Jing Liu

Phone: 617-621-7584
Email:

Position:
Research / Technical Staff

Senior Research Scientist
Education:
Ph.D., University of California, San Diego, 2019
Research Areas:

Biography

Before joining MERL, Jing was an Illinois Future Faculty fellow at the Computer Science department of the University of Illinois, Urbana Champaign (UIUC). Prior to that, he was a Postdoctoral Research Associate at the Coordinated Science Lab of UIUC. His research interests include Trustworthy AI, Distributed Learning and Inference, Robust and Efficient Internet-of-Things (IoT), and green AI.
Recent News & Events
- NEWS MERL Presents 4 Main Conference Papers and 6 Workshop Papers at ICML 2026
  Date: July 6, 2026 - July 11, 2026
  Where: COEX, Seoul, South Korea
  MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Stefano Di Cairano; Toshiaki Koike-Akino; Christopher R. Laughman; Jing Liu; Suhas Lohit; Kuan-Chuan Peng; Alexander Schperberg; Ye Wang; Gordon Wichern
  Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing
  Brief
  - MERL researchers are proud to present 4 main conference papers and 6 workshop papers at ICML 2026. ICML, taking place from July 6-11 in Seoul, South Korea, is a premier international conference in machine learning.
    
    Main Conference Papers with MERL Authors:
    
    1. Understanding Dynamic Compute Allocation in Recurrent Transformers by Ibraheem Muhammad Moosa, Suhas Lohit, Ye Wang, Moitreya Chatterjee, and Wenpeng Yin.
    
    2. LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior by Qinhong Zhou, Chuang Gan, and Anoop Cherian.
    
    3. Memory-Distilled Selection for Noise-Robust Anomaly Detection by Sirojbek Safarov, Jaewoo Park, Yoon G. Jung, Kuan-Chuan Peng, Wonchul Kim, Seongdeok Bang, and Octavia Camps.
    
    4. Partial Ring Scan: Revisiting Scan Order in Vision State Space Models by Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin Li, Ming-Ching Chang, Yu-Chee Tseng, and Jun-Wei Hsieh.
    
    Workshop Papers with MERL Authors:
    
    1. WISE: Weighted Iterative Society-of-Experts for Multimodal Multi-Agent Debate with Probabilistic Consensus by Anoop Cherian, Suhas Lohit, and Kuan-Chuan Peng. (Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE))
    
    2. MIRROR: Multisensory Implicit Rejection-sampled RObotic policy by Amisha Bhaskar, Pratap Tokekar, Stefano Di Cairano, and Alexander Schperberg. (Workshop on Structured Probabilistic Inference & Generative Modeling)
    
    3. Reinforced Neural Processes: Memory-Efficient Time-Series Forecasting with a World-Feedback-Trained Memory Policy by Nibraas Khan, Gordon Wichern, and Christopher R. Laughman. (Workshop on Reinforcement Learning from World Feedback (RLxF))
    
    4. Connecting Low-Rank Adapters and Policy Stability in GRPO Fine-Tuning by Antonin Rottman, Francesco Tonin, Yongtao Wu, Toshiaki Koike-Akino, and Volkan Cevher. (Workshop on Connecting Low-rank Representations in AI (CoLorAI))
    
    5. EinSort: Sorting is All We Need for Tensorizing LLM by Toshiaki Koike-Akino, Jing Liu, and Ye Wang. (Workshop on Connecting Low-rank Representations in AI (CoLorAI))
    
    6. Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment by Ye Wang, and Jing Liu, and Toshiaki Koike-Akino. (Workshop on Agents in the Wild: Safety, Security, and Beyond)
- NEWS MERL Papers and Workshops at CVPR 2025
  Date: June 11, 2025 - June 15, 2025
  Where: Nashville, TN, USA
  MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang
  Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
  Brief
  - MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:
    
    Main Conference Papers:
    
    1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee
    
    This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.
    
    Paper: https://www.merl.com/publications/TR2025-072
    
    2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.
    
    This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.
    
    paper: https://www.merl.com/publications/TR2025-077
    
    MERL Co-Organized Workshops:
    
    1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.
    
    Workshop link: https://marworkshop.github.io/cvpr25/
    
    2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.
    
    Workshop link: https://fadetrcv.github.io/2025/
    
    Workshop Papers:
    
    1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)
    
    With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.
    
    paper: https://www.merl.com/publications/TR2025-074
    
    2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.
    
    LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.
    
    paper: https://www.merl.com/publications/TR2025-078
    
    3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.
    
    This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.
    
    paper: https://www.merl.com/publications/TR2025-058
    
    4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.
    
    This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.
    
    paper: https://www.merl.com/publications/TR2025-076
    
    5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian
    
    Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.
    
    paper: https://www.merl.com/publications/TR2025-073
    
    6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand
    
    We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.
    
    paper: https://www.merl.com/publications/TR2025-075
    
    7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand
    
    To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.
    
    paper: https://www.merl.com/publications/TR2025-079
See All News & Events for Jing
Awards
- AWARD MERL Wins Awards at NeurIPS LLM Privacy Challenge
  Date: December 15, 2024
  Awarded to: Jing Liu, Ye Wang, Toshiaki Koike-Akino, Tsunato Nakai, Kento Oonishi, Takuya Higashi
  MERL Contacts: Toshiaki Koike-Akino; Jing Liu; Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Information Security
  Brief
  - The Mitsubishi Electric Privacy Enhancing Technologies (MEL-PETs) team, consisting of a collaboration of MERL and Mitsubishi Electric researchers, won awards at the NeurIPS 2024 Large Language Model (LLM) Privacy Challenge. In the Blue Team track of the challenge, we won the 3rd Place Award, and in the Red Team track, we won the Special Award for Practical Attack.
See All Awards for MERL
Research Highlights
MERL Publications
- Wang, Y., Liu, J., Koike-Akino, T., "Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment", International Conference on Machine Learning (ICML) Workshop on Agents in the Wild: Safety, Security, and Beyond, July 2026.
  BibTeX TR2026-094 PDF Presentation
  - @inproceedings{Wang2026jul,
  - author = {{Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki}},
  - title = {{Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment}},
  - booktitle = {International Conference on Machine Learning (ICML) Workshop on Agents in the Wild: Safety, Security, and Beyond},
  - year = 2026,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2026-094}
  - }
- Koike-Akino, T., Liu, J., Wang, Y., "EinSort: Sorting is All We Need for Tensorizing LLM", International Conference on Machine Learning (ICML) Workshop, July 2026.
  BibTeX TR2026-093 PDF Presentation
  - @inproceedings{Koike-Akino2026jul,
  - author = {{Koike-Akino, Toshiaki and Liu, Jing and Wang, Ye}},
  - title = {{EinSort: Sorting is All We Need for Tensorizing LLM}},
  - booktitle = {International Conference on Machine Learning (ICML) Workshop},
  - year = 2026,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2026-093}
  - }
- Hu, H., Huang, C.-T., Liu, J., Wang, Y., Suzuki, K., Brand, M., Koike-Akino, T., "ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies", arXiv, June 2026.
  BibTeX arXiv
  - @article{Hu2026jun,
  - author = {Hu, Haodi and Huang, Chung-Ta and Liu, Jing and Wang, Ye and Suzuki, Kei and Brand, Matthew and Koike-Akino, Toshiaki},
  - title = {{ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies}},
  - journal = {arXiv},
  - year = 2026,
  - month = jun,
  - url = {https://arxiv.org/abs/2606.09630}
  - }
- Wang, Y., Liu, J., Koike-Akino, T., "Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment", arXiv, May 2026.
  BibTeX arXiv
  - @article{Wang2026may,
  - author = {Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
  - title = {{Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment}},
  - journal = {arXiv},
  - year = 2026,
  - month = may,
  - url = {https://arxiv.org/abs/2605.13537}
  - }
- Bimbraw, K., Wang, Y., Liu, J., Koike-Akino, T., "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via a Large Vision-Language Model", IEEE Access, DOI: 10.1109/ACCESS.2026.3687477, Vol. 14, pp. 70724-70736, April 2026.
  BibTeX TR2026-054 PDF
  - @article{Bimbraw2026may,
  - author = {Bimbraw, Keshav and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
  - title = {{GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via a Large Vision-Language Model}},
  - journal = {IEEE Access},
  - year = 2026,
  - volume = 14,
  - pages = {70724--70736},
  - month = may,
  - doi = {10.1109/ACCESS.2026.3687477},
  - issn = {2169-3536},
  - url = {https://www.merl.com/publications/TR2026-054}
  - }
See All MERL Publications for Jing
Other Publications
- Aditya Deshmukh, Jing Liu and Venugopal V Veeravalli, "Robust mean estimation in high dimensions: An outlier fraction agnostic and efficient algorithm", 2022 IEEE International Symposium on Information Theory (ISIT), 2022, pp. 1115-1120.
  BibTeX
  - @Inproceedings{deshmukh2022robust,
  - author = {Deshmukh, Aditya and Liu, Jing and Veeravalli, Venugopal V},
  - title = {Robust mean estimation in high dimensions: An outlier fraction agnostic and efficient algorithm},
  - booktitle = {2022 IEEE International Symposium on Information Theory (ISIT)},
  - year = 2022,
  - pages = {1115--1120},
  - organization = {IEEE}
  - }
- Jing Liu, Chulin Xie, Oluwasanmi O Koyejo and Bo Li, "CoPur: Certifiably Robust Collaborative Inference via Feature Purification", Advances in Neural Information Processing Systems, 2022.
  BibTeX
  - @Inproceedings{liu2022copur,
  - author = {Liu, Jing and Xie, Chulin and Koyejo, Oluwasanmi O and Li, Bo},
  - title = {CoPur: Certifiably Robust Collaborative Inference via Feature Purification},
  - booktitle = {Advances in Neural Information Processing Systems},
  - year = 2022
  - }
- Jing Liu, Chulin Xie, Krishnaram Kenthapadi, Sanmi Koyejo and Bo Li, "Rvfr: Robust vertical federated learning via feature subspace recovery", NeurIPS Workshop New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership, 2021.
  BibTeX
  - @Inproceedings{liu2021rvfr,
  - author = {Liu, Jing and Xie, Chulin and Kenthapadi, Krishnaram and Koyejo, Sanmi and Li, Bo},
  - title = {Rvfr: Robust vertical federated learning via feature subspace recovery},
  - booktitle = {NeurIPS Workshop New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership},
  - year = 2021
  - }
- Aditya Deshmukh, Jing Liu, Venugopal V Veeravalli and Gunjan Verma, "Information flow optimization in inference networks", ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 8289-8293.
  BibTeX
  - @Inproceedings{deshmukh2020information,
  - author = {Deshmukh, Aditya and Liu, Jing and Veeravalli, Venugopal V and Verma, Gunjan},
  - title = {Information flow optimization in inference networks},
  - booktitle = {ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  - year = 2020,
  - pages = {8289--8293},
  - organization = {IEEE}
  - }
- Jing Liu and Bhaskar D Rao, "Sparse Bayesian learning for robust PCA: Algorithms and analyses", IEEE Transactions on Signal Processing, Vol. 67, No. 22, pp. 5837-5849, 2019.
  BibTeX
  - @Article{liu2019sparse,
  - author = {Liu, Jing and Rao, Bhaskar D},
  - title = {Sparse Bayesian learning for robust PCA: Algorithms and analyses},
  - journal = {IEEE Transactions on Signal Processing},
  - year = 2019,
  - volume = 67,
  - number = 22,
  - pages = {5837--5849},
  - publisher = {IEEE}
  - }
- Jing Liu and Bhaskar D Rao, "Robust PCA via ℓ0-ℓ1 Regularization", IEEE Transactions on Signal Processing, Vol. 67, No. 2, pp. 535-549, 2018.
  BibTeX
  - @Article{liu2018robust,
  - author = {Liu, Jing and Rao, Bhaskar D},
  - title = {Robust PCA via $$\backslash$ell \_ $\{$0$\}$ $-$$\backslash$ell \_ $\{$1$\}$ $ Regularization},
  - journal = {IEEE Transactions on Signal Processing},
  - year = 2018,
  - volume = 67,
  - number = 2,
  - pages = {535--549},
  - publisher = {IEEE}
  - }
- Jing Liu, Pamela C Cosman and Bhaskar D Rao, "Robust Linear Regression via ℓ0 Regularization", IEEE Transactions on Signal Processing, Vol. 66, No. 3, pp. 698-713, 2017.
  BibTeX
  - @Article{liu2017robust,
  - author = {Liu, Jing and Cosman, Pamela C and Rao, Bhaskar D},
  - title = {Robust Linear Regression via $$\backslash$ell\_0 $ Regularization},
  - journal = {IEEE Transactions on Signal Processing},
  - year = 2017,
  - volume = 66,
  - number = 3,
  - pages = {698--713},
  - publisher = {IEEE}
  - }
Software & Data Downloads
Videos

[AAAI 2026] LatentLLM: Activation-Aware Transform to Multi-Head Latent Attention

[NeurIPS 2024] MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track

[NeurIPS 2024] MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track