XTitle: Innovating Sizing and Digital Fit Systems: Nathan Ghabour '13

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[00:00:06,320] CHRISTA DOWNEY: Welcome to Engineering Career Conversations. I'm Christa Downey, Director of the Engineering Career Center at Cornell University. 

[00:00:14,240] TRACI NATHANS-KELLY: And I'm Traci Nathans-Kelly, Director of the Engineering Communications Program. We are excited to bring you this forum where we will host lively conversations that we hope will inspire you.  

[00:00:26,360] CHRISTA DOWNEY: We're happy to share today's conversation with Nathan Ghabour. Nathan, who currently works at Nike, is an experienced product leader in the computer software industry, skilled in computer aided design, biomechanics, surface modeling, and product design. Nathan's career launched with the Bachelor of Science focused in Biomechanical Engineering from Cornell University, class of 2013. Welcome, Nathan. 

[00:00:48,790] NATHAN GHABOUR: Thanks. Thanks for having me.

[00:00:51,329] CHRISTA DOWNEY:  Nathan, let's start by having you tell us a little bit about your current work. 

[00:00:55,010] NATHAN GHABOUR: Yeah, right now I work at Nike within the Innovation Department called NXT. NXT is a group of innovators that Nike created that allows us to build the future of sport. It's actually a really cool place to see such an investment in innovation. Unlike other apparel companies, Nike really wants to keep setting itself as a leader in the industry. I work in a department that's called NXT Digital. Because innovation existed before computers and Nike, I help lead a more digital wave focusing on machine learning and product creation. I use avatars and bodies to enable people to design and use a large set of data to allow us to understand all the different shapes and poses humans have. 

[00:01:52,190] TRACI NATHANS-KELLY: I think that that is really fascinating. What does your day look like then? I'm imagining all kinds of things, but I want to know what is actually true. 

[00:02:05,170] NATHAN GHABOUR: Yeah, I'm a director now, so I do a lot of different things. When I worked at a startup, I wore a lot of hats, and now as a director, I wear a lot of hats. Different hats though. Part of my job is solving hard problems. Really looking at what we can do with the datasets that we've collected over the last couple of years at Nike and what are ways that we can create better product using that. That's where I use my engineering skills to solve these bigger problems. Like what does a new sizing system look like? Or what are better ways to approach sizing across the industry? Then other parts of my job are really educating people. People don't necessarily know what machine learning means in and outside of Nike. Other things in terms of helping people understand the use cases and applications of the technology that we create. Then building the tools to do that. I also manage a group of engineers that also then are building the tools and capabilities that are going to create UIs and interfaces for people to actually do the work themselves so that they can use all the data that we've collected. 

[00:03:20,730] TRACI NATHANS-KELLY: With that in mind, I have a follow up question. I quite often, I'm talking to my undergraduates about using avatars or case histories. Right, Whether they're real or fictional, in order to get our heads around a problem or a circumstance. How do you develop those in house and then deploy them so you can help make a better product? 

[00:03:45,870] NATHAN GHABOUR: Yeah, ironically enough, I worked for the company that helped create a standard for this. My third job out of Cornell was at a company called Body Labs. And they were a company that really focused on understanding human shape and pose. At my first job, I was trying to make insoles for peoples' shoes. And I was doing that using CAD and designing. And I longed for a foot to put in my CAD system so that I can actually design the perfect insole. But getting that foot was quite hard. Scanning technology is still at a place where it's pretty basic, you're capturing a bunch of data and you're creating a point cloud and it's just a bunch literal points in an XYZ plane. Body Labs took those point clouds and we're able to actually give you shapes of humans. It's a statistical model that creates a really unified mesh. It's using a research paper called Simple, which is a single multi layered person model. And it basically looks at the variables that can understand shape and pose as separate components using thousands of data points of humans being scanned. And then creates an equation that solves those, that then creates a template. Then when anyone gets scanned, it fits the point cloud as best as possible. You can imagine it like a human shaped balloon that's being pumped to fit all the point clouds as possible. And then you have this wonderful unified mesh that you can import into any CAD system. You can analyze, you can predict and use it across all different points of product creation. 

[00:05:42,889] TRACI NATHANS-KELLY: I don't think I'm going to get over the idea of pumping up a human form. That's amazing. Thank you for the visual. It helps start making sense. 

[00:05:55,930] NATHAN GHABOUR: We had to really think about different ways of explaining the technology because explaining a Gaussian equation to non-engineers is difficult. I tell a lot of people my job is helping create multivariable calculus in an approachable way to people who have only taken algebra. I work with designers and I work with people who make patterns and clothes. And they really have not taken multivariable calculus, so they don't understand surface geometries, they don't understand Gaussian equations. And a lot of my job is trying to create these analogies that help take complicated things that we do in math and really get it to be in an approachable way that other people can understand. 

[00:06:46,629] TRACI NATHANS-KELLY: Already today, you're doing a good job. 

[00:06:48,290] CHRISTA DOWNEY: Yes, I'm envisioning this. I'm thinking about this and thinking about how many students have an interest in product design. I'm curious if you could go back and tell us more about your path of how you got to where you are today. 

[00:07:02,510] NATHAN GHABOUR: Out of Cornell, I was an independent major. I was already taking a path off the course that everyone can normally take. And with that, I saw a future of really wanting to be a designer. But I knew the traditional sense of engineering is a place where you start off as a test engineer. You're going to learn something. You're going to make the perfect screw or fixture for ten years. And then after those ten years you can start designing. I didn't want to do that. I started off at HSS right out of college. And I was doing internship there building tooling for measuring MCL circumferences to get surface areas. And I was like, this is really cool, this is part one of really understanding ligaments. I understood the value, but I also recognized that I was really not designing, so I was adamant to search for something different and that's where startup land really helped where I found the ability to find people who were willing to really iterate quickly and test things and have a problem that I felt confident I could solve. And that led to me helping start the company SOLS. We were a start up that was founded in 2013 that 3D printed custom insoles for your shoes. And so it solves a lot of things in me not knowing how to make things per se the way that a test engineer would, because it's 3D printing. And I did learn a lot about 3D printing though, and how it works and what to do with it. But I also got to design a product using my biomechanics and using my CAD experience that really have become foundational and how I think, what ways I look at product creation using a lens of mechanical engineering, where the industry is segmented into industrial designers and mechanical engineers. And sometimes they talk to each other and sometimes they don't. Whereas I see this combination really working harmoniously to create product that not only looks good but performs. 

[00:09:20,969] TRACI NATHANS-KELLY: Nathan, as you know, I help teach Engineering Communication within the College of Engineering. And something that you said a couple of minutes ago really intrigued me about how you have to daily explain things from one set of folks who are experts over here to another set of experts who are over there. So that's a communication skill, right? How do you maneuver that? How do you know what the right moves are or or tell a story about when you made a mistake and were able to correct it, or anything that strikes your fancy? I'm just interested. 

[00:09:55,149] NATHAN GHABOUR: Yeah, totally. My job as translator has gone from technical to technical to technical to business. And I think the way my career path has built, it has helped me gain the skills to do that. And, you know, when I was at SOLS, I transitioned from being an engineer to being more of a product manager, where I was taking the requirements of what we were doing in CAD and being able to tell a CAD engineer, hey, I need these features. Like I'm creating a surface offset and here's what a surface offset does, I need you to build it. And that started to create a framework of understanding needs and requirements and then being able to articulate them to someone to build this thing, which inherently felt like an easy thing to do as an engineer because we know how to build things. As things progressed, I went to Body Labs and that's where I was a full time product manager. I was then stuck with this like big topic of statistical modeling, machine learning, and really trying to wrap my head around it. I was not a computer science major, I was a mechanical engineer. I spent as much as I could to learn the work, and I dug up my old math books and try to understand these equations again, because it had been a couple of years. But it was like one, trying to gain an internal narrative of what the object is or what I'm trying to give. And then being able to just explain it without terms that people don't know. Taking a hard concept and trying to explain it with terms that people do know. Saying the word Gaussian, there's an assumption there that someone even knows what that is. It takes an iterative approach to distill what words are focused in engineering and what words aren't. A human shaped balloon is something everyone can imagine. Whereas a statistical model and using complicated words that are focused in engineering become a lot more confusing. You also pick up visual cues where you can definitely, as you talk to someone, recognize that if your technical words are too technical, you see this overwhelmed face or like this person is clearly getting overwhelmed with my words, then that has a moment for me to reassess like, okay, how far back can I go? How far up do I need to bubble this up? And saying less while saying more is the mantra people say, which is try to get this to a level of competence that you are still not diluting the work into something that's wrong. But also is in a way that people, it meets them where they are. And asking questions. I'm one to always just say like that makes sense. And it's not like ego thing for me to say that I didn't make sense and I failed. Again, it's this translation. And making sure that when you present things, it's a conversation. It's not a report. It's like here it is. This is what it is. It's really this dynamic thing that really requires feedback and honesty, and a little vulnerability from both ends. Recognizing where you are in your communication skills and where that person is, and just really getting to that common goal of understanding each other. 

[00:13:46,855] TRACI NATHANS-KELLY: I'm over here in this recording booth cheering because it's nice to talk to somebody who's out there doing the engineering work, but is still using some of the same vocabulary to try to explain that communication structure, translation, distilling your ideas, like all of those things that you were just mentioning, even the vulnerability to self-correct or ask input. It's really an important point. I was excited earlier too when you started to talk about having the mesh come together when you're designing something. Because I've been able to work with some professors and several classes where we get into mesh convergence. This is all we care about. 

[00:14:33,200] NATHAN GHABOUR:Yeah. 

[00:14:34,460] TRACI NATHANS-KELLY: And then we have to write up the report about it along the way so others can use the work. 

[00:14:39,100] NATHAN GHABOUR: Exactly, yeah. It's also monetarily incentivized for you to be good at that when you look at the industry, money isn't everything. But you are rewarded for being someone that can translate things well. The product industry is vast, but also highly paid, it really does create a ladder for career growth where you go from being an engineer to being someone who writes what engineers do. That gives you a much higher, robust level of control. You're thinking in a much bigger picture and at the same time you're gaining trust within your colleagues and the other parts of the business that need to exist to create said product or innovation. And the better you do that, the more people are like, wow, you really should be able to do more of this. Because you're really getting this company to work together and that's one of the hardest things to do, actually. Building the product is hard, but engineers can do that. People get that. It's getting all the pieces to connect that really is the powerhouse that someone can be. The more you do it, the more you're going to get recognized for it. 

[00:16:01,429] TRACI NATHANS-KELLY: Yeah, an engineering colleague of mine always says engineering is easy. It's the people that are hard. 

[00:16:09,210] NATHAN GHABOUR: Yes, fortunately, and unfortunately, they're complicated. 

[00:16:13,309] TRACI NATHANS-KELLY: Messy, wonderful things, people are. I want to take this idea of growing from being an individual contributor and you're now doing these magnificent things across technical groups, across development business groups. Let's take the lens a little bit wider here. We always like to ask folks who join us, how does your work contribute to a healthier, equitable, or more sustainable world? How do you see this all fitting in? 

[00:16:42,349] NATHAN GHABOUR: My work has always been towards being an engineer or being a product person and trying to create something the world or improves humanity. And that's very opaque. What does that mean? What application? But then with each step in my career, I made decisions that allowed me to have a more clear mind towards that. I wrote my major as biomechanics because as a 20 year old or 19 year old, when I wrote it, I was like, this is the closest thing to engineering in humans. That's logical. I want to do stuff for humans, I'm going to do biomechanics. Then as I grew, I had more context to realize biomechanics is one aspect, but there are other ways of applying this to ensure that you're creating something better, and with SOLS, I really wanted to look at the orthotic industry.  And see, here are a couple issues, orthotics are not precise. You're at the whim of someone as an artist in making something. They're really expensive and they are hard to replace. And I was like, let's solve these things. I would love for people who can't afford orthotics today to afford them. I also wanted to create a place where they didn't depend on orthotics to exist. What if we could create an Invisalign for orthotics, where we built your arch up and then slowly weaned you off. And then now you don't need a device to actually be aligned, but rather we've helped your muscles recover and train you to build the muscles to support yourself. It's really thinking about what are the problems in this world that exist, and then what are the ways that what you're working on can solve them? With avatars, the thought process is really giving more data and more information to a designer. Today, when you think about how things are designed, we follow these rules of ergonomics that are ancient at this time. They haven't been updated for a while. And to be frank, they're based on an average white male person. And that creates a level of assumptions that the rest of the non-white male world has to deal with. There's no, oh, this was incorrectly done. It's like that's the data that they had. Most of the ergonomic data that existed in the world is based on Army data and based on military data. And that's who the majority of the military were. Now we have more data. The work I do now really looks at how do we ensure that we can create this world of giving people all the data so that they're designing more inclusively. And also recognizing where are the places that people need products specifically to cater towards a demographic, whether it is a race or an age or a disability. And how do we then create the data that allows you to solve those problems? 

[00:19:53,309] TRACI NATHANS-KELLY: One of my favorite things is to explore the complexity of the data collection that then puts forward design decisions no matter what they are. I'm really glad that you were able to hit upon that. It's one of my current favorite topics. 

[00:20:09,120] NATHAN GHABOUR: It's been an interesting development in my career, especially that I've started to become more of an expert in machine learning. To start to see all the ethics and data vigilance that needs to exist. I spend a lot of time auditing the data we have and ensuring that we're weighing it correctly and not introducing bias, and when we do analysis even within the data we have collected, I'm constantly checking my assumptions and saying, hey, we separated the data by male and female. Did we need to do that? Or did we introduce a bias in just how we set up this data analysis that we were doing? What's interesting is it does require a lot of self regulating. There isn't really a person out there that's going to say like, hey, were you ethical in what you did or did you make sure that you weighed your data? I barely have anyone above me that understands the technicality of what I do. Then it becomes the self-regulation that needs to exist, where you are considering the things that you're bringing to the table. Then that really does call into question things where like how many of the engineers at your corporation are white and male, and do they think about those things? Or do you have a level of diversity in your engineers and in the way that your people work? Because we don't have these data ethics regulations that exist. We are all self doing it. And then if we're not going to create these regulations, are you as a company being responsible and who you hire to allow the people to be around a table to actually discuss these things to then make those decisions? 

[00:21:56,874] CHRISTA DOWNEY: Along those lines, I would say that your company is being very responsible in hiring you to oversee this team. It's such an important topic as you've both said. It's the future of data and how we use data and how we set up algorithms, and how we look at these algorithms and these machines to make decisions to point us in the right direction. There's so much room for error and there's so much room for really brilliant things to happen if done well. I'm grateful to have someone like you working at an industry leader, setting the pace for how these things are handled. It's brilliant. 

[00:22:48,940] NATHAN GHABOUR: Thank you. It's interesting to be called that as an expert because it's like there are jobs out there within Google and all those other big time companies that have created these data ethicists jobs. We all watch the revolving door that is those jobs, recognizing the stress and the unscopable work that exists within these groups. I recognize that I've tried to do my best with it, but I also am flying off the seat of my pants sometimes and wishing there was something out there that would be an expert that I could lean towards, rather than me working with the limited resources I have to create some level of ethics. 

[00:23:38,390] CHRISTA DOWNEY: Yeah, that brings me to the next question. I'm wondering about people, organizations, maybe conferences, professional organizations, or simply others that you collaborate with, and who else is working toward this grand challenge of ethical data that you have the opportunity to communicate with and learn from and share your work with? 

[00:24:06,679] NATHAN GHABOUR: Yeah, the easiest answer I have is the Max Planck Institute. The research and the model that the Body Labs used that I use at Nike is based out of that group, out of MPI. They have a whole department on perceiving systems and they have groups of people that talk about what is the best way to weigh data. I do principal component analysis, which is a method of looking at the data and finding all the linear relations that exist across the wealth of data. And there's a question of recognizing how much you weigh certain principle components and looking at how that reflects the population. I read papers like that, and some of the papers have nothing to do with bodies. They're just about weighing data and trying to then take that and apply it to the work I do. But finding a one for one is probably not just not going to happen because data is so expansive and so variable that you're not going to find, what is the ethics of body data analysis across a million person dataset? No one's going to write that paper. I could write the paper, but Nike's not going to let me. We have trade secrets. The balance of, how do we start building standards and existing in a way that is ethical but also balancing things like trade secrets and how do we create that in a world where we're also trying to make money and stay ahead of the industry? 

[00:25:59,479] CHRISTA DOWNEY: Yes. Okay, so where do you recommend if others are listening and they're interested in this topic, where would people go to stay current on what's happening in this field? 

[00:26:11,119] NATHAN GHABOUR: Two conferences that I follow and go to our SIGGRAPH and CVPR. That's where I feel like I'm going back to school when I exist outside of working every day. CVPR is really about the future of computer vision. And they do have talks there about ethics and what does it look like. Probably you've seen some of their talks circulate the media when synthetic data came about and the deep fakes trends started. The CVPR was where people were talking about the problems and what do we do about it. SIGGRAPH is the same thing, They're more focused towards machine learning and more about all the different new developments in modeling. And they also handle it on a different lens, but also have the latest tech when it comes to this work and what to do and how to use it. And inherently, sometimes the ethics of it. 

[00:27:15,329] CHRISTA DOWNEY: What's the greatest challenge you've faced in your work and how did you overcome it? 

[00:27:20,149] NATHAN GHABOUR: For me, the answer is more about my career path. It's always been an interesting A decision when or how to go to the next thing. Every job I've left has had its own narration. And the hardest thing was like when to leave. I recognize a fine balance of investing in a place and seeing things through and when things get tough to persevere in order to like you see something through. And then the other side of the coin of recognizing like you're not going to be the one person that solves everything you need to accept that if this isn't working, it's not going to work because you're not fully in control. And each time I've done that at a job, it's been a different narration. At SOLS I left because we had issues with our business and how we were operating, and selling was not reflective of what we had set our goals to do. So I didn't have any control of how we sold our product or how our sales team ran. I just made a really good product. With Body Labs, we acquired with Amazon. And when I was at Amazon, I was there for about two years. There was a bigger question for me about what am I doing here? Amazon bought us with a vision of what they wanted us to build for them. And I built one of them and was starting to still question the ethics of my own existential desire to work, whether or not I wanted to at Amazon. But then it was really what am I going to do next and how am I going to get after that? Is the next place going to be a small company or is it going to be a big company? And thankfully, Nike was an opportunity that walked up on my door. And I really ran for it and grabbed it, and it was really great, and I'm super happy to be here. But even now I think about, I'm solving this problem right now and at a certain point have to accept whether or not it's going to be something that's going to continue to be solved, or this is a problem that might not happen at either the pace that I want it to be, or at any pace? And then do I find the next job? Or do I find the next problem to solve? And then when does that happen? I'm still figuring that out. I think it's this continuation of career growth and career path. And it's a balance that I think about most careers, and how to go to the next one. 

[00:30:05,080] TRACI NATHANS-KELLY: I had a mentor way back when, who said part of the job that you currently have is looking for the next job. And what he really meant to say is lifelong learning, continuous development. Even if you're going to stay right here and keep the same title, you can change that job to be your next job, even in house. I thought that was very wise. 

[00:30:28,700] NATHAN GHABOUR: Absolutely. And that's what's unique about Nike, for me, is that I feel like I've built my career almost to be at Nike. Like I started in foot biomechanics and was working on insoles and 3D printing, and then made my way to bodies. And now I'm at Nike. And they're the people trying to build performance product for feet and clothing. I have a lot of opportunities within Nike not to just work on bodies, but to work on shoes, to work on 3D printing. There are these pockets where I can really reframe my job to be able to do those things. The challenge I deal with is also the idea of leaving things at a place that can exist without me. Not to toot my own horn, but if you're really putting a lot of effort into a field or work, there's going to be a you-shaped hole that exists because you're putting a lot of effort into it. The question is,  in leaving a place, have you created the right mechanisms that the you-shaped hole can get filled? And that's another job. On top of solving the problem and finding your next job. It's like handing off your job or the problem that you're trying to solve or the product you're creating to be set up for success without you. And you know, all three of those things take up a lot of your brainspace as you think about your career and think about what you're going to do next. 

[00:32:03,950] TRACI NATHANS-KELLY: I don't think that I've heard it put quite that way. The you-shaped hole. It's amazing, another good visual. Thank you. You're good at the visualization stuff. That's true. And I wish I would've known that for my very first jobs because what I was doing was exactly that. Someone said of a program that I had created, this was a long time ago, it's a cult of personality, Traci, and as soon as you leave, it's going to collapse. And they were right. Because I had created Traci-shaped hole and I had no concept of how to leave behind something that could carry forth without me. That's brilliant. One of the things that we asked you to do was to also think about what do you wish you knew when you were a sophomore? You have this great vision now of what to do and how to carry it forward, but it's sometimes hard to rewind, right? We ask about the sophomore level because that's about when people decide what their major is. And you said you were an independent major. 

[00:33:12,449] NATHAN GHABOUR: Yeah. Yeah. I'll take it really detailed. My advice is less about now and more about what I just needed to tell myself as a sophomore. I was an independent major in two parts. One, I took advantage of the independent major because I saw a path of shortcutting getting a Master's degree. I was aware that taking all the biomechanics courses I wanted to take would require me to not only get an MAE degree, but then go past that and get a Master's in Biomechanics, or get a PhD. Cornell was pretty hard and I was like, I don't need to do that more. I wanted to get what I need and go work. Part of it was that, and too, I actually struggled with my first two years. I had to withdraw from a class. I didn't have the best GPA and I was like, what am I doing? How do I get a hold of myself and figure this out? Part of it was going through the independent major route. The thing I wish I told myself or really could believe is to just remember that like you're at an elite university. And it is hard to consistently keep remembering, especially, you're still at one of the top schools in the world. And there's a bubble that gets created as a person in this world that like, you are not enough, or you're not at the mean, or you're not a deviation above the mean. And there's just self reflection or evaluation. And what I wish I was told was like regardless of where you are at Cornell, there's a world out there that isn't an elite school. The reality is like you're going to still be one of the smartest people in the room for the rest of your life. And it was a hit in the face, entering the industry and recognizing that the hard work that I put into Cornell is paying off five some. Till this day I will say Cornell was the most stressful time in my life. And that being said, it's like the rest of my life has been like, well, I can handle this. My job is to try to push people to do more, which I thought I didn't do enough. Yeah, the bubble pop is really recognizing, hey, you might be struggling right now and this might be really, really freaking hard. But the reality is like you're still one of the top people at this school, in the world, and you're going to make it. And to have confidence in that so that you can have a sense of relief from the minutia of, oh my god, I'm a deviation below the mean. I'm never going to make it in life. I used to catastrophize that I was going to drop out of school and work at McDonald's. Not that there's anything wrong with that, but for me, that was my personal failure nightmare. I wish I could have this reality check and this reminder so that I could continue to focus on my academics, but not have that immense pressure to succeed. 

[00:36:46,010] TRACI NATHANS-KELLY: I think that's such an important message. We happen to be recording this during finals have just concluded. The whole campus is feeling a little bit of relief today, I think as we look at graduation. So that's really wonderful. 

[00:37:06,090] CHRISTA DOWNEY: Nathan, if you were not doing this work right now, what would you be doing? And which is closest to what you dreamed of when you were a child? 

[00:37:12,789] NATHAN GHABOUR: I actually always dreamed of being a chef growing up, ironically. My uncle owned a restaurant and I took after him. And I was always cooking in my house with my mom and my sisters and really had a knack for it and thought, this is something I can do. I'm really good at it and I want to do it. But my parents were very quick to deny me that ability. I'm a son of immigrants and they were like, we didn't come across the globe for you to cook food. They pushed me to pursue something else that I was passionate about. I was passionate about engineering and went to Cornell and did this. And I recognize the importance of that now, and that I can cook and also not have to deal with the stress and financial instability that being a chef could be. I recognize that I probably could have also done a really great job as a chef because of the determination and work and drive that I have. But I'm also aware the restaurant industry is pretty rough and I'm glad I chose engineering. But career wise, when I was at Cornell, I did envision wanting to work at Nike one day. I set it as a goal, not knowing how I was going to get there. I was like, I don't think Nike even hires biomechanical engineers. I think shoe designers are, you know, shoe people that learn how to draw things or cobblers. But I kind was like, well I'll keep that in mind but maybe one day, somehow I'll figure out how to get there and I did. So here I am working at the place I had hoped I would work at. 

[00:39:05,410] TRACI NATHANS-KELLY: It's amazing when that works out. Yeah, I was just thinking you could have your team members answer yes, Chef really loud when they agree with you that might bring your worlds together. 

[00:39:17,740] NATHAN GHABOUR: Yeah, it was interesting. During the pandemic, I had a group of friends that we created a covid pod with, and it was six of us. I grew up working a Bodega in New Jersey. I was a Bodega kid and made sandwiches and did all this other stuff. When we were in the pandemic, people were like, hey, can you cook us breakfast sandwiches? Like, let's just do this pseudo diner thing. And it was really fun. I was just cooking for my friends, but took their orders and made paper sheets and then made them bacon, egg, and cheese. That was really fun. Finding ways to do that has been a good passion and hobby of mine. I've actually talked to my friends about doing like a chef's table where I would do a tasting menu and give people a five course meal and they'd just pay for the cost of it and I'd have fun cooking. Yeah, there's definitely ways to take your passion that might not have turned into a career and then finding ways to access it. Even today, I struggled to design sometimes because I'm busy doing machine learning work and doing all this other stuff. And I wanted to be a designer. My partner's on a rugby team, and I volunteered to redesign their jerseys. I've never designed a jersey, but it's still going to use all the design methods that I know. I'm still going to make a morphological chart of different aspects I want to create or what problems exist with their color scheme and then design something. There's definitely ways outside of just your career to pursue those things. It's just figuring out how, and how much work you want to put into it. 

[00:40:57,919] TRACI NATHANS-KELLY: Well, this last question we always ask of everybody, but I think you've already tapped into part of it. We like to check in with people about how they relax, have fun or re energize. We know some of yours already, but anything you want to add back to the list? 

[00:41:12,059] NATHAN GHABOUR: Yeah, definitely cooking is a really important way that I relax after gaining also my Cornell weight back through the pandemic, I got into working out a lot. I really tried to create a way where I could create a habit of working out, to let steam out. And I work out three days a week. And it's been really important for me to relax. I've unlocked the benefit of a bath. I did not know how to take a relaxing bath for a very long time, and now I know what that is. And it's really great. So baths, exercise, and cooking. 

[00:41:55,840] TRACI NATHANS-KELLY: That is an excellent one to add to the list. 

[00:41:59,659] NATHAN GHABOUR: Yeah, that and a dry sauna, which I don't own. But when I have the ability to get into a dry sauna, it's really nice to just sweat it out. 

[00:42:08,719] TRACI NATHANS-KELLY: Well, thank you so much for this enjoyable conversation. This has been quite eye opening. We appreciate so much your reassuring words to folks as they are working through their undergraduate experience that it's going to be okay, you're going to get there. And then all of your insights about how all these technical worlds and creative worlds that you have come together in your career. Thank you so much for your time. 

[00:42:36,079] NATHAN GHABOUR: Yeah, thank you for having me. 

[00:42:39,680] CHRISTA DOWNEY: Thank you for listening. If you are enjoying these conversations, please follow, rate, and review on your favorite platform. Join us for the next episode where we will be celebrating excellence and innovation among engineers whose impact contributes to a healthier, more equitable, and more sustainable world. 

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