Seminars

Overview Download the presentation slides Today in our AI/ML seminar, we were pleased to have Maliha Zahan Chowdhury as the presenter. Maliha is a first-year PhD student in Dr. Zhishuai Guo’s research group. Her talk focused on Direct Preference Optimization (DPO), primarily based on the original 2023 DPO paper. Although the paper was published only three years ago, it has already been cited nearly 8,000 times, reflecting its significant impact on LLM alignment and optimization research. ...

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 October 3, 2025, Md. Toyaha Rahman Ratul, a Ph.D. student at Northern Illinois University, presented a recent paper titled “On the Theoretical Limitations of Embedding-Based Retrieval,” authored by researchers from Google DeepMind and Johns Hopkins University. The paper formally examines and reveals the inherent limitations of single-embedding-based retrieval methods—approaches widely adopted in both industry and academia—thereby having a significant impact on the research community. ...

Overview On April 11, 2025, we were honored to host Dr. Imtiaz Karim, a Postdoctoral Researcher at Purdue University and incoming Assistant Professor at the University of Texas at Dallas, for a distinguished research talk titled “Systematic Security Analysis of Cellular Network Specifications and Implementations.” Dr. Karim presented a compelling overview of the security challenges in modern cellular networks, particularly 4G and 5G, and introduced CellularLint, a cutting-edge tool that uses large language models to identify inconsistencies in cellular specifications. He further discussed his methodologies for analyzing protocol implementations and revealed a range of vulnerabilities that have influenced updates to industry standards. The talk concluded with Dr. Karim’s vision for securing future wireless communication systems, including 6G. We sincerely thank Dr. Karim for sharing his impactful research and insights with our department.

Overview On March 28, 2025, Miftahul Jannat Mokarrama, a PhD student in the Computer Science department, presented a research topic on “LLMs as Judges,” a trending subject in the field of large language models. Her presentation provided a comprehensive survey covering functionality, methodology, applications, meta-evaluation, and limitations. Key conclusions included: LLMs as evaluators are versatile, evaluation is context-specific, challenges persist, human-AI collaboration is essential, and evaluation should extend beyond traditional research papers.

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.

Overview On March 7, 2025, Mosab Rezaei, a PhD student from the CS department, presented on the Forward-Forward algorithm for neural network models, originally proposed by Hinton. The talk explored the biological foundations and intuition behind the algorithm, its working principles, and related follow-up research. During the Q&A session, participants discussed neuron-inspired AI, alternative neural network architectures, and the differences and connections between the Forward-Forward algorithm and traditional feedforward networks.

Overview On 02/21/2025, the first NIU AI/ML Research Seminar was held in PM103. Ibrahim Al Azher, a PhD student from the CS department, delivered a general introduction to current studies on Large Language Models, their limitations, and recent advancements in Retrieval-Augmented Generation (RAG). The presentation, which lasted about an hour, was well received by the participants and sparked engaging discussions afterward. This event marked a successful start to the AI/ML research seminar series.