Center for Information Technology Policy Special Event: Election Security and Transparency in 2020 Date: Thursday, September 17, 2020 Time: 7:00 pm - 8:30 pm Event page: [ https://citp.princeton.edu/event/election-security-and-transparency/ | https://citp.princeton.edu/event/election-security-and-transparency/ ] This event is co-sponsored by the League of Women Voters of Berkeley Heights, New Providence, and Summit, New Jersey. Please register here: [ https://princeton.zoom.us/webinar/register/WN_g_huaPqzSqmGrfTTtKUxfQ | https://princeton.zoom.us/webinar/register/WN_g_huaPqzSqmGrfTTtKUxfQ ] “What can we as voters do to protect our elections and our representative government?”, asks the League of Women Voters of Berkeley Heights, New Providence, and Summit, New Jersey. Some of the answers include, be aware of election processes in use, and if those are inadequate, press for reforms. Be aware of election technology in use, and if that is insecure, press for reforms. Those processes and technology must ensure every citizen has the substantive opportunity to vote, and must count every vote (but just once!). Participate, as citizens, in running and observing elections. In the 21st century, new technology has led to new challenges in election security and transparency, but the 2020 pandemic has added special challenges of its own. Andrew Appel will offer his thoughts and insight on the 2020 election process. Bio: Andrew Appel is the Eugene Higgins Professor Computer Science (CS) at Princeton University, and served from 2009-2015 as Chair of Princeton’s CS department. His research is in software verification, computer security, programming languages and compilers, and technology policy. He received his A.B. summa cum laude in physics from Princeton University in 1981, and his Ph.D. in computer science from Carnegie Mellon University in 1985. Professor Appel has been editor in chief of ACM Transactions on Programming Languages and Systems and is a fellow of the ACM (Association for Computing Machinery). He has worked on fast N-body algorithms (1980s), Standard ML of New Jersey (1990s), Foundational Proof-Carrying Code (2000s), and the Verified Software Toolchain (2010-present).