Master’s Student Katy Jiang Wins 2020 Deepfake Education Competition

 

The Silicon Valley Leadership Group, in partnership with the CITRIS Policy Labs at the University of California, Berkeley, announced that Katy Jiang, ’21 MS Software Engineering, won the 2020 Deepfake Education Competition with her three-minute explanatory video. The competition challenged students of all levels to create engaging video content to educate the public on deepfakes, the use of artificial intelligence to manipulate images and video to influence public opinion.

“Deepfake is a form of artificial intelligence. The word deepfake combines deep learning and fake,” said Jiang. “It can produce a persuasive counterfeit by studying photographs and videos of a target person from multiple angles, and then mimicking its behavior and speech patterns. By making this video, I want to educate people about the technology behind their day-to-day entertainment application and raise people’s awareness that deepfakes can also be used in committing crimes such as frauds and scams.”

A 2019 Pew Research study reported that two-thirds of Americans (67 percent) say made-up news and information cause a great deal of confusion. Associate Chair of the Computer Engineering department Magdalini Eirinaki, who is teaching Jiang’s Web and Big Data Mining course this semester, recommended that Jiang submit her video, originally a class assignment, for the Deepfake Education competition.

“Identification and spread of fake news (whether in text or deepfakes) has been on my radar as a very interesting and critical research problem,” said Eirinaki. “This technology can be very easily weaponized and used to enhance the perceived credibility of fake news and disinformation campaigns. This can have even more devastating effects than current fake news, ranging from politics, to the environment (e.g. global warming), to public health (e.g. spreading disinformation about the COVID-19 pandemic). It is therefore more critical than ever for the research community to develop more sophisticated techniques to keep up with the deepfake technology to promptly identify and remove/flag them before too much harm is done.”

Jiang’s video explains how deepfakes can be used to impact how people interpret fake news—especially timely during a hotly contested presidential election and a global health pandemic. Using engaging visuals, music and voiceover to describe the dangers that deepfakes pose to democracy, she encourages viewers to assess content carefully before sharing it on their social media platforms. She demonstrates how many free apps are available to superimpose public images with false or misleading suggestions, inserting her own face over that of celebrities and politicians. Her winning submission will be featured on CITRIS media channels and through the Silicon Valley Leadership Group’s media channels. The recognition also includes a $2,500 prize.

“As we are in the pandemic and the election is coming, deepfakes pose a danger to democracy,” said Jiang. “Fake news will influence everything from stock prices to the election. People should be critical about what we see online.”

 

Physics Professor’s Latest Findings on AI in Quantum Physics Published in Nature

Fully-connected artificial neural networks are used to analyze ripple in electronic density in experimental images. Image courtesy of Cornell University.

Fully-connected artificial neural networks are used to analyze ripple in electronic density in experimental images. Image courtesy of Cornell University.

Ehsan Khatami, a professor of physics and astronomy in the College of Science, has his latest research published in the journal Nature today in an article entitled “Machine Learning in Electronic-Quantum-Matter Imaging Experiments.” The article shares research that is a collaboration between Khatami and colleagues at Cornell University as well as other institutions on AI-assisted discovery in images of an electronic order in a superconducting material.

A former student of Khatami’s, Kelvin Chng, is cited as an author on the paper and is now working for an AI company in the Bay Area.

“When I first heard about preliminary applications of machine learning methods in condensed matter physics at a conference in spring of 2016, I did not know anything about them,” he said. “I came back from the conference with some ideas on how to use them for quantum problems and quickly found out that Kelvin, who was working on a different project at the time, already knew a lot about artificial intelligence and their applications in industry.”

The pair began working and by the end of the summer had completed a paper that was published a year later in Physical Review X and highlighted in the American Physical Society News. They began collaborating with the Cornell group on designing and testing machine learning algorithms to categorize quantum electronic images of superconducting materials called cuprates.

The images were taken at Cornell using a method called scanning tunnelling microscopy, which maps out real-space patterns of electrons that have self organized into complex quantum states. The images are so noisy and naturally chaotic that conventional methods, like the Fourier analysis, have not been able to decisively pinpoint the type of electronic order found in samples that are close to becoming superconductors in a state dubbed by some physicists the ‘dark matter’ of cuprates.

The team used machine learning for the first time to make sense of data in this mysterious region. They trained a group of artificial neural networks using images that were generated via computer models based on a set of hypotheses, and found that the networks consistently discover the predominant features of a specific ordering pattern whose description dates back to the 1990s.

“It took a long time, many trials, and months of hard work for the collaboration at the beginning to carve out the best strategy to approach and solve the problem, but it all paid off over two years later.”

Updated: SJSU Students Take Home Three Awards at CSU Competition

On April 26 and 27, a dozen San Jose State University students competed for top honors at the 33rd Annual California State University Student Research Competition at CSU Fullerton, with SJSU competitors brining home two first place finishes and one second place prize in their categories. In true Spartan spirit, each of the student projects aimed to do some greater good– through improving fuel efficiency of aircrafts; converting greenhouse gases to liquid fuels; and creating chatbot tutors in support of student success.

Sarah Ortega, ’18 Aerospace Engineering, placed first in the category of Engineering and Computer Science, graduate level; Vanshika Gupta, a student in the College of Science placed first in the category of Physical Sciences and Mathematics, undergraduate level; and Sambhav Gupta, a student in the Lucas College of Business placed second in the category of Business, Economics and Public Administration for graduate and undergraduate level.

Ortega presented her research on designing a short to medium range hybrid transport aircraft that would use batteries as part of its fuel source. She worked closely with faculty advisor, Professor and Chair of Aerospace Engineering Nikos Mourtos.

“I knew there were electric aircraft, but current battery capabilities are limited,” she said. “I wanted to design a jet transport aircraft. I also knew I wanted to design something that could be feasible in the next decade or two, so we decided on a hybrid.”

She met regularly with Mourtos and also took an aircraft design class.

Vanshika Gupta, ’20 biochemistry, worked with Assistant Professor of Chemistry Madlyn Radlauer on her project “Investigating Macromolecular Structures for the Transformation of Greenhouse Gases into Liquid Fuels.” She has presented her research at the College of Science Research Day and as part of the CSU Program for Education and Research in Biotechnology conference in 2019.

Vanshika said she joined Radlauer’s research team during her first semester at SJSU.

“Dr. Radlauer trained me on the different instruments and polymerization techniques necessary for our projects,” she said. “For the competition in particular, she guided me in my presentation. I performed multiple runs in front of her and she advised me on improvements.”

The biochemistry student said she especially appreciated the opportunity to learn about the research projects that students from other CSU campuses presented.

“Since I started at SJSU in fall 2017, I have had the great pleasure of working with SJSU students in both the classroom and laboratory,” Radlauer said. “My research students are amazing and because each one of them comes to science via their own path, there is a wealth of perspective and experience in the group. I’m so proud to see them succeeding and sharing that success with one another.”

Sambhav Gupta, ’20 Business concentration in corporate accounting and finance, received second place for his project, “Artificially Intelligent (AI) Tutors in the Classroom: A Need Assessment Study of Designing Chatbots to Support Student Success.” Sambhav Gupta worked with Assistant Professor in the School of Information Systems and Technology Yu Chen on his project.

“There are advisors, and then there are mentors,” he said. “Dr. Yu Chen has helped me grow in both my academic career and as a person as well since I started working with her on this project back in October 2018.”

In February, the three CSU competition winners first presented their projects to a panel of judges as part of SJSU’s Student Research Competition. The students were selected along with nine others to represent the university at the systemwide event. At the SJSU Celebration of Research on April 23, the SJSU finalists were recognized in front of a crowd of students, faculty and staff.