much time did I spend on research related topics, such as reading, learning, thinking, doing, writing, teaching, and debating, etc. Ok, for me, I worked quite hard last week (Oct 30 – Nov 05, 2022), but a good chunk of my time was spent on attending meetings, replying to emails, grading homework, dealing with logistics/paperwork. I also spent a good amount of time teaching and thinking (which were productive), but had little time to read and write, and even less time to use my hands for doing anything other than typing …

Why am I not a better researcher than I am now? That was probably the main reason. I can blame it on not having enough time; but that probably is just an excuse. Maybe it’s because there are too many things (other than just research) that I chose not to give up? Or maybe the time I spent was just not effective enough. One progress I made was that I have learned, after a long while, not to waste time thinking that I am not good enough as a researcher.

A Trip to Mars… Desert Research Station (MDRS)

by Yu Gu, posted in Projects, Research, Robotics, Students

A couple weeks ago, a team of WVU students and I traveled to Utah to compete in the University Rover Challenge (URC) for the first time. It has been 5-years since I was last at a robot competition. This time, we have a new group of passionate and talented students, which brought back a lot of memory and excitements. We ended up doing well for a first-time team, but that was not without struggles and some luck.

Going to a robot competition is to get out of ones’ normal life routine. In a short a few days, unexpected events are rapidly unfolding in front of everyone’s eyes, followed by rapid and intense problem solving by the team members. In this post, I will mention just a few of these surprises.

Imagine you are sending a rover to Mars for a science mission. Your rover needs to be in other people’s hands for transportation and payload integration. It has to survive the rocket launch, months of interplanetary travel, and the short but horrifying landing process. It may not end up in the exact location on Mars as you hoped. Once it’s there, there is only so much you can do about the rover, and things start to break as the rover moves from one place to another …

URC was a bit like that. As a good robot challenge should be, there are many elements of surprises. Some of these surprises are imposed by the physical world, like a real Mars mission, and some are exclusively for the first timers like us.

Our launch vehicle was a brown UPS truck. We packed everything in five wooden crates and a cardboard box, with almost 300kg of gear. After traveling on the Earth surface for three days the shipment arrived at Denver. A two-person team picked it up with a van and completed the remaining 7-hour journey.

Several parts broke during this trip, mostly 3D printed ones. Luckily, we brought backups. Our 3D printer also had a motor mount broken. A team member (Tyler) zip tied the motor to print a new part to fix the problem, practically creating a self-repairing 3D printer. To our surprise, all the steel bolts on the rover were heavily rusted, as if the UPS truck took a sea route.

Getting the robot ready for the first two missions (Equipment Servicing and Autonomy) on the first competition day took a long time. Some testing were pushed to after dark. At close to 11pm (1pm Easter time), things started to look really good with everything working. When powering down the system, an (unpowered) GPS cable fall into the electronics box and got close (but not quite touching) the power distribution board. After a small flash under one of the darkest night skies in the US, everything went quiet.

The night of excitement renewed after the incident and sleep was no longer important. Close inspection of the power board revealed that an inductor melted down. The inline fuse was still intact and there was no way to tell if the electronics downstream (e.g., computer) were still ok. Swapping out the power board with a backup piece took some careful deliberation and planning. Luckily everything worked and there were still over 2 hours left to sleep before we need to get on the road.

It was a small miracle that the robot worked for the Equipment Serving task without having a chance to do a full system testing after putting everything back together. We probably wouldn’t do much better than what we did without more in-depth understanding of the tasks, which could only be acquired through being there.

The Autonomy task was more … dramatic, for a lack of better word. The robot held its position (like the picture below) for almost the entire duration of the 30-minute mission. At the very last moment, it took off and reached its first waypoint. For us outside of the command station trailer, a motionless robot can trigger many emotions and speculations. For the members inside the trailer, they were in a frantic problem-solving mode. Clearly, the time went by at very different rates just a few meters apart.

What turned out to be happening was that the terrain map loaded on the rover was centered around the habitats of MDRS. For the actual URC competition, the organizers split the four different missions at three locations about 1km apart. The starting point of the autonomy mission was just outside of our prior map. Knowing it’s not on the map, the robot did not know what to do. It took the team members just a few minutes to diagnose the problem, and then many trials and errors to place a blank map in the right place so the robot can move. It worked!  I have seen many “autonomous” robots made up its mind to not go anywhere during the competitions …, this was the first time that a robot changed the mind (with some human help, of course).

With a bit more time and experience, we were better prepared for the next two missions on the following days: Science, and Extreme Retrieval and Delivery. There was no shortage of surprises and issues, but the team (and the rover) held up well.

An adventure like the URC trip teaches us the meaning of real-world engineering. To know a system works, we need to put it through the test of truly new environments and unexpected situations, out of the control of the robot designers. The thought that we shipped a rover to one of the most uninhabitable deserts in the continental US thousands of kilometers away and still managed to make it work in all four missions is quite satisfying. Many other teams, especially international ones, had to cope with even harder constraints, like designing the rover to fit in airline carry-on cases.

When a new problem arises during a competition, and it almost certainly will, the problem needs to be understood and solved quickly, either by the robot itself or by team members. Luckily, robot designers and programmers are trained problem solvers, although their performance can be further improved with more systematic approaches. For autonomous robots? problem-solving is a much harder challenge and perhaps the greatest gap in the current robotics research.

Here is a group photo of the team along with our judge (second from the left), taken in front of a MDRS habitat after the Extreme Retrieval and Delivery mission.

Mastering the Master’s Study at IRL

by Yu Gu, posted in Research, Students

After spending 16+ years in school, and you still feel like needing more schooling, then a master’s program may be suitable for you…

This is the “super-undergrad” view of master’s study. Another way of looking at your journey as a master’s student is to consider it as a “pre-Ph.D.” Whether you want to get a Ph.D. later or not, you can build yourself into a capable engineer and an independent researcher in 2-2.5 years of time. If you take this latter view, the time spent on your master’s degree may become the most consequential period in your career. However, this would only come as the result of hard working and wanting to make a change.

The first challenge for a master’s student is the transition process. Life as an undergrad was quite structured. You come to classes, do homework, prepare for (one after another) exams, keep yourself fed, clean, and healthy, and squeeze in other things you want to do. Your schedule is largely dictated by the curriculum, the professors, and the computers. You don’t have to plan much; just responding to endless deadlines, and having things done on time, you would probably do well in school. The grad school is different though. Initially, about half of a master’s student’s time is spent on classes. The other half? Not so well defined (well, research is a pursuit of truth…). Just like you were giving the responsibility of managing your free times when going to the college, now you are giving the freedom (just another way to say responsibility) to manage half of your (potentially) productive time. Can you make it actually productive? Without the constant pressure of homework and exams, will you still learn as fast and as focused as you could? You will be giving guidance on research directions. The projects and papers do have deadlines. Beyond that, you will be responsible to manage your day-to-day research activities.

The second challenge for a master’s student is the transition process. When you were doing a class project, you would be pretty sure that the project was feasible, and the knowledge needed to complete it was mostly discussed already in the class. Now, you will be swimming in the ocean of human knowledge and trying to solve open-ended problems. Do you have a sense of direction? Can you find the right tools for the right task? More experienced people will be there to help you, but you need to be venturous, diligent, and resilient.

The third challenge for a master’s student is the transition process. The projects are getting a bit bigger now. Big enough that a few all-nighters no longer matter (e.g., writing a thesis). You are going to have to learn the slow and steady way towards success. Finding milestones (e.g., finishing a literature review) and base camps (e.g., submitting a paper) become important. Not kicking things that you tend to not like (synonyms of not good at) doing down the road is also important. You have to fight the principle of least effort with willpower!

The fourth challenge for a master’s student is the transition process. You wanted to be surrounded by smart, thoughtful, and knowledge people? Now you get your wish. When you hear people talking about things that are way over your head, what would be your response? Join the discussion, ask them to explain, and let people help you! When you feel ignorant, it’s probably the time you are learning.

A bit of structure for IRL master’s students (an experiment starting summer 2022):

  • 1st 6 months – identify a paper topic and complete the literature review for the paper. Present it to the lab.
  • 2nd 6 months – identify the thesis topic and complete the literature review for the thesis. Present it to the lab.
  • 3rd 6 months – complete and submit a conference paper. Present it to the lab.
  • 4th 6 months – complete and defend the thesis. Work on a second paper if possible.
  • 5th 6 months – fall back if needed.

A few heuristics for IRL master’s students:

  • Aligning the thesis topic with an ongoing project and IRL’s general research vision makes life simpler.
  • The thesis writing must be an individual effort, but the research is not. Good collaboration makes everyone better off.
  • Converting your written paper(s) into chapters of your thesis makes thesis writing a lot less stressful.

P.S. I wanted to go to grad school since when I was a kid. This was partially because I admired scientists and partially influenced by my uncle. He told me that when I become a grad student, I will get to work in a lab, and only on things I like to do. This turned out to be not entirely true. I did manage to get into a good grad school for my master’s study, and it made a big impact on me. I met many smart people and learned (collectively with them) that none of us can outsmart the rest of us. Each of us has some talent distributed in very different ways. Each of us has some serious flaws distributed in very different ways as well. I guess understanding those was a part of me growing up. After I got my master’s degree, I felt liberated, for two reasons. First, I was no longer so confused about what I could do (unlike when I graduate from college). I felt I could be useful in some ways. Second, I was confident I could always find an engineering job with a salary that can keep me alive, but I didn’t have to. This gave me the freedom and the power to try things that may not pay back. I became a life tourist.

An NSF REU Proposal on Human-Swarm Interaction

by Yu Gu, posted in Research, Robotics, Students, Uncategorized

I am sharing a REU (Research Experience for Undergraduates) proposal. When I first started writing this proposal, along with my colleagues Dr. Gross and Dr. Klink, we had no idea where to start. I searched the internet and found a couple sample REU proposals. Several friends also kindly shared their successful proposals. Now, it’s my turn to give back!

The project is about human-swarm interaction: how can one person effectively manage a 50-robot swarm in achieving a high-level goal? In the project, the undergraduate students need to design the robots, the test environment, a simulator, human-machine interfaces, along with swarm control algorithms. They would also perform a variety of experiments to demonstrate these capabilities.

During the first year (2019) program, eight undergraduate students from around the country worked together with several WVU students for 10 weeks. They designed and built 50 robots (!) and developed the basic swarm control software. Here is a video:

Here is a paper written by the students. One major difference between our REU Site and most other REU programs is that all students were working together on a same project. They had to work as a team (I called them a swarm…) to achieve the overall project goal.

This year (2020), however, we had to cancel the program due to COVID. We will be back in motion again next year!

The proposal received a C (Competitive) from the NSF review panel. The individual reviewer ratings were VG/G, VG, VG, VG/G (VG – very good, G – good). In general, the reviewers were excited about the project research ideas, but had concerns about our detailed program designs. Of course, we were very inexperienced in this area at the time.

Attachment: REU Proposal

The CV that Gets You the Faculty Interviews

by Yu Gu, posted in Faculty, Job, Robotics, Students, Uncategorized

If you are a Ph.D. student or a post-doc, you probably have thought about the prospect of becoming a professor at some point, even though this option may not be high on your list. If you only thought about getting a faculty position shortly before applying, you probably won’t get an interview; because it takes planning and time to build up a CV that is competitive for the faculty job market. When is the best time to start planning your academic CV? During your undergraduate years. Ok, if you missed that, and I don’t think I know anyone who didn’t, the earlier the better. I only started caring about my CV three years after my Ph.D., and I had to pay for that …

A strong CV is simply the most important part of your faculty job application package, and sometimes, it may be the only thing that a search committee member reads.

Sun Tzu once said: “If you know the enemy and know yourself, you need not fear the result of a hundred battles.”

There would probably be more than 100 battles for a faculty-job applicant. Let’s first think about your opponents: the search committee members. We are talking about a bunch of professors who may or may not be in your field. They have all failed numerous times in the past but those memories have faded. They are now trying to get through busy daily schedules and fulfill the service duty with the minimum time/energy costs. Reading tens, if not hundreds, of applications is just not that fun for them anymore. To prevent these people from making arbitrary decisions there is likely some kind of forms that the search committee members must fill. These are probably pre-determined performance metrics that evaluate your record or potential on teaching, research, and service. What does all this mean? It means your CV needs to be simple (easy to find relevant information) and well rounded. You shouldn’t be lagging your competitors in any of the major categories (e.g., publication, teaching, funding, and service). An easy way to find out the going credentials is to check out the CVs of newly hired faculty members in your field; they are not hard to find on the internet. Scoring a zero in any of these categories is not going to look good for you.

You might wonder how would you get teaching and funding experiences when you are still a graduate student? You can if you try hard enough. For example, you can offer to teach or co-teach a class; you can submit fellowship proposals; and you can attend teaching and grant writing workshops. A reasonable search committee would consider your available opportunities as a student, but you must demonstrate that you are proactive and have the potential. This would also help you later during the interview process.

Would this be enough? Not yet. Having all the boxes checked may get you a decent first impression. It may also take away some of the arguments for someone on the committee to strongly against you. What you really need now is a few strong supporters on the committee. In other words, you must impress them.

What makes your CV look impressive? Here is my rule of thumb: the most impressive things are the ones many people have tried but could not get. If you have done something unique but no one on the search committee has thought about trying, then sadly, it’s likely to go unnoticed. What are some examples of impressive things? publications in highly selective journals/conferences, best paper awards, prestigious fellowships/grants, a new theory, solving a known hard problem, …. Of course, you need to be good, work hard, and have abundance of luck, to get even one of these. Many people focus energy on achieving the seemingly least uncertain goal: increase the number of high-quality publications. However, if you have special talents, you may find a different path to success.

I want to quickly bring up the discussion of quality vs quantity here. I have seen CVs with over 100 papers not being appreciated by the search committee. A CV with just 1-2 good papers (unless they are on Nature or Science…) may also not be convincing enough. What if this person was just lucky or was working in a very small niche area? In robotics, for a freshly graduated Ph.D., I think 2-3 first-authored top journal papers and 2-3 first-authored top conference papers, plus a similar number of non-first-authored high-quality papers would make your CV look quite impressive. It’s certainly not easily achievable within the Ph.D. period. Adding more low-quality papers to the mix would only serve to reduce the perceived quality of your CV.

OK, say you have gotten a decent impression for your application package and the committee will meet next Wednesday to decide on a list of candidates to be interviewed. By the time the committee meets, the contents in many applications may have gotten mixed up in their heads, because they all look so much alike… The only chance you have is if you got something stands out, pointed out by one of your advocates on the committee. Other members would quickly flip to that page on your CV. If they also agree and cannot remember anything bad to say about you, then congratulations, most likely you would get an interview.

So, let me summarize it: create a CV that maximize the “expected” number of supporters (e.g., having something impressive) and minimize the number of naysayers (e.g., being well-rounded).

How to get there? This is the part about “knowing yourself.” Discover your true passions and strengths and come up with a plan for yourself. Well, …, that’s easy to say than done. Without known better, one way that worked well for me was to start by making myself look bad: creating the Google Scholar profile and making it public; posting the CV online; asking myself awkward questions like what are my top 3 contributions to the field? I found things usually get better after I learned to not avoid myself.

Attachment: my CV back in 2008, over 3 years after my Ph.D., which, unsurprisingly, did not get me anywhere.

Presenting Your “Whole Package” During Faculty Interviews

by Yu Gu, posted in Job, Students, Uncategorized

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.

Seventeen Mistakes that You Can Make in Writing Your First Paper

by Yu Gu, posted in Robotics, Students

Writing the first technical paper is hard. There are so many things that you want to write, and there are only a few pages that you are allowed to use. Once everything is put together, the manuscript does not read like any other paper that you have read. What went wrong?

Helping someone to write his/her first paper is also hard. I had my fair share of struggle with first time writers, whether English was their first language or not. The good news is that paper writing is not rocket science, everyone would eventually get it, sooner or later. To make this process a little less painful, mostly on my side :O), here is a checklist for you to use to avoid some commonly made mistakes:

  1. Not following the standard technical paper structure. There is no real need to innovate on the structure of the paper. Typically, we follow this order for a robotics paper: introduction/related work/contribution statement, problem statement, algorithm, experimental setup, results/discussion, conclusion and future work. The “related work” can be presented in the introduction section, as a separate section after the introduction, or near the end of the paper (seems to be a new fashion);
  2. Not paying attention to logic. Make sure to carefully design the paper before start writing. Every paper needs to have its overall purpose, theme, and flow. Every paragraph or sentence also needs to be logically connected to the ones before and after it;
  3. Not giving a clear and compelling argument at the very beginning of the paper.  Clearly identifying the objective in the first few sentences would reduce guesswork for the reviewers and channel their thinking to be aligned to what you want them to think. Pointing out the research gap and the potential impact would allow reviewers better appreciate the presented work;
  4. Not clearly framing the work in the literature. The main purpose behind the literature review section are: 1) to find the gaps/needs in the previous works to help identify the contribution of this paper; 2) to show that this work is built upon understanding of the history and the state-of-the-art (instead of coming out of thin air);
  5. Not clearly pointing out the innovation/contributions. Reviewers would be looking for this to make a decision, so try to help them out!
  6. Mixing problems with solutions in the problem statement section. This can have two negative effects: 1) it causes confusion and makes it harder to present the actual solution later; 2) it reduces the possible solutions to the problem and limits the imagination of the reviewer while reading the paper;
  7. Giving away the answer too early/easily. This is related to the previous point. Most of the time, if you present a solution right after stating the problem, people would trivialize it. This is like a movie spoiler. Present the problem, point out the challenges, let the reader fully appreciate the magnitude of the problem, then enlighten them. Writing a paper needs some serious storytelling skill!
  8. Not detailed enough so that the reader can reproduce the research or/and not concise enough so that the reader won’t get bored;
  9. The reasoning/argument is not bulletproof. Every statement needs to be backed up by facts and sound logic;
  10. Not providing proper evaluation of the results (e.g., metrics, statistics) and not providing confidence that this is a reproducible result. Spend some time to learn how to design experiments properly;
  11. Not providing insightful discussions of the results. Don’t just state the obvious!
  12. Not properly recognizing the limitations of the research. Every work has limitations. Typically this can be discussed in the assumptions (in the problem statement section) and in the future work section at the end;
  13. Not providing enough diagrams in the manuscript. A picture sometimes worth a thousand words. Try to have at least one figure, diagram, or table in each page. Attaching a cool video with the paper would also be important (especially for a robotics paper);
  14. Not fully describing every symbol used in equations;
  15. Not following the required format specified by the publisher;
  16. Having typos, grammatical errors, and formatting inconsistency;
  17. Forgetting to acknowledge sponsors, donors, other non-coauthor contributors.

Note that one of the most important parts of any form of communication is to understand the audience. For the papers, the reviewers are your peers but not necessarily very familiar with the specific problem that you are addressing. Their job is to make a binary classification (often) using the shortest amount of time (sort of like speed dating). Our job is to fight a series of cognitive biases of the reviewers. For example, it’s not uncommon for a reviewer to make a quick judgement after reading only the first few paragraphs (decisions based on incomplete information and heuristics). Once this judgement if formed, it’s difficult to change it. This is because they would be (subconsciously) looking for evidence to reinforce what they believed (confirmation bias). Now you see why the first sentence, the first paragraph, and the first page are so crucial to the success of a paper. Another human bias (availability heuristic) is that people value the most recent information more. This means you need to remind the reviewers all the good stuff about your paper at the end of it…

Well, so far we only talked about the organization aspect of paper writing. How to make sure what you published is not junk (instead, something that would make a technical impact to the community)? That’s another important topic, which we shall discuss at another time.

Looking for Talented and Motivated Students to Join IRL

by Yu Gu, posted in Job, Robotics, Students

My research lab is called the Interactive Robotics Laboratory (IRL). We are a group of creatively minded people that includes 10 graduate students, about a dozen undergraduates, and of course, me. Although we were only founded since 2012, IRL has made its name known in the robotics community. Our coming out party was the winning of NASA’s Sample Return Robot Centennial Challenge (total prize of $855,000). We were also the first group that developed precision autonomous pollination robot (just in case we won’t have enough bees in the future!). We are currently working closely with NASA’s Jet Propulsion Lab (JPL) in improving the autonomy of future Mars rovers (with seven students working for 10-weeks each at JPL).

We are also interested in the interactions among multiple robots. We are currently working on a cooperative UAV and UGV group in exploring underground tunnels. One of our bio-inspired ideas was recently selected by the highly prestigious NASA NIAC program. We are planning to send 100,000 micro ballooning spider probes into Mars’s global dust storm. With the funding form NSF, we are also working on human-swarm interaction: how can one human operator influence the global emergent behavior of a 50-robot swarm without directly controlling individual robots?

Our creative and futuristic work has drawn frequent media attentions. Our research was featured in over 65 news stories by media outlets such as the Discovery Channel, Wired, NASA 360, ABC News, Time Warner Cable, Associate Press, Aviation Week, and Air & Space Smithsonian Magazine.

Our success was building upon IRL member’s creativity, hard work, and ambitions. IRL provides a free thinking and collaborative environment that allow everyone to reach his/her full potential. We are also blessed for having the state-of-the-art facilities with more robots than human group members. At IRL, students are encouraged to develop their own research ideas, supported by resource provided internally and from external sponsors.

We are always looking for talented and motivated students to join us. Email me (yu.gu@mail.wvu.edu) if you are interested. As a diverse group, we are not looking for people of any particular background. For example, practical engineers and abstract thinkers are both appreciated in our group. Your GPA and GRE are also not as important as demonstrated ability to innovate and the obsessions towards creation (e.g.,  success in a related hobby). So make a case for yourself before emailing me.

Ph.D. Positions
We are always looking to fill 1-2 Ph.D. positions each year. These positions are fully funded (with tuition waived) by research projects or fellowships. To qualify, you should have excellent verbal and writing skills in English, should be motivated and capable of creatively working in a team environment. You can be enrolled in either the Department of Mechanical and Aerospace Engineering (for a ME or AE degree) or Lane Department of Computer Science and Electrical Engineering (for a CS, CpE, or EE degree). Please read the IRL guide for Ph.D. Study before applying.

M.S. Positions
IRL in general does not provide research assistantships to M.S. Students. If you are interested in pursuing a M.S. degree at IRL, you need to prepare your own funding or obtain an assistantship from the departments or WVU. IRL can assist perspective students with an outstanding credential to apply for Teaching Assistantships or Fellowships.

Undergraduate Research Positions
If you are currently a WVU undergraduate student who is interested in gaining research experience in robotics, you can participate in many different ways. For example, you can pursue a thesis or research credits at IRL; you can perform a senior deign project at IRL, or you can work as either a volunteer or hourly worker (limited positions available). Please contact me for details.

If you are from outside of WVU, please consider applying to our NSF REU program on human-swarm interaction.

Exchange/Visiting Positions

Please contact me if you are interested in visiting IRL. Generally, you would need to prepare your own funding support for these activities.

A Survival Guide for Ph.D. Study at IRL

by Yu Gu, posted in Students

I am writing this post to help perspective/new Ph.D. students to understand the culture and dynamics at the Interactive Robotics Laboratory (IRL), and to help existing Ph.D. students to evaluate their progress toward the completion of their degrees.

A. You May be Boarding the Wrong Bus

Getting a Ph.D. degree is an important decision for a student. I really don’t think everyone is suitable for a Ph.D. The first thing I tend to do when a student walked in my office talking about getting a Ph.D. is to try to talk them out of it. It’s not because I think they are intellectually incapable, rather I feel there are many other options in life then getting a Ph.D.  In particular, I would strongly discourage you from pursing a Ph.D. degree if the main motivations are:

  • to get a higher paid job – you could find a good job easier without a Ph.D. (getting a M.S.is probably the best bang for the buck) and would most likely be financially more sound to start working several years ahead;
  • to stay in school because you are not sure what else to do – go get a real job! Quite often, spending 16-18 years in school creates the momentum to spend 5 more years in school. This is just not good reasoning!
  • to have a title of ‘Dr.’ associated with your name – nothing is cool anymore after you own it.

For the rest of us, who are curious about exploring the unknowns, Ph.D. study can be a rewarding experience. Think about this: you get paid (and tuition waived) to be educated, to work on cool projects, to play with fancy toys, and to talk with other intelligent people on a daily basis! Is there a catch? Of course! but we will talk about it later…

B. Where You Sit on the Bus also Matters

So what should you expect by the time of graduation? That depends on what you want to do afterwards. There are just not that many places hiring Ph.Ds. and your choices are pretty much limited to academia, government, and industry, where each requires a slightly different skill set. Clearly you are unlikely to be able to teach well if you never taught, or run a large project if you never managed a team before. So make up your mind early and talk to me about what you want so you can receive a customized training experience.

Overall, before you graduate, you should have:

  1. the ability to do independent research. You should have accumulated enough knowledge of your field of interest to be able to identify new research directions on your own;
  2. the ability to effectively convey your ideas and findings both verbally and in writing;
  3. a bag of relevant skills that are tailored for your intended career;
  4. a Curriculum Vitae (CV) that is strong enough for getting your dream job.

If you are short on any one of these, you may not want to graduate yet…

C. Action Items

To achieve these objectives, this is what I expect you to be doing during the next a few years:

  • Read. Reading is the most important way for you to catch up with the fast evolving field. I will provide the initial papers, but you need to find a lot more on your own! Google Scholar is a great place to start. Exploring the reference section of a paper and who cited it often brings you more papers to read. The more your read, the more you will feel the need to read more (a rare case that positive feedback is actually a good thing);
  • Think. Independent thinking is what makes you a scholar. Standing on the shoulders of giants (after reading their papers), we should be able to think just a little bit further (or different);
  • Build. As engineers we create things with our hands. Working with physical systems is very challenging, but is also rewarding to see the stuff you built works. It also inspires the creation of new ideas. You will learn the problem solving skills through solving real world problems;
  • Talk. This includes ‘asking’ if you have a question; ‘discussion’ if you want other people’s opinions or to bounce ideas around; ‘presenting’ of your problems, solutions, results, and conclusions; and ‘teaching’ other team members with what you know;
  • Write. We should not keep the best only to ourselves. I expect each Ph.D. student to present at least one conference paper per year and have a minimum of two journal papers accepted for publication before dissertation defense. Write your first paper early, even if you are not completely ready and the paper may likely to be rejected. It takes some time to get into the game;
  • Lead. I want every one of you to be a leader at IRL because you will be the future leaders wherever you will be. Leading to me means taking a step forward when confronted with challenges, taking on responsibilities when others are hesitating, and be a source of inspiration to others.
D. Three Stages

In the next a few years, you would likely to experience three stages:

During the first year, you will be taking most of the required courses. You will also be assigned with specific tasks. Some tasks will be for training purposes, some will be related to research projects, and others will be related to housekeeping. You are expected to be integrated into the research group quickly (just shadow someone to get started). It is always a good idea to ask around and learn something from everyone.

From the second year on, you are expected to grow your own research independence. The tasks that you will be assigned will be at a higher level, without obvious answers. You are expected to read, think, come up with, and test your own ideas. You will also start to play leadership roles in different projects.

From the third year on, I expect you to have a good understanding of the research field, be able to identify gaps in the state-of-the-art, and be able to provide your own contributions. After banging your head against walls in different directions for a few years you will find a part of the wall that might be weakest. This would be the time to write a research proposal, so that you can continue to bang your head against that part of the wall until it goes through. At this point, you will know a lot more in your specific research area than I do. You are also expected to help mentor and manage the activities of your junior colleagues.

In general, you should expect to graduate in 3-5 years if you already have a Master’s degree. Direct-track students can expect one additional year (4-6 years).

E. The Responsibilities

We are working as a group. Our long-term survival and reputation depends on many factors. During your Ph.D. study, I expect you to:

  1. maintain a high level of motivation and academic integrity;
  2. efficiently manage your time and resources;
  3. keep a positive, open, and curious mind;
  4. be systematic and meticulous in doing research;
  5. be responsible and take ownership of your work;
  6. be persistent and not discouraged by failures;
  7. be professional and respectful;
  8. be a good citizen, team player, and be willing to help others;
  9. keep a clean and safe lab environment.

As your research advisor, you can count on me to:

  1. provide inspirations and general research directions;
  2. identify and respect your interest, strength, and limitations;
  3. work with you to identify interesting, feasible, and clearly-defined research topics;
  4. locate resources for conducting the research;
  5. monitor progresses, perform quality control, and provide feedback in a timely manner;
  6. learn, self-improve, and keep an open mind;
  7. provide support for scholarship, fellowship, and job applications;
  8. provide career advice and other support;
  9. host a yearly picnic.
F. Things to Avoid

You should not treat graduate school as a 9 to 5 job. You will need to spend as much time and effort needed to train yourself and to get the research going.

You should not be bothered by seeing other students getting away with an easy graduation. If they got a degree without received proper training, they will simply not be able to compete with hard working students graduating around the world each year.

G. Other Random Advices
  1. Failure. You may actually learn more from a failed attempt than successful ones, as long as you ask the right questions. Some people see failures as defeats, others see them as challenges. It is simply a matter of perspective. I often felt having a productive year after received my 10th rejection letter of the year.
  2. Pressure. The ability to handle pressure and stress will be an important part of your life, especially after your Ph.D. This includes two parts: be effective and positive when the pressure is high (i.e., don’t collapse under pressure!); be productive and self-motivated when the pressure is low (i.e., don’t collapse under no-pressure!);
  3. Ashamed. Don’t feel ashamed if you think that you are a Ph.D. student but have no idea about (serial port, ROS, Kalman filter, replace with any technical term). Feel free to ask. People will not laugh at you; Ok, maybe they will, but not for long.
  4. Confidence. Self-coubting is a human nature. You have no idea how many times I have doubted about myself (and am still doing it). Feel free to question yourself, but don’t let it bother you;
  5. Science. If you are not interested in following general science developments, you are unlikely to be very creative with your own research;
  6. Travel. The period of graduate study is the best time to see the world. Although you probably don’t have too much money, you also probably don’t have too many other things to worry about, such as children. I will give you extra vocation time if you present me with a good travel plan. Tip: presenting papers in conferences is another way of getting free trips!
  7. Health. Go out and play whenever you have free time! Last time I checked, Morgantown is still in the mountains!
H. Conclusion

Ph.D. study at IRL is demanding (yes), fun (should be), and rewarding (absolutely!)