• Secure and Assured Intelligent Learning (SAIL) Lab

    @ University of New Haven

    About Us

  • Secure and Assured Intelligent Learning (SAIL) Lab

    @ University of New Haven

    About Us

  • Secure and Assured Intelligent Learning (SAIL) Lab

    @ University of New Haven

    About Us

About Us

The Secure and Assured Intelligent Learning (SAIL) lab works towards laying concrete foundations for the safety and security of intelligent machines with both theoretical and engineering perspectives. Our research aspires to develop comprehensive models, metrics, frameworks, and tools for analysis, implementation, and mitigation of deleterious behaviors in AI systems.

In The Media

University’s SAIL Lab Enables Researchers to Navigate Sea of Opportunities in Intelligent-Machine Research
January 12, 2024 Empowering students in pioneering research on medical safety and BCI. The University’s Secure and Assured Intelligent Learning …
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CT Examiner: Connecticut Lawmakers Take on AI Regulation in New Bill
10 Oct 2023 Newsday : Jake’s 58 customers should monitor accounts after cybersecurity breach that closed casino, experts say 📰 …
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CNN Newsource: Romance Scams
10 Oct 2023 Newsday : Jake’s 58 customers should monitor accounts after cybersecurity breach that closed casino, experts say 📰 …
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NBC Connecticut: New Britain woman shares frustrations as celebrity deep fakes become more common
10 Oct 2023 Newsday : Jake’s 58 customers should monitor accounts after cybersecurity breach that closed casino, experts say 📰 …
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Latest Events

SEMINAR F’24-EFFICIENT AND ROBUST LANGUAGE ADAPTATION IN MULTILINGUAL LLMS
Presenters: Bibek Upadhata (Ph.D. Candidate SAIL LAB)Time: Friday 10/11/2024, 4:00 PM – 5:00 pm ETPlace: Maxcy 233ARecording: EFFICIENT AND ROBUST LANGUAGE ADAPTATION IN MULTILINGUAL …
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Efficient Language Co-Adaptation in LLMs using Translation in the Chain of Thought process
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Deception and Deception-Mitigation in Multi-Agent Systems and Learning Frameworks
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SEMINAR S’23-How AI Technology Developed for Deep Space Human Exploration Amplies Humans on Earth Today
Presenters: Seyed Sajjadi (Founder & CEO nFlux AI, NASA Engineer, Artificial Intelligence Consultant), Time: Monday 04/17/2023, 3:30 PM – 4:30 …
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News

🌐 University’s SAIL Lab Empowers Researchers in Intelligent-Machine Research
The University’s SAIL (Systems, Automation, and Intelligence Lab) is at the forefront of pioneering research, providing a dynamic platform for …
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🔬 Binesh and Bahareh’s Research Paper on Early Failure Detection in Medical Devices Accepted at ICMHI 2024! 🌐
🔬 Exciting Announcement: Binesh and Bahareh’s Research Paper on Early Failure Detection in Medical Devices Accepted at ICMHI 2024! 🌐 …
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Dr. Behzadan Appointed to Connecticut AI Task Force 
Dr. Vahid Behzadan, the director of the SAIL lab, has been appointed to the newly formed Connecticut Artificial Intelligence Task …
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Nancirose Embarks on an Exciting Journey at the Cooperative AI Summer School
SAIL LAB, University of New Haven – Jul 7, 2023 We are thrilled to share the remarkable news that Nancirose …
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Automated Penetration Testing using Reinforcement Learning

We aim to use Reinforcement Learning to allow an agent to be able to conduct penetration tests without a human operator.

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Combating Human Trafficking via Automated Intelligence Collection, Validation, and Fusion

We aim to leverage the artificial intelligence and machine learning methods for intelligence analysis to combat Human Trafficking.

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Automated Collection and Analysis of Open-Source Cyber Threat Intelligence

We develop machine learning models to collect data from sources such as social media, news websites, forums, and the darknet to discover and identify indicators of threat.

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Machine Learning for Cybersecurity

We study and develop reliable and scalable machine learning techniques to identify vulnerabilities, detect malicious behavior, and prevent attacks.

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FAULT DETECTION AND PROGNOSIS IN MEDICAL DEVICES

The medical device industry is leaping forward by relying on electronics to improve life-saving medical devices’ safety and performance.

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ADVERSARY ENGAGEMENT ONTOLOGY

Adversary Engagement Ontology (AEO) is a subset of the Unified Cyber Ontology that aims to define and standardize the representation of

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ADVERSARIAL ATTACK ON EEG-BASED BCI DEVICES

This project investigates whether Motor Imagery (MI) BCIs are vulnerable to Adversarial Stimuli, which are minor sensory perturbations.

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DECEPTION IN MULTI-AGENT SYSTEMS

Multi-Agent Systems (MAS) is the study of multi-agent interactions in a shared environment. Communication for cooperation is a fundamental construct for sharing information in partially observable environments

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News

Address

Buckman Hall 240, 300 Boston Post Rd, West Haven, CT 06516

Contact Information

Email: vbehzadan@newhaven.edu

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