Explorer Categories: Union of Alumni

I am an Argentinian PhD candidate with a background in mathematics, physics, and data science. My research lies in the intersection of novel data science methods and physics, with emphasis in glacier modelling and scientific software development. I am fortunate to be advised by Fernando Pérez (UC Berkeley, Project Jupyter) and Jonathan Taylor (Stanford).

I am a fourth-year EECS PhD candidate advised by Professor Somayeh Sojoudi. My current research broadly spans safety, interpretability, and robustness in machine learning. I double majored in CS and Mathematics as an undergrad at the University of Pennsylvania, and I like to use interesting mathematical tools in my research work today. I enjoy playing chess, guitar, and soccer in my free time.

Born and raised in Hong Kong, I’m a PhD student in theoretical cosmology studying statistical methods to search for axion cosmic strings using light from the distant universe. I’ve also been a key developer of the scientific critical thinking course, Sense and Sensibility and Science, which is now being taught in universities and high schools around the world. As a classically trained musician, I play the piano and the clarinet. As a tenor, I have sung in operas and am a member of the San Francisco Symphony Chorus. In my spare time, I contribute to mathlib, a library of computer formalised mathematical proofs, and write Wikipedia articles in English and Chinese. Outside of nerdy pursuits, I enjoy travelling and hiking in nature. I’m excited to meet and learn from people from different disciplines and different countries.

Amanda was born, raised, and educated in Austin, Texas. She went to The University of Texas at Austin (UT), where she earned a BA in Plan II Honors and a BS in Biomedical Engineering. At UT, her work focused on exploring the relationship between motivation and stress in engineering undergraduate students. She also helped develop and validate targeted drug delivery systems.

A PhD candidate in the UCSF-UC Berkeley Joint PhD Program in Bioengineering, Amanda is now wrapping up her work in Professor Dan Fletcher’s lab at UC Berkeley. Her research is at the intersection of CRISPR-Cas and viruses: she is engineering new viral-based vectors to deliver gene editing tools and developing CRISPR-Cas-based assays to detect viruses.

When not in lab, Amanda can be found in her garden, observing the beauty of nature and deciding what to plant next.

Yakira Mirabito is a PhD candidate in the Department of Mechanical Engineering at UC Berkeley. In the Cognition and Computation in Design (Co-Design) Lab, advised by Kosa Goucher-Lambert, she studies decision-making and organizational behavior within engineering design. She holds a BS in Materials Science and Engineering from Northwestern University and an MS in Mechanical Engineering from UC Berkeley. Yakira’s work has earned her numerous fellowships and awards. Notably, she has been funded by UC Berkeley’s Chancellor’s fellowship, NSF GRFP, and NSF InFEWS. Her contributions have been recognized by organizations such as the American Society of Mechanical Engineers and the Society of Hispanic Professional Engineers.

Beyond her academic achievements, Yakira is a passionate leader and advocate within the first-generation and low-income (FGLI) college student community. Her dedication to creating a sense of belonging for the FGLI community has earned her the Dean’s Award for Inclusive Excellence and a RISE! Leader Award. When she’s not immersed in her work, Yakira enjoys skiing and visiting art galleries.

I am a PhD candidate in the statistics department. My current research aims to improve gridded data products for snow water equivalent (water quantity in snow). Since snowmelt is a vital resource for meeting freshwater and agricultural demands, understanding the amount of water that snow will yield each year is essential for short- and long-term planning. Through my work, I hope to support water resource managers in making more informed decisions on water allocation, irrigation practices, and flood and drought control to ensure that communities have access to dependable supplies of food and water and are protected against snow-related hazards.

Hi! I’m a PhD student in chemical engineering working on hydrogen fuel cells and their components. Most of this work involves modeling using kinetics, thermodynamics, and transport phenomena from chemical engineering and electrochemistry with some numerical optimization tricks.

I’ve previously worked as an intern in supply chain consulting and full-time in semiconductors. I’ve been playing badminton casually for most of my life and hope that remains true from now on. I really like hiking, reading, cooking, cycling and eating way too much food! If you get to know me, I will probably try to rope you into running at some point honestly.

Boyan Xu is a Ph.D candidate in Mathematics and Computational Biology at UC Berkeley and a Department of Energy Computational Sciences Graduate Fellow (DOE CSGF). He obtained his B.S. in Mathematics from University of Illinois at Urbana-Champaign in 2018. His research in the Krasileva Lab at UC Berkeley applies mathematical methods towards characterizing protein structures involved in plant immune systems. He is also employed at Joint Bioenergy Institute (JBEI) developing bioinformatics methods for biosynthetic gene discovery. Boyan is also a researcher in fungal agriculture and co-founder of Ashby Fungi, an urban culinary mushroom farm located in Berkeley, CA.

I am a PhD student in EECS studying deep learning and artificial intelligence. I’m interested in developing reliable and robust methods for machine learning from large amounts of data and compute. I’m also very excited about the practical applications of these new technologies and how they might augment our innate abilities to understand the world and each other.

Jinyan received his Ph.D. in civil engineering from the Civil and Environmental Engineering Department. He has started a postdoctoral position at the NHERI SimCenter based at UC Berkeley. In his research, he will develop computation tools to explore resilience assessment solutions for interdependent civil infrastructure, such as buildings, transportation networks, and lifelines under natural hazards and climate changes. Jinyan’s research aims to mitigate damage and loss, choose the optimal risk mitigation measures, assess the adequacy of resources, and increase the level of preparedness for post-disaster responses. One of the biggest challenges in natural hazard engineering is the high dimensional uncertainty associated with the hazards’ occurrence, infrastructure conditions, and human behavior. Jinyan plans to advance the current hazard assessment methods with high dimensional uncertainty quantification methods so that more credible infrastructure behavior simulations can be achieved.