Dr. Bouton is the CEO of Vyasa Analytics, applying novel deep learning (ie A.I.) approaches for life sciences and healthcare clients. Previously, Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013.
_Prior to his role as the CEO of Entagen, Dr. Bouton worked as a computational biologist at LION Bioscience Research Inc. and Aveo Pharmaceuticals from 2001 and 2004, leading the microarray data analysis functions at both companies. In 2004 he accepted the position of Head of Integrative Data Mining for Pﬁzer and led a group of Ph.D. level scientists conducting research in the areas of computational biology, systems biology, knowledge engineering, software development, machine learning and large-scale ‘omics data analysis._
While at Pfizer, Dr. Bouton conceived of and implemented an organization-wide knowledge-base called Pfizerpedia for which he won the 2007 William E. Upjohn Award in Innovation. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.
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“Deep learning algorithms are basically the reason that everyone is talking about AI right now.”
As a kid, Chris Bouton loved sharks. Sharks turned into biology and biology turned into molecular biology, which evolved into computational biology. Chris followed his curiosity and received his Ph.D. in Neuroscience from Johns Hopkins University.
Grant Ingersoll: 00:18 Welcome everyone to the Develomentor podcast, your source for interviews and content on careers in technology. I’m your host Grant Ingersoll. For those new to the show, we have two goals. We want to showcase interesting people in technology across a variety of roles, both individual contributors and leadership roles, and we also want to highlight all the different paths people take to arrive to that particular place in their career. Today’s guest is a longtime friend of mine who has turned a PhD in neuroscience into a career in computational biology that eventually led him to founding not one but two companies focused on cutting edge analytics in the pharmaceutical industry. Please join me in welcoming to the show, Chris Bouton. Chris, great to have you here.
Chris Bouton: 01:06 Hey grant. Thanks. thanks for the opportunity. Great to talk with you.
Grant Ingersoll: 01:10 Now. You know, Chris, we’ve known each other for quite some time now. I think it’s, it’s probably going on 20 plus years. Not to date ourselves too much, but for the sake of our listeners, how about you give an intro to your background? You know, how you got started, some of the jobs, perhaps you’ve worked in the early years, that sets you up for where you are, where you are now.
Chris Bouton: 01:31 Sure. Yeah, absolutely. I think that I always had a love of science. And from a very early age, it started with an interest in sharks actually and marine biology. Then I got into molecular biology. Then I became interested in computational biology. And once I got interested in computational biology, that’s where I started to really identify what I loved about computational biology, which was having those high-level ideas about what I wanted to build. And then building teams who could actually build that software.
So from there, I worked at Pfizer for five years running a team doing data analytics on a range of data at Pfizer. And then ultimately made the leap and founded a company called Antigen where I was also running a software team and we were building new tech. That company was ultimately acquired in 2013 by Thomson Reuters. And then in the beginning of 2017, I founded VYASA my current company. So kind of a, definitely a path through multiple different kinds of careers, different kinds of things that I was doing. But it was, it always had to do with this love of science and different new ways of seeing and understanding scientific information.
Grant Ingersoll: 02:55 Yeah, that’s fantastic. I mean, it’s, it’s so cool how like that idea of being super interested in sharks as a kid than then led to the biology connection. For our listeners who perhaps aren’t as familiar with computational biology, kind of what, what is the day to day of, of those kinds of roles look like? I mean, is this a lot of math? Is it a lot of biology? Is a lot of programming? What’s kind of the mix there?
Chris Bouton: 03:22 Yeah, that’s, that’s a really great question. I mean, the answer really is that there are many different types of mixes and it really takes a full team of people to conduct computational biology research and, and more recently sort of data sciences research in general. So often you’ll have people who are hardcore software developers and are working with somebody else who understands the science and working with them to understand what they’re developing against.
Alternatively, you can have people who are coming things more from the scientific angle and are able to convey or communicate to others what they want to build as a tool to help them analyze these kinds of data. So it really is kind of a mix of many different types of skill sets that are needed to run computational biology projects. And just by way of background, computational biology really wasn’t that much of a pursued or as our career set until the genome finished up.
Chris Bouton: 04:29 When the human genome finished up, all of a sudden we had all of this data that we hadn’t had before. And so all of a sudden we needed computers to analyze it. You know, prior to that time point you know, you would run like a Western block and you’d have literally four bars or bands on a gel. And so you had four bits of data, right. And then, and then all of a sudden the genome happened and you can run on a microarray and generate millions of bits of data. And so that was, that was when computational biology really became important because we just had so much more data than we needed to analyze.
Grant Ingersoll: 05:05 Wow. You know, that’s, it’s funny that you bring that up because I think in my own career, the very first search engine I ever worked on was search over the human genome project. My sister’s actually a geneticist and she asked me if I could help her out in terms of making the data that comes off of those micro rays more accessible. So I hear, I’ve known you all these years that I didn’t even know that we had that shared inspiration, I guess for, if you will. Well, so, so you, so you did this Ph.D. and was there a lot of programming and that in your role or were you more on the science side of the equation as you, especially as you kind of got into Pfizer and, and were in these leadership roles?
Chris Bouton: 05:54 Yup. Yeah, I know in grad school in order to try to make that transition from bench research, you know, test tubes and pipetting and all that stuff over to computational biology, I did do some programming of my own to build some early pieces of software to do some of the kinds of analysis that I wanted to do. I think it was when I got to Pfizer that I realized that in the grand scheme of things I was nowhere near the same caliber of developer that plenty of other people in the world were.
Chris Bouton: 06:34 And so that’s a moment I’ve had this conversation with many people, right? Like they say, should I go and actually try to become a software developer right now? Right. And you know, they might be just coming out of grad school or something. And I, I almost inevitably say no, unless it’s something that you love. You wake up on a Saturday morning and want to do nothing more than spend 12 hours coding and are catching up quick. There is a level of skill associated with a top-notch developer that is really hard to replicate quickly, right? It takes experience. There’s a reason that they call them languages, right? It takes a long time to learn a new language.
And I think that one of the things that a lot of people fail to realize is that there are so many other facets of what it takes to build a high-quality data sciences team. And, and, and so those people who have, for example, a biology background are better suited to designing and understanding what to build and then working with the development team on, on how to build it right or should look like at the end. And so that, that was really where I started to realize that my, my job was more about this high level of understanding of what we wanted to build and then working with development teams to actually make those things happen.
Grant Ingersoll: 08:02 Well, and I imagine that the, at least the understanding of software you, you know, you may not be the expert and all the structures and processes around building quality software, like you said, that’s often a lifelong pursuit by somebody who wants to be a developer, but, but at least, you know, you had that understanding of what goes into it. So you can, you can then make judgment calls on, Hey, you know, is this, is this really accurate what we’re trying to do? Or, what’s, what’s the computers capable of doing when it comes in?
Chris Bouton: 08:36 Yeah. And I think, I think just another point there, I think it’s really important too, if you’re working with development teams to have a good understanding of what developing software is like. Right? It is very much, in my opinion, art, it’s a, it’s a fundamentally creative act. It’s something that requires a lot of skill. And time and, and deep thought about how you structure these things. And so it’s important for people working with software development teams to understand what it takes to build great software. Right? And so one of the best ways to do that is to try and do it yourself.
Chris Bouton: 09:22 You quickly realize just how hard it is to do it well. Right. and so I think that that was a really important part of my experience was, was coming to realize just how deep skillset good software development is.
Grant Ingersoll: 09:38 Yeah. I mean, and much like anything, I mean, I would probably say the same thing about the biology aspects of it or the neuroscience aspects of it, right. Is that, you know, there’s the real power I think you hit on is when you start to bring together multiple specializations and, you know, the proverbial saying the sum of the parts is or the is greater, right? Yeah. well, so speaking that when I, when I have people who manage people on the show here, I really like to ask the question of, you know, not just about your career, but what do you look for in people you’re hiring, right? And I think you’ve hinted at some of these things already, but you know, let’s call it out specifically. You know, you’ve been a CEO, you’ve been a manager at a big company. When you’re evaluating talent, what, what, what do you really look for?
Chris Bouton: 10:27 In one word, joy, right? People who can find joy in what they do. People who are excited to do this stuff. That’s the most important thing to me, right? I, I actually don’t care that much about pedigree. I don’t care that much about what they’ve done in the past. What I really care about is whether they’re super psyched to get up every day and do whatever it is that they’re planning on doing in, in, in my organization. And so actually the question that I asked the most often is if you wake up on a Saturday morning and you have nothing else to do what do you do? Right? And the people who say, well, you know, sometimes I wake up and I just spent three hours coding because like, it’s fun, you know, like that’s the kind of person who is gonna succeed.
Chris Bouton: 11:20 Because that’s the kind of person who’s going to persevere through the hard stuff. Because it’s fun for them. It’s a puzzle. It’s a way to work through a challenging problem. Another key thing that I look for are people who are willing to take big, challenging problems and figure out how to, you know, break them down into steps and, and work through them. Right. because there’s no answers. There’s no, you know, answer sheet to the, to the quiz once you get out into the real world, right? It’s really about like, okay, this is the new thing that we want to build. How are we going to do this? Yeah. And so, yeah, that’s, that’s the key thing that I look for in my teams.
Grant Ingersoll: 12:04 Yeah, no, that’s great. I think that reflects, I think the single biggest hiring characteristic I have is do you want to figure it out? Yup. Right. And, and especially in a startup, so speaking of startups, you know, so you’re at big pharma, right? You know, working for the man, right. And, and then, you know, you go off and you found Entagen, you know, what was that leap like for you? How’d, you know you were ready? Like, you know, walk me through that. What is probably a pretty big transition in your life and your family’s life, et cetera.
Chris Bouton: 12:39 Yeah, I think that I learned a lot from working in a large organization like Pfizer. And I think that and I, you know, ultimately or eventually also worked in another large organization, Thomson Reuters. And I actually equally value both of those experiences to founding and building startups. And, and that’s because they, they take different skill sets, but those skill sets complement each other. The kinds of work that you do in a larger organization is very complimentary to what you want to do on, in a, in a startup, right?
In terms of structuring your thoughts and building teams and understanding context. There’s, there’s a lot that’s important there. You know, that said I do, I love building startups. I think that once you make that leap and you, you start the process of building out what it is that you, you want to do in a startup you, you discover whether you have the set of sort of personality traits that are useful in that context or not, right.
Chris Bouton: 13:55 And you don’t really know ahead of time. It for me turned out to be the case that I love iteration. I love idea creation. I love being able to fail at something and then going back and trying to, you know, use what I just failed at to make a better decision or create something new and different.
You know, the Lean Startup is a great book for this type of stuff. Eric Ries, the guy who coins you know, the concept of MVP. He has another book out now called The Startup Way. There is a whole culture of how you think in the context of a startup that is a very dynamic and freeing in a way. And you just have to be willing to also take into account the possibility that you’re going to fail from time to time. Are you going to have to switch direction? And I just so happen to love that. So, you know, I got into the Entagen scenario started to identify those things and realized just how much I loved it. And went from there.
Grant Ingersoll: 15:09 Well, in some ways the, you know, the big company experience also shows you in some ways what the end goal is or the end result. Right. You know, obviously there’s, there’s a lot of stuff we don’t love about big companies and the bureaucracy, but, but you know, those things come for a reason. And so having that big company experience can, can say, okay, well this is what, what we’re working towards in the broader sense. Right?
Chris Bouton: 15:34 Absolutely, absolutely. And like any startup, if, if your startup starts to succeed you very quickly see larger-scale systems get built out in the context of what was originally a startup. And so there’s a whole process there by which you want to make sure that even as your company grows, you’re retaining things like innovation and ideation and iterative you know, the iterative nature to what you do. And so understanding both sides of that equation is a really important part of my experience and also how I like to run companies.
Grant Ingersoll: 16:13 Yeah. Well, and in those early days, vantage gen, right. If I recall correctly, it was, it was just you, right? And then you, you parlayed some consulting and hustle into building a product and then you were able to bring on people. Right. You know, how did you kind of know, how did you choose that path? Was it just, you know, it was the only obvious choice or, or you know, why did you take the choice to bootstrap versus try to go raise money on the idea behind Entagen?
Chris Bouton: 16:44 Yeah, no, that’s, that’s definitely true. I, you know, I started out thinking about just doing consulting you know, like me going out and doing consulting. And then I read a book called E Myth Revisited and E-Myth stands for entrepreneurial. And so the point of the book is that the entrepreneurial myth is okay, well I’m good at X, so I’m going to go out and do as much of X as I can. Right? so the example that he uses in the book is, you know, baking pies, right? I’m really good at baking pie, so I’m going to go out and make as many pies as I can. Right? But the challenge is the best-case scenario is that you’re really good at baking pies and then all of a sudden everyone wants to buy your pies.
Chris Bouton: 17:36 And the problem is that you quickly become a resource constraint, right? It’s just you. And so instead, what he outlines in the book is this idea that really if you want build a company, you have to build a business model that’s expandable and scalable. And so that was the key insight for me. And that’s why we went from being me as a consultant in the beginning of Entagen, to me building a business model around software development. And that software development had a custom software development component to it. And then we were also building alongside that our own products. And so the combo of those two things is what allowed me to scale the business and then ultimately develop something that was novel and our, our own IP.
Grant Ingersoll: 18:31 Yeah. Very cool. Yeah. I mean, you know, a lot of companies who are doing consulting find it a really hard to crack that nut that is switching to being a product company. But, but you guys definitely did that. You got yourself to an exit. You went and spent two years at Thompson as, as I understand, and, and then now you’re, you’re off to VYASA. I guess this makes you a, a serial entrepreneur at this point. So, so tell me about that. Tell us, you know, kind of bring us up to speed on, on Vyasa and what you’re doing now.
Chris Bouton: 19:05 Yeah, yeah, I guess so. I don’t think about it that way. I just wake up every day, run as fast as I can, go to sleep and do it again. Right. And you know, I think that’s the way you have to see it. You know, you have to just sort of love getting to get up and do whatever it is that you’re going to do that day. You know, back to what I was saying before, if, you know, if I’m not living by that same mantra, then, you know, no, use to ask the people who work for me to do that. Right?
Grant Ingersoll: 19:34 So true.
Chris Bouton: 19:36 I think that after I left Thompson, I went through what I call my wandering phase. And part of that wandering phase was sort of evaluating what I wanted to do next in the world. And ultimately I realized that I just love this space.
Chris Bouton: 19:52 I love working in the data sciences space. I love thinking about how novel technologies can be applied to some of the big hairy problems that we have in handling this massive digital deluge that we’re all experiencing, right? Where we’re generating vast sums of digital information. And we still don’t have great tools for handling all that information. But there’s also this continuous march forward in terms of the kinds of novel technologies that are applicable to it. And so for me, it’s a, this fun process of taking those two sides of the coin and trying to bring them together, you know, challenge a novel technology. And so that’s what we’re doing at the VYASA. We’re focusing on deep learning algorithms. Deep learning algorithms are basically the reason that everyone’s talking about AI right now. It is more fun to say AI than deep learning, but we’re really deep learning algorithms are the reason that we’re all talking about AI again, and they’re, they’re really cool.
Chris Bouton: 20:59 I mean, the simplest way to put it is that deep learning algorithms can be trained to identify patterns in data without being told how to see those patterns. In other words, pattern recognition by machines in large volumes of information without the human having to tell the algorithm explicitly what to look for. And that sounds like a very simple thing. It turns out to be incredibly powerful. And so the ideas, yeah. Yeah. I mean, and there’s, yeah, there’s lots of gotchas here. There’s lots of ways to understand what these things are good at, what they’re not good at, right? And there’s also a lot of work that we do just trying to separate the hype from the reality. Because it’s AI, people really like to anthropomorphize, what this technology is and imagine, you know, it doing everything that a human can do.
Chris Bouton: 21:56 And that’s simply not the case, right? These are just tools. They’re, they’re powerful tools. They’re new tools, but they’re just another tool in our toolkit. And so our job at is to really figure out what we can do with them, what kinds of data they’re best applied to, what sorts of use cases they’re most useful for. And then build software to help apply them in those use cases.
Grant Ingersoll: 22:19 Yeah, I guess in some ways the fact that you’re working on neural networks is come full circle and your career in the sense that you started off as a neuroscientist. And I know I’m anthropomorphizing there as well because that’s not exactly a one-to-one but, but there’s at least inspiration and so that’s gonna feel somewhat satisfying for you as well.
Chris Bouton: 22:39 Oh yeah, man. No, that’s that was a huge reason that I got interested in deep learning algorithms. Um you know, in the first place. You’re, you’re right. I mean, the reason that I went and got a PhD in molecular neurobiology was cause I was interested in how the brain works and to be able to come full circle back to working on algorithms that at least in part operate in the same way that our cortex is work is you know, in a much simpler way obviously is really, it’s really fun. It’s there’s plenty of analogies and there’s plenty of overlaps in these kinds of concepts. And it’s just going to be fun to see where all this technology goes.
Grant Ingersoll: 23:20 Yeah. This is very exciting. And, and, and like you, it’s always having to try to temper the hype behind it, but, but there’s been some really cool opportunities to arise out of it. You know, I think that brings us kind of up to date. You know, as you look back to where, you know, your career today, what’s been the most surprising thing? You know, like, you know, 20 year old Chris would say, gadda, you know, if I’m ever doing that, no way!
Chris Bouton: 23:53 I, I don’t know that I had preconceived notions around what I was going to do. And so it’s hard for me to answer the question about what’s surprising because I didn’t necessarily conceive of myself doing one thing or another. And I think that that probably that’s been a strength because, you know, for anybody who’s just starting out on this path in their careers, I would just say like, keep an open mind and listen to your heart and your gut around what’s fun. Because if you can find that joy in something even if that joy is coming from a place that’s totally not where you expected it to come from, right. It’s really valuable to listen to it. You know, I think that that’s, that’s probably some of the best advice that I could give would be just listen to your heart and your gut and, and listen to where your joy is coming from and then go and really evaluate whether there’s something there as a potential career path.
Grant Ingersoll: 25:05 Yeah. That’s fantastic. I mean, I think that that’s something that’s, you know, maybe it’s the fact that you and I have this liberal arts degree from Amherst, but this notion in a career, you know, there’s so much focus in, in our world right now around intentionality and like, you’ve gotta have all these plans and there’s a lot to be said for that. What I like to call is guided exploration is, you know, I know more or less where I want to be, but I don’t care about the particular, like I don’t have to go specifically down this road. Right. There’s other roads, there’s a road right alongside. Sometimes, you know, the road right next to the freeway is more enjoyable to drive on. Sometimes you just want to be on the freeway because you want to get there. Right. So it sounds like you, you know, you’ve had a pretty similar path that way of pulling on a thread and, and mixing our metaphors here are analogies and kind of seeing where it goes.
Chris Bouton: 25:58 Absolutely. Yeah.
Grant Ingersoll: 26:01 Yeah. Sorry, go ahead.
Chris Bouton: 26:02 It’s that continuous process? Yeah, I think you’re exactly right. And just being open to the new possibilities of you know, whatever it is. And then also keeping your mind open to the idea that there are plenty of different kinds of jobs out there, right. There’s not this one sort of set of things that everyone does. There really are all sorts of areas in between the standard things that people tell you about as, as career paths.
Grant Ingersoll: 26:31 Yeah. So true. Well, and I know even, you know, just for our listeners’ sake, they’ll get a kick out of I think a, you know, you in your wandering phase. This wasn’t just, you know, Chris sitting on a beach, he actually did some really cool art projects. I’ll link that up in the show note. You know, your Forest House, art car is just truly amazing. So it wasn’t like you were just sitting around either. So.
Chris Bouton: 26:54 Yeah. Yeah. I built a chat app. I built an art car. I did some crazy stuff. You know, it’s still out there on, on the app store and you know, people are using it and stuff. And so yeah, I was doing a lot of different kinds of exploration.
Grant Ingersoll: 27:10 There you go, man. And you know, but I think it also speaks to, you know, in many ways this human need to have something to do. You know, you can, you can make money from an exit, but that, that doesn’t give you a full-on satisfaction. Sure. It takes care of a lot of questions, but it doesn’t ultimately give you purpose. Absolutely. Yeah. So kind of, you know, looking a little bit forward, Chris, I mean, so you’re, you’re the CEO of a, you know, a startup. You’re in a, in a really interesting and challenging space doing, you know, for lack of a better word, AI in the biology space, you know, what are some of your main challenges as CEO these days? In terms of, you know, making that business viable, growing it ferreting out what’s, what’s real and not in the tech space?
Chris Bouton: 28:02 [Inaudible] Yeah, I mean, I’d say that one of our greatest challenges as a company right now is, is that hype cycle that we talked about. And that’s because there’s so much hype around AI right now that a standard knee jerk reaction to that hype is, well, there’s nothing actually there. And the truth is actually somewhere in between right there, there is something novel and interesting and valuable and powerful with these algorithms. But it’s, it’s not at the scale yet where, you know it’s full-blown AI the way that a lot of people are imagining in a way and the way that a lot of the hype cycle is talking about it. And so a big part of our job is to demonstrate with our clients what these algorithms can do where they do provide value and ultimately where they provide real return on investment.
Chris Bouton: 29:02 And once you actually show a client where these algorithms can do something new with manufacturing processes, with documents, search with image analytics the light bulb goes off and then they say, well, can you do that over here as well? And you say, yup, yup, we can do that too. So that, that I think is the key. One key challenge for us is just sort of like breaking through the hype cycle, identifying where the real value is of these algorithms, and then demonstrating that value for our clients.
So there’s obviously always plenty of other challenges, challenges with startup companies and scaling and all the rest of it. But we’re lucky to have a great team. People who love, you know, building these types of systems. I wake up, I’m feeling so lucky to be able to work with our team every single day. And we have a lot of fun building this stuff and that’s the most important thing. And so I’m, I’m excited to see where we go.
Grant Ingersoll: 30:06 Yeah, that’s awesome. And I know, especially in this space, because developers with this, these kinds of skills you’re talking about are very in demand. So you’ve gotta be, I’ve imagined, pretty creative around, you know, the talent acquisition and things like that.
Chris Bouton: 30:21 Yeah. Most important thing is respect your team, support your team. My job is to provide everything that I can to our team so that they can get their jobs done and have a fun time doing it. And so, you know, our, our culture is really important to us. A work-life flexibility is really important to us. Time flexibility is really important to us. And you know like I said before the people who can do this kind of stuff are, are true artists and respecting that and enabling that is the core of what my job is as a manager.
Grant Ingersoll: 30:59 Wow. There you go. I mean, folks, I mean I think you know, I usually end up asking about your best advice around a career, but I’m pretty sure you actually just gave answered that right prior to this. So, so Chris, I just want to, you know, finish up and thank you so very much for joining us on the show. I will be sure to link up a number of things that you hit on and in the show notes, the books you referenced as well as a link to VIASA so that folks can check out what all the great work you’re doing there. But yeah, thanks again for joining me here.
Chris Bouton: 31:33 Absolutely, man. Thank you so much. This is a great conversation. I really appreciate it.
Outro: 31:55 [Inaudible].
“I always had a love of science. From a very early age, it started with an interest in sharks. Then I got into molecular biology. Then I became interested in computational biology.”
“Once I got interested in computational biology, that’s where I started to really identify what I loved, which was having those high-level ideas about what I wanted to build and then building teams who could actually build that software.”
“Computational biology really wasn’t that much pursued as a career until the genome finished up.”
“When the human genome finished up, all of a sudden we had all of this data that we hadn’t had before. All of a sudden we needed computers to analyze it.”
“I think it was when I got to Pfizer that I realized that in the grand scheme of things I was nowhere near the same caliber of developer that plenty of other people in the world were.”
“It’s important for people working with software development teams to understand what it takes to build great software. Right? And so one of the best ways to do that is to try and do it yourself.”
“I actually don’t care that much about pedigree. I don’t care that much about what they’ve done in the past. What I really care about is whether they’re super psyched to get up every day and do whatever it is that they’re planning on doing in my organization.”
“The question that I asked the most often is if you wake up on a Saturday morning and you have nothing else to do, what do you do?”
“Deep learning algorithms are basically the reason that everyone’s talking about AI right now.”
“Because it’s AI, people really like to anthropomorphize what this technology is and imagine it doing everything that a human can do.”
“Even if that joy is coming from a place that’s totally not where you expected it to come from, it’s really valuable to listen to it.”
“I built a chat app. I built an art car. I did some crazy stuff.”
“My job is to provide everything that I can to our team so that they can get their jobs done and have a fun time doing.”
The Lean Startup - a radically influential book about creating a company that can adapt quickly.
E Myth Revisited - book that influenced Chris to really build a business instead of being a solo-entrepreneur
FORESTHOUSE - Check out the art car Chris and his team made for Burning Man!
Human Genome Project - The Human Genome Project had a tremendous impact on Computational Biology.
Artificial Neural Networks - Artificial Neural Networks are at the core of AI and Deep Learning.
Introduction to Neural Networks - Video explaining some of the concepts of Artificial Neural Networks.