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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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ACL 2025
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Paper “Can LLMs Understand Unvoiced Speech? Exploring EMG-to-Text Conversion with LLMs” is accepted by the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) main conference.
Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Anthony Goeckner
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Education
Sept. 2021 - Present: Northwestern University Ph.D. Student, Department of Electrical and Computer Engineering
2014 - 2018: Purdue University Bachelor of Science, Computer Science
Experience
- Northrop Grumman Corporation: Software Engineer, Robotics Research 2019-2022
- Northrop Grumman Corporation: Software Engineer, Embedded Software 2018-2019
- NASA Jet Propulsion Laboratory: Software Engineering Intern 2017
- GE Aviation Systems: Software Engineering Intern 2016
About me
Research interests: Robotics, multi-agent systems, communications & networking, learning-enabled cyber-physical systems.
Aria Ruan
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Test Hardware Controls Engineer at Tesla
Brooks Hu
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Danah Ansari
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Dante Bailey
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Devashri Naik
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PhD Student at University of Illinois at Chicago
Eric Yang
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Ethan Foong
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Frank Yang
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Software Engineer at Scale.Ai
Gyaan Antia
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Haijie Li
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Jinjin Cai
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PhD Student at Purdue University
Justin Liu
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University of Southern California
Lixu Wang
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Research Fellow at Nanyang Technological University (PhD 2024)
Nicolas Martinet
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Predoctoral Research Associate at INSEAD
Payal Mohapatra
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Philip Wang
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Qi Zhu
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Qi Zhu is a Professor in the Department of Electrical and Computer Engineering at Northwestern University.
Ruochen Jiao
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Applied Scientist at Amazon (PhD 2024)
Samuel Hodge
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Motor Engineer at Milwaukee Tool
Shamika Likhite
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Software Engineer at SpeechAce
Shichao Xu
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Software Engineer at Google (PhD 2022)
Shivi Shivistrava
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Controls Engineer at Merit Controls
Shuyue Lan
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Software Engineer at NVIDIA (PhD 2021)
Simon Zhan
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Education
2023 - Present: Northwestern University Ph.D. Student, Department of Electrical and Computer Engineering
2018 - 2022: University of California, Berkeley Bachelor of Art, Computer Science and Applied Mathematics
Sparsh Gautam
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Computer Vision Engineer at Tesla
Talia Ben Naim
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Software Engineer at Medtronic
Tia Rice
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Tianze Liu
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PhD Student at Michigan Technological University
Vincent Rimparsurat
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Software Engineer at Epic Systems
Weihe Gao
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Xiangguo Liu
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Research Scientist at Meta (PhD 2023)
Xiangyu Shi
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Education
Sept. 2024 - Present: Northwestern University Ph.D. Student, Department of Electrical and Computer Engineering
2020 - 2024: Zhejiang University Bachelor of Engineering, Smart Energy
About me
Research interests: llm foundation model based embodied system.
Xiayuan Zhang
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Xinliang Li
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PhD Student at University of Georgia
Xinyu Cao
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Yixuan Wang
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Software Engineer at Aurora (PhD 2024)
Yueyuan Sui
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PhD Student at Northwestern University
Zinan Wang
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publications
Kinematics-Aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
Published in IROS, 2024
We propose a new method that integrates kinematic knowledge into neural stochastic differential equations (SDE) and designs a variational autoencoder based on this latent kinematics-aware SDE (LK-SDE) to generate vehicle motions. Experimental results demonstrate that our method significantly outperforms both model-based and learning-based baselines in producing physically realistic and precisely controllable vehicle trajectories. Additionally, it performs well in predicting unobservable physical variables in the latent space.
Recommended citation: Ruochen Jiao, Yixuan Wang, Xiangguo Liu, Chao Huang and Qi Zhu, “Kinematics-Aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling”, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’24), Abu Dhabi, UAE, October, 2024.
Download Paper
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Published in NeurIPS, 2024
We introduce the problem of Universal Unsupervised Cross-Domain Retrieval (U^2CDR) for the first time and design a two-stage semantic feature learning framework to address it. In the first stage, a cross-domain unified prototypical structure is established under the guidance of an instance-prototype-mixed contrastive loss and a semantic-enhanced loss, to counteract category space differences. In the second stage, through a modified adversarial training mechanism, we ensure minimal changes for the established prototypical structure during domain alignment, enabling more accurate nearest-neighbor searching. Extensive experiments across multiple datasets and scenarios, including closet, partial, and open-set CDR, demonstrate that our approach significantly outperforms existing state-of-the-art CDR works and some potentially effective studies from other topics in solving U^2CDR challenges.
Recommended citation: Lixu Wang, Xinyu Du and Qi Zhu, “Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval”, 38th Annual Conference on Neural Information Processing Systems (NeurIPS’24), Vancouver, Canada, December, 2024.
Download Paper | Download Slides
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems
Published in ICLR, 2025
In this work, we propose the first comprehensive framework for Backdoor Attacks against LLM-based Decision-making systems (BALD) in embodied AI, systematically exploring three distinct attack mechanisms: word injection, scenario manipulation, and knowledge injection, targeting various components in the LLM-based decision-making pipeline. We perform extensive experiments on representative LLMs in autonomous driving and home robot tasks, demonstrating the effectiveness and stealthiness of our backdoor triggers across various attack channels, with cases like vehicles accelerating toward obstacles and robots placing knives on beds.
Recommended citation: Ruochen Jiao*, Shaoyuan Xie*, Justin Yue, Takami Sato, Lixu Wang, Yixuan Wang, Qi Alfred Chen and Qi Zhu, “Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems”, 13th International Conference on Learning Representations (ICLR’25), Singapore, April, 2025.
Download Paper | Download Slides
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.