Tuesday, July 5th, 2022


Dima Fayyad

AI has a lot of potential for cybersecurity applications, such as predicting attacker behavior, learning from existing cyber incidents, and taking proactive defensive measures to protect critical infrastructures.

Historically, we proposed techniques for detecting advanced cyberattacks in enterprise networks based on creating semantic representations of network logs and endpoint data, using a range of supervised and unsupervised learning methods. We deployed our algorithms in industry and on several university networks, where they detected unknown malicious activity, including Self-Propagating Malware attacks. 

At the Institute for Experiential AI, we're actively working to answer some open questions in the Cybersecurity space. For example, we're looking for the best representations of cyber data that model spatial and temporal relationships among various entities in cyberspace and the various types of information we can share across defenders to improve the effectiveness of models trained on a single network. 

We want to collaboratively learn global models that preserve the data privacy of individual contributors and achieve better detection capabilities than local models trained by a single enterprise. And we're striving to prevent future advanced cyberattacks by deploying intelligent AI agents to coordinate cyber defenses.

Latest Research

Responsible AI

AI Ethics and Responsible AI aim for AI systems that benefit individuals, societies, and the environment. It encompasses all the ethical, legal, and technical aspects of developing and deploying beneficial AI technologies. It includes making sure your AI system does not cause harm, interfere with human agency, discriminate, or waste resources.  AI Ethics and Responsible […]

Natural Language Processing

  GFT (General Fine-Tuning) Recently, deep nets have demonstrated significant progress with exciting results. Much of this work has been reported in leading media outlets and academic conferences such as ACL and NeurIPS. We have developed a “little” language, GFT, that makes deep nets look like regression. GFT is approachable to a broad audience, and […]


At the Institute for Experiential AI, we care about people’s health and wellness. We have several core faculty members with AI health expertise who cover AI for healthcare from multiple scales and various heterogeneous data sources. They work closely with scientists, clinicians, and healthcare providers in designing and developing AI algorithms over a wide range […]

Life Sciences

We live in the Golden Age of Life Sciences, driven in part by transformative advances across multiple fronts in our understanding of biological systems and their applications.  However, we are still only at the beginning of this new chapter.  A systematic strategy that includes humans and AI/ML-based approaches can accelerate the “exponentialization” of this value […]

Climate, Sustainability, and Environment

From climate science and water sustainability to urban resilience and disaster preparedness, Artificial Intelligence has shown significant potential to inform and enable transformative action in the areas of climate, sustainability, and the environment. For example, machine learning, graph analytics, agent-based models, and human-AI systems have shown potential with predictive understanding in earth systems science and […]