About Us

Nathan Good, Principal

Nathan Good

Dr. Nathan Good is Principal of Good Research and Faculty in Information School in UC Berkeley, where he teaches courses for the Master of Informations and Data Science (MIDS) and Master of Information and Cybersecurity (MICS) programs. He specializes in user experience research, modeling and investigating behavior where design overlaps with data. Past domains include systems for knowledge management, health care, sales support, consumer privacy, security and forensic tools, and recommender systems.

A fundamental goal of his work is helping companies create networked systems devices and services that are simple, secure and respectful of people's privacy. He is a co-author of the UC Berkeley web privacy census, and contributing author to books on privacy and the user experience of security systems. Prior to Good Research, Nathan was at PARC, Yahoo and HP research labs. At Berkeley, he worked with TRUST and the Samuelson Law & Technology Clinic and was a member of the 2007 California Secretary of State Top-to-Bottom Review of Electronic Voting Systems.

Nathan has published extensively on user experience studies, privacy, and security related topics and holds patents on software technology for multimedia systems and event analysis. His research has been reported on in the Economist,New York Times, CNN and ABC and he has testified on his research before the House, Senate and FTC. Nathan’s recent work on Privacy and Design was recognized for a best paper award at the Privacy Law Scholars Conference, and was featured in both IAPP and the Future of Privacy Forums top 6 Privacy Papers for Policy Makers. Nathan has a Phd in Information Science and a MS in Computer Science from the University of California at Berkeley

Maritza Johnson, UX Principal

Maritza Johnson

Dr. Maritza Johnson is a user experience researcher with a speciality in security and privacy. She aims to help people better understand the security and privacy decisions they encounter in their daily lives, with a specialty in how people think about data. In the past she was a user experience researcher on Google’s Identity team. Prior to that she was a technical privacy manager at Facebook where she served as a liaison to the external research community and contributed to the company’s privacy review process for new products. She completed her Phd in computer science at Columbia University and wrote her dissertation on end-user access control management with a focus on Facebook privacy settings.

Jennifer Chen, Research Sleuth

Jennifer Chen

Jennifer is a graduate of UC Berkeley's MIMS program (Masters in Information Management Systems), with a focus on UX research/design, as well as research overall. Before entering Berkeley, she got her bachelor's in Studio Art with a minor in Computer Science at UC Irvine, and worked as a QA engineer before deciding to learn more about technology and its relationship with people. After completing the MIMS program, she is pleasantly surprised by how much she enjoys doing research and analysis, and is continually striving to improve in this area.

Despite the different types of clients and projects, her most important goal is to engage each one with the same amount of detail and thoroughness as the next, regardless of the topic. Privacy and security will be tantamount as time passes, especially as the digital era increases exponentially in all factors of our lives. By utilizing her skills, she hopes to make a difference and help further educate people on various topics through her work, whether it's an app or a paper. She is especially interested in the cultural and social impact of online communities brought about by the Internet revolution, and how it's rapidly changing our society as whole.

When not running studies or combing through interviews, she enjoys Japanese anime and video games, as well as drawing and creative writing.

Will Monge, Research Machinist

Will Monge

Will (Guillermo) Monge is a data scientist and researcher at Good Research, where he focuses on privacy, algorithmic transparency, model accountability, and model risk by applying data science, ethnography, and UX research methods.

He was trained as a mathematician and worked as a methodological consultant/quant in the financial industry for 5 years focusing on risk modeling. During this time he ended up specializing in model risk — both from a governance and from a technical aspect, helping create structures for financial institutions to be able to work with models and automated data-driven decisions with sufficient transparency and controls.

Will has a Masters in Information and Data Science from the University of California at Berkeley, and equivalent masters degrees in Mathematics from Complutense University of Madrid (with a focus on computation and complexity) and Business Consulting (where he specialized on risk modeling) from ICADE (Madrid).