Burton’s award will support research in increasing the resilience of structures in response to natural hazards while simultaneously incorporating sustainable practices in building construction, maintenance and operation.
Specifically, he will explore the creation of a unified design and assessment methodology that accounts for the interconnections between building costs, structural resilience, consumption of resources and environmental and social impacts.
The grant will also help fund efforts to recruit and retain underrepresented minorities and women in structural and earthquake engineering. This includes working with undergraduate students in UCLA’s Center for Excellence in Engineering and Diversity. Burton will also develop an interactive exhibit to showcase the research at community-based institutions in and around Los Angeles.
Burton, who earned his Ph.D. from Stanford University, joined UCLA Engineering in 2014.
Chen’s award will her support research on engineering tumor-specific T cells for cancer immunotherapy.
Tumor-targeting T cells – a type of white blood cells that can be harvested from cancer patients, genetically modified to recognize tumor cells, and then re-injected into the same patients – have shown remarkable curative potential against multiple types of cancers that are in advanced stages. However, a major challenge for a broad application of this therapy is off-tumor toxicity, in which T cells attack healthy tissues that display the same biochemical markers as tumor cells on their surfaces.
Specifically, her research group will develop synthetic, cytotoxic molecules that can be delivered by T cells into target cells and conditionally trigger target-cell death if, and only if, the target cells contain proteins specific to tumor cells.
Chen, who earned her Ph.D. from Caltech, joined UCLA Engineering in 2013.
Meka’s award will advance his research into the structure of randomness. He will conduct research in three areas:
* Pseudorandomness: Pseudorandomness is central to complexity theory, a major area in computer science that organizes problems by how difficult they are, and how they connect to each other. This research will look at when randomness is necessary for efficient computing, versus when not-quite perfect randomness may suffice. The work may have applications in many areas, including potentially saving space in algorithms for the processing of big data.
* Optimization hierarchies and hardness of approximation: Identifying which algorithmic problems are hard to solve, even approximately, as well as when optimization techniques—the most powerful algorithm design methods currently available – can succeed in solving such problems.
* Communication complexity: Which problems can be solved with little communication, rather than a lot, between two entities.
Meka, who received his Ph.D. from the University of Texas at Austin, joined UCLA Engineering in 2014.
Photo: L to R: Henry Burton, Yvonne Chen, Raghu Meka.