Research

Overview Speaker: Md. Toyaha Rahman Ratul Date: Friday, October 2, 2026, 2:30 PM Room: PM103 The story in short: in 2024, Sakana AI introduced “The AI Scientist,” a system that tries to do the full research cycle by itself. It generates an idea, writes the code, runs the experiments, writes the paper, and even reviews it. In 2025, one paper fully generated by its second version passed peer review at an ICLR workshop, with scores of 6, 7, and 6, which was higher than about 55% of the human-written papers there. The team withdrew the paper before publication, as they had planned with the organizers. In 2026, the work was published in Nature. It has started a big discussion in the community about what AI can and cannot do in research. ...

Download the presentation slides Overview On April 10, 2026, Jichao Fang, a Ph.D. candidate in the Department of Earth, Atmosphere and Environment at Northern Illinois University, presented his research on deep learning and foundation models for Earth and planetary sciences. Jichao is also a Master’s student in Computer Science and is expected to graduate this year. ...

Download the presentation slides Overview and Motivation Presentation on integrating reinforcement learning concepts with LLM engines to improve performance Multiple approaches exist to enhance LLM performance: in-context learning, post-training (reinforcement learning), and fine-tuning Focus on efficiently integrating RL concepts with LLMs, particularly for multi-agent systems Covers differences between reinforcement learning on LLMs versus reinforcement learning on LLM agents Addresses RL implementation in HPC systems due to memory-intensive requirements Multi-Agent System Fundamentals Multi-agent systems consist of multiple LLMs, each with specific roles and functions State acts as history, compiling all previous agent turns, context, and evidence in multi-turn systems Communication Topologies ...

Overview On March 21, 2025, Dr. Mona Rahimi, an associate professor in the Computer Science department, delivered a presentation on An AI-Driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software, a problem she and her students have been investigating. With the rise of AI-based software, many traditional software engineering methodologies have become ineffective. In her talk, Dr. Rahimi discussed the redefinition of requirement specification and proposed methods for aligning AI perception with requirements engineering.