EECS Professor Emerita Constance “Connie” Chang-Hasnain has won the IEEE Nick Holonyak Jr. Medal for Semiconductor Optoelectronic Technologies. The medal, named in honor of the late Nick Holonyak, Jr., recognizes individuals or teams that have made outstanding contributions to semiconductor optoelectronic devices and systems, including high-energy-efficiency semiconductor devices and electronics. The award consists of a gold medal, a bronze replica, a certificate, and an honorarium. Chang-Hasnain was cited “for pioneering contributions to vertical-cavity surface-emitting laser (VCSEL) and VCSEL-based photonics for optical communications and sensing.” She is the inaugural recipient of this award.

EECS Department Chair and Professor Claire Tomlin has won the IEEE Mildred Dresselhaus Medal. The medal, named after the late Mildred Dresselhaus, is awarded to individuals who have made exceptional contributions to the fields of science and engineering, significantly impacting the IEEE's areas of focus. The award comprises a gold medal, a bronze replica, a certificate, and an honorarium. The selection process prioritizes individuals with a proven track record of impactful technical advancements, leadership in achieving meaningful goals, and a notable body of work reflected in publications, patents, or other evidence. Tomlin was honored “for foundational work in the design and verification of cyber-physical systems with applications to safety in autonomous systems.”

A team led by EECS Associate Professor Boubacar Kanté and EECS Professor Eli Yablonovitch has developed a machine-learning based optimization method for nonlinear and quantum optics. Inverse-design has been traditionally applied to linear optical systems, and it often leads to optimized structures that are unintuitive or experimentally unrealistic. In this study, published in Optica, the researchers attempt to tackle these challenges using a new inverse-design method for nonlinear photon generation. According to lead author and graduate student Zhetao Jia, they were able to achieve a compact, robust, and efficient source of entangled photon pairs based on spontaneous four-wave mixing in silicon, the most common material used in the semiconductor industry. This nonlinear quantum-optics approach could potentially be used for large-scale communication and quantum computing applications.

CS Associate Professor Ren Ng has been elected as an Optica Fellow. Optica (formerly OSA) has inducted 129 members from 26 countries to the Society’s class of 2024 Fellows. Founded in 1916, Optica is a global society that works to advance the science and technology of light. Ng was honored “for pioneering work developing light field cameras, as well as seminal contributions in 3D view synthesis and human visual perception.” Ng was named a Sloan Fellow in 2017 and a Hellman Fellow in 2019, the same year that he received the Jim and Donna Gray Award for Excellence in Undergraduate Teaching of Computer Science.

Professor Emeritus Chenming Hu, former chief technology officer at Taiwan Semiconductor Manufacturing Company (TSMC), has been awarded the Taiwan Presidential Science Prize "for advancing Taiwan's Semiconductor Industry." The award, established in 2001, is presented every two years to the most distinguished scientists in Taiwan and is given to innovative researchers who have made monumental contributions to international research in the fields of mathematics, physical sciences, life sciences, social sciences, and applied sciences. Particular emphasis is given to scholars whose work has had a major impact on these fields in Taiwan. The award was presented to Hu by Taiwan President Tsai Ing-wen. Hu, alongside Berkeley EECS colleagues, pioneered the FinFET transistor, which is widely used in high-performance processors around the world.

CS Associate Professor Alvin Cheung has won the 2023 Very Large Data Bases (VLDB) Early Career Research Contribution Award. The award, which includes a $2,000 prize, recognizes researchers who have made a significant impact through a specific contribution to the field since completing their Ph.D. Separately, a paper by CS Professors Joseph Gonzalez and Joseph Hellerstein, co-authored by Yucheng Low, Aapo Kyrola, Danny Bickson, and Carlos Guestrin, received the 2023 VLDB Test of Time Award. Their paper, "Distributed Graphlab: A framework for machine learning in the cloud," was published at VLDB 2012. The authors were nominated for this award by the research community, and the winner was selected based on the paper's impact through its consequent products and services, and follow-through research by the community. The VLDB awards recognize excellence in the field of database research and development. The awards are presented annually at the VLDB conference, which is one of the premier conferences in the database field.

EECS graduate students Yicheng Zhu and Jiarui Zou, and post-docs Ting Ge and Nathan Ellis have won the 2023 IEEE Control and Modeling for Power Electronics (COMPEL) Best Paper Award. Their paper, "A 48-V-to-1-V Switching Bus Converter for Ultra-High-Current Applications,” demonstrated a new dc-dc power converter topology and control technique for data center power delivery applications, capable of sourcing 1200 A of current at 1 V supply voltage. The hardware prototype used to validate the concept achieved the highest power density and efficiency combination of any prior work, academic or industrial. Next, the researchers are working with industry partners to transition this record-breaking concept to next-generation GPU/CPU computing platforms for AI and machine learning applications. IEEE COMPEL is the premier control and modeling conference for power electronics, having brought together world experts in the field for the last 24 years. Three best papers were selected this year from the total accepted 84 papers, based on originality, contribution to the field, and quality of presentation at the conference. 

EECS Professor Emerita Ruzena Bajcsy was awarded the Berkeley Citation, the university’s highest honor, at a special event on Tuesday, Sept. 5. The surprise announcement was made at the end of a special event to commemorate The Past and Future of Robotics and Machine Learning Based on 250 Years of Research Experience. Tsu-Jae King Liu, dean of the College of Engineering, presented the award. The Berkeley Citation is awarded to distinguished individuals whose contributions to UC Berkeley go beyond the call of duty and whose achievements exceed the standards of excellence in their fields. Bajcsy, whose storied career spans over 50 years, conducted seminal research in the areas of human-centered computer control, cognitive science, robotics, computerized radiological/medical image processing, and computer vision. Among her numerous awards and firsts, Bajcsy was the first-ever woman to receive a Ph.D. in electrical engineering in the United States. She is renowned for her intellectual leadership, tireless work ethic, and inspiring approach to research and mentorship. Bajcsy is widely considered the foremost role model of generations of educators and researchers in computer science and engineering.

A team of researchers from UCSF and Berkeley EECS have developed an implantable AI-powered device that can translate brain signals into modulated speech and facial expressions. The device, a multimodal speech prosthesis, and digital avatar, was developed to help a woman who had lost the ability to speak due to a stroke. The results have the potential to help countless others who are unable to speak due to paralysis or disease. The breakthrough study, published in the journal Nature, was led by UCSF neurosurgeon Edward Chang, EE Assistant Professor Gopala Anumanchipalli and Ph.D. student Kaylo Littlejohn. “This study heavily uses tools that we developed here at Berkeley, which in turn are inspired by the neuroscientific insights from UCSF,” said Gopala. “This is why Kaylo is such a key liaison between the engineering and the science and the medicine — he’s both involved in developing these tools and also deploying them in a clinical setting. I could not see this happening anywhere else but somewhere that is the best in engineering and the best in medicine, on the bleeding edge of research.”

A team led by CS Assistant Professor Anjoo Kanazawa has created Nerfstudio, an open-source platform to help speed up the development of Neural Radiance Fields (NeRFs). NeRFs are a type of 3D imaging technology that can be used to create photorealistic 3D models of objects and scenes from a series of images. The plug-and-play framework, called Nerfstudio, makes it easier for researchers to create and train NeRFs, allowing users to run NeRFs on real-world data. “Advancements in NeRF have contributed to its growing popularity and use in applications such as computer vision, robotics, visual effects and gaming. But support for development has been lagging,” said Kanazawa. “The Nerfstudio framework is intended to simplify the development of custom NeRF methods, the processing of real-world data and interacting with reconstructions.”