All right. So the first question for you is
just give us a brief review of who you are
and what you do here at Virginia Tech as well
as some background information such as undergrad and graduate schools
attended. Alright, great. So my name is
Bert Huang. I'm a new faculty member here in
the department of Computer Science. I just joined this
semester. So this is spring 2015, and,
uh, I earned my PhD at Columbia University in
2011. And since then, I've been doing a
post doc at University of Maryland. And before that
, I I did my undergrad at BRANDEIS University.
Awesome. Okay, So just before we get into
the swing of things where your current hobbies and interests
outside of computer science, Sure, so my hobbies
can probably be summarized with music, sports, and
I guess, taking care of my pet. So
I'm a big music fan. I listen to a
lot of different music. I play some musical instruments
, so I started do that completely amateur level stuff
, but, um and, uh, let's see
. And I'm a big sports fan following a lot
of the professional sports. Um, I'm excited to
get to know Virginia Tech football. I know that
. They're that they're That's the team to follow at
the school. Um, and, uh, yeah
, and I spent a lot of my free time
nowadays taking care of a pet parrot I have at
home. My wife and I spent a lot of
time at home taking care of him. All right
, Thank you. So can you just give us
a brief general overview of the research that you're currently
working on here? Sure. So my researches on
machine learning, Um, and specifically, my work
focuses on machine learning in settings where data and models
have complex structure. So this might not sound like
the type of data you might be interested in analyzing
. But the reality is that data that we measure
that would record in computer science that we want to
analyze using machine learning tools or data mining tools.
Um always comes from the real world, and the
real world has a bunch of entities interacting in complex
ways. So when we go to record that data
, that data inherently has some complexities that we have
to consider and that we want to consider and want
to have models that can efficiently reason about. So
my research tries to extend machine learning tools that we
that we have studied for the past few decades to
be able to handle these extra complexities and specifically for
anyone who's interested in maybe the, um, I
guess, the specific jargon within my field. Uh
, so within machine learning research, my work is
roughly characterized by two key words. One is relational
learning and the other was the other is structured prediction
and in relation to learning. The idea is that
we're doing machine learning where the data is described by
relationships between entities, much like you do in a
relational database and in structured prediction. The idea is
that we're doing machine learning for functions that output complex
structure. So you can understand how these characterized the
concept that was describing before. But those are the
key words. If you want to look for,
you know, maybe, uh, research areas that
my papers might appear in. So, looking at
some of your published papers that you've done, it
seems that you focus a bit on MOOCs. What
is your overall opinion on MOOCs and how did you
get into researching them? Sure, So first off
, in my opinion, on books, you know
, as a new university professor, um, as
somebody who I guess was a student not too long
ago, I think MOOCs are a fantastic, um
, development in web technology. And it's one of
the few Web technologies that have come out in the
past few years where the mission, the underlying mission
is inherently altruistic. Educating people is is one of
those things where it's really hard to do that in
a way that's, you know, nefarious or or
or or greedy. I mean, we're just going
to educate and empower people with knowledge with books.
So, uh, it's hard to imagine how those
can get corrupted the way that other, maybe,
uh, marketing platforms could on the Internet. So
So with that in mind, the other piece about
that's important to think about when when you think about
the people's opinions of on MOOCs is that some people
might have been worried when MOOCs started coming out and
becoming really popular, that they might encroach or or
on or damage academia in some way. But that's
not gonna happen. Uh, and and the reality
the reason it's not gonna happen is is just yet
another educational technology that we can use not only for
people on the Internet but for people in universities to
enhance their university experience and their educational experience. Uh
, you know, I see a lot of students
who are taking MOOCs and machine learning to to bolster
their education in the in the classroom, and and
that that really helps students and and helping students is
what we want to do. So it's a really
great technology. So my research in books comes along
with, You know, I really like the motivation
of books like I just described. But when you
think about the type of data that the MCX websites
record from the users, you see people you know
, watching videos and taking online quizzes. But they
also talk to each other. They answer each other's
questions. They ask questions, and they answer questions
. They up vote questions or down vote questions actually
don't know how much people down vote things, but
they up vote questions. Do they indicate that they
are interested in this question? There's a lot of
information there that we can use to ideally guide education
. Um, but that day that information is highly
interconnected. It's exactly exactly the scenario I described before
where the data depends on lots of parts of the
other data. So we need machine learning tools that
can think about how one students, uh, interactions
with the muck website affect another student's interaction with the
website. Um, you know, and you can
you can detect that from how they're communicating on forums
and things like that and my research. One of
the research projects I started working on with more data
was to take recordings of you know what, what
interaction students had with Amaq website and try to figure
out whether the students were engaged in the class.
And and so this is kind of a precursor to
whether they're going to do well in the class.
But it's sort of separate. It's not necessarily the
same thing, and it's not something we can observe
directly. So instead, we needed to consider How
are they interacting with other students, you know?
How often are they watching the videos? Or are
they pausing the videos and and taking some time to
think about things and starting them back up? All
these things are recorded bits of data that we can
use to try to figure out if students are engaged
. And the hope is that if we can identify
when students are engaged or rather identify when they are
losing engagement, maybe we can intervene in some ways
to try to get their attention back or help them
if they need help to help them complete the course
and and get the full educational experience. So I
just leave one more thought. I really love the
idea of doing research in books, because what we
It's an opportunity to use a lot of the tools
that we're building nowadays for analyzing, you know,
Web technologies like social media. Um, but we're
going to be able to use it for reasons other
than things like marketing, which is where most of
the money is nowadays in social media analysis. So
it's really special because we can do some, you
know, Social Media Analytics and help somebody you know
gain some more knowledge. But that, to me
, seems really special. So it also seems that
you've done quite a bit of research on finding patterns
from social media or open source sites on the Web
. This is a really interesting topic. How do
you get started on this? And do you see
a trend of research in this area? Yeah.
So Social Media is a great platform nowadays for for
people to to do research on on analyzing human data
. It's something that we didn't have. I guess
10 years ago or so it wasn't, uh,
wasn't as popular for people to be releasing information of
how they interact with their friends. And you know
what, What, what restaurants they like. And
all these things that people are now posting onto social
media sites that, um that we can now use
to figure out patterns in human behavior and, um
, and do analysis that can hopefully go into making
our lives better. And, uh, the other
thing that that I think is really important to consider
is that social media is just what where things are
now. But it's just one of the up and
coming examples of data where we're collecting more and more
more and more data from the real world about human
behavior. Um, and and you know, things
like the internet of things, the famous internet of
things where you have, uh, data coming in
from all of our appliances in our homes. Our
, you know, our thermostats are refrigerators. Um
, you can imagine you can include things like Apple's
Smart Watch and all the other smartwatches that have been
coming out, Um, people's health monitors. These
things can can be all combined together and combined with
the fact that humans live in this very complex social
world. If we can come up with the right
models to analyze these things, then we can find
ways to make people's lives better. And so yeah
, right now, social media is mostly people posting
things from their cell phones and laptops, and and
that's that's it's already very, very interesting, very
rich data. But to me it's a It's a
great platform, as essentially a test bed for what's
coming later. And what's coming later is is an
even richer landscape of data for which we need machine
learning tools to analyze this data that can handle the
richness, the complexity without incurring too much competition,
old cost or or cognitive cost. Alright, awesome
. Thank you. So the next question is,
do you have any cooler needs? Stories is something
that you've been able to do because of post graduate
or graduate level research that you wouldn't have been able
to do otherwise. Yes, there has been,
uh, the whole experience has been cool, but
there's been a few things that stand out that might
be interesting for people to hear about. So,
um, first off early on during my graduate student
education, I was involved with the collaboration at Columbia
University with the power company for New York City.
So this gave me the opportunity to interact with engineers
who are working directly on the power systems that powered
the city. You know, one of the most
important cities in the world. Um, and what
we were doing was we were we were analyzing their
data from their systems to try to try to build
tools that could help guide engineers in in in scheduling
, repairs or scheduling any sort of planning for for
improvement of the systems. Um, and And we
got to talk directly to the engineers working with,
you know, with their hands on these components to
decide, uh, what are the important problems they
want to look at? So that was a great
experience to sort of see things outside of computer science
and okay, other things that kind of stand out
to me during my internship at IBM Research in 2010
, I had the I didn't get to work on
the Watson project, but I was in the same
building, and I got to see all this.
I often saw the scientists working on, you know
, the Watson project, having conversations about jeopardy strategy
and all that in the cafeteria. So that was
really fun to sort of see the excitement around IBM
was that was a really exciting time to be at
IBM research just because, you know, that was
such an exciting project for for a I research,
Um, and then another sort of random, I
guess industry experience I got was a couple years ago
at the Neural Information Processing Systems Conference. So this
is the Nips conference. It's the one of the
Premier Machine Learning Conferences. Mark Zuckerberg, Uh,
you know, the CEO of Facebook, as everyone
knows, showed up to announce a brand new Facebook
ai research lab. But as a result, he
he actually came around to, uh a a bank
where we were having, and he shook the hands
of a bunch of people out. So I got
to take. I got to shake Mark Zuckerberg's hand
because he was there announcing this new research lab.
What's it? So I guess the last cool experience
I got to get I got to experience. So
the last couple experience I got to have as a
result of doing graduate research and postgraduate research is I
got to come here. I got to work at
Virginia Tech, uh, and do computer science and
teach computer science as a job, right? They
pay me to do this, so it's kind of
it's kind of awesome. So any grad students were
watching this Just just work hard. It's It's worth
it. You get, you get cool rewards in
the end. All right. Thank you. So
you've talked about some of the stuff that you've researched
in your field already, but what do you think
? Some of the up and coming topics of the
research in your field are, And along the same
lines. What are you looking forward to do personally
for research in the field? Sure. So,
uh, my research areas are up and coming.
They're not They're not fully mainstream right now because people
are very excited about using the classical machine learning tools
for their exploding data sets. Right? Everyone has
these growing datasets, and they're just applying things out
of, you know, python standard libraries. Um
, but but the key is that in a few
years they're going to be wanting to applying more complex
methods. So So my research and structure prediction in
relation to learning are these are a couple of these
up and coming fields. But if I'm gonna be
completely honest, they're not the most exciting, uh
, sub areas within machine learning that everybody's talking about
. Everybody's talking about deep learning. This thing called
deep learning and what deep learning is is first of
all, it's absolutely brilliant name for a fairly,
uh, intuitive concept. And that concept is the
is the idea that we can use a machine learning
or the machine learning methodology to learn models that make
decisions in sequences that make decisions hierarchically or or uh
in in in pipelines. And the idea is that
you can use special techniques to to learn all the
parts of that pipeline, um, in conjunction,
whereas in the past, people would sort of set
things up 11 piece at a time, maybe do
some learning for each piece and then just hope it
works. In the end, deep learning is all
about doing, learning from the input, the input
data all the way to the output, the target
output with all the pieces in the middle all at
the same time. And when you do that,
you get some really amazing results. So that's why
everyone's so excited. Because the people have seen recently
some really, really amazing results at a computer vision
and natural language processing, where you're able to,
you know, take pictures and and figure out how
to describe them in natural language using a deep learning
model. These are these are astounding results that have
come out. Um, but I I view that
I view deep learning and relation to learning and structure
prediction and, uh, you know, as I
view them as, uh, two or three directions
in a plethora of directions within machine learning research that
have pretty much the same goal, which is to
increase the complexity of our models but find ways to
reduce the computational and cognitive cost of using them.
So the idea is now these are going to be
easy to use tools where the computer is going to
do something really complex. But the human user doesn't
have to think about the complexity. So are there
any classes that you would recommend for undergrads to take
that are looking to do research some of the to
yours Also, are there any classes that you have
taught or have taken that you would highly recommend?
Sure. So in the in the fall, I'm
going to be teaching machine learning, which has a
funny name. So this is maybe a good platform
for you to try to explain the naming system.
So it's called. The class is officially called Advanced
Machine Learning for graduate students and just machine learning without
without the advanced, um, for undergrads. But
it's open to both undergrads and grads, and it's
actually what it should be, named as it should
be called an intro to machine learning course. It
is an intro course. You're not expected to have
machine learning background to take this course just because there
was some naming challenges when we were trying to register
the course with the registrar, So So that's the
result. That's the reason we have this advanced at
modifier that makes it sound like it's a second course
. But it's really the first course for people.
Interesting learning about machine learning. Specifically, it's going
to be more about, um, not just how
you use machine learning methods, but but how they
work under the hood. So you'll you'll learn about
how they work, which should give you the power
to to come up with your own machine learning methods
that are variants of what we learn, the textbook
or not, that we learn from the textbook types
of machine learning algorithms. Um, other courses that
I know that I'm still getting to know the curriculum
. Virginia Tech as a new faculty member. But
I know that there's a There's two Data Analytics courses
which are are really good boys to get into,
uh, pretty much the same kind of material,
but with a little bit more emphasis on, you
know how and when to use these models and a
little less emphasis on on how you implement them.
But you'll still get a mix of both in either
. Of course, um, there are 6000 level
courses for the grad students. Um that are much
closer to research. So next fall in the false
master of 2015, Professor Dr Batra, uh,
in the EC department will be teaching a deep learning
course on what I just described, uh, the
deep learning, uh, sub field of machine learning
. And then in the fall, Excuse me.
In the spring, I will be teaching probabilistic graphical
models and structure prediction, so that would be very
close to the research that I do. Let's see
. Yeah, And in general, I think I
don't know the exact course numbers here at Virginia Tech
right now, but but But if you want to
get into machine learning research, then it's good to
have a statistics background. So you want to take
some some form of intro to statistics or maybe advanced
statistics. If you've already taken some intro, Um
, and it would also help to learn a lot
about optimization. So that's that's that can be a
little bit advanced, But if you can take a
course on optimization that will give you a big leg
up on doing machine learning research. All right,
So do you have any recommendations of outside learning tools
for any currency s students being books, websites,
MOOCs or anything else. Yeah, so there is
a fantastic and very popular machine learning book that's run
out of Stanford. It's taught by Andrew Ng,
um, at Stanford, and it's, I believe
it's on coursera. But there's there's actually a bunch
of really, really good books on coursera on FedEx
and on Udacity. I think, um, and
these are really great tools to to do to,
to enhance your learning experience or if you if you
really, really, you know, do a lot
of self study. In addition to watching these videos
and make sure you pay attention, they could actually
big enough to get you the basic knowledge that you
need to go ahead and start applying machine learning to
your research. So, in addition to in addition
to MOOCs, there's, um, you know,
there's a lot. Of course, websites are really
good, so people tend to post really good slides
. Uh, I know that a lot of the
faculty at some of the top universities are just post
. They're not official MOOCs, but they post their
video lectures online for everyone, anyone to watch so
you can look at those, um, and I
guess outside of machine learning specifically for any computer science
student or any, uh, students within, you
know, science, uh, any of the sciences
or engineering disciplines? One of the important things that
is kind of surprising to people is that you should
get books and that let you study about the English
language usage. So my favorite book there is,
uh, Strunk and White's The Elements of Style.
It's a really short, relatively cheap book that I
think every student should own. Um, it's one
of these books that's it's so well written that you
can just read through it, sort of just it's
, you know, it's a book about essentially about
grammar and style. Um, but it's interesting enough
and well written enough with good examples that it's fun
to actually flip through and read. And as a
result, when you when you read it, you'll
you'll learn more about how to express your ideas better
, which is such a critical piece of of science
that is often overlooked in science education. So that
might be helpful tool for a everyone in adjacent fields
. So there's another resource that I think is really
exciting and fun nowadays, for people interested in learning
about machine learning and machine learning research. There's a
podcast now that's actually fairly popular. Um, that's
hosted by Professor Ryan Adams at Harvard University and Catherine
Gorman, who's a an interested layperson. So she's
doing. She does a really good job as the
host who's not, uh, scientist or not,
a machine learning researcher in keeping the conversation accessible to
, um to people who aren't within the machine learning
research fields and the podcast features, you know interviews
with researchers, um, and discussions about particular research
topics within machine learning. And I think it's a
lot of fun. And it's, you know,
I listened to it every week when it comes out
, and so do a lot of machine learning researchers
. And if you want to hear what the top
hot topics are nowadays, you can listen to that
. So it's called talking machines, and I don't
know how to access you. Can you can search
for on your favorite search engine talking machine. Awesome
. So the second to last question is, if
you give any advice to any current undergrad or graduate
students with their studies, what would it be?
Yes, uh, so so professors love giving advice
. And that's why we, you know, most
of our time is spent doing things called advising.
Uh, but we we we are. So I
have a lot. I have a lot to say
about this, Uh, but I'll just try to
say a couple things. So So So for the
one thing for students doing sciences. So specifically grad
students or or any undergrads we're getting into research.
Um, it's important to manage the highs and lows
of the scientific rollercoaster emotional roller coaster. And people
almost never talk about this stuff because because we we
as scientists, like to be very logical. And
we think about, you know, here we think
about very concrete truths. But one of the problems
or one of the challenges as a scientist is that
you often get either really well. The positive side
is, sometimes you get really high praise for the
work you do. But then on the negative side
is, sometimes you get criticism and it can hurt
a lot. Um, you know, you you
sent out you spent six months working on a research
project and write a paper on it, and you
send it out and it gets rejected by three reviewers
who may or may not have understood what you're trying
to express. And that can be very, very
daunting. And very, very almost devastating. Emotionally
. The day after you get your paper rejected is
a really tough day. Um, so So you
want to understand that that, you know, there
are things to learn from these experiences, and sometimes
there's actually not that much to learn. So you
can you should just quickly move on from it.
Uh, similarly, if you get high praise a
lot of times you want to not be too euphoric
about it, just so you don't crash the next
time you get a little bit of Christmas criticism,
Um, and then the other thing is, I'm
only describing the human aspect of it. Science itself
can be really harsh. You can have a hypothesis
that you think you have a really great plan for
research, you know, moving forward and you run
. An experiment of that hypothesis gets disproven on the
first experiment, and that's just the way you know
this is this is science. You discovered something about
the world, and that's what you discovered is that
you are very wrong. Uh, you just have
to You just have to learn to learn from it
, right? We have to learn to to experience
that, figure out what to do next, and
just figure out how to keep doing good work.
Moving forward. Um, there was another bit of
the other bit of advice I wanted to make sure
to include is just again for people in the sciences
for students in the sciences and engineering and math.
Um, don't forget that language and communication is over
half of what you need to do as a scientist
and as an engineer and as a as a contributor
to society. So it's easy to overlook these these
things. And when I was a grad when I
was an undergrad student, I probably didn't spend as
much time where I didn't spend. I definitely didn't
spend enough time practicing communication, writing, reading,
listening and talking. Um, and now I'm trying
to get better and better at it all the time
, and it's something that everyone should always try to
get better at because nobody is perfect and everyone can
be better and everyone's lives will be better. Their
careers will be better if they can do better at
communication down below the video. In the description,
we will include some links to your website. But
is there any specific way that you would like interested
, uh, undergrad graduates to get in contact with
you for graduate studies? Yeah, if you're interested
in getting into my research area and specifically working with
me on it, um, for sure. Look
at my website and have a look at some of
my recent papers to see if, um, anything
in their sounds interesting. A lot of it's very
hard to, um, accept access from the point
of view of, uh, you know, someone
who hasn't studied machine learning. Um, but that's
okay, because if if maybe, if the motivation
of the of the research sounds interesting to you,
then that might be a good, you know,
a good sign that that we could work together.
Um, but the other thing you should do is
read. I posted a document that describes, um
, you know, essentially, it's like a letter
to prospective student collaborators where I described the things that
I'm looking for in a good student collaborator. Um
, and it describes you know it talks about things
like being good at science programming at math, um
, and and that communication language and all those things
. Um, so that should give you an idea
of what I'm looking for. Um, And if
all that stuff sounds good, you know, then
then feel free to email me. I'll try to
get back to you as soon as I can.
You know, there's definitely, uh, as a
new faculty member. I'm discovering. I'm sort of
starting to understand a little bit why my professors,
when I was in college, were slow to respond
to email because it's it's It's hard to keep up
with all the all the messages. So So you
can you can email me. That's the best way
to reach me. Um, and I'll respond to
you as soon as I can. And if I
don't respond to you, then you can feel feel
free to repaying me. A lot of times,
things might just be getting much have just been buried
. It's not because I'm intentionally ignoring you just because
I had to put out a bunch of fires the
day before and then, uh and then I know
no longer see you at the top of my email
Less list, but yeah. So feel free to
repay me if you If I If you need more
response. Faster response. Awesome. So thank you
very much. And thank you for your time.
Thank you.