Ken Kennedy Institute AI Seminar Series | Tuesday 12:00–12:50 PM Central
The Rice Computer Science department, CS GSA, and Ken Kennedy Institute will host a weekly AI Seminar on Tuesdays from 12:00–12:50 PM at Rice University. This event is open to Rice University graduate students; no RSVP required. Join us each week at noon for a light lunch before the talk begins at 12:15 PM. See the list of seminars below.
| Date | Location | Seminar Details |
|---|---|---|
| 9/9 | TBD | / |
| 9/16 | TBD | / |
| 9/23 | TBD | / |
| 9/30 | TBD | Speaker: Prof. Kuan-Hao Huang (Assistant Professor, Texas A&M University) |
| 10/7 | TBD | / |
| 10/14 | TBD | Speaker: Prof. Sen Lin (Assistant Professor, University of Houston) |
| 10/21 | TBD | / |
| 10/28 | TBD | / |
| 11/4 | TBD | / |
| 11/11 | TBD | / |
Below is the Spring 2025 AI-Seminar Series:
| Date | Location | Seminar Details |
|---|---|---|
| 2/27 | DH 1046 | Speaker: Prof. Na Zou, PhD (Assistant Professor, University of Houston) Talk: Exploring and Exploiting Shortcuts in AI/ML: Algorithms, Challenges and Solutions For speaker bio and more information, please check the event page: Here AI/ML algorithms have made significant advancements and are extensively used in critical applications such as employment, personalized medicine, and more. Despite the success, mitigating shortcut features and spurious correlation in AI/ML remains a significant challenge. These algorithms may inadvertently rely on confounding factors or shortcut features in the data, resulting in unintended and misleading associations and thus poor model generalization. This issue hinders the widespread adoption of AI/ML in high-stakes applications. This talk will provide a computational perspective on shortcuts and spurious correlation in AI/ML, encompassing the measurement, detection, and mitigation of shortcuts and spurious correlation to address diverse challenges throughout the AI/ML life cycle. The speaker will first introduce real-world examples, fundamental concepts and the existing work. The speaker will also highlight her ongoing research across three key stages in AI/ML: enhancing data quality, refining algorithmic design, and optimizing model deployment. |
| 3/6 | DH 1070 | Speaker: Prof. Jessica Ouyang, PhD (Assistant Professor, UT Dallas) Talk: Generating Scientific Literature Reviews For speaker bio and more information, please check the event page: Here As researchers, we spend a lot of time interacting with literature reviews: we read them when we want to quickly get up to speed in a new research area, and we write them when we need to ground our own research contributions in the landscape of existing work. In this talk, I will discuss my lab's work on developing neural approaches to generate scientific literature reviews. I will begin by highlighting our contributions in the task of citation generation in the context of a larger literature review paragraph, including generation at the sub- and multi-sentence levels, enforcing coherence with the surrounding paragraph, and retrieving grounding sentences from cited papers. Then, I will introduce our work on full-length literature review generation and describe a detailed user study on the capabilities of current state-of-the-art language models on this task. Finally, I will conclude with a discussion of current challenges in literature review generation, as well as the ethical considerations that arise when automating part of the scientific writing process. |
| 3/13 | O'Connor 5th Floor | Speaker: Prof. Mauricio Araya Polo, PhD (Adjunct Professor, Rice; Senior R&D Manager, Total Energies) Talk: Addressing Geophysical Problems with Scientific ML For speaker bio and more information, please check the event page: Here Advances in machine learning (ML) are open new avenues for complex geophysical problems. This seminar explores how scientific ML techniques, in particular DL-driven inversion, can enhance traditional geophysical methods. We illustrate their application to seismic inversion, subsurface characterization, and CO2 monitoring through examples. Attendees will gain insights into current methodologies and future directions in integrating scientific ML with geosciences. |
| 3/20 | N/A | No Seminar — Spring Break |
| 3/27 | DH 1070 | Speaker: Prof. David Harwath, PhD (Assistant Professor, UT Austin) Talk: Speech Generation and Sound Understanding in Era of Large Language Models For speaker bio and more information, please check the event page: Here Transformer-based large language models (LLMs) have rapidly risen to dominance in the NLP field. One of the most exciting developments in this line of research is the finding that LLMs can be easily extended to handle multimodal inputs, such as vision or speech, via tokenization and concatenation with natural language inputs. In this talk, I will discuss several of my group's recent research directions into expanding the capabilities of multimodal LLMs to process speech and spatial audio signals |
| 4/3 | O'Connor 5th Floor | Speaker: Prof. Sunyang Fu, PhD (Assistant Professor, UT Health Houston) Talk: Advancing Aging Research with Natural Language Processing and Electronic Health Records: Opportunities, Challenges, and Future Directions For speaker bio and more information, please check the event page: Here Routinely collected real-world data, such as electronic health records (EHRs), serve as a valuable resource for aging research and geriatric medicine. However, the unstructured nature of these data presents significant challenges. To address this, natural language processing (NLP) techniques have been developed and applied to extract and standardize information from unstructured clinical text. This talk will provide an overview of NLP, highlighting its relevance to EHR data and its applications in aging research and geriatric medicine. I will also discuss some of the key challenges to adopting NLP in clinical research and practice and future research opportunities. |
| 4/10 | DH 1070 | Speaker: Jyoti Anand (Director of Data Science, Walmart) Talk: Gen AI in Retail Technology As the director of Walmart Data Science team, Jyoti has been integrating ML solutions with business functions to create the next generation of AI-powered capabilities. Her rich industry experience in Allegis, FDA, and Walmart has given her deep insight in Forecasting, LLMs, Deep Learning models. |
| 4/17 | DH 1070 | Speaker: Prof. Tianlong Chen, PhD (Assistant Professor, University of North Carolina at Chapel Hill) Talk: Multimodal LLM Agents: Profile, Memory, Planning, and Actions For speaker bio and more information, please check the event page: Here Large Language Model (LLM) agents are emerging as a powerful paradigm for enabling intelligent decision-making, planning, and interaction across a wide range of real-world applications. In this talk, I will introduce the concept of LLM agents and explore recent advances in designing more powerful, scalable, and efficient agent systems. I will cover core challenges in multi-agent collaboration, such as communication strategies, agent specialization, and memory management, and present recent innovations including Self-MoA, Graph-of-Agents (GoA), and Symbolic-MoE. I will also discuss retrieval-augmented generation (RAG) extensions, such as Chain-of-Agents and Reflective Memory Management, to improve reasoning over long contexts. Additionally, I will share recent progress in using LLM agents for embodied decision-making in robotics and industrial settings, as well as novel use cases in scientific discovery and judgment modeling. Through a series of real-world case studies and prototypes, this talk highlights both the promises and open questions in building next-generation LLM agent systems. |
| 4/24 | DH 1070 | Speaker: Prof. Cheng Zhang, PhD (Assistant Professor, Texas A&M University) Talk: See More From Less: Generative Human Modeling from Sparse Visual Cues For speaker bio and more information, please check the event page: Here Digital human reconstruction plays a critical role in many applications that benefit society, such as AR/VR, human-computer interaction, social robotics, and clinical diagnosis. However, generating accurate and detailed 3D humans remains challenging due to the scarcity of 3D data or multi-view inputs and the difficulty of capturing geometry and textures in uncontrolled environments. In this talk, I will discuss my recent work on steering generative models to reconstruct and manipulate 3D humans from sparse, or even single, visual inputs. The key idea is to combine generative modeling with human priors and appearance cues. I will illustrate how this idea can be adapted to address several key tasks. I will start by introducing a method for full-body reconstruction from a few RGB views using a new template-guided Gaussian representation. Next, I will describe a progressive novel view synthesis framework that turns a single image into consistent novel views, supporting both 3D human reconstruction and fine-grained editing. Finally, I will present a diffusion-based texture transfer method that recovers realistic textures from in-the-wild clothing images and maps them to 3D garments with unprecedented quality. I will conclude by discussing how these ideas extend beyond humans, offering broader potential for modeling the dynamic world from limited visual cues. |
To see a list of all upcoming events related to the Ken Kennedy Institute, visit https://kenkennedy.rice.edu/events.
For questions, please contact at hd31@rice.edu.
