Jing Liu

Jing Liu
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  • 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 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; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; 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
    •  
    •  NEWS    MERL Papers and Workshops at AAAI 2025
      Date: February 25, 2025 - March 4, 2025
      Where: The Association for the Advancement of Artificial Intelligence (AAAI)
      MERL Contacts: Ankush Chakrabarty; Toshiaki Koike-Akino; Jing Liu; Kuan-Chuan Peng; Diego Romeres; Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Optimization
      Brief
      • MERL researchers presented 2 conference papers, 2 workshop papers, and co-organized 1 workshop at the AAAI 2025 conference, which was held in Philadelphia from Feb. 25 to Mar. 4, 2025. AAAI is one of the most prestigious and competitive international conferences in artificial intelligence (AI). Details of MERL contributions are provided below.

        - AAAI Papers in Main Tracks:

        1. "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage" by M.R.U. Rashid, J. Liu, T. Koike-Akino, Y. Wang, and S. Mehnaz. [Oral Presentation]

        This work proposes a novel unlearning-based model poisoning method that amplifies privacy breaches during fine-tuning. Extensive empirical studies show the proposed method’s efficacy on both membership inference and data extraction attacks. The attack is stealthy enough to bypass detection based defenses, and differential privacy cannot effectively defend against the attacks without significantly impacting model utility.

        Paper: https://www.merl.com/publications/TR2025-017

        2. "User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search" by J.H.S. Ip, A. Chakrabarty, A. Mesbah, and D. Romeres. [Poster Presentation]

        This paper introduces a sample-efficient multi-objective Bayesian optimization method that integrates user preferences with gradient-based search to find near-Pareto optimal solutions. The proposed method achieves high utility and reduces distance to Pareto-front solutions across both synthetic and real-world problems, underscoring the importance of minimizing gradient uncertainty during gradient-based optimization. Additionally, the study introduces a novel utility function that respects Pareto dominance and effectively captures diverse user preferences.

        Paper: https://www.merl.com/publications/TR2025-018

        - AAAI Workshop Papers:

        1. "Quantum Diffusion Models for Few-Shot Learning" by R. Wang, Y. Wang, J. Liu, and T. Koike-Akino.

        This work presents the quantum diffusion model (QDM) as an approach to overcome the challenges of quantum few-shot learning (QFSL). It introduces three novel algorithms developed from complementary data-driven and algorithmic perspectives to enhance the performance of QFSL tasks. The extensive experiments demonstrate that these algorithms achieve significant performance gains over traditional baselines, underscoring the potential of QDM to advance QFSL by effectively leveraging quantum noise modeling and label guidance.

        Paper: https://www.merl.com/publications/TR2025-025

        2. "Quantum Implicit Neural Compression", by T. Fujihashi and T., Koike-Akino.

        This work introduces a quantum counterpart of implicit neural representation (quINR) which leverages the exponentially rich expressivity of quantum neural networks to improve the classical INR-based signal compression methods. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods.

        Paper: https://www.merl.com/publications/TR2025-024

        - AAAI Workshops Contributed by MERL:

        1. "Scalable and Efficient Artificial Intelligence Systems (SEAS)"

        K.-C. Peng co-organized this workshop, which offers a timely forum for experts to share their perspectives in designing and developing robust computer vision (CV), machine learning (ML), and artificial intelligence (AI) algorithms, and translating them into real-world solutions.

        Workshop link: https://seasworkshop.github.io/aaai25/index.html

        2. "Quantum Computing and Artificial Intelligence"

        T. Koike-Akino served a session chair of Quantum Neural Network in this workshop, which focuses on seeking contributions encompassing theoretical and applied advances in quantum AI, quantum computing (QC) to enhance classical AI, and classical AI to tackle various aspects of QC.

        Workshop link: https://sites.google.com/view/qcai2025/
    •  

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  • 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

    •  Koike-Akino, T., Liu, J., Wang, Y., "u-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts", arXiv, June 2025.
      BibTeX arXiv
      • @article{Koike-Akino2025jun2,
      • author = {Koike-Akino, Toshiaki and Liu, Jing and Wang, Ye},
      • title = {{u-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts}},
      • journal = {arXiv},
      • year = 2025,
      • month = jun,
      • url = {https://arxiv.org/abs/2505.18451v1}
      • }
    •  Chen, X., Liu, J., Wang, Y., Brand, M., Wang, P., Koike-Akino, T., "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation, June 2025.
      BibTeX TR2025-079 PDF
      • @inproceedings{Chen2025jun,
      • author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Brand, Matthew and Wang, Pu and Koike-Akino, Toshiaki},
      • title = {{TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-079}
      • }
    •  Koike-Akino, T., Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., "LatentLLM: Attention-Aware Joint Tensor Compression", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
      BibTeX TR2025-075 PDF
      • @inproceedings{Koike-Akino2025jun,
      • author = {Koike-Akino, Toshiaki and Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew},
      • title = {{LatentLLM: Attention-Aware Joint Tensor Compression}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-075}
      • }
    •  Liu, J., Koike-Akino, T., Wang, Y., Mansour, H., Brand, M., "AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent", arXiv, June 2025.
      BibTeX arXiv
      • @article{Liu2025jun,
      • author = {Liu, Jing and Koike-Akino, Toshiaki and Wang, Ye and Mansour, Hassan and Brand, Matthew},
      • title = {{AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent}},
      • journal = {arXiv},
      • year = 2025,
      • month = jun,
      • url = {https://arxiv.org/abs/2506.10205}
      • }
    •  Park, Y.-J., Germain, F.G., Liu, J., Wang, Y., Koike-Akino, T., Wichern, G., Azizan, N., Laughman, C.R., Chakrabarty, A., "Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models", arXiv, May 2025.
      BibTeX arXiv
      • @article{Park2025may,
      • author = {Park, Young-Jin and Germain, François G and Liu, Jing and Wang, Ye and Koike-Akino, Toshiaki and Wichern, Gordon and Azizan, Navid and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {{Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models}},
      • journal = {arXiv},
      • year = 2025,
      • month = may,
      • url = {https://arxiv.org/abs/2506.00630}
      • }
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  • 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}
      • }

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