[Naftali Tishby]Naftali (Tali) Tishby נפתלי תשבי     

School of Engineering and Computer Science and                                               
Interdisciplinary Center for Neural Computation and                                        
The Suadrsky Center for Computational Biology                                                

The Hebrew University ,  Jerusalem, Israel

Email: tishby@cs.huji.ac.il  Office phone: +972-2-65-84167, Fax: +972-2-658-6440

Office Hours: Ross 214.  Sunday, 14:00-15:00 or by appointment.                      Short Bio          CV          Publications

כתבות והרצאות לציבור הרחב:

 הרצאת "מדוע" (13.11.2005)   , הרצאה על השפה והמוח  ,וידאו של הרצאה קצרה באוניברסיטה הפתוחה ,   כתבה מ-2003 ב'הארץ',

ראיון עם מרית סלבין ל"מוסף הארץ" מיולי  2007. הבהרה: הראיון ניתן בעקבות פנייה של "הארץ" לצורך סדרת כתבות על "התייחסות המדע למוות ולמושג חיי-נצח". אין זה נושא שאני עוסק בו ישירות ואין באוניברסיטה העברית מחקרים בנושא. עם זאת, הרעיונות בראיון מבוססים על הרחבות עתידיות אפשריות עקרונית של טכנולוגיות שונות המתפתחות במהירות בחקר המוח ובלמידה חישובית. Also in English  

To put things in the right proportion… The Astro picture of the day

“Real progress does not come from new answers to old questions, but from entirely different questions…”

 Judge of a man by his questions rather than by his answers.  Voltaire
 French author, humanist, rationalist, & satirist (1694 - 1778)

 Machine Learning and Computational Biophysics

The interface between computer science, physics, and biology provides some of the most challenging problems in today’s science and technology. We focus on organizing computational principles that govern information processing in biology, at all levels. To this end, we employ and develop methods that stem from statistical physics, information theory and computational learning theory, to analyze biological data and develop biologically inspired algorithms that can account for the observed performance of biological systems. We hope to find simple yet powerful computational mechanisms that may characterize evolved and adaptive systems, from the molecular level to the whole computational brain and interacting populations.  An example is the Information Bottleneck method that provides a general principle for extracting relevant structure in multivariate data, characterizes complex processes, and suggests a general approach for understanding optimal adaptive biological behavior.

The Machine Learning Lab

The learning club

Ross building, rooms 59-63, phone: +972-2-65-85775

Recently Organized workshops and conferences:

Hebrew University Institute for Advance Studies program: From Neuron to Cognition 2007-2008 

NATO Advance Study Institute workshop on: Mining Massive Data Sets for Security 

NIPS 2006 Workshop: Revealing Hidden Elements of Dynamical Systems 

NIPS 2005 Workshop: Theoretical Foundations of Clustering

NIPS 2003 Workshop: The Information Bottleneck Method  

Current students and lab members:

Past students and postdocs:

 

My Courses:

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