Distinguished Lectures

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Monday, January 25, 2021

The Computer Science Department at Sapienza University of Rome is promoting a series of Distinguished Lectures held by renowned speakers on fundamental research topics in computer science. The goal of each lecture (approx. 45 minutes) is to explain why the theme is indeed fundamental, and to summarize the state of the art up to cutting edge research. 

Supported by MIUR under grant "Dipartimenti di eccellenza 2018-2022", of the Computer Science Department at Sapienza University.


Lecturer: Jeffrey David Ullman

Title: Abstractions and Their Compilers

Location: Zoom meeting
Date: September 16, 2021.
Time: 17.00-18.00 (CET)

Jeffrey David Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.
Abstract. An abstraction in computer science is a data model plus a "programming language"; the language is often far simpler than a general-purpose programming language. We shall consider four different ways that abstractions have been used. Especially important are "declarative abstractions," where you say what you want done but not how to do it. These abstractions require clever compilation, including some powerful optimization techniques, if they are to be used in practice. We shall talk about three such declarative abstractions: regular expressions, and their compilation into finite automata, context-free grammars and their compilation into shift-reduce parsers, and the relational model of data, and its compilation into executable code.

Lecturer: Michael Bronstein

Title: Geometric Deep Learning: from Euclid to drug design

Location: Zoom meeting
Date: April 27, 2021.
Time: 11.00-12.00 (CET)
Meeting recording

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-term scholar (2020)).
Michael is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.
Abstract. For nearly two millennia, the word "geometry" was synonymous with Euclidean geometry, as no other types of geometry existed. Euclid's monopoly came to an end in the 19th century, where multiple examples of non-Euclidean geometries were shown. However, these studies quickly diverged into disparate fields, with mathematicians debating the relations between different geometries and what defines one. A way out of this pickle was shown by Felix Klein in his Erlangen Programme, which proposed approaching geometry as the study of invariants or symmetries using the language of group theory. In the 20th century, these ideas have been fundamental in developing modern physics, culminating in the Standard Model.

The current state of deep learning somewhat resembles the situation in the field of geometry in the 19h century: On the one hand, in the past decade, deep learning has brought a revolution in data science and made possible many tasks previously thought to be beyond reach -- including computer vision, playing Go, or protein folding. At the same time, we have a zoo of neural network architectures for various kinds of data, but few unifying principles. As in times past, it is difficult to understand the relations between different methods, inevitably resulting in the reinvention and re-branding of the same concepts.

Geometric Deep Learning aims to bring geometric unification to deep learning in the spirit of the Erlangen Programme. Such an endeavour serves a dual purpose: it provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers, and gives a constructive procedure to incorporate prior knowledge into neural networks and build future architectures in a principled way. In this talk, I will overview the mathematical principles underlying Geometric Deep Learning on grids, graphs, and manifolds, and show some of the exciting and groundbreaking applications of these methods in the domains of computer vision, social science, biology, and drug design.

(based on joint work with J. Bruna, T. Cohen, P. Veličković)

Lecturer: Alessandro Chiesa

Title: From Zero Knowledge to Private Transactions

Location: Zoom meeting
Date: February 24, 2021.
Time: 17.00-18.00 (CET)

Alessandro Chiesa is a faculty member in computer science at UC Berkeley. He conducts research in complexity theory, cryptography, and security, with a focus on the theoretical foundations and practical implementations of cryptographic proofs that are short and easy to verify. He is a co-author of several zkSNARK libraries, and is a co-inventor of the Zerocash protocol. He has co-founded Zcash and StarkWare Industries. He received S.B. degrees in Computer Science and in Mathematics, and a Ph.D. in Computer Science, from MIT.
Abstract. The integrity of digital currencies such as Bitcoin rests on the fact that every payment is broadcast in plaintext. This fails to provide any meaningful privacy guarantees for users. In this talk I will explain how a beautiful cryptographic tool, zero knowledge proofs, can be used to design digital currencies that provide strong privacy guarantees for users. These ideas have been deployed in the wild, as part of the cryptocurrency Zcash and in several other settings.



Lecturer: Luciano Floridi

Title: AI, Digital Utopia, and “Asymptopia”

Location: Zoom meeting
Date: December 1, 2020.
Time: 11.00-12.00 (CET)

Luciano Floridi Professor of Philosophy and Ethics of Information at the University of Oxford, where he is Director of the OII Digital Ethics Lab. He is a world-renowned expert on digital ethics, the ethics of AI, the philosophy of information, and the philosophy of technology. He has published more than 300 works, translated into many languages. He is deeply engaged with policy initiatives on the socio-ethical value and implications of digital technologies and their applications, and collaborates closely on these topics with many governments and companies worldwide.
Abstract. AI promises to be one of the most transformative technologies of our age. In this lecture, I will argue that AI can actually support the development of a better future, both for humanity and for the planet. In the course of the lecture, I will outline the nature of utopian thinking, the relationship between it and digital technologies, and introduce what I shall call “asymptopia”, or asymptotic utopia, the possibility of a progressive improvement of our society steered by regulative ideals (Kant).

Lecturer: Luca Trevisan

Title: P versus NP

Location: Aula Seminari, Via Salaria 113, 3rd Floor.
Date: February 6, 2020.
Time: 11.30-12.30

Luca Trevisan is a professor of Computer Science at Bocconi University. Luca studied at the Sapienza University of Rome, he was a post-doc at MIT and at DIMACS, and he was on the faculty of Columbia University, U.C. Berkeley, and Stanford, before returning to Berkeley in 2014 and, at long last, moving back to Italy in 2019.

Luca's research is focused on computational complexity, on analysis of algorithms, and on problems at the intersection of pure mathematics and theoretical computer science.

Luca received the ACM STOC'97 Danny Lewin (best student paper) award, the 2000 Oberwolfach Prize, and the 2000 Sloan Fellowship. He was an invited speaker at the 2006 International Congress of Mathematicians. He is a recipient of a 2019 ERC Advanced Grant.

Abstract. The P versus NP problem asks whether every time we have an efficient algorithm to verify the validity of a solution for a computational problem we must also have an efficient algorithm to construct such a valid solution. It is one of the major open problems in mathematics and computer science and one of the six unresolved "Millenium Problems" with a million dollar prize on its solution.

We will trace the origin of this question, and its conceptual implications about the nature of mathematical proofs and the notions of expertise and creativity. We will see why certain proof strategies cannot possibly resolve this problem, and why fundamentally different ideas are needed to make progress. Finally, we will discuss "average-case analysis" extensions of the P versus NP problems, which are needed to reason about the security of cryptographic protocols and blockchains and about the complexity of problems arising in machine learning.

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