Putri Karunia's Typedream allows users to build no-code websites

EECS alumna Putri Karunia (B.S. '19) who co-founded 2022 Forbes 30-Under-30 Enterprise Tech company "Typedream," is the subject of a profile titled "Putri Karunia proves that women not only belong in tech startups, but will actually make them more successful and profitable." Karunia, who was raised in Indonesia, graduated cum laude from Cal in 2019 and joined a team that included fellow EECS student Anthony Christian (B.S. '19) to found start-up Cotter, a passwordless authentication service that allows users to add a one-tap login to websites and apps in less than 15 minutes.  While developing Cotter, they came up with the idea for Typedream, a fast, user-friendly website-building tool that enables Notion (platform) customers to publish attractive websites in just 10 minutes, without prior coding experience. The design offers an intuitive text-editing interface with enriched web3 functionality, like gradients, blur navigation bars, cards, and text or buttons over images. "With a community-driven approach, our users help us prioritize the features we build and define our roadmap for the foreseeable future," said Karunia. "Listening and observing our community also led us to see glimpses of what the web could be like in the next 5-10 years."

Noam Nisan, Kimberly Keeton, Bruce Hajek and Nickhil Jakatdar named 2022 Berkeley EECS Distinguished Alumni

Congratulations to the winners of the 2022 EECS Distinguished Alumni Awards!  The CS winners are Noam Nisan (academia) and Kimberly Keeton (industry); and the EE winners are Bruce Hajek (academia) and Nickhil Jakatdar (industry). Noam Nisan (Ph.D. 1988, advisor: Richard Karp), currently a CS professor at Hebrew University of Jerusalem, was cited "For fundamental contributions to computational complexity theory and the creation of the field of algorithmic mechanism design;" Kimberly Keeton (M.S. 1994/Ph.D. 1999, adviser: David Patterson), currently a principal engineer at Google, was cited "For leadership in the research and the production of computer data and storage systems, and for mentoring the next generation of computer scientists and engineers;"  Bruce Hajek (Ph.D. 1979, advisor: Eugene Wong), currently an ECE professor at the University of Illinois at Urbana-Champaign, was cited "For his prodigious and fundamental research contributions to stochastic processes, information theory, and communications and computer networks; for his sustained and worldwide influence as a beloved teacher and mentor; and for his major leadership role in electrical and computer engineering;" and Nickhil Jakatdar (Ph.D. 2000, advisor: Costas Spanos), currently the CEO of GenePath Diagnostics, was cited for "For serial entrepreneurship and visionary leadership across several sectors, with profound impact to the microelectronics industry and to the developing world." Their awards will be presented at the 2022 Berkeley EECS Annual Research Symposium (BEARS) on April 25th.

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

A paper with lead author EECS postdoc Efrat Shimron and co-authors EECS graduate student Ke Wang, UT Austin professor Jonathan Tamir (EECS PhD ’18), and EECS Prof. Michael Lustig shows that algorithms trained using "off-label" or misapplied massive, open-source datasets are subject to integrity-compromising biases.  The study, which was published in the Proceedings of the National Academy of Sciences (PNAS), highlight some of the problems that can arise when data published for one task are used to train algorithms for a different one.  For example, medical imaging studies which use preprocessed images may result in skewed findings that cannot be replicated by others working with the raw data.  The researchers coined the term “implicit data crimes” to describe research results that are biased because algorithms are developed using faulty methodology. “It’s an easy mistake to make because data processing pipelines are applied by the data curators before the data is stored online, and these pipelines are not always described. So, it’s not always clear which images are processed, and which are raw,” said Shimron. “That leads to a problematic mix-and-match approach when developing AI algorithms.”

3 UC Presidents and Gary S. May

UC Davis Chancellor and EECS alumnus Gary S. May (M.S. '88/Ph.D. '91, advisor: Costas Spanos) took the stage with UC President Michael V. Drake and Presidents Emeriti Janet S. Napolitano and Mark G. Yudof  for the UCD Chancellor's Colloquium on March 8th.  The four discussed the challenges they faced and lessons learned during their tenures in office.  Topics included the impact of the pandemic on campus communities, the importance of public health, and the efficacy of remote learning; the university's federal lawsuit over the Deferred Action for Childhood Arrivals (DACA) program; approaches to managing UC funding cuts, including maintaining access to retirement plans and student aid;  and America's cultural and democratic future, including ways that universities might help shape it.

Tiny switches give solid-state LiDAR record resolution

A new type of high-resolution LiDAR chip developed by EECS Prof. Ming Wu could lead to a new generation of powerful, low-cost 3D sensors for autonomous cars, drones, robots, and smartphones. The paper, which appeared in the journal Nature, was co-authored by his former graduate students Xiaosheng Zhang (Ph.D. '21) and Johannes Henriksson (Ph.D. '21), current graduate student Jianheng Luo, and postdoc Kyungmok Kwon, in the Berkeley Sensor and Actuator Center (BSAC).  Their new, smaller, more efficient, and less expensive LiDAR design is based on a focal plane switch array (FPSA) with a resolution of 16,384 pixels per 1-centimeter square chip, which dwarfs the 512 pixels or less currently found on FPSA.  The design is scalable to megapixel sizes using the same complementary metal-oxide-semiconductor (CMOS) technology used to produce computer processors.   Additionally, large, slow and inefficient thermo-optic switches are replaced by microelectromechanical system (MEMS) switches, which are traditionally used to route light in communications networks.  If the resolution and range of the new system can be improved, conventional CMOS production technology can be used to produce the new, inexpensive chip-sized LiDAR.

Colin Parris elected to the NAE

EE alumnus Colin Parris (M.S. '87, Ph.D. '94, advisor: Domenico Ferrari) has been elected to the National Academy of Engineering (NAE).  After a career at IBM Systems & Technology and General Electric (GE) Research, Parris is currently Senior Vice President and Chief Technology Officer at GE.  He is known for his life-long commitment to "the development and enhancement of STEM programs across minority communities," and serves as a board member of the Annual Multicultural Business Youth Educational Services Embarkment (Ambyese), which prepares multicultural secondary school students for the challenges of pursuing careers in the corporate sector through self-esteem-building and exposure to successful role models in industry.  While a student Berkeley, Parris helped start the Summer Undergraduate Program in Engineering Research at Berkeley (SUPERB) and was deeply involved with the group Black Graduate Engineering and Science Students (BGESS).  At GE, Parris, whose expertise spans engineering, software, and AI-driven analytics, leads teams that leverage digital technologies in the energy industry and other industrial environments.  He created and leads the Digital Twin Initiative company-wide and is currently working to "accelerate business impact and transformation by combining lean principles with digital solutions."

Black Women Matter: Arlene Cole Rhodes, Valerie Taylor and Melody Ivory

Three EECS alumnae are featured in a 150W Black Women Matter web page recognizing the legacies of Black women at Cal as part of the 2022 Black History Month celebrations.  The web page, which was put together by EECS Emerita Director of Diversity Sheila Humphreys, highlights 31 Cal pioneers whose lives spanned the past 120 years.  The EECS Department is represented by: Arlene Cole Rhodes (Ph.D. '89, advisor: S. Shankar Sastry), the first Black woman to earn an EE doctorate from Berkeley; 2020 EE Distinguished Alumna Valerie Taylor (M.S. '86 / Ph.D. '91, advisor: David G. Messerschmitt ), the first Black chair of the Department of Computer Science and Engineering at Texas A&M University; and Melody Ivory (M.S. '96/Ph.D. '01, advisor: Marti Hearst), the first Black woman to earn a CS doctorate in from Berkeley.

EECS Black History Month: Lee Julian Purnell (EE M.S. 1929)

Lee Julian Purnell is the first Black student who is known to have graduated from the EECS department. He was born in Washington, D.C. in 1896, graduated from Berkeley High in 1915, was a superb track athlete, and earned a B.A. from Cal in 1919.  He got his B.S. in Electrical Engineering at MIT in 1921, where he and another student were said to be the first pair of Black students to graduate from MIT in the same class together.  He received his M.S. in Electrical Engineering from Berkeley in 1929, and eventually settled into a career at Howard University, where he served as the Dean of Engineering for 20 years.  Learn more about Lee Purnell in the EECS Newsletter.

Marti Hearst inducted into 2021 ACM SIGIR Academy inaugural class

CS alumna Prof. Marti Hearst (B.A. '85/M.S '89./Ph.D. '94,  advisor: Robert Wilensky), whose primary appointment is in the School of Information, has been named to the 2021 inaugural class of the ACM Special Interest Group on Information Retrieval (SIGIR) Academy. SIGIR Academy membership recognizes the "principal leaders in IR" who have made "significant, cumulative contributions" to the development of the field, and whose "efforts have shaped the discipline and/or industry through significant research, innovation, and/or service."  Hearst literally wrote the first book on Search User Interfaces in 2009.   She is known for her early work on automating sentiment analysis and word sense disambiguation, including the invention of an algorithm known as "Hearst patterns" which is widely used in commercial text mining applications including ontology learning.  She also developed a now well-known approach to automatic segmentation of text into topical discourse boundaries, called TextTiling.  Hearst is an Edge Foundation contributing author and a member of the Usage panel of the American Heritage Dictionary of the English Language. Her current research interests include user interfaces for search engines, information visualization, natural language processing, and MOOCs.

Eric Fosler-Lussier and Luca Daniel named 2022 IEEE Fellows

Alumni Eric Fosler-Lussier (Ph.D. 1999, advisor: Nelson Morgan) and Luca Daniel (Ph.D. 2003, advisor: Alberto Sangiovanni-Vincentelli) have been named 2022 Fellows of the Institute of Electrical and Electronics Engineers (IEEE).  The grade of Fellow is conferred upon a members of IEEE "with an outstanding record of accomplishments in any of the IEEE fields of interest."  Fosler-Lussier, now a professor Computer Science and Engineering, Biomedical Informatics, and Linguistics, and the Associate Chair of Computer Science and Engineering at Ohio State University, was cited "for contributions to spoken language technology by integrating linguistic models with machine learning." Daniel, now a professor of Electrical Engineering and Computer Science at MIT, was cited "for contributions to modeling and simulation of electronic systems."  IEEE is the world’s largest technical professional organization for electronic and electrical engineers.