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.
Shiyin is a 5th year Ph.D. candidate at U.C. Berkeley, where she studies the biomechanics and material properties of the spine. Prior to attending Cal, she earned her B.S. in Bioengineering at Santa Clara University, where she participated in Engineers Without Borders and did research at NASA Ames Research Center. This connection with NASA grew into a formal collaboration, and now her dissertation research focuses on the effects of spaceflight on the mouse spine, specifically intervertebral disc composition and joint mechanics. Shiyin is passionate about building communities, improving human health, and increasing female representation in STEM. When she’s not in the lab, Shiyin enjoys hiking, backpacking, and skiing.
My research interest is aerosols, or particulate matter (PM). Up until now, I have been working to understand what has historically driven high PM events in urban spaces with the goal of working towards achieving meaningful reductions in pollution going forward. Specifically, I have been studying the effect of temperature on PM levels, and how the relationship between the two has been evolving over time.
I’m Luis! I’m a PhD student in the Berkeley BioE department. I engineer microbes to produce valuable molecules such as antibiotics or fuels from plant-derived feedstocks in the Keasling lab. I have recently become interested in the possibility of using microbes to mine metals from water sources.
I am a 4th year PhD student at the Earth and Planetary Science department. I integrate original field observations with laboratory data sets to reconstruct the history of Earth’s magnetic field and tectonic plate configuration. In particular, I study Proterozoic intrusive rocks to gain new insights into the long-term evolution of the intensity of Earth’s magnetic field. I also set up a Quantum Diamond Microscope at UC Berkeley Paleomagnetism Laboratory which enables us to characterize rock magnetic properties at micron-scale. Currently I am also working on integrating paleomagnetic data with thermochronology data to study the uplift history of the Adirondack Mountains, NY.
