Colloquium

We will survey recent advancements in the world of custom cloud HW, discuss Microsoft’s unique approach for building FPGA based accelerators and why all that should be interesting for CS faculty and students, rather than be discussed exclusively at dedicated electrical engineering schools.

Verification of concurrent systems is a difficult task, and often becomes undecidable.

The world of computing platforms, from hardware to software and to systems, is going to change dramatically in coming years as a result of the spreading of machine learning. This talk will survey the current trends in parallel computing systems and heterogeneous platforms. Then, I will deep-dive into the domain of deep learning and its acceleration by hardware and software.

Women's rates in STEM (Science, Technology, Engineering and Mathematics) in academia as well as in the industry are meaningfully lower than those of men internationally and in Israel too. The present lecture will present international and local findings on that as well as influencing factors and interventions aimed at promoting change in Women's participation in STEM.

A Nondeterministic Finite Automaton (NFA) defines a function from finite words to True/False, corresponding to whether or not the word is in the automaton's language. A Nondeterministic Discounted-sum Automaton (NDA) is a weighted automaton that defines a function from finite or infinite words to real numbers. It is an extension of NFAs, realizing the concept that an immediate reward is better than a potential one in the future.

Learning graph connectivity has broad-ranging applications from 3D
reconstruction to unsupervised learning. In this talk I will introduce
a new method to learn the graph structure underlying noisy point set
observations assumed to lie near a complex manifold. Rather than
assuming regularity of the manifold itself, as customary, we assume
regularity of the geodesic flow through the boundary of arbitrary

Big Data in combination with Machine Learning are transforming the world around us. This talk will cover the following topics:
We will highlight key concepts and programming paradigms in this exciting technological frontier, including Hadoop, NoSQL Databases, Kafka and Spark, and how they can be used to store, process and analyze massive data sets.
Until recently it was practically impossible to teach big data courses at IDC. We will review a new project which created a Big Data Infrastructure for IDC on AWS, with the following key achievements:

Natural language processing (NLP) is about analyzing and synthesizing texts for understanding the message expressed in written and spoken human languages. NLP is used in many technological applications, such as virtual personal assistants, machine translation, document summarization and named entity recognition.

The ability to plan collision-free motions is an important aspect of robots' autonomy: While performing tasks in cluttered environments, the robots need to avoid obstacles as well as fellow robots. The motion-planning problem has been extensively studied over the past four decades.  It was primarily investigated as a theoretical problem in computational geometry and has since been the subject of research in robotics as well as computer graphics, computational biology, architectural design, artificial intelligence, and more.
 

We present new tradeoffs between space and query-time for exact distance oracles in directed weighted planar graphs.
These tradeoffs are almost optimal in the sense that they are within polylogarithmic, sub-polynomial or arbitrarily small polynomial factors from the naive linear space, constant query-time lower bound.
These tradeoffs include:  
(i) an oracle with space O(n^{1+\epsilon}) and query-time ~O(1) for any constant \epsilon>0,
(ii) an oracle with space ~O(n) and query-time O(n^{\epsilon}) for any constant \epsilon>0, and