UC Berkeley

Name: Feng Wang
Title: study physical properties of atomically thin two-dimensional materials
Description: fabricate two-dimensional materials; material characterization with atomic force microscopy, electrical transport, and optical spectroscopy.
Preferred qualifications: good understanding of undergraduate physics

Name: Hernan Garcia
Title: Live imaging of single cell transcriptional input-output functions

Our lab is interested in uncovering the quantitative and molecular information that would make it possible to predict how gene expression patterns are governed by input activators and repressors, and the arrangements of DNA binding sites for these transcription factors. This project involves simultaneously imaging the concentration of an activator input and corresponding transcriptional output using fluorescent reporter constructs in live, developing fruit fly embryos. Past studies have measured these input-output relationships using a combination of data from two separate experiments, one to measure each variable. However, the averaging necessary to bring together multiple datasets erases information about the fluctuations in time across the single-cell distribution of input-output functions. By instead conducting simultaneous measurements, we can compare features of this distribution to predictions for different quantitative models of gene regulation to narrow down possible molecular mechanisms through which activators influence the timing and intensity of transcription.
There are several possible research threads for a summer student on this project, potentially involving measuring the input-output functions for repressors instead of activators, or for reporter constructs with varying numbers and orientations of activator binding sites. Depending on the student's interest, they could also try analyzing the input-output functions through an information theoretic lens to help understand the rate and dynamics of information transmission in the early fruit fly embryo. Regardless of the specific research question, any summer student will learn confocal microscopy, fly husbandry, and familiarity with our lab's image analysis pipeline.
Preferred qualifications: 

Name: Luke Kelley
Title: Gravitational Waves and Supermassive Black-Hole Binares from Cosmological Simulations
Description: Experiments called 'pulsar timing arrays' have recently made the first discovery of 'low-frequency' gravitational waves (GWs). These incredible new signals are believed to be produced by binaries of supermassive black holes (SMBHs), the enormous objects at the centers of galaxies. Pairs of these SMBHs are brought together following galaxy mergers, but the two SMBHs are believed to go through a complex (and largely unconstrained) binary evolution process before they can reach the small binary separations required to emit detectable GW signals. Modeling the formation and the evolution of these binaries is challenging, but we have new, state-of-the-art cosmological simulations that we believe contain novel insights into what SMBH binaries might be out there, and what additional signals we can hope to detect from them.
A variety of exciting projects are available within our group in exploring these new simulations, and studying gravitational waves from SMBH binaries. For example, we can explore, (1) what distribution of galaxy orbits initially bring the pairs of SMBHs together, (2) how much SMBHs typically accrete from the surrounding gas during their binary inspiral, (3) how binary populations change for different simulations and different simulation parameters, or (4) how often two galaxy mergers occur in the same system, producing SMBH triples. All of these projects provide the opportunity to work with and learn data analysis and coding (python), in addition to performing analytic (paper and pencil) calculations. Feel free to reach out for more information.
Preferred qualifications: Required: enthusiasm to learn. Helpful: any experience in programming/python.


Name: Greg Tikhomirov
Project 1:
Project 2: Nanomedicine
Project 1: Nanorobotics We cannot simply miniaturize a Roomba robot to clean up plaque in our arteries – the laws of the nanoworld are different from those to which we are accustomed. We are investigating the challenges and opportunities of constructing and programming robots on nanoscale. Nanorobotics holds the potential to transform science, medicine, and engineering. This emerging discipline is positioned to address several emerging society needs, such as disease diagnostics and therapy.

Project 2: Nanomedicine All diseases are molecular in nature, and to fight them we need precise molecular machines. Currently, our therapeutic constructs are either too simple (e.g., small molecule therapeutics) or complex but not sufficiently well understood (e.g., CAR-T cell therapy). We are developing a new toolkit for molecular programming, in which simple building blocks with well-understood behavior can be composed into arbitrarily complex architectures using well-understood algorithms, and using this toolkit to build molecular machines customizable for any challenge in medicine.
Preferred qualifications: We are looking for creative individuals from diverse backgrounds who like to learn, think, and build and who disregard traditional discipline boundaries. We value strong work ethic, independence, creativity, optimism, openness to new ideas, clarity of thought, and mathematical/programming skills.

Name: Priya Moorjani
Title: Genomic insights into human evolution, adaptation and disease
Description: Recent advances in sequencing have opened up unprecedented opportunities to use genetic data to advance our understanding of human evolution and biology. In our lab, we use genetic data from ancient specimens and present-day individuals to study the processes that have shaped genetic variation (mutation and recombination), reconstruct evolutionary events (selection, founder events and admixtures) and identify key genetic variants related to adaptation and disease. The summer project will involve applying computational methods and statistical analyses to genomic data to infer demographic history and identify variations associated to human adaptation. The student will be responsible for conducting population genetics simulations to train published methods and test the performance of the method. This will include simulating various human demographic histories and selection scenarios. The student will also have the opportunity to work with other lab members to explore and improve methods for detecting regions of adaptation in humans and other species. For more details, please see recent publications from the lab:
Preferred qualifications: Python programming skills, basic statistics.