Personal History

Growing up as a curious girl in the Japanese countryside

I spent my early years in the Japanese countryside as a very curious girl. I grew up in Tottori Prefecture, the least populated prefecture in Japan. To be honest, I do not remember many details from that time. What I do remember is that I really wanted my parents’ attention, especially since I had a younger brother. I tried hard to get their attention. I was not particularly into studying or sports. But I loved and was good at observing and understanding things around me. For example, I liked playing medal games, which are a type of arcade game in Japan where we can win metal coins, similar to arcade tokens. I strategically observed other players and machines to figure out which machine might let me win. I enjoyed observing how people behaved, guessing what they might be thinking. I liked thinking about what I should do based on those observations and thoughts. The best moment for me was when my strategy worked as I imagined and I won the game.

Realizing the importance of environment and people around me to shape my personal growth during adolescence

As I grew older, I started to realize how much the environment and the people around me influenced my personal growth. This became especially clear during my teenage years.

I began playing field hockey when I was in elementary school because my best friend joined the team. Hockey was not very popular in my hometown, and our school was not particularly strong. There were schools in neighboring prefectures that consistently performed much better in competitions. Based on past results, I did not truly believe I could beat them, and I lacked confidence in my own skills. Still, I wanted to improve and win, so I practiced hard every day. From elementary school through my second year of middle school, though, I never felt ready to compete at a national level.

Everything changed in my third year of middle school. A new teacher and coach joined our school, someone who had previously led a team to win the national hockey championship. His arrival completely changed my life. From the moment he came, everything felt different. He taught us not only how to practice hockey more effectively, but also how to think differently about the sport and about daily life. Even though my teammates were the same, his influence transformed how we played and how we approached challenges. I started practicing from early morning until evening, in addition to regular team practice. After about six months of this intense training and learning, my confidence grew dramatically. I began to believe that I could compete with the best players in the country. Although I did not end up winning a major competition that summer, the experience taught me something far more important. Where you are and who you are surrounded by can completely change how much you grow.

As high school graduation approached, I faced a major decision about my future. Hockey had been the center of my life for many years. One option was to continue playing hockey at university and aim to become a professional player. The other option was to focus on studying, take university entrance exams, and pursue an academic path. Many of my friends had been preparing for these exams for a long time, while I had spent most of my time playing hockey. I only had a few months left to prepare. My teacher told me that it would be tough for me to get into the university I wanted to. So I knew that the second option would be very challenging. However, I was intrigued by that hard challenge and the future opened by studying at university. I decided to take the second option – study very hard and take a university entrance exam. After retiring from playing hockey, my extreme study days started.

For my major, I decided to study mathematics, which was the subject I loved the most for a long time. For university, I decided to apply to Nara Women’s University, even though my teacher told me that it was a very difficult choice. I worked hard and successfully got into my first-choice university. Later, I found out that I had barely passed. One more mistake, and I might not have made it.

Diving deep into math during undergraduate years

I joined Nara Women’s University, which is the home of many female students who love math and science, just like me. During that time, women in STEM field were rare, especially in the countryside, so I always felt that I was different from others. This feeling was uncomfortable in Japan which has a strong collectivistic culture. However, in this new environment, I felt more accepted. I began deeply studying math with a lot of women who shared my interests. But math at the university level turned out to be extensively hard. I used to enjoy math, solving problems with numbers and equations, but now it was about learning theories with abstract concepts. I started struggling with it because I could not find the connection between math and real-life applications and could not intuitively understand the meaning and value of learning it.

At the university, there was no hockey team, so I started playing lacrosse. It was a fun experience and I made great friends through it. We even won a big competition. My university life was a mix of struggle in math and fun in lacrosse.

When my undergrad study got close to the end, I decided to look for an industry job rather than going to graduate school. I didn’t have a strong interest in anything specific industry, but I wanted to find a job where I could use my math skills. I learned that many graduates from the math department become a system engineer (SE) who uses the logical thinking skills obtained by learning math. I applied for various SE positions and got offers from some companies (though I didn’t get into my top choice).

Discovering my passion for data science and becoming interested in international experiences while working as a data scientist

Upon joining as an SE to my first company, I discovered my role was in a new field (at that time) called Data Science. The assigned job was to create bidding algorithms for an online advertising system known as Demand Side Platform (DSP). This job required skills in data analysis and machine learning. Based on my math background, the company thought I would be a good fit for this role. It was a completely new experience, but this transition was transformative for my career. Data Science applied math to real issues, which was the connection between math and practical application I had been looking for throughout my undergrad study. I loved this job. Unveiling insights from big data felt like discovering buried treasures in a big mountain.

While working as a Data Scientist, I got the opportunity to represent my department at an international digital marketing conference held in Japan, which exposed me to the broader world. Listening to speakers from overseas made me realize there was so much more to explore, motivating me to learn English to gain international perspectives.

This realization and motivation made me decide to move to an environment where I can use English more frequently and interact with international employees. I moved to Rakuten, where I continued working as a Data Scientist. My role involved analyzing data from merchants on Rakuten’s e-commerce platform to build strategies for boosting their sales. The main reason for joining Rakuten was its unique environment. English was the internal official language, and the company had a strong international presence. My life in this new environment further motivated me to learn English hard and nurtured my hope to work internationally.

I thought that this was the perfect working environment, but, over time, I started questioning the meaning of my work. My daily tasks were demanding, and I struggled with a misalignment between the company’s goals and my personal interests. I felt that the vision of this company did not align with my passion, which made me unmotivated toward my job. But I was not exactly sure what my passion was at that time. This realization led me to introspect about my true passions and what I really wanted to do.

While exploring my passions, I considered starting my own business and applied for Tottori Startup Camp, an event supporting aspiring entrepreneurs in my hometown. Through this event, I felt lacking in a clear sense of self and purpose, which affected my commitment to any business idea. At the event, I met with Hiromi Okuda, a Japanese female entrepreneur and a person who changed my life. To find my true self and what I want to do, she encouraged me to attend a training held in Silicon Valley. Feeling that this was the opportunity I had been seeking, I promptly decided to participate, despite the significant cost (about double my monthly salary at the time). This decision marked a significant turning point in my life.

Deciding to go to the US through training at the Women’s Startup Lab

In January 2018, I traveled to Silicon Valley for the first time to attend the training. At that point, I was more worried about my limited English skills than excited about the experience of being in the U.S. I joined a group of 10 participants, including myself, for the training. During the training, we introspected deep into our lives, reflecting on everything from childhood onward, aiming to discover our core being. Night after night, I remember lying in bed, puzzled about what truly made my core being. Local entrepreneurs also participated as mentors in the training. While I couldn’t fully grasp their discussions at the time, their enthusiasm and powerful words left an impression on me, providing a surge of energy. Although I didn’t fully uncover my core being through this training, I returned with something even more valuable. It was the unmoved desire to go to the US. I wanted to live in this thrilling environment, communicate with the people there, and experience entirely new aspects of the world. My excitement led me to decide that I had to find a way to go to America.

While the decision to go to the US was made, how to go about it and what to do there remained unclear to me. However, my strong passion and excitement pushed me forward without a clear plan. My initial idea was to utilize the network of the global company Rakuten to secure a position at Rakuten’s U.S. branch. However, given my English proficiency at the time and the available positions, I couldn’t find a job in the U.S. that was aligned with my interests. I considered waiting for any opportunity to arise at Rakuten, or starting with any job in the U.S. and then promoting as I gain more experience. But I felt that this path would take too much time. Instead, I believed that by investing in myself and polishing my skills, I could more quickly achieve what I desired in the US. Thus, I reached the conclusion of pursuing a graduate degree in the U.S. By studying at a university, I could pave my way to stay in the US and gain expertise in a short time in a field that genuinely interested me. I made this decision just a few days after returning from the training, and I submitted my resignation letter to Rakuten the next day. While some might see this as a hasty decision, I believed (and had confidence) that this path would make me grow significantly and bring me to a better place to live happier.

Starting in March 2018 after leaving Rakuten, I dedicated myself to studying English for the US graduate school admission. From morning till night, I focused on preparing for the TOEFL exam, aiming to score at least 100 points as quickly as possible. English had never been my favorite subject; during my undergrad study, I used to joke that I would finish my life without studying English. Thus, studying English daily was not easy for me. However, I had a strong enough passion to overcome that challenge and achieve the target score within the deadline.

Another challenge was selecting a field of study that would align with my passions. Although I wanted to go to the US, I hadn’t yet clarified what I wanted to learn or do. My experiences in data science and interactions with a supervisor at Rakuten played a significant role in helping me decide.

As I dived deep into machine learning and AI as a data scientist, I often became disappointed by AI. At the time, the world was buzzing about how AI, exemplified by AlphaGo, would soon surpass human intelligence. However, in practice, AI was nowhere near capable of independently conducting data analysis tasks I did daily at Rakuten. Instead, I began to appreciate human intelligence, cognition, and learning abilities. This realization ignited my curiosity to understand how humans think and learn, and brought the interest in creating AI capable of human-like thinking and learning.

Another realization stemmed from my supervisor at Rakuten, who was exceptionally intelligent in various ways. In meetings, I presented data analysis results and my interpretation to him. He would then respond with incredibly deep and interesting insights. Despite looking at the same data and graphs, he consistently derived fascinating observations and provided clear explanations. This difference between his and my abilities significantly intrigued me.

However, I noticed that he had acquired his skills over time through experience and couldn’t easily explain his thought processes. His knowledge was internal and inaccessible. I wanted to understand how his brain worked to produce such outputs and learn his skills. But it was just impossible without understanding the process within his brain.

These two interests converged in the field of computational cognitive science, a branch of psychology. This field uses machine learning and data science to understand cognitive processes, including human learning, and tries to leverage this understanding to the development of AI. This field resonated perfectly with my interests in human learning processes and my expertise in data analysis and machine learning. Therefore, I decided to pursue cognitive science studies.

After applying to several US universities, I decided to enroll in the Master’s program in psychology at New York University, starting in the fall of 2019.

Finding my interests in neuroscience while studying cognitive science at New York University

During my master’s program, I had an important realization: the intersection between my interests and my expertise might actually be in neuroscience.

The beginning of my life at NYU was tough. I had to adjust to English-taught classes, a completely new culture, and life in the US. On top of that, there was another big challenge waiting for me – finding a research lab. NYU’s Psychology master’s program was flexible, and technically I could graduate just by taking classes and passing exams. But I really wanted hands-on research experience. So I started reaching out to professors. As someone new to both English and US academic culture, explaining my interests and skills in a convincing way was extremely difficult. It became even harder as I tried to understand each lab’s research focus, especially since I didn’t yet have much research experience. To make things worse, there were very few labs that both accepted master’s students and aligned with what I wanted to study.

After some rejections, I finally got a chance to meet with Prof. Roozbeh Kiani. That meeting became a turning point for me. His lab studies how the brain processes information and makes decisions by working with monkeys, and I was offered the opportunity to analyze monkey behavior related to decision-making. At the time, I was more interested in studying humans, but I decided to take the chance and try something new. Looking back, that decision completely changed my career path. I am deeply grateful to Roozbeh for welcoming me into his lab, especially when my English was still far from perfect.

My project focused on analyzing monkeys’ decision-making behavior, so I didn’t work directly with neural data like spikes back then. However, as I spent more time in the lab and continued taking classes in cognitive science and psychology, I started to realize something important. Neuroscience might actually be a better fit for my background in data science. In psychology, data tends to be very lab-specific such as behavioral data from a particular task or questionnaires designed for a single study. That makes it hard to combine datasets across labs and take advantage of machine learning. In contrast, neuroscience data is often collected using common modalities. Signals like spiking activity or local field potentials are ubiquitous, even if they come from different subjects or brain regions. That made me excited about the potential of neuroscience: integrating large datasets and applying modern machine learning to improve people’s lives. It felt like the right direction for me.

Then, the world changed. COVID happened.

It was my first spring semester, right after surviving my first fall at NYU. I was living in on-campus housing, and I still remember an email sent on Monday, March 16. NYU announced that student housing would close, and we were required to move out by March 22 (preferably within 48 hours!). Until then, there had been no indication this would happen. It was shocking, especially for international students who didn’t have a home to return to in the US. I had no choice but to go back to Japan and stay at my parents’ house. I booked a flight for two days later, packed everything, and left. Classes that used to be in person suddenly went fully online. Because of the 13–14 hour time difference, many of my classes were held in the middle of the night for me. In total, I stayed in Japan for about half a year before returning to the US in August to continue working with the monkeys on my project.

Since my master’s program was two years long, by the time I returned, it was already time to think seriously about what to do next. Initially, I wanted to go into the industry, which was my original motivation for coming to the US: improve my English, gain expertise, and find a job here. But during the pandemic, the job market was terrible, especially for international master’s students. The chances didn’t look good. So I started seriously considering a PhD. Staying in the US had become my top priority. Even during the pandemic, I loved my life there and felt much happier than I ever had in Japan. The diversity in the US made me feel like I could truly be myself. People were different, and that was okay. I am Yuki.

In Japan, the culture often feels more pessimistic, and people don’t really praise each other. In contrast, in the US, people are much more positive and encouraging. Simple things like hearing “that’s a great question” in class made a huge difference for me. That kind of environment helped rebuild my self-esteem, which had been extremely low before coming to the US.

For my PhD, I chose neuroscience over cognitive science or psychology for the same reason as before. I believed neuroscience sits at the best intersection of my interest in the brain and my background in data science and machine learning. While exploring programs, one stood out immediately, the Neural Computation and Machine Learning joint program at Carnegie Mellon University. I applied to several programs, but I was incredibly fortunate to be accepted into the one I was most excited about. It offered exactly the training I wanted, which is to learn how to apply machine learning to neuroscience research. After graduating, I decided to join CMU.

One last fun coincidence. During my master’s, I actually met one of my future PhD advisors without realizing it. Dr. Byron Yu was on sabbatical at NYU during my final year, and he sometimes visited the Kiani lab. At the time, I had no idea how well-known he was in the field. To me, he was just a professor who occasionally came by, and we had a few casual chats. I never imagined he would become my advisor. Later, while browsing labs in the CMU program, I saw a professor whose photo looked very familiar, and someone working exactly at the intersection of neuroscience and machine learning. I checked with my lab mates and confirmed it was indeed the same person. What a coincidence. Life is funny like that. From that moment on, his lab became my top choice at CMU.

Feeling, for the first time, that my research truly mattered and that I was growing as both a person and a scientist at Carnegie Mellon University

I started my PhD at Carnegie Mellon University in September 2021. In the US, a PhD is expected to take about five years, and in neuroscience it often takes even longer, often six or seven. Until then, my life had changed in big ways roughly every three years, so I was honestly a little worried about whether I could commit to one thing for that long. In the end, that worry turned out to be unnecessary. These five years flew by, filled with intense learning, excitement, and a deep sense of fulfillment.

Our PhD program used a lab rotation system. During the first year, students rotate through several labs, spending one semester in each, to figure out where they want to settle. In the summer before starting the program, I reached out to several professors to explore rotation opportunities. During the application process, I had already been in touch with Professor Matthew Smith, and I immediately felt that he was an amazing person to work with. He also collaborated closely with Byron Yu, whose work I was most excited about. I asked whether it might be possible to be co-advised by both of them. I was so lucky that both were accepting rotation students that year and were open to taking me on. That was how I started my first rotation with their labs.

At the beginning of the rotation, I was most interested in working on brain computer interfaces (BCIs). BCIs create a direct communication pathway between the brain’s electrical activity and external devices, allowing people to control computers, robotic limbs, or other machines using neural signals. CMU is one of the pioneers in this field, and to me, BCI felt like the perfect intersection of neuroscience and machine learning. The core challenge is translating neural activity into meaningful control signals using models and algorithms.

Instead, Matt and Byron suggested a different project focused on brain electrical stimulation. At the time, a postdoc in the lab, Joana, had been leading that work, and they proposed that I work closely with her to learn the project. To be honest, I was a bit disappointed at first. I thought the BCI project sounded more exciting. But once again, things turned out better than I expected. This stimulation project ended up being exactly the right place for me to combine my interests and my technical background.

During the rotation, I learned how to work with monkeys, built basic data analysis pipelines, and spent a lot of time discussing potential research directions. Through those conversations, one idea came up, closed-loop optimization of stimulation parameters. This idea immediately resonated with me. When I was a master’s student at NYU, my advisor Roozbeh Kiani had once mentioned optimizing stimulation parameters using algorithms, and I had been fascinated by it ever since. Now the same idea was resurfacing, supported by Matt and Byron’s interests as well. After more discussion, we decided that this would be my main direction, developing a closed-loop stimulation framework to control neural activity. I loved the idea, and the people in both labs were incredible, so I decided to join their labs without doing additional rotations. Writing this now, in my fifth year and very close to graduation, I can confidently say that joining these two labs and working with Matt and Byron was one of the best decisions I have ever made.

Of course, the path to make good progress on my research was initially not smooth at all. After officially joining the labs, I struggled a lot, especially in the first couple of years. The coursework at CMU was extremely demanding, particularly the machine learning classes. As part of the joint program in Neural Computation and Machine Learning, I had to take core courses in both neuroscience and machine learning. At that point, my math skills were quite rusty, and many of the ML core classes were highly theory-focused, with heavy mathematical content and relatively little programming. My first two years genuinely felt like survival mode. I leaned heavily on friends to work through homework together, and I spent most of my time studying. Trying to make progress on research while keeping up with coursework was exhausting. But in the end, it was worth it. Those years gave me a strong foundation in machine learning, which I now rely on deeply in my research.

Coming more soon!