University Of Michigan, Td Ameritrade, Michigan discussed on DataFramed
We've got somebody who has some level of expertise or considerable amount of experience in all those fields. And that makes it a very collaborative environment for our teams exploring. That's really cool. How big is the team? So about twelve or so people that's a really nice size with those types of skills since it does so own ask you about a challenge. Such teams can present part of my background is working in a cell biology lab in biophysics and thinking about silk roads, still division that these subsidies. We had physicists chemists biologists mathematicians such as myself or working together collaborating on so biological questions. Which means that you get a lot of different points of view, in essence, a lot of different types of creativity in a knowledge, everyone brings them up to the table. But a problem with that is that sometimes you not having the same conversation. Absolutely. That maybe that's the the benefit of having myself come from an academic setting where we had a, you know, I worked in large lab of twenty five plus post office in grad students and experimental researchers at the university of Michigan here and many of them to your point were either coming from physics engineering computer science mathematics about chemistries. That's my actually my background. Doing this computational chemistry type work and being able to understand the different viewpoints and understand what people bring to the table in itself is a very important thing. And having spent a good amount of time, they're realizing that there's no way even as a scientist or let alone a data scientists that you can know at all I think once you can set aside your eagle and realize that that the synergy that can be had with your collaborators can do amazing things. And I think that that's what, you know, having experienced that in the past when as we're building out our team here at TD Ameritrade. I think that's something that we very consciously keep at the top of mine and that we're not hiring the same people over and over again. In fact, my biggest thing is I'm not interested in hiring another Sean, right? What's important is that we have that diversity of experiences and thinking that really helps push the boundaries of what our team can look at for. Sure, I'm not sure I could handle another Hugo data champion. In all honesty. So you actually hiring at the moment right now, we're not, but we're always have openings up and coming in the future. But we also do have a data science team within the company here that also sits here in the office with us, and we're actively looking I'm also part of sort of enterprise wide initiative, which is an AI council. So I serve as a an advisory member of the internal council where we're looking at. How might we apply? It assigns an aspects of official diligence namely deep learning methods to help solve some real problems within the company and for those types of initiatives. I think that we're obviously going to be expanding into that realm. Even more over the next months. And so I'm sure that adding new team members will be a priority. So we'll put a linked to the you'll careers page in the show notes in any other resources that may help people who are interested in of the conversation. Like this great you smoke a bit to your background in scientific research in a lab. I'd love to know just a bit more about your background. And how. Got into data science originally. Yeah. Through maybe going a little bit further back when I was growing up I was actually coming from an Asian family. I thought that it would do me. Well, did venture down a path of becoming a physician and that never really panned out. I went to school for actually was a biology major with the with an emphasis on biochemistry, but I was actually doing that being very data driven. If you will in that, I chose that major because that was the major that had the highest probability of being accepted into a medical school in Canada. But that didn't pan out, but in school growing up, I was always very very good at math. So while doing biology, I actually did a minor in applied mathematics, and I spent a good amount of time doing research some research in computational field or in geometry field where I was exploring protein flexibility, and how that has fix on how proteins bind are different types of liggins and. And once my undergraduate career was coming to a close thinking about what was the next step in? It was recommended to me that I'm hey, maybe you should consider graduate school. And now it's up until that point something that had never considered and but looking into it. It was really a fantastic over opportunity. So I know that you had several I guess past data framed podcast guests such as Sebastian Rasha. Randy Olson who all I went to school at Michigan state. And that's right. That's actually why I went, and maybe a side note is that a Sebastian actually is sort of came after the my time there, I overlapped at the time that the that Randy was at Michigan state too. But it's the Bashan actually worked in a lab that I also rotated in as well. So we have a lot in common tool. And of course, you'll still and Sebastian and Randy roll like Strom members of the pie data community as well. Which is really cool. Yeah. Yeah. Maybe that's definitely that. That's important to us as well. So I I one of the coal. Organizers along with Ben Zayed Linden, Patricia. She choose to here in the community. And we run the high data and Arbor a monthly meet up. It's hosted here in the TD Ameritrade office. And we really spent a lot of time thinking about what the science means. And what value we can bring to the local data signs seen as well. As to some extent the startup scene. In fact, last month, our speaker is a senior legal counsel, and here TD Ameritrade who was invited to give a talk, and she talked about a topic that was titled privacy is isn't dead, which I thought was fascinating. And something that data. Scientists is important for all of us think about it's not so much of a, you know, necessarily putting your head down and crunching the numbers, right? There are people behind the data. Right. And it's important for us to all always consider the privacy aspect, but most Oliva talks are recorded in posted on YouTube. So I invite everybody to go check him out willing to them in the show notes as well. So grad school what happened after grad school during grad school. I did a lot of competition. Work. So I worked strictly in a dry lab. So did computer simulations of like protein DNA interactions some of the largest simulations of its time. Genomic stuff. Right. Right. Right. I think there's some overlap with some of your Michelle Lynn Gila as well. Who did some MDC simulations and? And. Let me if I'm wrong about molecular dynamics is simulating stuff on a really short timescale, but all the interactions, and you need a lot of computing power to do this right absolately. So I feel like a curmudgeon these days, right? Because when when when I was doing computer simulations, which is realistically not that long ago. We were using CPU's right in parallel computing on computers, and it probably took me about six months to year to produce several hundreds of nanoseconds or microseconds simulations, then we're talking about simulation time steps of PICO seconds here. And then now with the, you know, the growth of an usage of GP us people are basically reproducing simulations that Iran, right? But within weeks if not days, so people are maybe spoiled is it an exaggeration. But definitely not. I do have this vision of you being like back in my day. We never had GP us way. Did went got by with block. You know? Right. I love it. Right. So yeah. Grad school, molecular dynamics, then what happened. And so I. That's when I'm booth from Michigan state to the university of Michigan to become a post, doc. So as also in a very similar competition lab in this case of his doing simulations of protein protein, interactions protein are a and what sort of called core screen simulations where you can think about different scales of dynamics. So usually at the at Tom mystic level. You're looking at Adam to 'em at interactions. But would you are able to skill out using a more coarser grain type of model than you're looking at larger enlarge dynamics and being able to study that's always looking at so binding of proteins that might affect transcription? And so during that journey during essentially my entire PHD in post career, I think what people refer to as data science today. I was just referring to to doing science out of necessity arise. So things like applying. She analysis or k means clustering to look at what protein structures look very similar to each other. What are some of those dynamics people you call it machine learning these days? Around my colleagues. It was just a necessity again. Sure. Sure. And so then you transition from academia to industry, right? Yeah. I think a large part of that. I think may perhaps even some something that is not often talked about especially when moving from industry into from academia into industry, right or you in staying in. Academia is what happens when when you start growing up and becoming an adult and need to raise children nothing. It was precisely at the moment that I had a child that. I thought started thinking what is life mean? Right. Has it maybe perhaps existence you'll crisis? But in surpassing about whether or not I want to stay in academia, and also the what was a very competitive environment. In the academic Senate that that I was in start thinking about well, what would it mean to move into industry right into open also open up the options, especially with a funding scientific funding being very very challenging in also realizing too that maybe for the rest of my life. I'm not actually doing science and that probably end up either, you know, teach. Ching which I do enjoy. But also spending majoria my time reading papers and just writing grants and not actually doing fundamental research. I was a little bit depressing for me. And so I was very lucky a post that I had worked with who is now a faculty of the university of Michigan. Doctor Erin Frank who is a close collaborator. He worked with me. And he kind of asked me the question of did you ever consider data signs, and this was back in two thousand fourteen and up until that stage. I actually had never even heard of the phrase before. Right. Even with the digital sort of popularizing that term again data signs, and as I looked into some of the job postings that were out there. And I just kind of thought to myself like I feel like I have ninety percent of these skills that people are looking for in the other ten percent. I would be absolutely interested in learning more about and having so that curiosity, and so that's what I made the change and transition over to industry start looking for jobs out there.