Souli is a neural engineer and entrepreneur from Morocco. He has a deep passion for neurotechnology. He has both a bachelor's and masters in bioengineering from UCLA and he's actively involved at UCLA Crux. He was leading the club as president in his senior year. Additionally, he's also the founder of Neuron. Neuron is a cutting edge neurotechnology startup and offers neuroscience services for researchers, businesses and students.

Additionally, Neuron quantifies cognitive states and wellness, and this includes stress, fatigue, and it's focused on EEG data. In addition to that, Souli also has extensive experience in deep learning and machine learning, and he has used this to push the boundaries of neurotech innovation and become a key player in the neurotech industry.

{ 1:56 }

Crux was the first time that I was able to actually work with Neurotech. I joined Crux my sophomore year at UCLA and that was the first time that I was able to learn about it through the Crux workshops. I was able to get more familiar with my Python coding and then for the first time learn about signal processing, which as a sophomore I hadn't learned at all throughout my classes yet. And more specifically, all those signal processing skills related to analyzing brain data. So the first time that I ever learned about that was through Crux. And then the first time that I was able to apply those skills was also through Crux. My sophomore year, I worked on a team as a team lead. So the fall quarter of my sophomore year was the workshop series.

And then winter and spring, I was leading a team that was trying to build a lie detector based on brain waves. That was my first ever project working on it. It was for COVID, so it was all done remotely. Even the team members were all remote. So it was a little bit tough. And that was my first time ever dealing with it. So it was the first good experience in terms of being able to collect data myself. Getting experience with the whole pipeline of setting up an experiment and designing the protocol, actually collecting data and analyzing it was nowhere near the proficient skills that I I needed to get the project complete. So we weren't really able to be successful in bringing that project to fruition. But that was still a very good first experience.

And then my junior year, I was the BCI team's coordinator for CRUX, so overseeing all the BCI Crux teams, advising them, seeing if their project ideas were even feasible, and then advising them on the technical side. And then in my senior year I was president of CRUX. So growing throughout the organization was definitely key for me to even find out that I wanted to start a startup because I went within crux from the technical standpoint, team lead to the more managerial side, but still all within Neurotech. And that's how I found out my senior year when we worked on the California Neurotech Conference, that leading a group of people within this space is truly what I wanted to do. So that was a key moment that made me convinced that I wanted to launch a startup in the space.

{ 5:11 }

On top of the club work that I did with Crux, I also did several internships. I interned both at large biotech companies and with student led start-ups. One of them being Valence Valence Vibrations that Chloe Duckworth Co founded.  So I actually interned with her my sophomore year. I also interned for another student-led startup my sophomore year. Then the summer of my junior year I worked with another neuro tech company and a large biotech company.

And so I was able to get a sense of what it's like to work in a corporate job, what it's like to work in a startup environment. And I found out that the startup environment was what I was drawn to and what I was actually more interested in going to work for. When I worked in the corporate job, I literally thought that I would not want to live this life of just very repetitive tasks. You basically do the same thing every week. I just have a desk job. You are just one employee in this sea of employees, you're so far from the actual mission and tangible impact of the company, even if the company is actually doing great work.  So that was another stepping stone in finding out what I enjoy doing in terms of work outside of the street of a student club environment. And so those were two key things that made me realize, OK, this is really what I want to do.

Even after having the idea for Neuron, it took a year to think about it. I got more experience during that year when we founded and organized the first California Neurotech Conference. And that really solidified it for me that I was like 100% this is exactly what I want to do and then went all in on Neuron.

{ 7:33 }

Yeah, I learned a lot from those experiences working with student start-ups.  So at the same time I was working for two different start-ups that were at the time taking different approaches. One of them was a very large Co-founding team.

{ 7:48 }

It was basically a group of friends that all decided to found a startup and it was COVID. So for that semester they actually took the whole semester off. And so the time that I was working with them, it was very high quality work. Everybody was really engaged, everybody was doing their work. And at the time I felt OK, this is most likely like out of the two start-ups, the one that I imagine is going to stay alive with Valence at the time they were the two Co founders were still in school and it wasn't as structured as the other one simply because the Co founders had to basically prioritize their schoolwork while working with their start up and and this group of interns.  So what I realized then is you definitely cannot found a successful startup while being a full time student. You got to pick one or the other.

And what ended up happening is the startup with a large Co founding team at the end of my internship when the next semester rolled, came through. So they went back to school and that sort of died because those Co founders had to prioritize school. That is always a priority and so they were able to dedicate a good ton of time when they took that semester off to the start up. But then after that they went back to school. They couldn't keep working on the start up and making it successful because most start-ups fail even when the founders are working full time on them. So I'll let you imagine what it's like to be a full time student and trying to build a successful start up on the side. And then Valence is the startup that is still going strong and successful today. Because what their strategy was was, Ok, let's rush through school and try to graduate early, getting her degree and then very quickly go full time and dedicate 100% of her resources to the start up.

And that I think is a key thing that allowed them to be successful and for that start up to go strong. So that was a key learning. And so what I did was I started working on my startup after I graduated from my bachelor's. I still went ahead and did my master's, but I finished my master's with my thesis in one year instead of two. And I was able during that year when I was still a student at UCLA to take advantage of all the entrepreneurship resources that we have access to. We have so many resources that are made available to us, whether it's pitch competitions, workshops, entrepreneurship classes. And so I took that year to start launching the startup by taking advantage of all of those participating in competitions, being very involved with even the MBA student community that before then I was never in touch with. So that's basically how I took that learning and how it helped me launch Neuron.

{ 11:33 }

I feel like I'm very lucky in the sense that I would definitely not be able to do what I'm doing right now back home in Morocco. So in that sense, I'm very fortunate and grateful that I'm able to build a start up that I don't think would even be possible back home.

And that is, again, I am grateful for all the resources that are available here that I want to make sure that everybody makes sure to take advantage of because we're very lucky to have access to all of this. And I know, for example, UCLA can be very overwhelming with the number of resources that are available, but that's really what helped me make the most of my time is that every time I got a newsletter through my email, I would actually read it. And then if there's no resources that are interesting to me, then I'll just skip it. But I'll make sure to open and read every single e-mail. And then if there's anything that actually does appeal to me, then I make sure to take advantage of it.  And so that is, I think something that comes from me being from Morocco, from out of country, understanding how valuable the resources we have here and making sure that I do take advantage of all of them.

{ 13:34 }

So the SAS is software as a service that is for neuroscience researchers who collect their own data, have the personnel to collect that data, and we offer this automated data analysis platform to have them automate their data analysis process, making it faster and cheaper. And so that platform is for you to only do the analysis. So you're expected to already have the brain data.  That is our first offering of our software as a service. But then for the rest of the world, they do not have access to the personnel who are experiencing collecting that data. They don't have access to the hardware that is required to collect this data. And so that's why we have the second service that's neuroscience as a service or Nas. And so that one is for business teams that don't have access to these resources. And so we do everything for them. It's a comprehensive neuroscience study service where we do everything from designing the study, collecting the data and analyzing it. So they don't need to have any knowledge of this.

And the purpose of it is to work with companies that have a mental Wellness product that claim that it improves your mental well-being. So what we do for them is we run the neuroscience research study to actually provide the evidence, the objective neuroscientific evidence of those claims for those companies to then use for marketing and sales purposes. So that's right now our main focus for this service, but it also has other applications and other applications for marketing agencies. For them, it's very hard to quantify how effective the marketing content that they put out is. And so that's another application. And lastly, the last client that we worked with was for this service, the Neuroscience as a service. And they were actually a corporate training company. So they have a six month program where they train managers, they're called management queues. And so we ran A 7 month study to quantify directly from brain waves the effectiveness of their program.

{ 16:50 }

Yeah, Wellness is definitely a big one because nowadays wellness is a booming industry. And there are countless products that have these claims that they improve your focus or they improve your stress or your mood or even your cognitive function. But these products are not regulated by the FDA. The FDA, the Food and Drug Administration only regulate these products that claim they're effective when it supplements.

{ 17:46 }

And so our goal is to bring that scientific validity to the space to increase customer knowledge awareness. And then ultimately for the companies, that's actually how they can stand out because right now this is not the norm in the industry.  And so this is how these companies using our service, can one prove their effectiveness, make sure that clients can trust them. They have the scientific objective data directly from the brain and the neuroscience, and so it's a way for them to stand out from the competition. So that's the main industry that I think we can disrupt with this. But then as I mentioned, there's those other applications. Marketing is also in a similar space where companies have a very tough time quantifying how effective the marketing content they put out is.

{ 18:41 }

They have tools that look at how many clicks they got on an ad, for example, but then you don't know why they clicked on it. So through the brain you can actually see the effect that it had on their subconscious, the effect that it had on their brain awareness and quantify their marketing content in this novel brain quantitative way that again can help a marketing agency stand out from the crowd.    Wow.

{ 19:52 }

Yeah, I'm really glad to see politics starting to catch up with the tech.  I actually wrote an essay in undergrad about the ethics of brain computer interfaces and at the time Chile was the only country that had a law about neuro rights and privacy of brain data. And now I'm very happy that I believe Colorado and very recently California have added brain data as a part of the personal identification data that companies need to keep private and have data privacy around. So I'm glad to see that politics is finally catching up. So even before that law or that bill was passed in California, that was already always be a huge priority that we have neuron.

And so one main way that we're able to keep the data private is by anonymizing the data, making sure that the information about the subjects, whenever we do have to collect it, is completely separated from the actual brain data. And so right now, even in the worst case scenario of that brain data being leaked, it would still be impossible to trace it back to the individual it was collected from. It would be completely anonymized, which is basically even how researchers make their data available. Even with health data, which is the paramount of sensitive data, as long as it's anonymized, it can be made public for other researchers to use, for example. And so that's the main way that we use to make sure that everything that we collect is very highly, highly sensitive. And so therefore we make sure their privacy policies are very high and paramount.

So I'll give a tangible example of what we did with our last client. So our last client, they actually were the management training company called Management Cues, but who we collected the data from or the managers that went through their training program. And so the data that we provided, we provided to Management Queues as aggregate data so that they can now use that for marketing and sales purposes. But we also provided it to management queues clients and their leadership team because the data that we collected was from the managers of this company. So we also provided the data to them as aggregate data only to show to them how effective the training program that they paid for actually was. The way we actually provided the results was in two separate ways.

We had aggregate anonymized data provided to the companies. And so that had absolutely no information about the individuals. It didn't even have individual comparisons, obviously no names, no identification. And then on top of that, we separately created individual personalized reports for each of the managers who went through the study to get their own personal data. And so each of the managers independently knows what their own data is. No, no one else does. And so that's basically ensuring that there's no repercussion from the company on the employees, the company not even knowing who the each data point is from, but still giving the employees the ability to understand where they stand in the crowd, where they excel, where they need to work on by comparing their personal data to the aggregate data. And then if they wish, it is still their right.

And they have now the ability to, if they want, share their personal data with their met, with their leadership, If they want to, for example, use it to make an argument for a raise, let's say, then they want that option, but we're not going to share that data without their consent. So they have their individual report. If they want to make it available to their leadership team, then they're totally able to.

{ 26:24 }

Yeah. So one thing that I made a huge point about in the essay that I mentioned was the current field with invasive brain computer interfaces. So what we do when they're on is completely non invasive, but invasive neurotechnology is extremely important specifically for patients who need it. And so that's the point that I had made in that essay that I, I still believe, is that those technologies can be life changing for this population that are paralyzed, that have lost the ability to communicate through physical means.

And so that is truly revolutionary technology that can give those people back the ability to communicate. That is actually the reason I fell in love with neuroscience in the 1st place and with this field.  So I think that that work is extremely important for that population and I hope that until it becomes safe for non patient populations to use it, that it will not even be possible for them to use it. Because right now most of those invasive brain computer interfaces require brain surgery, which is still a life threatening surgery. And so I want to make sure that those procedures are only made available for the people who have a risk benefit ratio that makes sense. For those people who are paralyzed that have lost that ability, it does make sense for them to take that risk of going through brain surgery for the reward that comes out at the end for healthy individuals. I don't think that that's the case today.

So that's something that I hope will not be regulated such that even the healthy people that just want to go through that procedure because it looks cool are not able to until the procedure is made safe enough for anyone if they wish to actually go through it for us at Neuron. So everything we do is completely non invasive. So there is no risk to your health, to your life going through our studies wearing this completely non invasive headset or head cap. And the other thing I want to mention is thankfully the NIHI believe has already proven that it is impossible to identify individuals based on their brain data. So as opposed to for example a fingerprint, if you have if you find a fingerprint out in nature, you can directly trace it back to the individual it's from. But brain data that's not the case.

If you have anonymized brain data, it's impossible to trace it back to the actual individual. And so that's how on our end when they're on the non invasive nature and the anonymized data policy for data privacy are the two ways that we're making sure that all ethics are respected when it comes to brain interfaces, neurotechnology and brain data.

{ 30:35 }

Yeah, I was just talking about it at a conference at UCLA a few weeks ago.   And what we were talking about is basically no matter how fancy complex the architecture of your model is, at the end of the day, the quality of the data is really going to make or break your model. That's the reason why foundation models, large language models today are so expensive. It is obviously the resource intensive resources that they need to build those models from a technical standpoint in the architecture. But what's really expensive is getting the high quality data to feed into those models. And so that has been a challenge finding the right high quality and large amounts of data.

And so one thing that we're doing is we're actually collecting our own data to work to help with our models. So in that last study that we conducted, we also had the subjects performed tasks that induced cognitive states, that induced the mental Wellness states that our algorithm quantifies so that we can collect your own data, so that we make sure that it's high quality, we make sure that it's collected the right way. And that is how, on top of being able to source Open Access data sets, which are always great and something that I hope will continue to expand, still collecting our own data to make sure that our models are as good as possible.

{ 32:35 }

Yeah. So even on our team right now, and we have people with all of these different backgrounds, just like when at Crux we would put on a team and we'd try to get people from different majors, same. Now we want people with neuroscience backgrounds, with data science, engineering, electrical engineering, CS, computer science. And that diversity of perspectives is also very important. But mostly in this field is just the complementary, the complementarity of skills. We want people who are extremely good at what they do. So extremely good. Add the signal processing part, add the machine learning part, add the neuroscience.

And so it's great to have people and we should have people who are great at each of these individual skill sets and then bringing them together into a complimentary team is actually what is at the end, the best way to get a high quality result. 

{ 33:46 }

Yeah. And so those are the different backgrounds that are needed just to build the model. But then as a company there are a lot more needs. And so now we're, we already started expanding and having a lot more people with different backgrounds, whether it's business backgrounds for sales, team design, cognitive science, UI/UX design work, having people with those backgrounds and even now people with psychology because we started a corporate training program based on neuroscience. And so our team has very different backgrounds, but all with this passion for neuroscience. And so that's what truly at the end of the day helps us foster this community.