“Being able to take initiative and decide what to do, and help lead others in what to do — I think a lot of that comes from the critical thinking skills that I developed doing research.”
Helene Moorman, Neuroscience PhD Program alum (entering class of 2009)
Helene Moorman is a Senior Systems Engineer at Cruise, where she is helping to develop their self-driving electric car. As a Neuroscience PhD student in Jose Carmena’s lab at Berkeley, she studied brain-machine interfaces (BMI) — specifically, how the brain can learn to control a virtual arm. Although her current job may appear to be a far cry from her research at Berkeley, Moorman says the skills she gained in graduate school have been critical to her career in tech.
Moorman became interested in BMI as an undergraduate at MIT, where she earned a BS in Brain & Cognitive Science. She came to Berkeley to do her PhD with Carmena, who is in the department of Electrical Engineering and Computer Sciences. Moorman has broad interests, and says she appreciated the interdisciplinary nature of the Neuroscience PhD Program, where engineers interact with researchers from fields such as psychology and molecular biology. As a graduate student, she also served as the art director of the Berkeley Science Review.
Throughout her time in graduate school, Moorman was open to the idea of working in industry. After earning her PhD, she took a position as a Human Factors Scientist at Exponent, a technical consulting firm. She then moved to Cruise, where she uses her scientific mindset and data analysis, visualization, and coding skills to help develop the safety and performance requirements for their robotic car.
Read our Q&A with Moorman to learn more about her career path; what she finds compelling about her work; advice for graduate students interested in industry; and life as a new parent. This interview has been edited for length and clarity.
Rachel Henderson: How did you become interested in neuroscience?
Helene Moorman: In high school I was interested in a little of everything. My high school offered an independent research course where you would choose a topic of research, read literature on it, find a mentor at a local university, try to actually do some kind of experiment, write it up, and enter science competitions and stuff. The research that I ended up settling on had to do with false memories, so it was kind of in the psychology realm. That was sort of the beginning of my interest in human behavior.
When I went to undergrad at MIT, again, I was sort of open to everything. I took a long time to pick a major — I waited until almost halfway through college to declare a major. I took a lot of different courses in a lot of different subjects, but I really loved the introductory neuroscience courses that I took there. There was a seminar class, especially, where the professor talked about brain-computer interface technologies and showed some of the early videos, from some of the big players in that field, of paralyzed human subjects. There was one of a woman bringing a drink to her mouth, and drinking out of a straw using a robotic arm that she’s controlling with her brain. I was completely blown away by that. I was like, ‘This is so cool, I want to be a part of this.’
So I declared neuroscience as my major, and went to one of the professors at MIT who was [studying] motor control and sort of dabbled in brain-computer interface. I said, ‘Can I do undergrad research in your lab?’ I ended up working in that lab for a couple of years in undergrad and then a year after I graduated as a research assistant, doing primate and human research on motor control. I loved that, so I decided to apply to grad school in neuroscience with the aim of joining a BMI [brain-machine interface] lab. That’s how I ended up at Berkeley in Jose Carmena’s lab.
RH: Why did you choose Berkeley? I’m assuming Jose Carmena was a big draw.
HM: He was definitely a part of it. We had a lot of discussions before I made a decision. I was basically choosing between Berkeley and Stanford. There was also a lab at Stanford that did kind of related stuff that was really exciting. But Jose did a really good job of convincing me. He was a pretty new professor at the time that I started. He didn’t have tenure yet, he just had a couple of people in his lab, and he hadn’t graduated anyone yet. He basically said, ‘If you come join me, you can have almost complete freedom to do whatever kind of research you’re interested in. I’ll give you all these resources and you can kind of go wild, and I’ll help you with that.’
So that was a pretty nice offer (well, I thought it was a nice offer, it’s different for everyone) versus the other option [which] was more of an established lab that was bigger. There would have been less interaction with the PI [principal investigator], even though it was also a really great program. Also, I just liked Berkeley much better than Palo Alto, and it seemed like a place that I would rather live.
RH: Tell me about your thesis project.
HM: The focus of my thesis ended up being how the brain adapts and learns to control a motor system with kinematic redundancy. In the body, kinematic redundancy means that — especially when you’re thinking about reaching movements, about hand and arm movements — there are multiple solutions to the problem of reaching out to a certain point in space, like getting your hand to a certain spot in terms of all the joint configurations. So if you think about reaching out and touching the table in front of you, you can move your arm and elbow and shoulder into different configurations and still be able to reach the same point because you have more degrees of freedom than are strictly necessary to do that.
I was interested in using brain-machine interface as a tool to look at: how does the brain choose between those different redundant solutions, and what is the effect of practice on how those solutions are formed? I was [working with] monkeys with electrodes implanted in the motor cortex [of the brain], and I would teach them to control this virtual arm on the screen that we called the ‘tentacle arm’ because it had extra joints. The monkey had to learn this task where he had to get the end point of the arm, which sort of represented the hand, to different targets on the screen, but the joints could move in multiple ways. I looked at how the strategy consolidated with practice, and how the patterns of neural activity associated with the different kinds of movements in this weird, sort of unnatural, system.
RH: What was your experience like in the Berkeley Neuroscience PhD Program?
HM: It was great. I had a wonderful experience. My first sense of the program and the community when I came to interview was that it was super friendly, super welcoming, tons of really smart people, and a lot of support for the students. I liked that it was a relatively small program compared to some of the other ones that I was looking at, and it was really well-funded. I think all that turned out to be pretty true throughout the time that I was there.
A lot of the professors in the program were incredible. The fact that it was an interdisciplinary program was great. My advisor Jose was joint with neuroscience and electrical engineering, and I liked that there were psychology professors and MCB [molecular and cell biology] professors, and everybody kind of mixing together.
The research had its ups and downs. I definitely went through the ‘third-year depression’; I think a lot of people do. [But] I was happy with my thesis in the end. I didn’t feel like it ended up being super groundbreaking or necessarily as exciting as I had envisioned when I started, so I had a little bit of disappointment with that. But I think that’s just part of the coin flip of research sometimes. Overall I really enjoyed my experience, and even though I didn’t continue in academia, I was really glad that I went through the program and that I got that experience.
RH: I noticed that you did some work with the Berkeley Science Review [a graduate student-run magazine about scientific research at Berkeley]. What did you do there and why did you do it?
HM: Most of the work that I did at the Science Review was related to the design and layout aspects of it. I worked as a design editor for a couple of issues, and then I was also the art director for a while, so I led the design team. I came to it partially because I had a friend who was working on it. I would see what she was working on, and I was like, ‘Ooh, that looks so fun. I want to do that.’ But I’d also be making PowerPoint presentations to present my work or writing a paper, and I just really liked making the figures. I thought it was so fun to try to make them clear and beautiful, and to communicate that stuff visually.
So [the Berkeley Science Review] was a chance to do that even more, and also learn about other kinds of research that were [happening] on-campus outside of my neuroscience world. And [also to] learn how to use some of the design tools — we used Adobe tools, and I got pretty good at those. I thought, maybe this will be useful to me in the future. Maybe I’ll go into scientific editing or illustration or something. So that’s why I got into it, and then it ended up being a lot of fun to see the finished product, every issue.
RH: At what point did you start thinking about going into industry?
HM: I started thinking about the future for real probably around year five — year five of six of grad school. I started grad school, actually, not even really necessarily sold on the idea of staying in academia. I was kind of thinking that brain-computer interface was just on the cusp of being advanced enough for commercialization. I thought maybe I could do this research, and then that would lead to maybe some kind of industry opportunities in that space, like developing products; clinical products or something like that. And by the time I got near the end of grad school, I realized that the timing was not right. I sort of miscalculated how quickly the technology would advance. It wasn’t really there yet, and there weren’t going to be that many of those kinds of opportunities. It was more going to just be a research thing for a while longer.
So I started thinking about what I wanted to do. When I really sat down and looked at: What is it like to be a postdoc? What is it like to be a professor? I kind of didn’t like a lot of the elements of those. I just didn’t think it was going to be a good fit for me. That’s when I started thinking about alternatives.
RH: What was your path after graduate school?
HM: Like a lot of people that I’ve talked to in the program and in other PhD programs, I was kind of concerned about: How do I use my skills in a different context? How do I convince someone to hire me? I went to the Beyond Academia conference for the last couple of years of grad school to get ideas about what [other people did]. I ended up with a shortlist of types of jobs that I might be able to pursue. I had planned to just kind of apply for anything and everything. I thought it would maybe take a long time to find a job. But actually, [I was offered] the first two things that I applied for.
One was a technical sales role with a neurophysiology hardware company that I had a connection to because … we had worked with them a lot in the capacity of my research in the Carmena lab. They supplied the electrophysiology equipment, and I had developed a really close relationship with the engineers there because I had beta tested a lot of equipment for them. So they offered me a job.
I also had a friend who had graduated a year earlier [from] a totally different program who was working at Exponent, which is a technical consulting firm based in the Bay Area, but it’s a national company. They have experts, mostly PhDs, in all kinds of different engineering fields. They also had a human factors group, which was psychology and human behavior experts. They did consulting for all kinds of different clients. A lot of what they do is related to litigation. They’ll serve as expert witnesses and offer opinions on failures, accidents, and stuff like that.
So [my friend] was working [at Exponent] and I thought what she was doing sounded pretty interesting. She referred me, and I applied and was offered a job in the human factors group. I ended up thinking that a sales role wasn’t really right for me, so I went with the consulting role. It also seemed like consulting would be a good transition into industry because I’d be working on a lot of different kinds of projects, meeting a lot of different people, and would maybe be developing a network that I could use to move on to something later when I didn’t want to consult anymore.
I started [at Exponent] a couple of months after I graduated, and I worked there for two and a half years before kind of feeling like I was a bit done with consulting. I enjoyed it, but I was kind of annoyed by the lack of control, because it’s a client-focused business. … You have to meet these deadlines dictated by the clients and you don’t really get to have too much control over that stuff. There would be times where nothing was happening and then crazy times, and I just didn’t like that stress.
So I started to look around again and decided, hey, I’m in the Bay Area, I want to make more money [laughs]. I should look at tech companies. I felt like I could either pivot to UX [user-experience] research or data science, because I had a lot of coding and computational skills from grad school, design skills from the Science Review, and research [experience].
I applied to several jobs in those realms and ended up at Cruise. Initially it was more of a UX research job, but it kind of morphed [and] I’m now a systems engineer there, … where I help develop the safety and performance requirements for the robotic car that we’re developing. [For instance], what requirements does it need to meet such that we consider it ready to launch, ready to put on the road safely. I do a lot of data analytics as part of that role.
RH: What are some things that you enjoy about your job?
HM: There are a lot of things I enjoy about it. I really, really like the problem and the mission of the company. It’s super interesting. We’re trying to make self-driving cars that have the potential to save a lot of lives if they drive more safely than humans. They can’t drive drunk or have a heart attack or anything like that while driving. Also, the car that we’re developing is electric. So if we can get a lot of people to use these self-driving cars someday, it could take a lot of older gas vehicles off the road, which is great for the environment. And it’s just interesting — robotics is really interesting. It’s a really hard problem. There’s sort of an endless amount of interesting aspects of it to look at. So I like that, and I think the overall goal is very motivating.
I also really like the people that I work with. I think I have a great team, and really kind and smart co-workers for the most part. Just getting to go and have these intellectual discussions about how to build this robot with them every day is really fun.
I like that it’s a more technical role than what I was doing before in consulting. My consulting job was a lot of literature research, writing reports, and it was mostly focused on psychology and behavior. But in this job, I have a data analytics, data science-type role. So I spend a lot more time playing with data, writing code, doing data analysis and visualization, and helping make decisions based on the data that we collect from our vehicles testing on the roads of San Francisco. I find that really fun.
RH: How do you think your experiences in graduate school helped prepare you for what you’re doing now?
HM: It definitely helped prepare me. I couldn’t have gotten either of these jobs without my grad school experience. The analytical skills from doing research are hugely useful, especially at Cruise. [Also,] I guess a little less so now than when I started, but it’s kind of a startup environment. We do have leadership, but there are a lot of decisions to be made and not necessarily a lot of guidance as to how to make them because it’s a field that hasn’t really been that fully explored. And also it’s an organization that doesn’t have much of a hierarchy and everybody is somewhat inexperienced, I guess. So being able to take initiative and decide what to do, and help lead others in what to do — I think a lot of that comes from the critical thinking skills that I developed doing research. Like looking at problems very scientifically, making sure we make decisions and draw conclusions based on evidence, evaluating the strengths and weaknesses of the evidence that we have, and convincing others, like, ‘Hey, this is not enough. We need more information before we should move forward.’ All that comes from research and from my PhD.
In terms of the hard skills, I started learning to code as an undergrad. I took a few computer science courses, but really I learned Python in grad school. I needed it to develop my experimental setup and also to analyze all the data that I collected and visualize it and all that stuff. So the data analysis skills and the coding skills for this particular job were directly applicable from grad school.
RH: What advice do you have for PhD students who are looking to go into industry?
HM: Develop your network as early as possible. Don’t be shy about making connections and keeping in touch with people, especially outside of your immediate [field]. You want people who are not doing the same type of thing as you are, necessarily. Use LinkedIn, try to go to conferences, or whatever avenue that you have to meet people and make connections. Have people sort of know who you are and respect your work and like you as a person — those are super useful later. Both my jobs I got through being referred by somebody that I knew at the company. And I think that’s true for most of my friends that have gone from grad school into industry as well. Those connections really, really help. That’s how most people get jobs in the Bay Area, really.
[Also,] if you are interested in a tech job, especially in software or something, try to do an internship during grad school. I didn’t do [an internship], but it would have [made it] even easier. It sort of takes a little bit of convincing for one of these companies to take a chance, I think, on somebody that has an academic background and not an industry background, because they have all these preconceptions. [Such as] PhD people are smart, but they’re going to take forever to do things, they’re not going to be pragmatic, they’re going to be really pedantic about everything [laughs]. Which, honestly, is kind of true sometimes. Those are sometimes the urges that you have.
So I think if you can point to something on your resume that shows that you already have some experience in [an industry] setting, that alleviates that fear a lot, and makes you look more desirable. Also, it’s a great chance to understand if it’s a work environment that you’re interested in. I know not that many people do internships in neuroscience, but it’s really common in other [programs], like in the EECS [Electrical Engineering and Computer Sciences] department. Everybody pretty much does at least one internship before they graduate. I think more people should be doing that. I know there are differing levels of support from professors in biology because it’s not that common. But hopefully, people can get the support of their advisors to take a couple months over a summer to do an internship if they think that they might be looking to move into industry.
RH: You just had a baby — is this your first child? How are you doing?
HM: Yes, he is my first baby, probably my only baby [laughs] because it’s a lot of work, I’m finding out. But it’s great. He’s three months old now. It’s been a little bit tough adjusting. He was pretty colicky, so we’ve had a lot of crying and sadness, but that’s getting better now. He’s starting to sort of have a personality and be able to pay attention to things around him. That’s really fun to watch.
I’m really lucky that my and my husband’s income hasn’t been affected by what’s been going on [Ed. note: this interview was conducted on May 15, 2020 during the COVID-19 pandemic]. I’m able to work from home. Also, my mother sort of got quarantined here with [us], so she’s able to help us take care of him while we’re working now. We’re super lucky in that. So overall, things are great, and I’m happy and super excited to get farther into parenthood and see how it all goes.
RH: Many of the people reading this will be members of the Berkeley Neuroscience community, particularly students. Is there anything else you’d like to share with them?
HM: Just that in the spirit of ‘people should be developing their networks’, people are welcome to reach out to me if they have questions or want advice or just want to make a connection on LinkedIn or whatever. I’m totally open to that.