publications

Jun-Yan Zhu wins ACM SIGGRAPH Outstanding Doctoral Dissertation Award

CS alumnus Jun-Yan Zhu (Ph.D. '17, advisor: Alexei Efros) has won the Association for Computing Machinery (ACM) Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) Outstanding Doctoral Dissertation Award. Zhu is a pioneer in the use of modern machine learning in computer graphics. His dissertation is arguably the first to systematically attack the problem of natural image synthesis using deep neural networks. As such, his work has already had an enormous impact on the field, with several of his contributions, most notably CycleGAN, becoming widely-used tools not just for researchers in computer graphics and beyond, but also for visual artists.

Lensless Cameras May Offer Detailed Imaging of Neural Circuitry

EECS graduate students Nick Antipa and Grace Kuo, along their advisor Associate Prof. Laura Waller, have penned an article for Photonics Media titled "Lensless Cameras May Offer Detailed Imaging of Neural Circuitry" about a new architecture which could enable simultaneous monitoring of millions of neurons in 3D space at frame rates limited only by image sensor read times.  Instead of using a large, lens-based light-field microscope to image individual brain neurons, the DiffuserCam lensless imaging architecture consists of a diffuser placed in front of a 2D image sensor. When an object is placed in front of the diffuser, its volumetric information is encoded into a single 2D measurement.   Borrowing tools from the field of compressed sensing, a 3D image is reconstructed by solving a sparsity-constrained optimization problem.

PerfFuzz wins ISSTA18 Distinguished Paper Award

"PerfFuzz: Automatically Generating Pathological Inputs," written by graduate students Caroline Lemieux and Rohan Padhye, and Profs. Koushik Sen and Dawn Song, will receive a Distinguished Paper Award from the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2018 in Amsterdam in July.  PerfFuzz is a method to automatically generate inputs for software programs via feedback-directed mutational fuzzing.  These inputs exercise pathological behavior across program locations, without any domain knowledge.   The authors found that PerfFuzz outperforms prior work by generating inputs that exercise the most-hit program branch 5x to 69x times more, and result in 1.9x to 24.7x longer total execution paths.

Aviad Rubinstein wins 2017 ACM Doctoral Dissertation Award

CS alumnus Aviad Rubinstein (Ph.D. ' 17, advisor: Christos Papadimitriou) is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “Hardness of Approximation Between P and NP.”  In his thesis, Rubinstein established the intractability of the approximate Nash equilibrium problem and several other important problems between P and NP-completeness—an enduring problem in theoretical computer science.  His work was featured in a Quanta Magazine article titled "In Game Theory, No Clear Path to Equilibrium" in July. After graduating, Rubinstein became a Rabin Postdoc at Harvard and will join Stanford as an Assistant Professor in the fall.

Editing brain activity with holography

The research of Associate Prof. Laura Waller is highlighted in a Berkeley News article titled "Editing brain activity with holography."  Waller is co-author of a paper published in the journal Nature Neuroscience that describes a holographic brain modulator which can activate up to 50 neurons at once in a three-dimensional chunk of brain containing several thousand neurons, and repeat that up to 300 times a second with different sets of 50 neurons. The goal is to read neural activity constantly and decide, based on the activity, which sets of neurons to activate to simulate the pattern and rhythm of an actual brain response, so as to replace lost sensations after peripheral nerve damage, for example, or control a prosthetic limb. “The major advance is the ability to control neurons precisely in space and time,” said Waller's postdoc Nicolas Pégard, who is a first author of the paper.  “In other words, to shoot the very specific sets of neurons you want to activate and do it at the characteristic scale and the speed at which they normally work.”

A feasible way for devices to send data with light

Researchers, including Prof. Vladimir Stojanović, have developed a method to fabricate silicon chips that can communicate with light and are no more expensive than current chip technology.  Stojanovic initially led the project into a new microchip technology capable of optically transferring data which could solve a severe bottleneck in current devices by speeding data transfer and reducing energy consumption by orders of magnitude.  He and his collaborators, including Milos Popović at Boston University and Rajeev Ram at MIT, recently published a paper in Nature where they present a manufacturing solution by introducing a set of new material layers in the photonic processing portion of a bulk silicon chip. They demonstrate that this change allows optical communication with no impact on electronics.

HäirIÖ: Human Hair as Interactive Material

CS Prof. Eric Paulos and his graduate students in the Hybrid Ecologies Lab, Sarah Sterman, Molly Nicholas, and Christine Dierk, have created a prototype of a wearable color- and shape-changing braid called HäirIÖ.  The hair extension is built from a custom circuit, an Arduino Nano, an Adafruit Bluetooth board, shape memory alloy, and thermochromic pigments.  The bluetooth chip allows devices such as phones and laptops to communicate with the hair, causing it to change shape and color, as well as respond when the hair is touched. Their paper "Human Hair as Interactive Material," was presented at the ACM International Conference on Tangible, Embedded and Embodied Interaction (TEI) last week. They have posted a how-to guide and instructable videos which include comprehensive hardware, software, and electronics documentation, as well as information about the design process. "Hair is a unique and little-explored material for new wearable technologies," the guide says.  "Its long history of cultural and individual expression make it a fruitful site for novel interactions."

Making computer animation more agile, acrobatic — and realistic

Graduate student Xue Bin “Jason” Peng (advisors Pieter Abbeel and Sergey Levine) has made a major advance in realistic computer animation using deep reinforcement learning to recreate natural motions, even for acrobatic feats like break dancing and martial arts. The simulated characters can also respond naturally to changes in the environment, such as recovering from tripping or being pelted by projectiles.  “We developed more capable agents that behave in a natural manner,” Peng said. “If you compare our results to motion-capture recorded from humans, we are getting to the point where it is pretty difficult to distinguish the two, to tell what is simulation and what is real. We’re moving toward a virtual stuntman.”  Peng will present his paper at the 2018 SIGGRAPH conference in August.

Atomically thin light emitting device opens the possibility for ‘invisible’ displays

Prof. Ali Javey,  postdoc Der-Hsien Lien, and graduate students Matin Amani and Sujay Desai have built a bright-light emitting device that is millimeters wide and fully transparent when turned off.  The light emitting material in this device is a monolayer semiconductor, which is just three atoms thick.  It opens the door to invisible displays on walls and windows – displays that would be bright when turned on but see-through when turned off — or in futuristic applications such as light-emitting tattoos.  “The materials are so thin and flexible that the device can be made transparent and can conform to curved surfaces,” said  Lien. Their research was published in the journal Nature Communications on March 26.

A step forward in Stephen Derenzo's search for dark matter

Prof. Stephen Derenzo is quoted in an article for Australia’s Particle about a new material for a proposed detector of weakly interactive massive particles (WIMPs).  Derenzo is the lead author of a study published March 20 in the Journal of Applied Physics about a crystal called gallium arsenide (GaAs) which features added concentrations, or “dopants,” of silicon and boron.  This material possesses a scintillation property--it lights up in particle interactions that knock away electrons. According to Derenzo, who is a senior physicist in the Molecular Biophysics and Integrated Bioimaging Division at Berkeley Lab, the new ultrasensitive detector technology could scan for dark matter signals at energies thousands of times lower than those measurable by more conventional WIMP detectors. “It’s a privilege to be working on such an important problem in physics, but the celebration will have to wait until clear signals are seen,” he says. “It’s possible that dark matter particles are even lighter than what we can see with GaAs, and their discovery will have to wait for even more sensitive experiments.”