How to maximise your extracurriculars and time

Everyone likes extracurricular activities.  They are a big part of college life, and help to properly round out your education.  However, they are often large time commitments.  Having been part of way too many programs at one time myself, I thought I might offer some of the strategies I used in order to pull it all off.  But first, a brief glimpse into my life spring quarter, 2013.

4:37 am – Wake up, get dressed, jump on my bike and head over to the Recreation Center for practice.

5:15 am – Meet up with my teammates and get into a van for the ride up to Lake Cachuma, about a 30 minute drive up into the Santa Ynez mountains.

6:20 am – Out on the water for another gruelling practice; today is 5000m of the great Cachuma pairs race

9:00 am – Finally back on campus, with just enough time to run to quantum mechanics before the lecture starts.

10:00 am – Back to the erg (indoor rowing machine) room for second practice.

12:00 pm – Home; time for lunch, a quick shower, and then it’s off to class / teaching lab / research lab

6:00 pm – Finish up the last few experiments for the day, and bike back home again, getting started on dinner and homework as soon as I get back.

7:00 pm – The resident I work with at the hospital lets me know that tonight is going to be a great shadowing night, so I head over to the local hospital to meet up with him.

8:00 pm – Cases start rolling in and I follow my resident around the hospital, trying to take note of the procedures he is carrying out, all whilst listening to an impromptu lecture by an x-ray tech on the correct way to read an x-ray scan.  As the night continues on, my resident and I go from room to room, constantly checking up on patients and occasionally heading over to the OR to observe a procedure.

1:00 am – The hospital is starting to quiet down a little.  I look at my watch and realise that it’s already 1am, and after quickly thanking the resident for an extremely informative evening, I head back home.

1:30 am – and back to bed.


So, with days  stretching into the 20+ hour mark, how is it possible to actually remain functional?  Well for one, you don’t have to sleep 8 hours every night.  After getting used to it, it is very possible to get by on around 5 hours a night, just take lots of naps!  A short, 20 minute nap every time you find yourself getting too tried to focus is enough to get through the next 90 minutes of work or study.

Another key to maximising your schedule is getting things done earlier in the day, rather than later.  By starting at 4:30 in the morning, I was able to squeeze a large chunk of my day into the hours before 9am, when class typically starts.  Because of this, I can fill the hours 9-5 with class, work, and research, while only have to fit  another 2 hours of practice in wherever I have time.

This idea builds into another important component of maximising your time: scheduling.  If you want to do more than you ever though possible in a day, you really have to schedule your time well.  Sit down and write out a loose schedule for the week on sundays, then as the week goes on adjust and update that schedule with more concrete details, to make sure that you don’t fall behind and use all the time that you have effectively.  But make sure to allow for some room for error, and be realistic with what you think you REALLY can do in an allotted time slot.  Start small, and just build up as you get more comfortable with an intense daily routine.  Soon enough you will know the limits of what is possible to pull off in a single quarter, and hopefully be better able to exceed them in the next quarter.

So if you’ve ever thought to yourself, “I wish I had time to do ____”, you definitely can!  With enough planning and commitment, it is possible to do just about everything that you would want to in college.  And when going to a school like UCSB, there is so much to do here that it would be a shame not to take advantage of some of the really fun opportunities UCSB offers outside of the classroom.

Optimizing lab experiences

I recently participated in an undergraduate research panel discussion for a Biology course. A group of pre-bio majors interested in research asked us questions on our experiences of applying to labs, contacting professors, and project progress. The discussion was interesting and would be beneficial to those planning on trying such lab experience. So, here is my advice on the path to and excitement of undergraduate research:

1. Time commitment: Initially starting in the lab requires lots of time and effort. You learn two broad categories of new ideas – the project concept, and experimental techniques, – which can take weeks or commonly even months to understand comfortably.

2. You will make mistakes! Unlike intro course labs, which are time tested classic experiments and graded of a scale of “0 – perfection”, lab research experiments are not meant to be flawless. Especially in the beginning, when you start learning new techniques and experimental methods, you will make mistakes. Many will be small, errors (that should decline rapidly over time), like miscalculations in newly introduced equations, or incorrectly assembling a gel apparatus; and some will be fundamental errors in understanding the concept. Although some of these mistakes are easily avoidable, they are part of the process of learning and experimentation. This is also an opportunity to learn from your mentor who has experience with the procedures and concepts at a very high level. Over time, you will reduce these small mistakes, and will feel confident using the instruments, conducting procedures, and even troubleshooting problems!

3. Go to lab meeting – Each week, if my schedule permits, I look forward to Tuesdays when we have lab meeting. The meetings are stimulating discussions between grad students, post docs, faculty and undergrads, in an informal and intimate environment. The group meeting allows me to understand different lab-members’ projects, start thinking more deeply about various mechanisms and pathways, and to see effective ways that data is presented.

4. Be friendly – The labs especially at r1 universities like ours are composed of intelligent, talented and accomplished experts in their fields. They have interesting paths, stories, projects, and can give meaningful advice as you start looking at your future. Don’t feel intimidated to make friends with others around your bench, as these grad students, post docs and professors all share a passion for science just like you.

From my lab opportunity, I have gained one of the most fulfilling and stimulating experiences in my undergraduate education. And as I continue working on my project, I hope to keep the above four ideas in mind, just as I hope you do, if starting in a lab.

Learning a New Language – Computer Code!

Hey everyone!  Luckily my research has finally progressed past the experimental design phase and w are now in the data analysis phase! (WoooHoooo!) However, this phase of research comes with many of its own challenges.  To begin with, depending on your project type, there may be alot of data to handle, and there may not be a program already in existence that can analyse your data in the appropriate ways.  This was the case with my project, with most experiments having about 3200 data points each!  This meant that I had to learn a new language in order to properly design and write a program that would handle this large amount of data.  Even though this was a daunting task, it was all possible through one of the greatest tools of this age: the internet!

So, how do you go about going from a total novice at computer code to writing your own program in as short a time as possible?

Well the first thing to do is to write out by hand exactly what you want your program to do.  For example, if your experiment results in a data file, the first thing you will want your program to do is pull out the relevant data to be analysed from the output file.  Now that you have your data pulled into the program or stored in a file your program can access, what do you do with it?  This portion of the programming is highly dependent on your specific requirements, but for the purpose of example lets just say that your data files contain several position measurements you want to create a histogram of how frequently  these position measurements fall into certain bounds.

The first step to doing this is to come up with a very small sample of your data and do the calculations by hand or with the help of a program such as excel.  For our histogram, we would want to count the number of cells with values in a certain range, tally the results, and then plot them on a bar graph.  This step is crucial to writing a program that both works, and works as your expect (a big distinction).

Now it’s almost time to start coding! But before you can do that you have to decide what type of program to use.  This would be a good time to talk to your research mentor, and to see what types of coding languages they know, and what software the lab has available to write your code with.  In my case, I chose to use Wolfram’s Mathematica to write my program.  This particular piece of software isn’t strictly a platform to write programs, but is more based in a data analysis platform that accepts commands as code, which makes it easy enough to use by versatile enough to perform the tasks I wanted.

So, you’ve got your data files, a written out example of what needs to be done to the data for proper analysis, and software in which to write your program.  Now what?  This is probably the most difficult step, as staring a blank page with the hopes of filling it with correct code is quite daunting.  But this is where you written out plan comes in!  Looking back at our example, the first thing we needed to do was import the data.  Assuming that no one we know already knows how to do this, a simple google search for “importing data <coding language or software here>” will most likely yield a result.  Once you figure out the correct function to import your data, it is important to run your program and make sure that nothing strange is happening to your data during the import process.  This somewhat tedious process of checking each step of your program as you write is may seem annoying, but it is much better than writing a 200 line program and finding an error back in like 5 that requires substantial reworking of all the subsequent steps.  Keeping this check as you go philosophy in mind, continue searching the internet and the software documentation for ways to make the program do what your plan requires, and with enough time and effort a custom made analysis program will come out of it!

Of course, we’re not done yet.  Now that you program works for one data file, it is time to make sure it works for all of them.  This is where designing your experiments beforehand so that the data files come out in the same format every time comes in handy.  If all your files are in the same format, you should be able to feed in each file and run the program.  If they are not all similar, you might either have to adjust your program so that it can handle these differences (which might be impossible depending on how different each file is) or to slightly tinker with your program each time you feed in a new data file.  A good way of doing the latter option is to place lines of code at the beginning of your program that define variables for the parts of your data files that are different.  For example, if your code has a line that says “while column number is <5, perform function x and ++1 to the column number” and you have data files with different numbers of columns, change the 5 in the line of code to a variable, which you can define in the first few lines of code and then edit later one without having to sift through your functions to find the correct line to change.

Hopefully this information will help someone who, like me, is trying to write a program without knowing any computer languages beforehand.

Best of luck, and happy debugging!

Equipment failure – not always a bad thing!

The research I am currently working on recently took a very interesting turn due to a unexpected turn of events. During the summer, my #1 piece of equipment broke down.  Unfortunately this means that my main project is at a standstill until the can be repaired.  However, this rather sad turn of events meant that I was able to switch projects for the time being and work more closely with my graduate student mentor on some new materials science. The focus of the research at the moment is to make smart jello – but out of DNA.  The most exciting part of this research, besides its promising applications as a drug delivery method, is that this project requires learning a whole new set of techniques and procedures that are new to both my mentor and I.

During fall quarter my mentor and I spent two weeks figuring out how to correctly ‘cook’ our pieces of DNA in order to get them to form into gel structures, and at least a month testing different ways in which to observe and test the gels.  Several failed ideas and broken devices later, we eventually found the best way with which to observe our gels.  While this was somewhat frustrating, it was a very rewarding experience, as I was able to participate quite a lot in the design and troubleshooting process.  Finally solving our testing problem also meant that we were one step closer to being able to start collecting a lot of data on our gels, which was a relief given that we were unable to produce any really great data (aside from a lot of preliminary results) until we found the right way to test the system.

The trials still haven’t ended yet though.  The methods my graduate student and I came up with to test our gels are still very difficult, and we have a lot of practice to do before we truly master them.  Even though the failure of a critical piece of equipment can be crushing, it is not always such a bad thing.  Though all the work I had completed during the summer had to be put on hold right when it was at the most exciting point, there was another great opportunity to learn about a new system available and I’m very happy that I had the chance to take part in this research, and look forward to continuing work on the gels until the equipment is fixed.

Balancing Research with a very Busy Senior Year

Summer is awesome. The weather is great, no classes or tests to worry about, and all week to focus on research!  Alas, Fall quarter is not quite so carefree.  With classes starting up again, it is difficult to find the time to balance undergraduate research and coursework, along with other extracurriculars.  For me, this quarter has been a particularly challenging one.  With 19 units of coursework, research, graduate/medical school admissions, and fall rowing season all at the same time it was difficult to find just a few minutes a day to relax.  But over the course of the quarter I found that there are many ways to manage all of these activities without compromising the quality of your work.  Here’s a couple of things I found useful when trying to manage a very busy quarter:

  • First, you have to plan ahead.  At the beginning of the quarter, try to print out a copy of your schedule and fill it in with where you plan to do all of your extracurricular activities.  Better yet, try to do this prior to setting your schedule for the following quarter.  A great online tool to do this for UCSB students is, which pulls data from GOLD and generates all the possible combinations of schedules you could have based on the classes you want to take.
  • Be Flexible.  Often, even though your schedule may seem to be set in stone, there will be times when you need to make changes.  Be it a medical emergency, a club event that requires travel, a research conference, or even a special experiment that requires uninterrupted attention: be prepared to make changes and don’t freak out about it!  The more time you spend worrying about missing a certain section of your schedule, the less time you have to make up for it.  There is always time to get things done, you just have to look for it in interesting places.
  • Wake up early.  During my freshman year, many of my dorm-mates were most excited about not having to get up early to make it to school by 8am.  While this is a dream come true for almost everyone, it also saps a lot of your extra study time.  During my time at UCSB I’ve tried both pulling the classic college all nighter and just trying to get up early in the morning, and the latter method wins every time.
  • Make time for yourself.  With all the stress of trying to balance all your schoolwork and extracurricular activities, it is important to make a little time for yourself to spend not thinking about school or work and just relax.  This should hopefully keep you from stressing out too much during the week, which when weighed against the time you spent doing nothing school related will actually work out in your favor.

Hopefully some of these tips might help out anyone having a tough time trying to balance their workload this quarter!