SAFERR AI Lab
Advancing research in Safety, Reliability, and Robustness of AI
At SAFERR AI Lab, we focus on developing and testing AI systems that are safe, reliable, and robust. Our interdisciplinary research addresses critical challenges in ensuring AI systems operate dependably in real-world settings.
Latest News
May 7, 2025
New paper accepted at ICML 2025
We're excited to announce that our paper `Inference-Time Alignment of LLMs via User-Specified Multi-Criteria Transfer Decoding` has been accepted at ICML 2025. This work represents a inference-time alignment of LLMs that can be used to align LLMs with user-specified criteria. Read more
December 12, 2024
New paper accepted at AAAI 2024
Our paper titled `Align-Pro: A principled approach to alignment of LLMs` has been accepted at AAAI 2024. This work represents a principled approach to alignment of LLMs that can be used to align LLMs by employing a trainable prompter Read more
August 19, 2024
Welcoming a PhD student to the lab
We're delighted to welcome a new PhD student, Avinash Reddy, joining our lab this fall semester. He will be working on the broad topic of `Alignment of Language Models`.
Research Areas
Our interdisciplinary team works across these key areas to address the critical challenges in AI safety, reliability, and robustness.
Safety
Designing AI systems that proactively avoid harmful behaviors and operate within clearly defined safety boundaries, even under uncertain real-world conditions.
Reliability
Ensuring AI systems deliver consistent, predictable, and verifiable behavior across tasks, environments, and deployment scenarios.
Robustness
Developing AI systems resilient to adversarial inputs, sensor noise, and distributional shifts, enabling reliable performance in dynamic and imperfect settings.
Explainability
Building interpretability tools that clarify how and why AI systems make decisions, enabling trust and deeper understanding of model behavior.
Human Alignment
Aligning AI behavior with human intent, preferences, and values through preference learning, value modeling, and robust evaluation protocols.
Ethical Governance
Studying the societal implications of AI and building frameworks to embed accountability, fairness, and transparency into development workflows.