When : Thursday, September 29, 2016 - 14:30
Speaker : Maurizio Corbetta
Affiliation : Professor of Neurology, Neuroscience, Radiology, Biomedical Engineering, Washington University School of Medicine St.Louis, St.Louis, MO, USA & Professore Ordinario Neurologia, Direttore Clinica Neurologica Azienda Ospedaliera Padova, Dipartimento di Neur
Where : Aula Magna "A. Lepschy"
Short Bio :
Maurizio Corbetta is Chair of Neurology and Full Professor of Neurology at the Department of Neuroscience, University of Padova, Italy. He received his medical degree from the University of Pavia in 1985 and then completed his postdoctoral and residency training at the University of Verona. He then moved to Washington University in St. Louis in 1992 for a fellowship in neuro-imaging and cognitive neuroscience, and rounded out his training with an internship and residency at Barnes-Jewish Hospital. He joined the faculty as an assistant professor of neurology in 1996, was named head of stroke and brain injury rehabilitation in 2002, and three years later became a professor in three departments: Neurology, Radiology, and Anatomy and Neurobiology. In 2016 he moved from Washington University to University of Padova. Prof. Corbetta is internationally known for his multidisciplinary research on the neural basis of human cognition and the neurological mechanisms underlying stroke recovery. He is a member of several professional societies and has received many honors for his work, including the J.S. McDonnell Foundation Award in Cognitive Sciences, the Marie Curie Chair in Cognitive Neuroscience from the European Union, the Norman Geschwind Award in Behavioral Neurology from the Academy of Neurology, and the outstanding Clinician-Scientist Award from the American Society of Neurorehabilitation. He published more than 150 peer-reviewed publications and 30 reviews. 27 of these papers have been cited more than 300 times. Prof. Corbetta has been nominated ‘Highly Cited Researcher’ by Thompson Reuters in the decade 2002-2012. This honor is shared with only 128 researchers worldwide in the field Neuroscience/Behavior.
Abstract :
The brain is organized as a network of connections between nerve cells. Recent advances in functional neuroimaging show that large scale connections are organized in networks of brain regions. These networks are visualized at rest and surprisingly resemble networks recruited by common behaviors. An important question is the relationship between spontaneous and task-driven activity, not just at the level of topology, but also information patterns, and dynamics. I will argue that spontaneous activity patterns reflect the statistical history of co-activation, and represent the ensemble of possible brain states driven by behavior. In this regards, spontaneous activity patterns represent spatiotemporal priors for task patterns, which explains why abnormalities of brain networks at rest relate to behavioral deficits. In the second part of my talk I will show that focal lesion can cause specific patterns of brain network abnormalities that relate to behavioral deficits and recovery. These patterns represent a simplification of entropy of neural states. I will argue that treatments of brain disorders should focus on treating not only behavior but also abnormal information patterns.
When : Thursday, July 14, 2016 - 11:00
Speaker : Seth Lloyd
Affiliation : Nam P. Suh Professor of Mechanical Engineering and Professor of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
Where : Aula Magna "A. Lepschy"
Short Bio :
Seth Lloyd is Professor of Mechanical Engineering at the Massachusetts Insitute of Technology. He is the director of the WM Keck Center for Extreme Quantum Information Theory at MIT, the director of the Program in Quantum Information at the Institute for Scientific Interchange, and Miller Fellow at the Santa Fe Institute. Lloyd earned his A.B. degree in Physics from Harvard University, his Masters of Advanced Study in Mathematics and M.Phil. in History and Philosophy of Science from Cambridge University, and his Ph.D. in Physics from Rockefeller University. After postdoctoral fellowships at Caltech and at Los Alamos, he joined the MIT faculty in 1994. Professor Lloyd teaches and performs research in quantum information theory and complex systems, by focusing on the role of information in physical and mechanical systems, with an emphasis on quantum mechanical systems, and on the characterization of complex systems, including problems of design and control of such systems. His collaborations resulted in the first experimental demonstrations of quantum algorithms (using NMR), the first demonstration of a quantum optical logic gate, and the first demonstration of superconducting quantum bits, and the first demonstration of coherent quantum feedback control. Recently, he has worked on the role of quantum coherence in living systems, participating in the demonstration that quantum coherence plays a crucial role in guaranteeing the efficiency of energy transport in photosynthesis.
Abstract :
The understanding of the Universe where we live has changed by the introduction of novel ideas based on the laws of the microscopic world. From these principles, novel technologies are under study for entering the everyday reality. Moreover, the interactions of quanta appear to be more fundamental than space and time, changing then the way we describe the dynamics of things and life itself. The colloquium is organized with a talk by Prof. Seth Lloyd and a conversation on the subject of Quantum Information and Computing together with Prof. Gianfranco Bilardi (DEI-University of Padova). The two parts aim to provide the audience with a traditional presentation by the speaker and a dialogue on the change of scenario provided by the Quantum Information.
When : Tuesday, June 21, 2016 - 14:30
Speaker : Michael I. Jordan
Affiliation : Pehong Chen Distinguished Professor, University of California, Berkeley, CA, USA
Where : Aula Magna "A. Lepschy"
Short Bio :
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Abstract :
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" t be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as "runtime" in core statistical theory and the lack of a role for statistical concepts such as "risk" in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and relationships between computational and statistical lower bounds.
