Sohrob Kazerounian

Distinguished AI Researcher

Sohrob Kazerounian is a Distinguished AI Researcher at Vectra AI where he develops and applies novel machine learning architectures in the domain of cybersecurity. After realizing that his goal of becoming a skilled hacker was not meant to be, he focused his studies on Artificial Intelligence, with a particular interest in neural networks. After receiving his Ph.D. in  Cognitive and Neural Systems at Boston University, he held a postdoctoral fellowship at the Swiss AI Lab (IDSIA) working on Deep Learning, Recurrent Neural Networks, and Reinforcement Learning.

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Sohrob Kazerounian

AI and the Future of Cybersecurity Work

In recent years, new studies and institutes have emerged to explore which future jobs will stay human-driven—and which will be handed over to machines.

Near and Long-term Directions for Adversarial AI in Cybersecurity

AI in cybersecurity enhances human capabilities and uncovers hidden threats—while raising the inevitable reality that attackers will also weaponize AI.

Choosing an Optimal Algorithm for AI in Cybersecurity

A look at the misunderstood No-Free-Lunch theorems—and how a crude generalization can still reveal a valuable insight about search and optimization.

Types of Learning That Cybersecurity AI Should Leverage

Despite the recent explosion in machine learning and artificial intelligence (AI) research, there is no singular method or algorithm that works best in all cases. In fact, this notion has been formalized and shown mathematically in a result known as the No Free Lunch theorem (Wolpert and Macready 1997).

Neural Networks and Deep Learning

Deep learning refers to a family of machine learning algorithms that can be used for supervised, unsupervised and reinforcement learning. These algorithms are becoming popular after many years in the wilderness. The name comes from the realization that the addition of increasing numbers of layers typically in a neural network enables a model to learn increasingly complex representations of the data.

How Algorithms Learn and Adapt

There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories-supervised, unsupervised, and reinforcement learning.

AI vs. Machine Learning

"The original question "Can machines think?' I believe to be too meaningless to deserve discussion. Nevertheless, I believe that at the end of the century, the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted." - Alan Turing

The Rise of Machine Intelligence

Can machines think? The question itself is deceptively simple in so far as the human ability to introspect has made each of us intimately aware of what it means to think.

Alan Turing: Pioneering Machine Intelligence and Modern Computing

Explore Alan Turing's pioneering contributions to machine intelligence, the Turing Machine, and his perspectives on the development of modern computing.