Colloquium

Statistical hypothesis test is a fundamental tool in statistics, where one can determine whether to accept or reject the null hypothesis (the default statement of no difference). Rejecting the null hypothesis means that the probability of getting the observed results is very low - lower than a predetermined significance level. This probability is called a p-value and the lower the p-value is the more significant the results.

Many of the basic algorithmic questions deal with the geometry of graphs. We know many variations on the theme of computing the metric of edge-weighted graphs. E.g., “all-pairs shortest paths”. As I will try to convey in this lecture, there is much that we know beyond that as well as broad barely charted territories. For example, let G=(V,E) be a graph and let w(e)>0 be given positive real numbers associated with every edge e in E.

Byzantine agreement (BA), the task of n parties to agree on one of their input bits in the face of malicious agents, is a powerful primitive that lies at the core of a vast range of distributed protocols. Interestingly, in protocols with the best overall communication, the demands of the parties are highly unbalanced: the amortized cost is polylog(n) bits per party, but some parties must send \Omega(n) bits.

A growing number of malware detection methods are heavily based on Machine Learning (ML) and Deep Learning techniques. However, these classifiers are often vulnerable to evasion attacks, in which an adversary manipulates a malicious instance from being detected.

Pegasus is a zero-click attack: The target is sent a text message, and that's it – the device is compromised – the message doesn't even have to be read. According to a recent Google report: "Short of not using the device, there is no way to prevent exploitation... Pegasus is a weapon against which there is no defense... This is one of the most technically sophisticated exploits we've ever seen".

Advances in technologies and interface design are enabling group activities of varying complexities to be carried out, in whole or in part, over the internet, with benefits to science and society  (e.g., citizen science, Massive Online Open Courses (MOOC) and questions-and-answers sites).

Anomaly detection in computer networks is a classic, long-term research problem. Previous attempts to solve it have used auto-encoders to learn a representation of the normal behaviour of networks and detect anomalies according to reconstruction loss. In this talk, I'll present the concept of "auto-encoder losses transfer learning".

Reconstructing and semantically classifying observed natural images from novel (unknown) fMRI brain recordings is a milestone for developing brain-machine interfaces and for the study of consciousness. Unfortunately, acquiring sufficient “paired” training examples (images with their corresponding fMRI recordings) to span the huge space of natural images and their semantic classes is prohibitive, even in the largest image-fMRI datasets.

I'll present an educational approach that synthesizes many abstractions, algorithms, and data structures learned in key CS courses, and makes them concrete by building a complete computer system – hardware and software – from the ground up. The methodology is based on guiding students through a set of 12 hands-on projects that gradually construct and unit-test a simple hardware platform and a modern software hierarchy, yielding a surprisingly powerful computer system.

It is said about Socrates that he is the father of Western philosophy because he focused his love of wisdom on ethics. Socrates seemed to have already realized that regardless of scientific or technological developments, there are basic questions that human beings will always continue to ask.