Walter Piper
Dr. Walter Piper is a Research Engineer at Neurable, a brain-computer interface company whose vision is to allow people to control software and devices using only their brain activity. Dr. Piper has a PhD in Neural Science from NYU where he studied the temporal dynamics of amygdalar norepinephrine during pavlovian threat conditioning. He has 11 years of experience with data analysis, statistics and research design and 3 years of experience with machine learning engineering and computational modeling. At Neurable, Dr. Piper’s goal is to develop brain-computer interfaces for psychology related use cases. His tenure in neuroscience has shown him that there is a world of low-hanging fruit when it comes to real-time applications related to mental health and human behavior.
Can you describe your academic and professional background? What path led you to pursue this field?
In undergrad, I was on a pre-med track completing a biochemistry and biophysics degree. I was getting to a point where I was looking for inspiration and realizing that even though I was interested in the subjects I was studying, I wanted to apply it to something else. Biochemistry can be applied to so many different fields, so I started looking on the internet and reading papers on google scholar to try to get a feel for what actually excited me. I was interested in psychology from going through my own mental health journey and I was interested in combining biochemistry with psychology. From there, I finished my degree and stayed to work and do my masters with a psychology professor who had a neuroscience background. Then for my grad school career, I was very interested in molecular neuroscience and where that crosses over with behavioral and cognitive neuroscience. A lot of my grad school career involved this molecular and psychology crossover with experimental and theoretical work.
The program at NYU had a computational focus so I started picking up computational neuroscience skills there. Then towards the end of my time in the PhD, I started to get a sense of what I wanted to do after graduating and felt like data science was a very practical skillset. I upscaled with data science which, along with my computational neuroscience training, is the skillset I’m using now. I started focusing on the concept that there are so many discoveries in neuroscience and how I wanted to bring those out of the lab and into people’s lives and this was the track that led me to Neurable.
How did you find this position, and what was the hiring process like? Is there a typical structure for this in your field?
When I was finishing my PhD program, I had an 11-month long job search. Industry job searches can take a while. You have to do a lot of networking since there is no system to link people with jobs. Finding a company where I was a really good fit was difficult. Most people in industry don’t have a PhD and if they haven’t gone to grad school at all they have no idea what a PhD means. If you get a PhD, people look at you as extremely specialized and if you go for a job that doesn’t align perfectly with that specialization, people won’t take you seriously. I learned that I had to find a job that aligns perfectly with my interests and skills. For the brain-computer interface space in industry, the skills that people are looking for include computational neuroscience skills and data science as well as how algorithms fit in with a software ecosystem which goes slightly beyond the computational neuroscience space.
I found Neurable through their discord community right around the time I was finishing my PhD. I was active in that community, but I wasn’t confident enough to apply for jobs with them yet at that point. However, I started from a place where I thought it would be more realistic to apply for general data science jobs, but I learned quickly that’s not how it works since people will look for the fit with your extreme specialization.
To boost my confidence, after my PhD I did a data science bootcamp which included mentorship and that’s when I realized I needed to rely on my background and find companies that align super well with my background. Eventually, networking with Neurable through their discord community and building those data science skills gave me more confidence to apply.
Can you tell us about your current responsibilities? What is a typical day or week like in your role?
I help run the data collection efforts since we do have a research lab at our headquarters, and we run experiments every week. There is a lot of data analysis for that as well so that part of my job follows a traditional science workflow. I also prototype new features like real-time algorithms and real-time tools to respond to neural signals in real-time. I write python code to prototype those algorithms and test those out. There is also data science aspect that isn’t limited to neuroscience so if there is any problem that people can’t figure out, then generic data science methods can help me get a better handle on that and help the company as a whole figure out those problems. It’s a mix of traditional science with some software engineering and data science.
What do you enjoy about your current job and work environment?
I like my team a lot. My favorite part is the teamwork aspect. My day-to-day motivation has a lot to do with social responsibility and being there for my teammates. As opposed to academia, which has more of an individual focus, industry in general has more of a team emphasis and individual achievements don’t matter as much. It’s about being there for your team. There is also a sense of making history which goes along with being in a place where you can have high impact.
What are some of the challenging aspects of your job?
Some of the challenging aspects are adapting to a high teamwork environment. For example, remembering to not work alone for too long without checking in with other people. You don’t have your own independent project. Plugging in with other people’s projects is hard since everybody has to be on the same page so I had to develop an intuition about when I should make my own decisions about work versus when I should be consulting with my team. Learning to deal with the intensity was a challenge as well. We’re trying to do the science aspect and develop engineering tools that will be helpful for the product and we’re trying to launch the product. We’re covering more ground which takes a lot of scientific experience since we don’t have time to do large academic studies. We have to be engaged full time to be able to handle it.
Do you have any professional plans for the future? What are some future career paths that could open up for someone in your position, 5-10 years down the road?
I’m happy at Neurable and am motivated by Neurable’s focus on health and well-being. I’m less worried about building up my career since I know it will happen naturally as I’m doing things to make sure the company is doing well. For the career path I’m on, it’s similar to the career progression you would expect with a data scientist or machine learning engineer. It’s highly technical and you can do a lot of well compensated work from a purely technical perspective. There are two career paths from here and one of them does focus on individual technical contributions and the other is middle management so having technical skills but being able to lead a technical team.
What’s changing in your industry? Are there any future trends we should be aware of?
Everything is changing. Especially for a company like Neurable at the heart of brain-computer interfacing. There are not a lot of companies working in this space: non-invasively monitoring for the purpose of software integration and software tools. There are not many companies that do that. We will be putting out the first system that’s designed with all these neuropsychological use cases in mind and it’s going to be robust enough to operate well when you are doing activities and have your eyes open. I think this could open a lot of new use cases since once we have neural metrics that are psychologically relevant and reliable, then we can have integration with anything that’s computerized.
Is it common for people in your field to have a scientific/academic background (i.e. have PhDs)? Can you think of any advantages or disadvantages someone with a PhD might experience in your field?
It’s different between companies. At some companies, no one has a PhD but everybody doing neuroscience at Neurable has a PhD. People who are dedicated software engineers might not have a PhD, but they’ll have master’s level computer science training. For us, the core neuroscience work is at a PhD level. I think a PhD helps with vision: the ability to have a vision that’s realistic. If you have PhD level training, it’s easier to know what’s realistic and what kind of new technologies can be invented and implemented in a given time frame. The disadvantages are about people’s perception. If you’re trying to get into a company where none of the leadership team has a PhD, then they might not appreciate what the PhD brings. However, if people don’t see the value of a PhD, it might not be where you want to work anyway. A company is limited by the skillsets that all the people have collectively and if you don’t have anyone with PhD level neuroscience training, then you’re going to have difficulty with realistically achieving some the technological dreams.
Do you have any final words of advice for those navigating these career questions? Is there anything you would have done differently given what you know now?
One thing I did well was developing a vision and finding a company that shares that vision. When your vision aligns with the company’s vision, it makes things much better. People should always be focused on the importance of vision especially people who are getting a PhD since the people who have the PhD training actually have the skills to implement that vision.