I am a fourth year Physics PhD candidate working on quantum error correction, quantum simulations, and tensor networks. In my research, I synthesize and develop graph inference algorithms for decoding quantum codes. In addition, I work on developing (approximate) tensor network methods for large scale quantum simulations. I am currently interested in constructing theoretical methods to learn correlated effects on quantum devices to supplement decoding. Beyond physics, I enjoy STEM outreach, singing and listening to music, and am fascinated with maps and history.
Archives: Explorers
Frederick Dehmel
Hi, I’m Frederick Dehmel, a computer science and pure mathematics student at UC Berkeley. My academic and research interests centre on theoretical computer science, cryptography, and quantum coding theory, particularly formalizing algebraic constructions in Lean 4 and exploring pseudorandom error-correcting codes. I hope to pursue a PhD in Theoretical Computer Science after college.
Alongside my research, I’m deeply passionate about education and community engagement. I’ve had the privilege of serving as Head Teaching Assistant for Berkeley’s computer security course and teaching math and CS to incarcerated students at San Quentin. Beyond academia and teaching, I love playing tennis, going on hikes and riding my bike!
Madelyn Rahimi
Madelyn Rahimi is an undergraduate student studying Physics and Astrophysics at UC Berkeley. Her research has involved quantum optics and open quantum systems, where she helped develop simulations to constrain the measurement sensitivity of the prospective Quantum Invisible Particle Sensor. She is also interested in the intersection of quantum measurement and high-energy theory, particularly in questions involving quantum gravity and cosmology. Her time at Université Paris Cité strengthened her interests, where she was introduced to the intricacies of de Sitter space and its role as a geometric background for describing our accelerating universe. Beyond research, Madelyn co-founded Astrophotography at Berkeley and enjoys shooting film photography in her free time.
Haruki Ebina
I am a PhD candidate in the Physics department at UC Berkeley, working with Martin White. My research is on large-scale structure cosmology, which uses the distribution of galaxies and matter to constrain fundamental physics and the model of the universe. I am broadly interested in modeling and interpreting data from international survey collaborations. I was born in Tokyo, Japan, grew up in San Diego, CA, and completed a double major in Physics and Mathematics at Cornell University. During my undergraduate studies I worked under Michael Niemack, working on Cosmic Microwave Background experiments. In my free time, I enjoy rock climbing, museum visits, and traveling.
Yuhan Chen
I am an undergraduate student at UC Berkeley majoring in Physics and Astrophysics, and I’m currently taking a semester off to do research and explore more areas of physics. I’m working on 3D hydrodynamical simulations of close binary systems to model how supernova ejecta interact with a binary companion star and how the newly formed neutron star can capture gas from the companion. From these simulations, I study the formation of an accretion disk around the neutron star through the capture of gas from the shock-inflated companion star. More broadly, I’m interested in transients and compact stellar objects. Outside of research, I enjoy figure skating, cycling, backpacking, listening to music, making crafts, and spending time with my dogs. I’m always happy to chat if you’re interested in any of these things, and I’d also love to learn about something completely new!
Emmi Wyttenbach
Emmi Wyttenbach received both her B.S. and M.Eng. in Electrical and Computer Engineering from Cornell University and is currently a graduate student studying power electronics in Professor Robert Pilawa’s group. Her research focuses on the control and modeling of hybrid switched-capacitor converters in the data center application space. She is additionally exploring high frequency inverter design for MRI. Outside of the lab, she enjoys a variety of sports including lifting, rock climbing and swimming.
Mina Huh
Mina Huh (https://minahuh.com/) is a 5th-year Ph.D. student at UC Berkeley EECS, advised by Amy Pavel. Her research focuses on accessible Human-AI collaboration. I build systems that help people inspect AI outputs, act with AI guidance, and intervene during AI execution – especially when visual access or attention is limited. She is also passionate about teaching and mentoring research, and has served as a lecturer for CS160 at Berkeley. Outside of research she enjoys tennis, hiking, and baking pies.
Marçal Comajoan Cara
am a Computer Science PhD student at the Berkeley AI Research (BAIR) Lab. My interests include AI for scientific discovery and design, mechanistic interpretability, and modeling tabular and graph data, with applications in genomics and population genetics.
I am originally from Spain, and before coming to Berkeley, I studied Data Science and Engineering at the Polytechnic University of Catalonia, with exchanges at ETH Zurich and Stanford. My research path has taken me across quite different scientific domains: before working on AI for genomics and population genetics, I did research on quantum machine learning for high-energy physics.
Outside of research, I enjoy traveling and discovering new places. I’m equally drawn to contemplating the sun and thinking about the immortality of the crab.
Charlotte Shi
Hi, my name is Charlotte, and I am a senior at UC Berkeley’s Rausser College of Natural Resources, majoring in Microbial Biology. My research focuses on the immunology and microbiome associated with Chlamydia trachomatis infection, particularly in low-resource regions with high burdens of infectious diseases. Outside the lab, I provide volunteer clinical support and advocacy for immigrant and uninsured communities. I also serve as a volunteer EMT, caring for people during unexpected and vulnerable moments. I hope to build a career that moves between the bedside, the lab, and the community, turning systemic needs into actionable solutions.
Kylie Morgan Huch
My name is Kylie Huch, and I am a neuroscience PhD student specializing in theoretical research. My work focuses on representational geometry and neural architectures, particularly how the brain’s encoding and representation of information complement the computations performed on that information, and how the brain’s structure supports these processes. My background is in computer architecture and biophysics. Before beginning my PhD, I worked on developing dedicated, low-energy hardware accelerators for fast and robust edge-computing applications, using non-standard logic paradigms and emerging technologies such as superconducting quantum interference devices (SQUIDs). I completed my undergraduate studies at the University of California, Santa Barbara (UCSB), where I majored in biophysical chemistry and computer science.