Episode 4 features Bill Bauer, the Education and Diversity Director at Hauptman Woodward Medical Research Institute located in Buffalo, NY. Bill talks about advice for networking and starting a data analysis career in the biomedical field.
(upbeat music) [Heather] This is Buffalo State Data Talk. The podcast where we introduce
you to how data is used and explore careers that involve data. Hello, and welcome back to another episode
of Buffalo State Data Talk. I am your host, Heather Campbell, and we appreciate you
joining us for episode four. Today, we will be talking to Bill Bauer, the Education and Diversity Director
at BioXFEL NSF Science
and Technology Center at Hauptman Woodward Institute Biomedical research organization, located in Buffalo, New York. Thanks for joining us today, Bill.
Why don't you start us off by
telling us about in general, what kind of work Hauptman
Woodward Institute does? - [Bill] So how Hauptman Woodward
Medical Research Institute or HWI is a nonprofit organization that's focused on foundational research.
And this means that we're most
interested in figuring out how things work at the
very fundamental level at the atomic scale. Our specialty is in structural biology so this means that we study
structures of biologic molecules
like DNA or proteins. And these are really
important molecules because they are the molecular
machines of our bodies that keep everything
running and keep us alive. And are usually involved
as some type of disease
so if you're interested in a disease, it's usually caused by a
malfunction of a protein or some type. So for better able to understand
how these proteins work, we can have a better idea of how to treat
them specific scenes
as they're involved in. - [Heather] So specifically you're part of the
BioXFEL Technology Center. Can you talk about what this stands for and what work is specifically
done by that center?
- [Bill] Yeah. So BioXFEL stands for Biology with X-ray Free Electron Lasers and is actually a science
and technology center that's funded by the
National Science Foundation and managed by HWI in Buffalo.
We often use x-rays to study
these very small proteins and an x-ray Free Electron Laser or XFEL is a very intense, very
fast type of x-ray. In fact these are now the
brightest x-ray sources in the world.
And it's the center's
responsibility to generate new technologies and methods that will enable
researchers around the world to take advantage of
these new types of x-rays and then use them for their own research.
- [Heather] So I'm assuming
this must be kind of like how we figured out the structure of DNA by taking x-ray photos,
is that a similar idea? - [Bill] Yeah it's exactly the same thing only with much more advanced technology.
- [Heather] Thank you so much for telling us about your background. So as the Education
and Diversity Director, what are your main job responsibilities? - [Bill] Right. So I create and manage
all of the center's Education
and Diversity Programs, and we have a lot of them. We have internships, fellowships, workshops, professional
development events and conferences. So I run all of these programs and then
write up the reports on them. - [Heather] So now that
you've talked a bit about your responsibilities and
what the Institute does, could you tell me a bit about
what a typical day or week would look like for you?
- [Bill] Yeah, so it looks
like a lot of emails, particularly now that I've
been working from home so much, I spend a fair amount of my
time checking in on the students and making sure that they're
getting the proper support they need and making
sure that they are still
considering their own career advancement and helping them out
with that aspect of it. - [Heather] So it sounds
like you help with organizing a lot of personal development, but do you get the
chance to spend any time
working on your own personal development? - [Bill] Yeah. Good question. So I do spend most of my time organizing events for other people, but I do occasionally get the chance
to do this for myself too. Most recently I went to a
workshop in diversity training in Buffalo, and I do
participate in workshops typically when they're
a part of a conference. And so I'll sign up for whatever
it looks interesting to me
and do that on the side of
the conference when I want to. - [Heather] So a lot of conferences are known to have specific, you know, happy hours
or time for networking. So do you have any suggestions or tips
for students while networking? Because I know a lot of
students get really nervous when they're thrown
into a networking event and aren't really exactly sure
how to connect with people. - [Bill] Right. That's true.
I do have a lot of tips for them. And so this is something
that we focus a lot on in our internship programs, and it's been really difficult
lately for obvious reasons. We no longer have live events to go to,
but there are still
opportunities to network online. For example, I asked all the insurance
to create a LinkedIn account when they first started
with the internship program and then asked them to
connect with me on there
and then find people in my network that they may be interested in contacting. And I can virtually introduce
them through LinkedIn. It may be more awkward this way, but it may be the best that
we can do at this time.
A lot of networking can
also be done through email or through Twitter, where you can find groups
that have similar interests and find people that may
be willing to help you. But I think the best way
to do this virtually is,
or at least start out and
learn more about this is to watch this video that we
had all of our summer interns watch this summer. And this is a seminar run by Elena Levein and was sponsored by the
American Chemical Society or ACS.
And I would recommend everyone watch this. You can find this video
on the ACS website. It's very useful. - [Heather] We can add the link to this description of the episode.
As a part of your position you work with both undergrads
and graduate students, and you do a lot of
mentoring of these students. So as a student, looking for a mentor what would you recommend
they look for in somebody?
And what kind of questions would you suggest that they're asking? - [Bill] So I have a few
recommendations here, but it depends on where they're looking. So if this is someone
who they already know,
it's probably the best
and easiest scenario. They can email them
directly or call the person and explain what it is that
they're trying to accomplish. And then explain why they
think that this person might be able to help
them achieve their goals.
If they haven't met them before, it could still work out this way, but they may not be as inclined to help if this is a blind email. There are other resources out there
that can help connect people with mentors that have faced similar
challenges as the students. There's something called the National Research
Mentoring Network or NRMN, they have a mechanism to connect people
and then guide them through a step-by-step mentoring process. So if you don't have any
suitable mentor available to you, this is probably the better way to go. - [Heather] That's a great suggestion.
And we can also add that link to the description of the episode. Could you tell me a little
bit about some of the projects that the BioXFEL staff
and students work on and the data analysis that
they do on this research?
- [Bill] Sure, so all of them are working
on a particular protein that they're interested in. Some of them are working
on things like photosystem, which is the protein
complex is responsible for
harvesting light and
turning it into energy. Some of them are working
on something called GPCRs G protein-coupled receptors, which are common drug targets, and regardless of which
target they're working on,
and they're all interested in using the X-ray Free Electron Lasers to study. So most of them are using this to look at the dynamics of the protein. So this means looking at
how they're actually moving
or how they're interacting with substrates with other proteins. And we're able to use this information to construct something like movies, and we can watch these things happen.
Watch the changes happen over time when this particular change is happening. - [Heather] So once they
figure out the structure of one of these molecules, what exactly is it that you
can do once you have that data?
- [Bill] Right. Good question, so it depends on what
you're looking for really. So this could be something like looking for more insights
into how to design a drug to shutdown the protein.
You can be looking at particular
mutations that are known, that are shown to cause issues, but the protein that leads some disease and it's, you know exactly
where that mutation, where that change in the protein is
you'll have more information on how that affects the
function of the protein. Some people just want to know what it looks like so
they can better study it. So everyone that works with a protein
in any field wants to know
what the structure looks like because they're making
more rationally design their own experiments. So they can see where the consignment, where the actual chemistry
is happening inside a protein
and then make mutations to it,
to see what the effects are. - [Heather] So the
people that are analyzing these x-ray data that you have versus the person who's
actually taking the protein and putting it into the machine.
Are they the same
person, different people? Do they have the same background? - [Bill] No. No. So we have really interdisciplinary teams working on these projects.
It takes a lot of people to
run an experiment in an XFEL. So we have typically the people that are preparing the samples
who will have a background in structural biology or
biochemistry, molecular bio. And they had to generate a lot of protein,
like gram quantities of purified protein, which is a lot for
people who wouldn't know, but it would sometimes take
them weeks of working, you know, straight through the night, they work in shifts just to
generate this amount of protein.
And then they take this protein and they grow crystals of it. So maybe a different group
that's working on that. And so we had to crystallize a protein so that when the x-rays headed
it makes it a fraction
pattern (indistinct) weak. Single molecule will attract, but the signal is too weak we can't actually take it. Not yet, we're getting
there. We're making progress.
And so we have a team of
people that work on that part. And now we have a different team of people that work on sample deliveries. So these people will just
work on getting our sample in front of the XFEL.
And we have a lot of people in the center that work on developing
technologies in that area. And they often have physics
or engineering backgrounds. And then we have data analysts who have computer science
and physics backgrounds.
And then there's all
the people who actually work in the national
labs and run the XFEL. And so they have varied backgrounds too, but mainly in physics
and particle physics.
- [Heather] So the people
who are working on the data analysis side of things, did they have any background in biology or were they coming strictly
from Computer Science IT background.
And they had to learn a whole new field coming to work for this center. - [Bill] So everyone has to start in one of those areas, right? It's unusual that they'll be learning
computer science and
biology at the same time. But what we try to do is provide
cross training experiences for all of the people that
are involved so that they can learn the biology while
they're still doing their job as a data analyst.
And it is important
for them to understand, at least at a basic level, how all of these things work because they need to communicate with the rest of their team.
They have to understand what's going on and it can be challenging because there's very different
backgrounds required for biology and for computer science. So I think in the end,
after they go through all of the programs, they have a pretty good
understanding of both sides of it. - [Heather] So it's definitely possible for people with a strong
biology background to kind of move into the
data science data analysis,
and then also people with a
really strong data analysis, coding background to use
that, to work in biology. - [Bill] So if I had to pick one, I would say it's probably easier to start in computer science and data science
and then transition over to biology side. And the reason is, is that I think that's a more
difficult thing to learn. It's more like a different
language that you have to learn. And once you get that down,
you can move into really
any area of science. So if you decide that you like biology, you can learn more about biology and then learn how to apply
your data analysis skills towards a field biology.
The great thing about computer
science and data sciences, is that you can apply this to
virtually any type of field. It doesn't have to be
just computer science, but if you decide you'd
like environmental biology, you can program on
science, you can go there,
you can apply it to material science, you can apply to virtually any field. - [Heather] So you already
touched on this a little bit talking about what background you think students should come from
if they're interested in
the data science side of things, but could you say maybe a
little bit more specifically about what kind of skills or education you think people would need to go into this kind of field,
specifically the kind
of hard or soft skills that people might need to be working in early data-driven field? - [Bill] Right. So I think being proficient at coding
is really the best hard skill
that you can develop. A lot of people I work with
use Python to create algorithms that we use to process data in
real time has it's collected. And if you can do this
really well, like I said, you can apply this to virtually anything.
For soft skills I think being able to communicate
really well on a team is really helpful for what we do and also being flexible and being able to adapt to situations.
That's very often the case that you go into an experiment
thinking you know exactly how are things going to go, and then it goes completely wrong and you have to adapt and its like,
maybe even write new code to deal with the problem
that you're facing. And I think if the people that can do that are really the best people on the team. So I don't know how you learn that,
how you learn to be flexible
and adapt to new situations, but it's definitely
something that's helpful. We work in a really big data field, but also really fast data. And some art centers
kind of unique in this way I think in that we have a typical
experiment and the XFEL we'll collect maybe
one to 2 million images of x-ray diffraction
patterns in a 12 hour period, and then each image or a
frame is about five megabytes.
So that's five to 10
terabytes in raw data, which is typically reduced
to maybe three terabytes after you do your processing. And this is just for a 12 hour shift. People often have multiple shifts too.
So that's a lot of data and but it's also coming in really fast. And so at the XFEL we typically use in California and this
one operates at 120 Hertz. So that's 120 images
per second, potentially.
Like I first said like a human
obviously can't process that. So you have to have algorithms
that are fast enough to deal with it as it comes in and either accept or reject it. It's about to get even
more difficult though.
So the newer XFELs that are coming online like the European XFEL,
they're even faster. And they operate in about
27,000 pulses per second. - [Heather] That's a lot of data. So what kind of technologies
are the people using when
they analyze this data? Are they using Excel? I mean, you mentioned Python, but you mentioned some of the technologies of the programs people use.
- [Bill] So most of the
programs that we use for data reduction and
processing analysis are specific to our field. So many of them have been
created by members of our center. They're not something that
are commercially available.
We have to generate them on our own. And then once we do, we typically will make them
freely available to the public. Three of the interns helped to optimize some of these
programs this summer.
And so they did things like
created better interfaces, improve the coding so that
it would run faster on a GPU or improve the stability and
reliability of the programs. So this was really a big
part of our project is making the XFEL more accessible
is that we have to make the
programs that we use successful. And so we make them as
easy to use as we can and then distribute them
to the rest of the groups. - [Heather] That's
really cool that you guys basically make your own novel technologies
to analyze your data. - [Bill] Its active, this is
all new cutting edge stuff and we're the ones that are
kind of leading the way and a lot of the program development. - [Heather] Could you talk about briefly
a conclusion or a result that
you guys found at the center, or maybe, you know, maybe the
structure then helped to lead to some other discovery
that was really important or really interesting and contributed to the biomedical field.
- [Bill] Right. So much of what we do is linked to some type of
disease or some type of cancer or diabetes and you know, it has to be interesting in
order to get signed across. So that's kind of, it goes not saying
that we're all doing
something that's interesting, but I think of all the
work that's been done, at least at HWI one
that's been most impactful has probably been the work
that was done by our founder, Dr. Herbert Hauptman.
So much so that he received
a Nobel prize in chemistry for his accomplishments. He essentially made
structural biology possible by solving what's known
as x-ray's problem. So when we use x-rays to study molecules,
we can only measure the
intensity of the x-ray wave or the amplitude of the wave we can't see the phase or how the waves lineup is one another. And so he came up with a
wave and using some math,
he's a mathematician
that using equations that allowed us to determine the phase and then solve structures
of small molecules. And he did it all of this
without using a calculator or a computer.
He only had a yellow notepad and a pencil, which was very impressive. - [Heather] Wow. - [Bill] So the work that he did enabled the structural biology
in a very early stage.
And he's also the only person in Buffalo to have a Nobel prize. - [Heather] Speaking of Buffalo, could you tell us a bit about
how you ended up in Buffalo and why you chose to live and work here?
- [Bill] Sure. So I was
born and raised here and so is my wife. And so we've been trying
our best to stay here and raise our kids here
with our families around. We certainly could have
left at some point,
but we really like it here. And we enjoy the changes in seasons, even the winter. So it's been a conscious
decision just to stay in Buffalo, trying to work.
- [Heather] Assuming you've lived here for a number of years. So what could you say potentially
about how you feel about the environment for biomedical research or new companies and
stuff coming in Buffalo?
How has that changed recently? - [Bill] This has really
been changing a lot. So I work on the Buffalo,
Niagara medical campus, and they've been expanding. There's always new companies coming in
are always putting up new buildings. So this is a really great
time to be in Buffalo. This is the area that
you're interested in. Most people aren't aware of
the companies that are here on campus
and so we've been trying to promote this and every time we have workshops, we mention the companies
that are around here. We've been taking students on tours of the different companies
and talked about what they do here. So just trying to make people
aware of the opportunities that exist and make sure that
they have the opportunity to stay in Buffalo if they'd want to. - [Heather] So a lot of our listeners
are younger and are students. So as somebody who's in a leadership role who may have been responsible
for hiring or mentoring new employees or young students, what advice would you give to
somebody who's interested in
working with data and has
just entered this field? - [Bill] So first I would tell them that they've made a good decision. So we are always in need
of new data analysts. And they're very hard
to come by and, you know
we always are looking for people
who are interested in this and good at it. The first thing that they should work on is getting a good foundation
and programming, like I said, and at least one language,
but potentially more.
Most computer science programs will have some type of coding component to it that will include like
artificial intelligence, machine learning or image processing. And all of that is gray area.
The earlier you can get that better, but if your program doesn't
include things like this, I would recommend looking
outside of the program to get experience and
training in these areas. And once you're comfortable with this,
then we can do what we
were talking about before and going on to find
other areas of research that are interesting to you and trying to apply your
skills to that different area. There's a lot of other programs out there.
So Buffalo has one
called the Coder School. So if you're looking for an
introduction to Python coding, this is an actual physical
location you can go to, but they also have remote learning now where you can sign up for workshops
that they'll train you
on how to code in Python and even apply this to things
like Minecraft and Roadblocks and other popular gaming platforms. - [Heather] Yeah. So we can add
the link to the Coder School in the description for this episode.
So finally, before we let you go is there anything else you'd
like our listeners to know that we didn't get a
chance to cover today? - [Bill] I think one thing
I should emphasize is that the best way to get started
on your career is internships.
And it's a great way to
try out different fields and figure out what you like. So this should be a
priority for every student every summer that they have
the opportunity to use. Applications for these are
typically open in the fall
and closing January or February. So you should consider
looking for these soon, and I'm running a bunch of
these internship programs too. So maybe we can link those. - [Heather] Definitely,
we'll add that in the description too. - [Bill] But even if it's not one of mine, there's a lot of online resources that can help you find internship programs that you're interested in.
- [Heather] Great. Well, Bill, thank you so much for joining us today and to all of our listeners if you've not already
checked out previous podcasts available wherever you listen to podcasts
and for more information
about starting your career as a data scientist, go to dataanalytic.Buffalostate.edu and don't forget to subscribe so that you get a notification
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