Show Notes
The weather; everyone talks about it, so the old joke goes, no one does anything about it. Dr. Leela Watson, founder and CEO of InitWeather, says that by using advanced algorithms and machine learning, we can make faster and more reliable predictions about the weather that can help a wide range of industries, including agriculture, energy, and aerospace. "When I started this," said Dr. Watson, "it was, oh , let's just do this. And then when you dive into it, you realize why not so many people have been using machine learning within weather, because it is such a big problem. And just sorting through all the different ways that it can be done is a challenge."
TRANSCRIPT:
Intro: 0:01
Inventors and their inventions. Welcome to Radio Cade, a podcast from the Cade Museum for Creativity and Invention in Gainesville, Florida. The museum is named after James Robert Cade, who invented Gatorade in 1965. My name is Richard Miles. We'll introduce you to inventors and the things that motivate them. We'll learn about their personal stories, how their inventions work, and how their ideas get from the laboratory to the marketplace,
Richard Miles: 0:40
The weather: everyone talks about it, but so the old joke goes, no one does anything about it. However, using advanced algorithms and machine learning, we can make faster and better predictions about the weather that can help a wide range of industries. Welcome to Radio Cade . I'm your host, Richard Miles. Today I'm very pleased to welcome Dr. Leela Watson, a former NASA meteorologist, and the co-founder and CEO of InitWeather. Welcome to Radio Cade, Leela.
Dr. Leela Watson: 1:05
Thank you. Thanks for having me.
Richard Miles: 1:06
So Leela, I'm guessing that as a meteorologist, you have probably heard lots of jokes about the weather. Are there any good ones out there, because it's not exactly comedy gold, right?
Dr. Leela Watson: 1:15
No, it's definitely not. I mean, I've heard them all, so I'm waiting for that really great one. That real zinger, that--
Richard Miles: 1:20
That actually makes you laugh, right? Yeah. There are a lot of lame weather jokes, but none that I've heard that are really good. So that's a tragedy for your profession, but anyway, why don't we start out by explaining for the listener exactly what it is that InitWeather does. And as I understand it, you crunch a lot of existing data to come up with faster and more reliable forecast. Something that is very particularly useful for things like the agricultural industry. And of course, utility companies, as we saw in Texas a few weeks ago, actually. So, how does it weather differ from other weather forecasts?
Dr. Leela Watson: 1:51
So, at a really basic level, what we do is we use machine learning to create better weather forecasts. So, the way that we're different is that weather forecasts are generally made from computer weather models that are out there and forecasters take that information and make their weather forecast . So what we do in our product takes that weather data that's available. We run it through our machine learning algorithm, and then we do create better weather forecasts than what are currently out there now.
Richard Miles: 2:20
But you're taking obviously, from more than one data set, when you see a weather forecast on the TV or in the newspaper, are they all drawing from the same pool of data or do they all have different data sources that go into that forecast?
Dr. Leela Watson: 2:31
So when you watch a TV program, say you're watching the evening news and they're showing you graphics of the weather that's coming through the front, that's coming down, or precipitation that's going to impact that area. Those are graphics that are produced from a weather model. So, a weather model at the most basic level is really just a series of equations that govern the atmosphere and create forecasts of temperature, precipitation, and all sorts of other variables. So, those forecasters on TV and the government, anywhere, are taking those weather models and then creating their own forecast, refining them a little bit and specifying for their particular area of interest. So, that's one source of data for weather forecasts, and of course you have observational data. So what's happening. now? You have weather stations that can give you your precipitation amounts, so your temperature, humidity, and all that. So, it's kind of a combination. Forecasters look at tons and tons of data to make their forecasts . So not everybody uses the same thing, but generally there's a certain set of weather models that everyone's looking at. And of course, using your observations for your own local area to help make your forecast
Richard Miles: 3:38
So InitWeather then, just so I understand, it's not a person like you or somebody on your staff that's sort of sorting through this like a news station might; you've got a software program, I'd take it right? That sucks in all this data. Is it faster guesses about what the weather's going to be like, or do they turn out to be more reliable or is it both?
Dr. Leela Watson: 3:56
Most specifically, it's more reliable forecasts because we do rely on other data that's out there. So, whenever that data becomes available, then we take it and run it through our algorithm . So, sometimes in some cases, yes, we can be a little faster than what's out there, but really what we're shooting for is more reliable, so that various industries, people who work in those industries, can then use our forecasts more reliably than what's already out there and available. Of course, everybody wants a better forecast. So, that's what we're trying to give them.
Richard Miles: 4:24
So, Leela, if you could give me an idea of what's sort of the degree of precision that say a farmer or a large agricultural company needs in order to count as a better forecast for them? Let's say next week, a normal weather forecast say, well, we think we're going to have between three to five inches of rain in your area. If you say, okay, it's only going to be two to four. Is that valuable information to that farmer, that agricultural business? What degree of precision are we talking about?
Dr. Leela Watson: 4:47
That's absolutely valuable. And for the general public, a three-day to five-day forecast that you get from the local weather from the national weather service is good enough, but for a lot of industries, they need to have more specific forecasts , especially tailored for their areas. For example, in agriculture, temperature's actually obviously a huge, huge issue, especially frost or freezing temperatures, or if there's excess of heat. So for example, if we talk about when are we going to reach freezing? So say that we have a forecast that says, oh, it's going to be about 30 to 32 degrees on this particular night. Well, that's a big deal. If somebody is trying to protect their crops from being damaged by freezing temperatures, if they're off, if it turns out to be 35, well, then they've gone through all these procedures to protect their crops and then they didn't really need to do that. They could have saved that time and money doing something else. On the other hand, if the forecast is saying, oh, it's going to be about 35 degrees, and then all of a sudden it hits 30, well, that's a big problem too. They have to take these preventative measures to protect their crops. So, having something very accurate is very important for them.
Richard Miles: 5:58
I see. So if you can even buy them a few hours, for instance, if you know it's going to hit freezing at exactly 2:30 AM, as opposed to 4 or 12 or wetter, then that could make the difference between getting out that equipment, say to save a citrus crop or something like that and not. Is that more or less accurate?
Dr. Leela Watson: 6:11
Absolutely. It's not only how low the temperature will go, but like you mentioned, what's the timing, especially with precipitation, also. We can do pretty good with precipitation forecast, but usually we're off with timing or maybe location, and that stuff is very important to the farmers that are relying on precipitation for their crops and definitely in other industries as well.
Richard Miles: 6:33
So give us an idea of the other industries. I mean, I mentioned that the utility industry, the whole experience in Texas, where they had to sub freezing weathers for a long time, they didn't really know, is that a one-off thing or is that a common problem for utility companies as they try to forecast demand and so on? Would something InitWeather give them as much of an advantage as say a farmer?
Dr. Leela Watson: 6:52
Absolutely. Yeah . Weather impacts almost every industry in some way or another. Of course, we hear a lot about severe weather and that has very detrimental effects on many industries. And then there's of course, just the mundane weather. When are we going to reach freezing like I mentioned, or is it going to be very, very hot or lots of rain, but we work in many different industries, and one area that we also work in is aerospace. So, they're not really concerned with temperatures at the surface; their big problem is upper-level winds. So, for them having accurate upper level winds forecast is very important, and it doesn't even have to be a large wind event to make an issue for them. So, it's just blowing slightly harder at the upper levels, well, that has an impact on their rocket. So, that could change their trajectory, it could blow the rocket, it could topple over. So there's many, many different areas that, industries that the weather will impact.
Richard Miles: 7:46
That's fascinating, because that's obviously a case in which being a little bit off can cost you a hundred million dollars or a lot of money if you lose a rocket or something like that. Is it correct? Are you partnering now with a company to get into the unmanned aerial vehicle space to collect very, very high altitude weather? Is that correct?
Dr. Leela Watson: 8:03
That is actually a project we're working on right now. So, we would love to be able to have better upper level observations, especially wind observations for the aerospace industry. And actually, it can be useful to other industries as well. Right now we rely on weather balloons. That technology is almost over a hundred years old and it does its job and it works okay, but we're thinking let's look to the future. How are we going to improve that some more? So the project we're working on is to take a UAV and use it like a weather balloon and send it up vertically, collect weather observations, and bring it down and be able to do that multiple times in the leadup to a launch, so they can get that information and use that for their rocket trajectories and forecasting for launch.
Richard Miles: 8:47
So very much like a custom design solution. This would be for a particular client who wants a very particular, say, launch window or period of time that you would put up that UAV. This wouldn't be an ongoing service, because I imagine that'd be pretty expensive.
Dr. Leela Watson: 8:58
Yes, it would be pretty expensive. Um, but so it would be designed for launches, and launches, they're doing many, many more of them now, and there's more launch sites that are opening up worldwide. So, there is a market for that. It's still in its infancy, this project, but we're hoping that it takes off soon.
Richard Miles: 9:16
Tell us a little bit, Leela, about the origin stories of InitWeather. For some young companies or inventors, it's the classic Eureka moment: all of a sudden you have this blinding insight. Was it like that for you and your co-founders? Did it just dawn on you: hey, we've got something that we can package into a model that is very useful? Or did you just sort of iterate or stumble your way towards that model?
Dr. Leela Watson: 9:36
I would say it was years in the making for me. My job before I started this company was working for NASA's applied meteorology unit. So, we had a contract and I worked on that contract and we worked with NASA and supported their space program. And, I was the resident weather modeler, so I ran weather models and I came up with solutions specific to Kennedy Space Center and Cape Canaveral Air Force Station. And of course, everybody wants a better model, better output, but there's only so much you can do with what you have. Now, what's out there is , is pretty amazing, but it comes to a point where it's very hard to keep improving that model. So, a series of events happened , um , and I started this company, and the idea was: let's come up with a different way to improve the models instead of the same old way of throwing more computing resources at it, which equals more money for very little improvement. Let's come up with a new way to create a better forecast. And of course, artificial intelligence machine learning is not new, and it had been being used in other industries and even within weather, but I hadn't seen it implemented in the weather industry, maybe in little pockets, but that was my push to develop this and to create that better weather model.
Richard Miles: 10:48
And Leela, remind me what year was that? That was 2016. Is that right?
Dr. Leela Watson: 10:51
Correct.
Richard Miles: 10:52
Yeah. And how has it been since then? This is a fairly common path, or it's not an unusual path that a university researcher or somebody sort of in advanced research working in the private sector will say: Hey, I've got a new idea. They form a company and they take their technology invention to market. How has it been for InitWeather? How many employees do you have right now? And what has that journey been like so far?
Dr. Leela Watson: 11:10
So we're still very small. We're only three of us, and it's been a very slow process, I would say, because I think at the end of the day, it's great to say: Oh yes, we're going to use machine learning, create a better weather forecast, but there are so many different ways that you can use machine learning to create these better forecasts. I always call it the original, big data problem. There's so much data, and so it takes some time to gather that data, run it through the algorithm, decide what's the best way to proceed, what the best data is. So, it has kind of been a learning process along the way as well. When I started this, it was: Oh , let's just do this. And then when you dive into it, you realize why not so many people have been using machine learning within weather, because it is such a big problem. And just sorting through all the different ways that it can be done is a challenge, but we've gotten really great results. So, now we are in the process of selling product to a lot more customers, and we're starting to grow now.
Richard Miles: 12:06
That's great news. I know you've done well in competition, including the Cade Prize competition, when you entered there and you were a finalist. I imagine it's a different world, right? This is what always fascinates me, is that a lot of these pitch competitions or so on, you rarely encounter a bad idea. I mean, all the ideas are pretty good and you , you see them and you, you hear about them and go: well that, yes, it's plausible, it's useful. But then, getting from that stage where people will say, yep , good idea to , okay, who exactly is going to buy it or pay for this great idea, how do you do it in such a way that it's sustainable and not just a one-off thing?, And that is particularly for people from a research background, sometimes can be frustrating I imagine.
Dr. Leela Watson: 12:44
Absolutely. It can definitely be frustrating. And then within weather , we have to compete with free data, free weather, national weather service. They have issued their forecasts all the time and that's free. You can just go to their website and look at it. But you know, private weather industry really focuses on specific industries and specific problems, and so that's kind of what we're going after. We want to help the industries and our customers that the national weather service forecast is not going to help them. You know, they need more specific information, and they need more consistently better information. So, I think that's really important too . You know, there's a lot of times that weather models or forecasters really hit one event really well. They nailed it, but we want to be able to do that all the time. And so that's our goal with our machine learning product is be better and be consistent.
Richard Miles: 13:30
Right, it's a reliability factor. I think you're probably onto something, concentrating on the agricultural field. I interviewed last year, the national director of the 4H Foundation, and she pointed out to me that farmers in particular have always been actually early adopters of technology, because they have to be. When you present them with something that actually saves them money right away, they'll try it out, and they don't need a whole lot of convincing. And then if it doesn't work, well, then they stop. But if you can show them that it's going to increase the yield or protect crops in your case, then they're willing to give it a try. And a lot of the innovation that is actually later made it to the broader market starts in the agriculture market because of farmers trying to solve problems or agricultural companies trying to solve problems.
Dr. Leela Watson: 14:09
Yeah, absolutely. Our first customers, the first people that came up to us were all within the agriculture industry. And that wasn't actually even our focus at first, you know, we were kind of looking more towards commodities, which is agriculture to that in aerospace. And then we just had farms, farmers, anybody in the agriculture industry come up to us and say, well , this is really great. We could definitely use this. And that's kind of how we got into that industry. So, they've been great. They've been willing to try our product and use it and it's worked for them. So, we're happy about that.
Richard Miles: 14:41
I don't know how much you work with commodities traders or if you're pitching this product to them. But this idea of being able to seize on a market opportunity, even a few hours before somebody else, it hadn't dawned on me that wow, that of course would be a valuable service that you could provide. If you were able to provide that again, that reliable data, you can only be really wrong once, right. People will quit using it .
Dr. Leela Watson: 15:01
Absolutely. They do, they need that information and they need it quickly. And we're definitely aware that we need to be correct. We're not on a local news. We can't be wrong and still keep our job if we don't perform and produce something really great, they can just say: Hey, we're done and move on.
Richard Miles: 15:15
I was going to say probably the second, most proper type of weather joke is complaining about the weather man , right, who got it completely wrong? I think you're part of a trend that's obviously been underway for a while of being able to take large data, big data and crunch it and use it, really add value to a certain segment of the market. Obviously, probably not the retail market for quite a while . I've mentioned this before my daughter works in the insurance industry and the car insurance industry, and she was telling me that there's one company that basically just has a huge data set on every single car produced, every single feature down to the nth degree, so that a car insurance company knows exactly how safe or unsafe that car is particularly now with semi-autonomous vehicles. And those companies that aggregate that data and sell it to the car insurance companies do pretty well, because that's extremely valuable set of information that a handful of customers out there are willing to pay a lot of money for.
Dr. Leela Watson: 16:05
Yeah, absolutely. I've noticed that there are many different industries that need that weather data and they don't know how to get it, process it, and use it for their industry. So that's definitely a big thing for weather companies too, is being able to get all that data, put it together in a way that is useful to them, presenting it in an easy to use way as well.
Richard Miles: 16:25
So Leela, you mentioned you're still very small ,about four people. How do you spend most of your day now? I don't imagine it's in sort of the research and , or is it? Or are you on the phone talking to clients or potential customers, or where do you put your energies at this point?
Dr. Leela Watson: 16:39
A little bit of both. So I have a really great business partner, Jordanna, and she takes on a lot of the business side of things and allows me to keep my hands in the research part. So, I do a lot of coding still, and I really love that. I mean, that's my bread and butter type thing. Of course, I am on the business side of things as well, but the idea was born out of my experience using weather models. So, we found that it was important for me to keep my hands in that side of things. I spend a lot of my day doing that. And then of cours,e we're a new company or new-ish company, I should say, we're small. So, we all have to do our part and do all the other administrative and business things that occur, so.
Richard Miles: 17:15
So, I have to wonder with my limited experience of starting institutions, just the Cade Museum, we had a staff of four forever. And then all of a sudden we went to a staff of 30 almost overnight. And you always worry about what if we fail, but you never really think, well, what if we succeed? Then all of a sudden your life gets a lot busier and actually more complicated. So managing success is often part of the managing decline. So just a word of caution. So Leela, you were born and raised in Massachusetts, like a lot of people from the Northeast, you ended up in Florida. You picked up a bachelor's degree at the University of Miami and then a Master's and PhD at Florida State University. And you said your first real interest in the weather was after experiencing or surviving a hurricane in Miami, I'm not sure which. Tell us what was that like and how did that steer you into meteorology?
Dr. Leela Watson: 17:56
It was actually just a small, small, I put in quotes, hurricane that actually had a big impact out of Miami, as far as flooding. I wasn't very worried about it, and then I found that I was trying to get home--I was working actually--and the storm was coming up from the South, and I had to drive South back down to Miami. I was working in Fort Lauderdale and had to drive back down South. And so, when I left Fort Lauderdale things weren't too bad, but by the time I got down to Miami, it was a different story, and my road was flooded and I had to find a different way of getting home. And it actually just left a big impact on me because for a small, again quote small storm, it had a big impact. So that really fascinated me. I'd always been fascinated by weather, but that was kind of the nail in the coffin that made me realize: Hey, I think this is what I want to do and study this. And so, that's how I got into meteorology and decided to go to grad school and get my degree there.
Richard Miles: 18:48
So you'd already graduated with your undergraduate degree and you were working after that, and that's when you figured out, okay, this is pretty interesting.
Dr. Leela Watson: 18:53
Yeah. So my undergraduate degree was environmental science. And so, I decided to move on to meteorology. I was still interested in environmental science, but meteorology just really fascinated me. So I moved onto that.
Richard Miles: 19:07
So inventors often marched to the beat of a different drummer, and we've talked a lot of inventors and entrepreneurs on this show and , and often they have very interesting paths. What were you like as a kid? Were you a good student? What were your interests? Do you remember when you were small, let's say in grade school first or second grade, do you remember what you wanted to be when you grew up?
Dr. Leela Watson: 19:24
I was definitely an introvert, and I was a good student. Weirdly enough, when I was young, I wanted to be a stockbroker. I don't know why.
Richard Miles: 19:39
Oh wow! That's a very unusual first grade dream.
Dr. Leela Watson: 19:39
I know, very weird, but as I grew up and grew older, I realized science was what I was really interested in. And so it was just a no-brainer for me to go down that path. I never thought I would do anything else. And as I got older, I knew again, I was going to go to grad school. When, I didn't know, if I was going to take some time off and then go. But my path was actually pretty clear. And then the whole entrepreneurship thinking about business, it kind of had always been there. My father was a doctor, he had his own practice, and he always talked about business and how he wanted to be in business. So, that kind of got ingrained in me, and I started thinking about it early. I didn't know if I would actually ever start my own business, but that seed was planted early. And then of course, just working and having so many ideas of how we could do things better from the science side, from the business side, from the marketing side, it became clear. Alright, this is my time. I need to do something about it and start my own business.
Richard Miles: 20:31
So these things or these inclinations often run in families. You mentioned your dad was a doctor. Anyone else in your family, extended family, that is in the general field of numbers, number crunching? I think it's really interesting you wanted to be a stockbroker. Ultimately, right, that's processing big data every day for a lot of money or maybe a lot of money. Anyone else? Do you have siblings that do something similar, or?
Dr. Leela Watson: 20:50
My brother is an engineer. My other brother is in computers. My sister was a stay at home mom, but she's in the political arena now. So not quite the same path as me. I think it is interesting. The fact that I did want to be a stockbroker and then ended up meteorologist I guess my path is always to try to predict the future. That was the path I was on for my job. So,
Richard Miles: 21:11
And certainly in both fields, stock picking and picking the weather, people know when you're wrong, right?
Dr. Leela Watson: 21:16
Yes, absolutely.
Richard Miles: 21:17
There's no hiding from your prediction, like oh I didn't mean that.
Dr. Leela Watson: 21:20
Yeah. At least I don't have to worry about other people's money. It's just the weather. Is it going to rain on their head or not.
Richard Miles: 21:24
Exactly. Exactly. You're at the stage in your career now where you've had some early success and you're probably asked to speak maybe to groups or people ask you for advice. What do you tell them? Are there any things that you know now that you should know known , say when you were a college freshmen or recent college graduate that you've learned either at NASA or in starting up InitWeather that you would impart to say a younger version of you,
Dr. Leela Watson: 21:46
As far as choosing meteorology, I would definitely say, be ready for math and physics. Because when I got into meteorology, you see on the weather channel the graphics and talking about the weather, but then when you get into school, you realize the fundamentals behind the meteorology are all math and physics. And I've heard that a lot with students that came in to the meteorology program, they didn't realize that was what it was. So you have to love science. You have to love math in order to succeed. Um, as far as starting my own business, what advice would I impart? Just be ready to work. It's a lot of work you're going to work all the time. I work pretty much every day of the week, but on the flip side, it's something you love. It's something that gives you happiness. I wake up and I'm happy to start work, which doesn't always happen for everybody. So be ready to put your head down and grind and the rewards will come.
Richard Miles: 22:38
So one thing I find fascinating is someone who goes from working for fairly large organization. So you've worked at NASA, a subcontractor, right? For NASA. So you worked for one of the largest organizations out there and you go to a four person business compare and contrast. What is that like? In terms of just the whole psychology of on one hand was my case. I worked for the Federal Government for a long time, you show up at work and you're at a large office building with hundreds of thousands of people. You never have to worry. Who's going to pay the light bill. You never have to worry about that sort of stuff. And then you go to this existence where you worry about that all the time. So was it a psychological hurdle? Was it exciting? Was it terrifying? All of the above?
Dr. Leela Watson: 23:15
All of the above. Definitely. So working for a larger organization, your voice is not heard as much when you have a conference, there's 30 people in the room talking and you can put your ideas out there, but there's a whole lot of other people and ideas floating around. So sometimes you'd feel like you're not heard. So going to a small group. It's nice because everybody's heard everybody's ideas are taken into consideration as far as paying the electric bill and , and keeping me awake at night. Yes, I've definitely gone through that. I definitely wake up sometimes and think we need to do X, Y, and Z. It has to be done right now. And it is somewhat terrifying, but it's very rewarding at the same time. And I like being small right now. I don't think if we grew overnight, like you mentioned earlier, it's a different set of problems. So it's nice that we're a small group. Now I can handle that now and hopefully we'll grow and I'll be able to transition into that managing a larger group as well. But I like it, how it is now,
Richard Miles: 24:10
I think you put your finger on something you said earlier, and this is my own experience as well, going from the federal government to starting a little nonprofit . And when you're working for larger organization, often you're working on very big, exciting, important things, but you're not exactly sure what your contribution was sometimes where you're one of a cast of dozens or hundreds or thousands, but in your own little micro company or nonprofit by golly, you know, exactly at the end of the day, what you did get done or didn't get done. And what your role in that success or failure was, there's never any doubt about the importance of your role, whereas you do have sometimes at , and in much larger corporations, you think, well, I did a good job. It doesn't matter. So what I tell people sometimes particularly if you were relatively young in your twenties and so on, consider working for a small corporation, small company, or a startup, because a lot of people that age want responsibility. And then unfortunately the large organization, you may not get that in your career until you're considerably older before you get real managerial responsibility or decision-making authority. So it sounds like you're happy with that trade off as well that you get to look back on your day and know precisely where the Leela Watson played a role and what it meant.
Dr. Leela Watson: 25:15
Yeah, absolutely. Every day is different. And I love that. And every problem and challenge is different. And I love that. And I absolutely agree with somebody who wants to come into a smaller organization. Yes, you're taking a risk because who knows what the future of that organization is, but you will absolutely be given responsibility when we hire anybody. We want them to take the reins . We want them to think outside the box and come up with new ideas and go off on their own and be innovative. And we hope that's what they'll do, because quite honestly, we don't have time to micromanage everybody. So that's what we're looking for. And so if that's what interests you in a small organization is absolutely a great place to be.
Richard Miles: 25:54
So Leela, great advice. Thank you for being on Radio Cade and at a minimum, we now have at least one more person we can blame if the weather doesn't turn out right now. We can be more precise about our blame. Like, Hey, it didn't freeze exactly 2:30. Like they said it was going too.
Dr. Leela Watson: 26:07
Yeah, we'll take it. We're used to it.
Richard Miles: 26:10
Best of luck InitWeather you all have gotten a strong start and you look like you're headed to a big things. And so I hope we can have you back at some point after your IPO, right. And you're cashing out your millions of dollars. Anyway. Thank you very much for joining us on Radio Cade.
Dr. Leela Watson: 26:25
Thank you, thanks for having me .
Outro: 26:28
Radio Cade is produced by the Cade Museum for Creativity and Invention located in Gainesville, Florida. Richard Miles is the podcast host and Ellie Thom coordinates inventor interviews. Podcasts are recorded at Heartwood Soundstage, and edited and mixed by Bob McPeak. The Radio Cade theme song was produced and performed by Tracy Collins and features violinist Jacob Lawson.
