Colloquium

The IDC CS Colloquium
 

Danny Harari: "Developing perceptual capabilities in AI systems: Towards human level understanding of the visual world"

Rapid developments in the field of automated learning have caused a major shift in the approach to the learning of intelligent systems, from explicit instruction to the automatic learning from a large number of labeled examples. Deep neural network models are integrated in the core of new AI technologies such as the autonomous car. Yet, there are still fundamental differences between current AI technologies and human intelligence.

02/02/2017 - 13:30

Tzachi Pilpel: "Survival of the Thriftiest: Cost-Cutting Strategies in the Cell"

Paying less and getting more is not just a goal of wise consumers. Living cells, too, strive to make the most of their resources. This is particularly true when it comes to the most expensive cellular process in terms of energy expenditure: gene expression, or the translation of the genetic code into proteins. In my talk I will present a new study, done jointly with my students Idan Frumkin, Dvir Schirman and others, to decipher the economy of gene expression in living cells.

19/01/2017 - 13:30

Yakir Vizel: "Achieving Scalable Formal Verification through Generalization and Abstraction"

Modern computerized systems are complex designs that include hardware and software components. Designing and implementing such systems requires extensive engineering. Yet, unlike other domains of engineering, software and hardware engineers often lack the mathematical tools to help them specify the requirements and verify that the implementation conforms to the specification. Formal Methods aim at bridging this gap.

26/01/2017 - 13:30

Adi Makmal: "Artificial Intelligence and Quantum Information"

Recently, there has been a growing interest (both in academia and in the industry) in establishing connections between artificial intelligence (AI) and quantum information (QI), which opens up new and exciting interdisciplinary research directions. In this talk I will present a novel AI model, entitled Projective simulation (PS), whose information-processing scheme, which is based on random walks on a directed graph, provides a natural route for quantum extension.

12/01/2017 - 13:30

Ofra Amir: "Intelligent Systems for Supporting Loosely-Coupled Teamwork"

Teamwork is a core human activity, essential to progress in many areas. A vast body of research in the social sciences and in computer science has studied teamwork and developed tools to support teamwork. Although the technologies resulting from this work have enabled teams to work together more effectively in many settings, they have proved inadequate for supporting the coordination of distributed teams that operate in a loosely-coupled manner.

05/01/2017 - 11:30

Merav Parter: "Graph Algorithms for Distributed Networks"

I will describe two branches of my work related to algorithms for distributed networks.
The main focus will be devoted for Fault-Tolerant (FT) Network Structures.
The undisrupted operation of structures and services is a crucial requirement in modern day communication networks. As the vertices and edges of the network may occasionally fail or malfunction,
it is desirable to make those structures robust against failures.

02/01/2017 - 14:30

Moran Yassour: "The natural history of the infant gut microbiome in health and disease"

The gastrointestinal tract harbors one of the highest densities of microorganisms on Earth. This collection of microbial genomes is an extension to the human genome, with clear implications on human health and disease. Early events in microbial colonization have a profound effect on physiology and immune education in the gut, thereby impacting disease susceptibility (including obesity, asthma, and other inflammatory disorders later in life).

19/12/2016 - 14:00

Dan Garber: "Faster Projection-free Machine Learning and Optimization"

Projected gradient descent (PGD), and its close variants, are often considered the methods of choice for solving a large variety of machine learning optimization problems, including empirical risk minimization, statistical learning, and online convex optimization. This is not surprising, since PGD is often optimal in a very appealing information-theoretic sense. However, for many problems PGD is infeasible both in theory and practice since each step requires to compute an orthogonal projection onto the feasible set.

29/12/2016 - 11:30