Prediction algorithms score individuals, or individual instances, assigning to each one a number in [0,1] that is often interpreted as a probability: What are the chances that this loan will be repaid? How likely is the tumor to metastasize? What is the probability this person will commit a violent crime in the next two years? But what is the probability of a non-repeatable event? Without a satisfactory answer, we cannot even specify the goal of the ideal algorithm, let alone try to build it.

This talk will introduce Outcome Indistinguishability, a desideratum with roots in complexity theory, and situate it in the context of research on the theory of algorithmic fairness.

Speaker Bio:
Cynthia Dwork, Gordon McKay Professor of Computer Science at the Harvard Paulson School of Engineering, and Affiliated Faculty at Harvard Law School and Harvard Department of Statistics, uses theoretical computer science to place societal problems on a firm mathematical foundation. Dwork’s earliest work in distributed computing established the pillars on which every fault-tolerant system has been built for decades. Her innovations have modernized cryptography to withstand the ungoverned interactions of the internet and the eventuality of quantum computing, and her invention of Differential Privacy has revolutionized privacy-preserving statistical data analysis. In 2012 she launched the theoretical investigation of algorithmic fairness. She is a winner of numerous awards and a member of the NAS, the NAE, the American Academy of Arts and Sciences, and the American Philosophical Society.

Data is a key driver of modern economy and AI/machine learning, however, a lot of this data is sensitive and handling the sensitive data has caused unprecedented challenges for both individuals and businesses. These challenges will only get more severe as we move forward in the digital era. In this talk, I will talk about technologies needed for responsible data use including secure computing, differential privacy, federated learning, as well as blockchain technologies for data rights, and how to combine privacy computing technologies and blockchain to building a platform for a responsible data economy, to enable more responsible use of data that maximizes social welfare & economic efficiency while protecting users’ data rights and enable fair distribution of value created from data. I will also talk about new paradigms that this approach enables including decentralized data science and data DAO. I will also discuss new frameworks on data valuation.

Speaker Bio:
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in AI and deep learning, security and privacy. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an ACM Fellow and an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur. She is the Founder of Oasis Labs and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Memory-safety issues have been a long-standing concern of the security practitioners. According to Microsoft [1] and Google [2], memory-safety bugs still represent 70% of the exploited vulnerabilities in complex, real-world programs like OSes and Web browsers. However, it doesn’t mean that academics and practitioners haven’t tried hard to alleviate the problem. Advances in automatic techniques like fuzzing and sanitizers revolutionize the way we tame the memory safety bugs, but the increasing volume of new software simply outpaces the adoption rate of these promising new techniques, setting the legacy programs aside. In this talk, I’d like to share “our” own journey to fight against memory-safety bugs – “our” is important as all research is conducted together with the brightest hackers in SSLab at Georgia Tech. First, I’d like to talk about our group’s research agenda in the memory-safety world ranging from binary exploitation, programming analysis, fuzzing, symbolic execution and security education. Second, I will share our group's journey to participate in the DARPA CGC, DEFCON CTF and pwn2own competitions. Third, I will also present where our group is heading to: a promising new memory/thread-safe language, called Rust. Lastly, I will conclude the talk with an important projection by using our recent work on finding bugs in the Rust packages [3]: like COVID-19, the memory-safety bugs likely stay with us for the next decade, if not more.

Speaker Bio:
Taesoo Kim is an Associate Professor in the School of Cybersecurity and Privacy and the School Computer Science at Georgia Tech. He also serves as a director of the Georgia Tech Systems Software and Security Center (GTS3). Starting from his sabbatical year, he works as a VP at Samsung Research, leading the development of a Rust-based OS for a secure element. He is a recipient of various awards including NSF CAREER (2018), Internet Defense Prize (2015), and several best paper awards including USENIX Security’18 and EuroSys’17. He holds a BS from KAIST (2009), a SM (2011) and a Ph.D. (2014) from MIT.