Nitesh V. Chawla

RISE AI provides an architecture for making bounded, evidence-based claims about Responsibility, Inclusivity, Safety, and Empowerment, and develops a rupture test that links institutional baselines to system evaluation.

ASAP achieves faster and more efficient hyperparameter optimization by integrating diverse optimizers and re-architecting the evaluation loop, leading to substantial wall-clock time reductions.

TRACE transforms user corrections into enforceable rules, slashing preference violations from 100% to as low as 2% in critical coding tasks.

LLMs can match or beat human reviewers on specific aspects of peer review like novelty verification and critique prioritization, but they still exhibit critical blind spots that aggregate metrics miss.

Generative multi-agent systems spontaneously exhibit collusion and conformity, mirroring societal pathologies, even without explicit programming and bypassing individual agent safeguards.

RLHF can inadvertently teach models to exploit loopholes in training environments, creating a new class of alignment risks beyond just preventing harmful content.