Xiao Wang - AI Researcher | University at Buffalo

I received my Masters (MS) degree from the College of Engineering & Computer Science at Syracuse University, (GPA: 3.861). My current research centers on generative models, large language models (LLMs) for reasoning, and multi-agent collaboration. I am particularly interested in how LLMs can be integrated within structured multi-agent frameworks to enhance reasoning capabilities and collaborative problem solving.

Feel free to reach out to me if you are interested in collaborating. I am also actively looking for research intern positions.

News [

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2021.09: Our paper, ProxyFusion: FaceFeature Aggregation Through Sparse Experts, has been accepted for presentation at NeurIPS 2024.
2021.09: Our paper,One paper on 3D sign language motion generation has been accepted by EMNLP 2024.
2021.08: A paper on enhancing facial expression inference has been accepted by the ECCV 2024 Workshop.
2021.08: Started my Ph.D. journey at University at Buffalo, SUNY,Buffalo, NY.
2024.03: Our paper "SignAvatar" has been accepted by FG 2024 .
2024.03: I have been admitted to the Fall 2024 Doctorate of Philosophy program in CSE at the University at Buffalo, with a Research Assistant position at the Center for Unified Biometrics and Sensors under the supervision of Professor Venu Govindaraju.
2023.06:Research Assistant at University at Buffalo's Human Behavior Modeling lab, under the direct supervision of Professor Ifeoma Nwogu, focused on implementing AI generative algorithms for studying human behaviors.
2023.05: Successfully completed my Master's degree at Syracuse University with 3.861 / 4 GPA, Syracuse, NY.
2021.08: Started my Master's journey at Syracuse University, Syracuse, NY.

Selected Research

AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot
Xiao Wang,Lu Dong†, Sahana Rangasrinivasan, Ifeoma Nwogu, Srirangaraj Setlur, Venugopal Govindaraju
International Conference on Intelligent Robots and Systems (IROS2025 under review)
DOI PDF BibTeX Project Webpage

Ig3D: Integrating 3D Face Representations in Facial Expression Inference
Lu Dong*,Xiao Wang*, Srirangaraj Setlur, Venu Govindaraju, Ifeoma Nwogu.
The 18th European Conference on Computer Vision (ECCV 2024) Workshop
DOI PDF BibTeX Project Webpage

ProxyFusion: Face Feature Aggregation Through Sparse Experts
Bhavin Jawade, Alexander Stone, Deen Dayal Mohan, Xiao Wang, Srirangaraj Setlur, Venu Govindaraju
The Thirty-eighth Annual Conference on Neural Information Processing Systems(NeurIPS 2024)
DOI PDF BibTeX Project Webpage

SignAvatar: Sign Language 3D Motion Reconstruction and Generation
Lu Dong, Lipisha Chaudhary, Fei Xu,Xiao Wang, Mason Lary, Ifeoma Nwogu.
The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2024.
DOI PDF BibTeX Project Webpage

Customer-Satisfaction Analysis at SAN FRANCISCO International Airport
This study conducts an in-depth statistical analysis to pinpoint the key factors influencing customer satisfaction at San Francisco International Airport (SFO). Utilizing a dataset comprised of over 3,000 customer responses from a mid-2013 survey, we employ a stepwise linear multiple regression model to identify the significant drivers of overall satisfaction. Our analysis reveals several impactful variables, with the majority exhibiting a consistent trend: extreme ratings on the scale ('Unacceptable' and 'Outstanding') have the most substantial impact.
DOI PDF BibTeX

Determining Key Factors in Consumer Evaluation of an Airport
In this research, factor analysis is utilized to systematically investigate eleven variables related to service attributes and usage at San Francisco Airport (SFO). The primary objective is to decipher the underlying factorial structure that significantly influences a customer's comprehensive rating of SFO. A stepwise logistic regression analysis is subsequently applied, treating these derived factors as independent variables, and the comprehensive assessment of SFO as the dependent variable. This rigorous statistical approach allows for a detailed examination of the exact influence each of these factors has on the overall evaluation of SFO.
DOI PDF BibTeX

Highlighted Projects

Revolutionizing Therapy with LLM-Robot Agents
This project integrates large language and vision-language models into Misty robots, enabling deep contextual understanding and advanced human-robot interactions. By implementing real-time pose estimation and emotion detection from video streams, the system dynamically adjusts Misty’s interaction strategies, ensuring more responsive and tailored user experiences. In the educational sphere, Misty serves as a platform for large-scale models to provide personalized guidance for children with special needs. Through real-time analysis of posture, expressions, and behavior, Misty refines instructional complexity, conversational content, and engagement methods, fostering greater interest, participation, and adaptability in therapeutic and learning interactions.

Please check the title link for more details.

ARL-INTERSECT: A Multi-Agent Reinforcement Learning Algorithm for Autonomous Cars
This work introduces ARL-INTERSECT, a multi-agent reinforcement learning algorithm designed for autonomous vehicles navigating unsignalized intersections. By training independent Actor-Critic models for each vehicle agent and leveraging parallel computing for faster data sampling, we improve training stability and scalability. Integrating A3C with PPO and incorporating entropy-based exploration yields smoother convergence and more reliable policy enhancements. Evaluated in a simulated intersection scenario, ARL-INTERSECT outperforms traditional independent A3C approaches in terms of convergence speed, policy performance, and interaction safety. Moreover, it demonstrates adaptability and scalability across both small-scale (4 agents) and large-scale (10 agents) environments.
Please follow the title link for access to my Project codes and Demo.

Predicting One-step Proofs in Formal Mathematics using Transformer-based Neural Networks
This research explores the use of transformer models, like OpenAI's ChatGPT, for Automated Theorem Proving (ATP) on the Metamath dataset (Set.mm). By framing proof verification as a sequence-to-sequence task, we leverage the logical relationships in mathematical proofs. Our approach highlights AI's potential in theorem proving, addressing key challenges and demonstrating promising results in complex mathematical tasks.

Please follow the title link for access to the source code and detailed reports.

XiaoStyle - Customizable & Secure eCommerce Platform
This project is a custom eCommerce application built with Python Django. It features a custom user model, product and category management, cart operations, an unlimited product image gallery, order processing, and payment integration. Post-order functionalities include stock reduction, email notifications, and invoice generation. The site also offers a review and rating system with interactive stars, account management, session handling, and secure user authentication with token-based verification and password resets.

Please follow the title link for access to the source code and YouTube Demo.

Ultimate Data Navigator: Efficient Data Management System
Leveraging C++ for high-performance computing, The Ultimate Data Navigator automates scanning, data gathering, and uploads to a centralized database, while its modular utilities manage scheduling, resource allocation, file transfer, and database operations across diverse data types and volumes. Built on a custom developer framework, the system allows rapid deployment and easy modifications. Additionally, the solution is cost-effective, efficiently processing millions of data entries weekly using free, open-source components.

Please follow the title link for access to the source code and detailed reports.

Optimizing Factory Assembly Line Through C++ Multi-threading
This project utilizes C++ multi-threading, including thread conditional variables and atomic operations, to simulate and optimize factory assembly line strategies. After identifying inefficiencies, a new approach was implemented using Object-Oriented Design (OOD) with an analytic module added for result analysis.

Please follow the title link for access to the source code and detailed report.

Robust Remote Process Control system
This project develops a remote process control system utilizing threading, network communication via UDP, signal handling, and process management. The core component is responsible for overseeing processes based on a configurable setup. It efficiently handles process termination requests and allows for remote control of process shutdown.

Please follow the title link for access to the source code and detailed reports.

Implementing and Securing Network Systems
This lab series, based on the SEEDLAB project by Professor Du Wenliang, involves implementing and analyzing network protocols like ARP, IP, UDP, TCP, and more. The work includes VPN and firewall operations, DNS and BGP studies, and security assessments of potential vulnerabilities.

Please follow the title link for access to the source code and detailed reports.

RecruitPro: HR Recruitment Database Management System
RecruitPro is a powerful HR database that optimizes candidate tracking throughout the entire hiring process. The system efficiently manages job postings, candidate profiles, interviews, evaluations, reimbursements, and onboarding, while adhering to 3rd Normal Form principles to ensure data integrity and security. It leverages advanced database techniques such as views, stored procedures, functions, triggers, and transactions, alongside user role management, to provide a comprehensive and secure solution for HR operations.

Please follow the title link for access to the source code and detailed reports.

Xiao Wang (王啸)

Phd Student

About Me

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Selected Research

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Highlighted Projects