My Guest Appearance on The Talking Block

I'm on a podcast now!

I was recently featured as a guest on “The Talking Block”. You can find the full interview here on Spotify. You can also find the full transcript below:

Transcript

1) Hi Matthew, could you give our listeners a quick sense of what you’ve done professionally since graduating from Brown?

Sure! So since graduating (I was class of 2015, molecular biology) I’ve worked in several labs working on research related to regenerative medicine. Worked in a Lab working on Alzheimer’s, a lab working on stem cell vesicles. I helped organize a biohackerspace, which grew to have paying members and a full biosafety level 1 lab space, and I worked on a company for making anti-aging metabolites cheaper. In the middle of this work ended up transitioning from biomedical research into machine learning engineering. I did freelance machine learning for companies ranging from facial recognition to software for DJs. I even worked for a long stretch with the Tensorflow team at Google on probabilistic programming. And now, I do machine learning work for a passwordless authentication startup. That’s…wow, it’s only been 4 years?

2) It is my sense that throughout your time at Brown, and perhaps even before then, you’ve been steeped in biology oriented research. Could you speak, perhaps very broadly, to what you did in this capacity and what your goals were?

I think when I really started focusing on biology was when I was about 12, I started helping out at a wildlife hospital, the kind that took in injured and sick wild animals, rehabilitated them, and then released them. I was eventually able to pass the veterinary intern test at this place and do things like help out with the medical wards, or handle tasks outside the sterile field of surgeries.

Around this I became interested in aging. I wondered why certain animals like the hawks would be outlived by the tortoises, even if the former was otherwise healthy and in its prime. I began to look up researchers in this field and I saw, “hey, actual scientists have really been looking into this”. I just became fixated on aging after that. I interned at a lab working on osteoporosis in high school. Became president of the biology club at my school. Interned in at the Vyss institute. Went to Brown so I could learn from Sedivy, Helfand, and Tatar. And did research in a lab working on fruit fly aging. I took pretty close to every single advanced molecular biology course at Brown. My goal was to learn as much as humanly possible about this space, and then from there figure out the best course of action. My goal was to find something that could reduce the chances of many of the age-related diseases that run in my family. You could say that this was a pretty personal motivation for all this.

**3) Before we delve further into this interview, could you give us a sense of how you got interested in Computer Science, and maybe Science more generally. Your list of accomplishments within scientific research or otherwise suggests a deep passion for the space. Could you perhaps explain how this passion developed? **

Computer science, mainly started out like any other hobby. I think it was when I first started messing around with the HTML tags on websites, which was how I made my foray into JavaScript. I was using languages like R for projects in school like analyzing medical records for the wildlife hospital I mentioned earlier. One of my later high school internships was working on DNA origami designing software with the Vyss institute. Basically, I saw CS as a useful skill for pursuing my other goals in biology. You could even say I first got the basics of machine learning accidentally. During college I worked in one of the robotics labs at MIT, created software for processing actuarial data for longevity research. Even after college I was doing algorithm work for one of the biomedical engineering labs at MIT, doing computer vision work for multi-photon microscopy. It was really only after trying to start a biotech company that relied heavily on genetic algorithms after college that I thought (hey, maybe I should focus on this computer science thing a bit more deeply). For computer science, I guess I realized that improving the analysis tools themselves for aging research would have the biggest return on investment. So, I decided.

As for science itself, I wish I had a more compelling story. I’ve wanted to be some kind of scientist for as long as I can remember, except maybe when I was 3 years old and I wanted to be Big Bird from sesame Street. At one point it was astronomy, at one point it was paleontology. I guess at some point I just started collecting as many books as I could from garage sales and second hand stores that my parents took me to that I got hooked.

4) You also founded another company by the name of Project AgeTuneUp. Could you explain what this company does, perhaps in a way that those less scientifically inclined might understand?

So, Project AgeTuneUp was founded in response to some interesting research on NAD precursors and aging. So NAD is one of these molecules that is responsible for many different processes in cells, including in cell and genetic repair. One of the hypotheses for aging is that cells run low on the charged, useful form of NAD and accumulate the less useful version NADH. Multiple labs have tried supplying cells and model organisms (like mice) with precursors to NAD. One of the more promising ones was a molecule called NMN. David Sinclair’s lab at Harvard Medical school had published experiments back in 2013 on how this could do amazing things in mice. The problem at the time was that this molecule was incredibly expensive to produce. So much so that these two-week mouse experiments were almost impossible to fund for humans.

My solution was to create microbes that could manufacture a lot of this molecule. Much more than they could consume themselves. I had a lab space, had several PhD biologists helping me out, and even made several living prototypes of the microbes. I even ended up raising a half million seed round.

Ultimately, I ended shutting down the company and returning money back to the investors. At the very least I can say that all the investors got a 1X return. THere were a few issues. Some of the incumbent companies were in a pretty vicious legal battle (and whoever won, would probably come after me next). There was also the fact that I was not a graduate student or otherwise affiliated with the labs that produced the NMN research. All I had was an undergrad. The memory of Elizabeth holmes was still fresh in every biotech investors’ mind. Some of the VCs I talked to also pointed out that if I failed, this wouldn’t just damage me, but the entire field of aging as a whole (which is still pushing for much-needed legitimacy)

**5) As you intimated earlier, you’ve worked in a number of different tech oriented roles, having worked, it seems, extensively with machine learning and encryption tools. Could you speak broadly to the work you’ve done in these spaces, your view on the prospects of machine learning and perhaps any ethical concerns that arise, and your view on establishing robust security protocols for large corporates in a time where data breaches seem all too common? **

So, when I went into tech, I focused on becoming good at machine learning first. This was an area I had the most exposure to, even as an undergrad. Of course, unlike in academia, it’s not enough to just run a machine learning model on some data and print out a report. Machine learning as a skill becomes much more in-demand when it can be combined with other stacks. I’ve created machine learning models that can be deployed on websites, desktop apps, mobile apps, and even distributed and blockchain apps.

Being able to explore all those areas meant that could get a better sense of where machine learning still struggled. One of those areas was with keeping the big data you’re working with safe.

6) What advice would you give you to those interested in getting smart on the different spaces within technology?

I think for the first dive, it’s important to get a sense of how these different spaces relate to each other. When we talk about tech, it seems like most are referring to web apps or mobile apps, or the backend that supports those. I’d recommend walter issacson’s “The Innovators” if you want an idea of the historical context of how these different technologies came to be, and the thought process behind the inventors.

For a deeper understanding, focus on learning by doing. It’s important to read as much as you can for the first intuition (you can speed up that process with something like Audible or Speechify), but once you have a sense for the landscape I recommend following some actual tutorials, and learning by building.

I think one of the worst things you can do when learning a new subject is going it alone. You can become

I actually recently posted about lessons from my particular journey into machine learning on Hackernoon

7) If you could give yourself advice coming out of college now, in light of what you’ve seen, what would that advice be?

I’ll focus on advice that would apply to more than just my unusual circumstances. If I could give myself advice coming out of college, I think the SEC would need new rules for insider-trading by time-travel.

I guess the advice I would give myself is to maintain a growth mindset. I know that’s the advice often given to people entering college, but I think it’s even more important afterwards. Many people view education as something that stops once formal education ends. The world is not only changing, but it’s changing faster. It’s important to keep a habit of constantly learning, and always keeping in mind that you have the ability to improve yourself a lot.

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