Four UC Berkeley faculty members have been awarded the 2023 Bakar Prize, which is designed to give a boost to innovators as they translate their discoveries into real-world solutions.
The prize is given annually to former Bakar Fellows and provides additional resources to ensure a successful transition of their technology from academic research to industry applications.
Now in its 11th year, the Bakar Fellows Programs has supported 64 Faculty Fellows and more than 50 Innovation Fellows as graduate students or postdoctoral fellows. The program continues to attract and support innovative research teams that are committed to moving their basic research discoveries into real-world applications.
The four prize winners are:
Alessandra Lanzara, professor of physics: A New Quantum Detection Tool for Quantum Information Science
There is a worldwide race to build quantum computers that will allow for complex and currently impossible calculations in fields such as cryptography and protein modeling. Key to this revolution in computing is an understanding of how to build a quantum bit, or “qubit,” the basic unit of information in a quantum computer. Spin, an intrinsic property of all elementary particles and the quantum-mechanical counterpart of angular momentum in classical physics, is viewed as one of the leading candidates for qubits. Scientists will need new tools to access and control the spin quantum number, especially in those materials such as superconductors and topological insulators that hold great promise for quantum computing. Lanzara’s research group has developed a one-of-a-kind tool called spin-Time of Flight (spin-TOF), which allows mapping and manipulation of the spin property of materials and is a thousandfold more efficient than other existing tools. Her Bakar Fellows Award will enable her to commercialize spin-TOF for use by researchers in the fields of quantum materials and computing and also undertake research and development of the next generation tool for industry applications.
Markita Landry, associate professor of chemical and biomolecular engineering: Bypassing plant regeneration with nanotechnologies to deliver DNA, RNA, and protein
Plants are vastly underrepresented among the many biological systems in which genetic engineering is routine. Technologies to genetically manipulate plants yield random DNA integration into the plant genome, are inefficient and require transgene segregation through laborious breeding if labeling as a genetically modified organism (GMO) is to be avoided. With current approaches, it can take months or years to obtain and test a plant genetic variant. One main bottleneck facing efficient plant genetic modification is efficient biomolecule delivery into plant cells through the rigid and multi-layered cell wall. Landry’s lab has developed a nanotechnology that enables high-throughput delivery of biomolecules to plants without requiring expensive equipment or refrigeration of reagents. The method results in transient protein expression without incorporation of foreign DNA into the plant genome.
Rikky Muller, assistant professor of electrical engineering and computer sciences: Ear EEG: Hearables That Read Your Mind and Your Dreams
Smart earbuds and hearing aids known as hearables have transformed the headphone and audiology landscape. In addition to enhancing enjoyment of music and conversation, technologically advanced electronic in-ear devices controlled by touch, movement or voice are being applied to medical monitoring, fitness tracking and more. Muller’s research group has developed EarEEG, which uses lightweight in-ear earbuds to detect the brain’s electrical activity. Electroencephalography (EEG) is recorded non-invasively from the ear canal and transmitted wirelessly to the user’s smartphone. Her goal is to enable the seamless connection of mind to device in a variety of user interface, consumer and health care applications.
Jaijeet Roychowdhury, professor of electrical engineering and computer sciences: Oscillator Ising Machines for Combinatorial Optimization
In our information-dominated world, combinatorial optimization problems are ubiquitous. Logistics and transportation, intelligent robots, autonomous cars, smart grids, drug design and communication networks all involve finding the optimal solution from among millions or billions of possibilities. “Quantum annealing” machines have been proposed to solve such problems quickly, but they are large, expensive and difficult to scale to solve ever-larger problems. Roychowdhury and his graduate student, Tianshi Wang, have invented a new approach called an Oscillator Ising Machine (OIM), which solves combinatorial optimization problems using coupled electronic oscillator circuits. Because OIMs are based on conventional integrated circuit technology, they are a far smaller, less expensive and more scalable alternative. OIM chips may become a standard technology, as GPUs are for graphics computations.