2006年から2011年までEU（欧州連合）の推進するFP6におけるガンの臨床治験の革新基盤となるIT基盤の研究開発プロジェクトACGT (Advancing Clinico-Genomic Trials on Cancer) に正規メンバーとして参画。2011年より2016年までEUの推進するFP7におけるガンの個人化医療の革新基盤となるIT基盤の研究開発プロジェクトp-medicineに正規メンバーとして参画。
Christos H. Papadimitriou
Christos H. Papadimitriou is the Donovan Family Professor of Computer Science at Columbia. He did his undergraduate studies in Greece, and has a PhD from Princeton. Before joining Columbia in 2017 he taught at Harvard, MIT, Stanford, the Technical University of Athens, and since 1996 at UC Berkeley. In his research he uses mathematics to explore two fundamental questions in Computer Science: How quickly can computers solve problems? And what obstacles – what manifestations of inherent complexity – come in the way? Over the past four decades, he has pursued these questions in several domains such as optimization and control theory, robotics and artificial intelligence, databases, networks, and the Internet. He has also engaged from this perspective some fundamental questions arising in the sciences. In social sciences, he established that the key concept of the Nash equilibrium is plagued by serious complexity impediments, while in the life sciences he is studying from the computational point of view evolution and the brain. He is a member of the National Academy of Sciences, the American Academy of Arts and Sciences, and the National Academy of Engineering, and has been awarded the von Neumann medal, the Gödel prize, the Knuth prize, and eight honorary doctorates. In 2013, the president of Greece named him commander of the Order of the Phoenix. He has written, in addition to many research papers and some of the standard textbooks in computer science, three novels: Turing, the New York Times bestseller Logicomix, and his latest Independence.
Abstract of the Key Note Speech “A Computer Scientist Thinks about the Brain”
When key problems in science are revisited from the computational viewpoint, occasionally unexpected progress results. This unexpected power of computational ideas, sometimes called "the algorithmic lens", has manifested itself in these past few decades in virtually all sciences: natural, life, or social: in statistical physics through the study of phase transitions in terms of the convergence of Markov chain-Monte Carlo algorithms, and in quantum mechanics through quantum computing. In this talk I will discuss three other instances. Almost a decade ago, ideas and methodologies from computational complexity revealed a subtle conceptual flaw in the solution concept of Nash equilibrium, which lies at the foundations of modern economic thought. In the study of evolution, a new understanding of century-old questions has been achieved through surprisingly algorithmic ideas. Finally, understanding the ways in which the Brain gives rise to the Mind (memory, behavior, cognition, intelligence, language) is arguably the most challenging problem facing science today. As the answer seems likely to be at least partly computational, computer scientists should work on this problem --- except there is no obvious place to start. In this talk I shall recount one possible starting point, the way in which assemblies of neurons (sparse representations of elementary entities such as words, memories, and concepts) can be the basis of a surprisingly comprehensive computational model and programming system. I will also reflect on why assemblies and their operations may provide clues about the way we make language.