Fireside Chat with Stefanie Jegelka

Fireside Chat with Stefanie Jegelka


>>Welcome, Stefanie.>>Thanks.>>It’s a pleasure to have you here. Like we were just chatting, this is not your first time here. So you spent summer here
as a research intern?>>Yeah. Many years ago in 2012.>>Yeah.>>I had a good summer
and I’m just recognizing the building and memory.>>Yeah, it’s almost like
a rite of passage, right? It’s amazing to see how many prominent researchers
in our field at one point or another have passed
through an internship in this or one of the other labs.>>Yeah. This is actually
what I got to know then, like all these other people also have interned in the same
lab back in the day.>>So of course that was still not a very early point in your machine learning
research career. You’ve been doing machine
learning research for a while. If I remember we crossed paths, I think it was 2006 in Tubingen.>>Yeah.>>When we were both undergrads. So tell us a little bit
like how did you get, I guess maybe more common these
days but it was not very typical at that time for
undergrads to get started in research and especially in
machine learning research, so what made you get into machine learning research
as an undergrad at that point?>>Yeah that’s kind of an interesting story with
lots of coincidences. So I think I started hearing about machine learning when I was actually an exchange student at UT Austin->>I see.>>- back when I was
an undergrad, and so I took some courses there and
I took a data mining course. I actually enjoyed that course. That’s where first time I heard about clustering and
such kinds of things. Then I did a research project, actually on neural networks and
computational neuroscience. Then back when I was at home, I needed a bachelor’s thesis project. That time I had roommates who were research assistants
at Max Planck Institute.>>I see.>>I was like, hey it
sounds really cool what they’re doing and it sounds very interesting and also a bit more towards the mathematical
side and algorithmic side, and I kind of enjoyed that. I was like, “Hey is
there may be someone I could ask for a project?” So I asked if they have a project
for me for bachelor’s thesis, so that’s how it started.>>So I guess the fact that you were actually doing your
undergrad in Tubingen, which was like this machine
learning powerhouse already at that time
probably played some part.>>Yes I think so. Then some random coincidences of
having met people who said like, I’m doing this and I was like, wow that’s really interesting. Then I just stuck
with it essentially. So I started working actually on independent component analysis
and matrix approximations, and I continued working on that and then expand it to
other topics. Just like that.>>Yes. So let’s talk a
little bit about that. Right you’ve had a pretty
interesting arc in terms of the variety of
research topics you’ve pursued. You started, like you mentioned, with algorithms for independent
component analysis type things. You went a fair bit on
clustering during your PhD and then it seems like
you got interested in discrete optimization
and just the body of research kind of
exploded from there. At least that’s the impression
we get from the outside. So how did you get interested in this discrete optimization
and maybe just for people who might
watch this conversation, maybe also tell us a
little bit about what discrete optimization is
and where it comes up, and how it interested you?>>Yeah, so I’ve always been
interested in algorithms. That’s almost one of the
fun things I enjoyed. Actually, the way I got into
discrete optimization was partially. I was working on
clustering and that is one specific discrete
optimization problem, meaning I have data points I need
to assign them into clusters, so I have to partition them. So there’s a discrete
solution and I have some objective function
that I want to optimize, but then of course there’s many other discrete optimization
problems meaning I have usually some binary variables and I want to optimize
over those variables. For example doing assignments, finding maybe clusters
or paths and graphs. Usually this involves graphs or sets, or partitions, something
along these lines, and where does it come
up in machine learning? In many places. So
one is in clustering. There’s many places where I say
I want to select data points, maybe I want to do exploration, I want to see which
measurements should I take to gain as much information
as possible about the world and also in
inference problems, when I want to make
discrete prediction. So one classical example that time was predicting where in
an image is an object. So I basically make a prediction for each pixel in the
image of zero or one, or is it foreground or background? So it’s an inference
problem where I’m making predictions of
all discrete variables. There’s many other problems
where maybe I want to do predictions over
graphs or networks, I want to find communities, I want to find discrete labels. Somewhere all of these are essentially discrete
optimization problems. So the interesting thing
is that typically, computationally these
can be very hard. So typically, I’ve some
objective function that I want to optimize. This can be NP-Hard, meaning it’s hard to find any algorithm that works
in polynomial time. But now, sometimes they
have specific structure. So what I was interested in that
time was a specific type of structure that enables good
algorithms, good approximations. That structure is
called submodularity. So the idea of submodularity is
that I have diminishing returns. So if I think for example
about information, I want to find a set of items that maximizes the
information that I get from them. If I have a set of items, I add one more item to the set, how much does my value
or objective increase? The more I have, the less it increases from
this one additional item. That’s the idea of submodularity, there is diminishing returns.>>Correct.>>It’s pretty simple
and hence it applies widely but it’s very
useful algorithmically. It has very nice connections
to convex optimization, greedy algorithms
combinatorial optimization.>>So speaking of more continuous
optimization and connections, so in terms of the touch points between the broader
optimization community and the machine learning
community, definitely there’s been like over the last decade, a huge growth of that literature in the
continuous optimization area. There partly it was
fueled due to this aspect that when we started thinking about the kinds of optimization problems that
arise in Machine Learning, they were maybe introducing some structures that were
not necessarily seen before or maybe some
regimes of parameters, data sizes, things
like that that are not necessarily explored enough in the optimization
literature and that led to the mushrooming of different
algorithmic styles and what people were studying. Now when you look to again
discrete optimization, it’s obviously not a young field. It’s a field that has very rich, very old literature
both in mathematics and theoretical computer science
amongst other areas. Where has Machine Learning influenced or changed a narrative in that field? Has Machine Learning added an interesting dimension
there in a similar manner?>>Yes. I think so too. So one is that Machine
Learning has also brought new problems like different
clustering problems or different inference problems
where I want to make structured predictions
or so that lead to new optimization problems. But the other one is that
maybe the focus shifts. So if you think about the classical combinatorial optimization community, theoretical computer science, the main goal was to get a polynomial time
algorithm for some things. So it could be like
something that scales on the fifth or sixth
order with the input, but it’s polynomial time. In Machine Learning,
you care also about, even linear can sometimes be very expensive and quadratic
is also very expensive. So it’s a completely different shift and there may be you are okay to have a bit of an approximation
but to be much, much faster. So then the question is, is there some trade off in-between? That is one question and the
other one is even if I have a polynomial time algorithm
and it’s a good algorithm, it’s maybe not fast enough. So one example that’s maybe very easy to understand is
the greedy algorithm. So one way to do sub-modular optimization is to
run a greedy algorithm meaning, I’m picking out of the
many items I have, I’m picking one item that’s
the most valuable and then I’m picking the next item as the best augmentation
tool dataset, and I do this over
and over until I have K items if I want to select K. So
that’s a very simple algorithm. It works very well in practice and it works very well if your
dataset is not too large. But if your dataset is large
then searching through all these many different items every time you augment your
dataset is very expensive. So now our new questions, can I accelerate this
and how much do I have to trade off the quality
of my solution for speed? Can I parallelize? This is inherently sequential, maybe I can parallelize it
or can I do sub sampling? So one reason line of work has looked at like how
parallel can I actually make these queries to not lose the approximation
guarantees that I had.>>This is the work from Harvard.>>Yes. This is the
work on adaptivity. There’s been many recent papers especially from Harvard
but also other groups. Yeah, so this is one question that is motivated by looking at
sub-modularity on larger datasets. There have been earlier
works that used other ideas such as just
sub sampling your data, how much do you have
to sub-sample and just doing the
optimization over that. Smaller set of items or
just using sub-modularity itself to do a better organization of your data and you don’t
have to query everything. So there’s been several ideas. All of these are very
interesting and then there’s also another
literature just on streaming algorithms for
greedy and so on and so forth. So all of this essentially was
motivated or fueled by saying, “I have large datasets
in Machine Learning, can I somehow run these methods on large datasets because they
work well in practice?>>In terms of the communities, does the Discrete
Optimization Research in Machine Learning and the
Discrete Optimization research in theoretical computer science
or in math and other disciplines, are they at the point where they talk to each other
and learn from each other or they’re still happening mostly
in their own ecosystems?>>I think they are increasingly
talking to each other. So they’re still theory conferences and there’s the Machine
Learning conferences but more and more you see people actually collaborate
in that space and theoretical computer science
researchers actually starting to publish at the
Machine Learning conferences. Another example is that even
mathematicians are starting to look into problems that are
interesting for Machine Learning. So another example, so
I recently also got interested in discrete
probability and distributions that
are essentially log sub-modular distributions and
a stricter version of these. That has a lot of mathematics
and algebra in this and I know that mathematicians
are starting to look at, “Hey, actually sampling is a problem that’s interesting for
Machine Learning tool.” So there’s a new dimension, a new interesting directions for those people as well and
looking at what distributions are these people interested
in and vice versa as the Machine Learning researchers learning from these people too.>>Now of course, a lot of the concerns you’re pointing
out which are leading to these new quests for theoretical research are very motivated from concrete applications. I know in your own work you’ve
always maintained a thrust for also doing research
in those applications. So tell us a little bit about
some of your applied work and often people
complain about the fact that there is theory
but it has little bearing to do on what’s going
on in practice and vice versa. So in your experience
as you’re working on better algorithms from a
theoretical standpoint and you’re also working
on just trying to get the best performing algorithm in the context of some
computer vision problem, how easy do you find progress
in one maps to the other?>>So I will say sometimes there’s a discrepancy and
sometimes they actually come together very nicely
and I like to think about things like them from a theoretical perspective,
trying to motivate. So I think a very good example is my first paper in computer vision. So that time, it was a very classical problem
of segmenting images, basically finding foreground and
background, supervised studied. Lots of research work on this and I had never written a
computer vision paper. So I had this idea of well, one problem with these methods
that time was that they were essentially penalizing the length
of the boundary of an object. I said well maybe we
can relieve this by instead saying I want to input some priors that the
boundary of the object is coherent so we can implement this via some other
mathematical tools. So the sub-modularity
tools I was mentioning. I had an approximation
algorithm for that which actually solved the
problem, approximately of course. So I just tried it and after
a little bit of trying, basically finding
the right parameter, actually worked quite well. So I said, “Well, let’s submit it to a Computer Vision conference.” I remember that time there
was another PhD student in our lab and I showed him the results and I said,
“What do you think of this?” He was like, “Wow, this
actually seems to work.” So I submitted it and it was selected for an
oral presentation at that time. So actually that was completely
motivated from an idea coming from a theoretical research which was accepted by the researchers in the application fields at that time. So that’s one example that it
can actually work quite well. I think nowadays also a lot
of theoretical research is inspired by looking at phenomena
people have observed in practice, especially a lot of the work
on theory of deep learning, trying to explain that
behavior that’s another route. That’s also fruitful. There’s sometimes still a gap between what you can
actually prove and what is actually the real model which
is decreasing but in general, I think it’s a very good
perspective to think about at least like what
theoretical tools I have. It doesn’t always work as smoothly as that first
paper I mentioned, often it needs much more engineering to actually make something
work in practice. So that was maybe a lucky case where not much of that was required.>>So speaking of like trying to explain some of the properties of deep learning
from a theoretical standpoint, I noticed that’s what you’re
going to be talking to us about in your seminar today and you’ve been working
in this area more recently. So what are the questions
you’re studying in this domain?>>So yeah, what I’ll talk about
today is a specific class of deep neural networks
that actually applies to a combinatorial objects
or graphs and networks. So in many learning problems what we want is we
want to actually make predictions on inputs that
are graphs or networks. I though we’d just put
a label on a graph say I have a molecule that
I can model as a graph. I have some information
about what are the nodes and maybe
the edges, the bonds, and the atoms essentially, and I want to predict
properties of this molecule. Or I have a social network I want to make predictions about
the nodes in that network, could also be another network of
interactions and it is used for recommender systems
when people click on items or interactions
between drugs also. So what I need is a good
representation both for nodes and of the
entire graph itself, and this has been a field
of research for long. People have long thought
about how can I best find a good vector representations
of graphs and more recently what people are doing is neural networks which are essentially
learnable representations. The way they work is they essentially find a representation
for neighborhoods of each node and then
they put them together into a representation
of the full graph. So what we have been interested in is more theoretical properties
of these networks, so one question I’ll be talking about is what is the discriminative
power of these networks? So meaning if I give it
two different graphs, will it always map them to different vector representations
or sometimes to the same one? The problem is that if you map
them to the same representation, you can never learn to assign
different labels to these graphs. So there’s an implicit restriction in what kinds of
functions you can learn. We are studying this from basically the perspective of what choices
you make when you design this neural network architecture and what you can show is
basically there’s a limit to how much they can discriminate
so they will like in the current architectures these so-called aggregation based
architectures that people use, there’s always a limit on
what you can discriminate and the limit is a popular
graph isomorphism test.>>I see.>>It’s called device
vital Lehmann test. Now the question is do all the
neural networks that people are using actually possibly achieve that limit or are they
bigger than that? It turns out that actually some of the popular architectures
are much bigger than that and you can see this by studying what are the
aggregation functions. I should maybe explained
what the aggregation is. So the way this neural networks
work is that for every node, it looks at the neighbors in the graph and collect
information from the neighbors, basically the feature
vectors of the neighbors, combines them in some way to get
a new representation of itself. Now this combine the
neighbors operation is the aggregation operation. There’s a few restriction. So for example, it has to
be permutation invariant because there’s no natural ordering of like this is
neighbor number 1 or 2. The other one is that you’re
using the same kind of aggregation operation on each
node to reduce the number of parameters and hence this
neighborhood aggregation has to be invariant to how many
neighbors you have because not every node has
the same number of neighbors. So these are the restrictions and typically the way
they work is that you apply some transformations of the neighbors and then
you do an aggregation, maybe a sum or a mean or a max pooling and then you do another non-linearity and you
do this over and over again. These aggregation operations
there are some conditions on these under which you are
more or less discriminative. At the end of the day, it boils down to an injectivity condition on
so-called multiset function. So injectivity means
different inputs lead to different outputs and multiset means that the functions
you’re actually having here get sets as inputs, this is a set of neighbors
and the neighbors can repeat, and the number of neighbors with their specific label is important. So you’ll need to actually
preserve the information about that and that helps you and you
see actually in practice also. So this is the theory
and now the question is what is the implications in practice. So you actually do see that
the networks that respect these injectivity conditions do fit the training data better
and also generalize better. So the generalization is a phenomenon we don’t
explain in this favor but it happens but you basically fit the
training data much better. So it actually does have an
effect also in practice. So this is one question
we had looked at. There’s others, for example, there’s an interplay
between the structure of the graph and the depth of the network that works
well for these graphs.>>I see.>>So there is a way to explain these interactions and
you’ll also see again in practice that there does seem
to be this phenomenon happening. More recently, we’ve
been looking at how the structure of the network aligns
with the structure of the task that you’re trying to do so this pertains mostly to questions
of reasoning where the answer is also
actually computed by an algorithm and the
network essentially has to learn to execute
that algorithm. So there’s also some interesting
theoretical questions that you actually see
reflected in experiments too>>So I guess does this way of thinking about the
architectures which are better or worse for
capturing the data, does it also give any intuitions
in terms of algorithm design for the underlying
optimization that we need to solve or is that
still being done in basically using the same
SUD style algorithms, or are there any other tricks we get to bring from the
theoretical underpinning?>>So that’s something we
are currently looking at. I think that there’s lots
of open questions there. I don’t have an answer yet but
it’s a very good question. Yeah. So basically looking
more at how the structure actually relates to the
algorithm that you’re solving. I think there are interactions
there and there’s things you can exploit
and explain probably, but I don’t have a good answer yet.>>Okay, we’ll look
forward to maybe learning about it in the next conferences. We have you here for a distinguished seminar today
but you and I both of us have, of course, quite a long way to go still in our research careers hopefully and I’m
very curious you’ve. like the nature of problems
that you’ve been studying has really become richer and more diverse as it naturally
does over the years. Really long-term, what’s something
that drives you that you would like to get to in terms
of research goals.>>I think I have a few goals
that are inter-related. So one of my goals is basically to improve machine learning
with structured objects. So all the discrete
optimization of trying to do inferences and predictions with sets and partitions
is related to that. The graph neural networks
are related to that. Basically, trying to get better at the computational
hurdles that are there, and better understanding the
models and their properties. Understanding the algorithms,
understanding how we can do much. Basically, how we can
get algorithms that we understand how they
work and why they work, and when they don’t work, and that are actually
practically applicable. So that is one thing. The other one that we
started working on is to make machine
learning more reliable. That of course relates to understanding what your
models can and cannot do, and what your algorithms
can and cannot do, and to actually
specific optimization, like so we have looked
at robust optimization. How he can, like that’s
one way how you can actually bring that
robustness into the problem that has regularizing properties and to understand these trade off. So basically using prior knowledge,
using structural knowledge, using optimization
formulations to guide the problem and I like there’s
some understanding of how we can encode what we know and what we know about the problem domain to
get better models and algorithms, and then better
optimization strategy. So that’s one big set
of inter-related goals.>>Well, that seems like it’s going
to keep you busy for a while. Now, you also had this experience of two very
different educational systems. So you grew up in the European
system and now of course you’re teaching in
the American system. I’m very curious of what you see as the relative or maybe I would say metrics
and weaknesses of the two. I will ask you a more specific
question about that in a moment, but I first just want to hear
your general impressions.>>That’s a very big question. So yes I did my undergraduate
and graduate studies in Europe. At that time, I would say my
PhD program was a bit special. It is less sold than what
typically now is done in Europe. Is basically, I did my master’s degree and then I
only did research after that, and it was only a few years. Like only three to four years. Versus in the U. S. you do your bachelors and then
you start research, and you will spent
much more time there. So these are very different systems. In between I visited the US. So I found that what I had learned
actually in the German system was very adequate to follow what
was in the American system, which I was very happy about. So I think actually the education
I received was very good. Also actually at that time at least the German system
was very much focused on giving you first abroad
foundation of mathematics etc, and then going into
the technical things. Most also possibly because
at that time we didn’t have a degree after three years it
was actually after five years. So there was a lot of time. So what I would say
though is like that, in the end I liked those
PhD programs better, where you don’t only do research, but you also have a few
courses and you can target coursework to the
research that you have, and sort of continuously
actually learn something else because it’s never easier to learn about something than if you actually have a course that you can take. Even if it doesn’t have
to be a required course. If it’s just like some fun course you can take is just more structured. So in that respect, I think that the more
structured PhD programs I would say basically you get a broader
exposure to different topics. So because if you just
work on one thing, you get more focus on
what you’re doing, but you’d see less about what else is there like
many other directions. So this is something that
when I visited the US, I notice that there’s a broader exposure you can get
just because maybe there’s a big university behind it and that is something that’s
a bit different on Europe. But at that time it was
a bit more focused. I think the PhD programs in
general are a bit different. Because in Europe it used to be
the case that you were admitted, like not into a program, but you’re basically hired by the professor and that’s
like your advisor. Versus in the U. S. you’re
admitted to the research program. So when principle you can
still change advisors. It’s a bit more flexible than that, which is sometimes a good thing
if you’re coming in and you don’t actually know what
exactly you want to do as is the case with many students.>>Yes.>>Even though it’s good
to focus soon enough.>>Now, the more specific thing
that I wanted to ask also was, so one of the things that of course, we’re becoming increasingly aware of and there’s a lot of
desire to do something, but we don’t always
have good answers is the overall lack of
diversity in STEM fields. In particular, when we
look at the statistics for machine learning conferences,
it’s pretty damning, and if you start going narrower
into machine learning you realize, sometimes in fact all of the machine learning
is still maybe being, there’s like a handful
of areas that are maybe making all of machine
learning still look good, relative to what it might really be. So I was curious in terms
of your own experiences in the German system and then
now what do you experience, what you see the students
going through as a faculty like are
there things that we can learn that we
can incorporate from the German or other
European systems that maybe are helpful in terms of improving the
diversity in STEM fields. Just what was your experience
like when you were, I’m sure you had to overcome more than a handful of challenges
to get to where you are. So what kept you going?>>So first the thing is I think the same problem
persists in many countries. Many times also in Europe I
have been the only woman.>>I see.>>So I’m not sure there’s good
things to learn from either side. I think there’s other countries
where this is different. But I think in general, so the thing is there needs to
be support at multiple levels. So one thing is getting
people interested to even start say a college education or graduate
studies in this field. So it actually needs
to start very early. I remember when I was
in high school and I was saying I’m going
into Computer Science. People are like, why do
you want to do that? There’s like go into
Management or something, but Computer Science
that’s so uncool.>>That’s not necessarily
gender-specific. I remember my dad, so my dad was an Electrical Engineer. He was like you know
these computers there they’re done like the dot
com boom has happened. This is just a fact, look like nobody is leaving
electricity behind. You should do double E.>>Okay. So there’s that one, but is?>>But I still understand
what you’re saying.>>I think just the thing of
the thought of as a woman, what are you going to NSR? All the other women were going into business or something like that, and maybe made similar. So it was just not a typical thing to consider and basically
to change that mind. I remember I had some physics teachers who also
made some remarks etc where I was at that time. So basically, even showing people that there’s interesting
things to discover. I remember, I like
mathematics at that time. So we had this thing
where you could go to some advising and they will tell you about what are maybe
good job choices or so. I said, “I like math.” They were like, “You
could study math.” I asked them, “So what do
you do when you study math?” They’re like, “Yeah, you can
work in an insurance company.” I was like, it sounds very exciting. The way they described it, sounds like I’m not
going go into math. In it maybe, I think I would
have been happy there too, but like at that point, so basically, just
knowing what exists. At that time, I also had no idea what all you may be able to
do with Machine Learning. I didn’t even know that
Machine Learning exists. Didn’t like what is the breadth of computer science. I didn’t know. I actually went for
Bioinformatics at time. I was like I like math, I like biology, I like many
of the things that you do. I wasn’t actually even specifically interested to do computer science, I have to admit, at that time. So that’s how it devolved. Then I actually went much more. I went into computer science studies. I didn’t really know how to program. We had to do a little bit
at school that was it. So at that level, just getting
people interested is one point. Then the other one is support. So I see acceptance of women in leadership positions
is very different often. There are these things of you
just got this because you’re a woman, like judgments. Then the other thing is support. So I see also other
women who say this is too much or say
being a faculty job, it’s just too stressful. I don’t want to do that. So there’s also these things
like apart from discussions we’ve had about basically
insulting and offending, but at different levels. So one other level of support is
for example, say you’re a woman, you have a child, how flexible is
the work experience with that? That I have to say actually, I’ve been very lucky. I had a child two years ago, and I had lots of support
from the department. I wasn’t sure at that time. It’s a male-dominated
field. I don’t know. In many cases in my career, I have been the only woman
starting from PhD student. For some time, the
only other woman was the person who was the administrative
assistant in the office. So I wasn’t sure, but actually, I was very lucky. I will say it was a good thing that actually there was
support and flexibility, and there’s childcare
options available. Otherwise, it would be hard for me to do the job and just follow up. So I think if you want to support
women, there’s many dimensions. So there’s this thing
about getting respect, but there’s also this thing
about starting early to get people interested just to have enough women
interested in the field. If there’s only two women applying, what can I do in 100-men? The other one is to continue
giving support and saying, “Okay, it’s actually feasible.” Say took on my own family and
job and all of these questions, I think are important.>>So one dimension of support, I also heard at least or perceived
in your response was that sometimes it’s important if you have role models
that you can look up to. Did you have such people when you were just getting
started in the field that helped you overcome
those initial hesitations?>>Actually, I didn’t
have a female role model. Frankly speaking, I think all the people I knew
in the field were male. I mean, at least in
the more senior roles. But what I did have was very
good support from my advisors, people who have been working with, who were continuously
basically believed in me, and were encouraging me. That is I think very important. That there’s some people who
believe that you can do it even say when paper gets rejected, Tetra, these things happen, and there’s people who make your
believe that you can do it, and that things work out. That is I think very important. So someone who gives
you basically support, and that can also be
a network of people. These can be role models as well. I think it’s good to
have role models, but I think very important
role is played by advisors and people that
you’ve worked with. If they believe in
you and they give you the feeling that you’re valued
and you’re strong, you can do it. If they give you the
feeling that know everything you do is just worthless, then I don’t think you’ll go very far unless you’re really
strong as a person also. That is maybe less women
specific and more general. I’m sure this isn’t for everyone.>>Absolutely, I think
we’ve all or the course of our PhDs had difficult moments where we’ve doubted our
ability to get through things. Having a good support structure and having encouraging mentors
absolutely plays a big role. I think in particular, often we also see that make people hesitate when taking up more mathematical or
more theoretical topics in terms of their research areas. In those it’s even, I would say all the more important to have the right support structure. So I think we’re getting
up on time here. Are there any final words of
encouragement or wisdom for our viewers who might
just be thinking about how to get started
in their research or how to navigate this difficult terrain that
you would like to offer?>>I think for all young researchers, I would say honest, be open-minded. Learn about many different
things, don’t discard anything, that’s a very important
thing and be courageous, explore on your things,
and just have fun.>>Yeah. I think the last
bit often gets ignored. Well it was a pleasure. Again, we’ll look forward to learning more technical
aspects of your work in the seminar today. Thanks a lot.>>Thank you.

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About the Author: Oren Garnes

2 Comments

  1. This different approach by Microsoft is a nice innovation. (Maybe I have missed others). And it's classy in having a well prepared host chatting to someone interesting who is able to followed up on other vids.

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