Matthew Brand

Matthew Brand
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  • Biography

    Matt develops and analyzes optimization algorithms for problems in logistics, control, perception, data-mining, and learning. Notable results include methods for parallel solution of quadratic programs, recomposing photos by re-arranging pixels, nonlinear dimensionality reduction, online singular value decomposition, 3D shape-from-video, and learning concise models of data. In addition to academic "best paper" awards, this work has garnered several industrial awards for commercialized technologies.

  • 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 Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
    •  

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  • Research Highlights

  • MERL Publications

    •  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}
      • }
    •  Basu, S., Lohit, S., Brand, M., "G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups", Transactions on Machine Learning Research (TMLR), May 2025.
      BibTeX TR2025-056 PDF Software
      • @article{Basu2025may,
      • author = {Basu, Sourya and Lohit, Suhas and Brand, Matthew},
      • title = {{G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups}},
      • journal = {Transactions on Machine Learning Research (TMLR)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-056}
      • }
    •  Pan, C."., Brand, M., "Inverse Design of Multilayer Broadband “RGBP” Freeform Metalens for Dual-Functional Color-sorting and Polarization Imaging", Conference on Lasers and Electro-Optics (CLEO), May 2025.
      BibTeX TR2025-055 PDF
      • @inproceedings{Pan2025may,
      • author = {Pan, Cindy "Hsin" and Brand, Matthew},
      • title = {{Inverse Design of Multilayer Broadband “RGBP” Freeform Metalens for Dual-Functional Color-sorting and Polarization Imaging}},
      • booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-055}
      • }
    •  Chen, X., Wang, Y., Brand, M., Wang, P., Liu, J., Koike-Akino, T., "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2024-157 PDF Presentation
      • @inproceedings{Chen2024dec,
      • author = {Chen, Xiangyu and Wang, Ye and Brand, Matthew and Wang, Pu and Liu, Jing and Koike-Akino, Toshiaki},
      • title = {{Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation}},
      • booktitle = {Workshop on Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning at Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-157}
      • }
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  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Metalens 3D-From-Polarization Camera"
      Inventors: Brand, Matthew E.; Kuang, Zeyu
      Patent No.: 12,250,442
      Issue Date: Mar 11, 2025
    • Title: "System and Method for Generating Optimal Lattice Tool Paths"
      Inventors: Brand, Matthew E.
      Patent No.: 11,392,105
      Issue Date: Jul 19, 2022
    • Title: "Machine Learning via Double Layer Optimization"
      Inventors: Zhang, Ziming; Brand, Matthew E.
      Patent No.: 11,170,301
      Issue Date: Nov 9, 2021
    • Title: "Uniform-irradiance extended-source freeforms"
      Inventors: Brand, Matthew E.; Birch, Daniel
      Patent No.: 10,995,932
      Issue Date: May 4, 2021
    • Title: "Methods and Systems for Freeform Irradiance Tailoring for Light Fields"
      Inventors: Brand, Matthew E.; Birch, Daniel
      Patent No.: 10,837,621
      Issue Date: Nov 17, 2020
    • Title: "Compound Optics with Freeform Optical Surface"
      Inventors: Brand, Matthew E.
      Patent No.: 10,234,689
      Issue Date: Mar 19, 2019
    • Title: "Freeform Optical Surface for Producing Sharp-Edged Irradiance Patterns"
      Inventors: Brand, Matthew E.
      Patent No.: 10,119,679
      Issue Date: Nov 6, 2018
    • Title: "Tailored Freeform Optical Surface"
      Inventors: Brand, Matthew E.; Aksoylar, Aydan
      Patent No.: 9,869,866
      Issue Date: Jan 16, 2018
    • Title: "Method for Determining a Sequence for Drilling Holes According to a Pattern using Global and Local Optimization"
      Inventors: Garaas, Tyler W; Brand, Matthew E.
      Patent No.: 9,703,915
      Issue Date: Jul 11, 2017
    • Title: "MPC controller using parallel quadratic programming"
      Inventors: Di Cairano, Stefano; Brand, Matthew E.
      Patent No.: 9,618,912
      Issue Date: Apr 11, 2017
    • Title: "Method for Generating Representations Polylines Using Piecewise Fitted Geometric Primitives"
      Inventors: Brand, Matthew E.; Marks, Tim; MV, Rohith
      Patent No.: 9,613,443
      Issue Date: Apr 4, 2017
    • Title: "Method for Generating Trajectory for Numerical Control Process"
      Inventors: Brand, Matthew E.; Agrawal, Amit K.; Erdim, Huseyin
      Patent No.: 9,513,623
      Issue Date: Dec 6, 2016
    • Title: "System and Method for Planning a Radiation Therapy Treatment"
      Inventors: Brand, Matthew E.
      Patent No.: 9,251,302
      Issue Date: Feb 2, 2016
    • Title: "Method and System for Cutting Features From Sheet Materials With a Laser Cutter According to a Pattern"
      Inventors: Garaas, Tyler W.; Brand, Matthew E.; Josef, Cibulka
      Patent No.: 9,248,525
      Issue Date: Feb 2, 2016
    • Title: "Method for Reconstructing 3D Lines from 2D Lines in an Image"
      Inventors: Ramalingam, Srikumar; Brand, Matthew E.
      Patent No.: 9,183,635
      Issue Date: Nov 10, 2015
    • Title: "Determining Trajectories of Redundant Actuators Jointly Tracking Reference Trajectory"
      Inventors: Shilpiekandula, Vijay; Brand, Matthew E.; Srikanth, Manohar; Bortoff, Scott A.
      Patent No.: 9,170,580
      Issue Date: Oct 27, 2015
    • Title: "System and Method for Controlling Machines According to Pattern of Contours"
      Inventors: Brand, Matthew E.
      Patent No.: 9,104,192
      Issue Date: Aug 11, 2015
    • Title: "Method and System for Detouring Around Features Cut From Sheet Materials with a Laser Cutter According to a Pattern"
      Inventors: Garaas, Tyler W.; Brand, Matthew E.
      Patent No.: 9,046,888
      Issue Date: Jun 2, 2015
    • Title: "Method for Scheduling Cars in Elevator Systems to Minimizes Round-Trip Times"
      Inventors: Brand, Matthew E.
      Patent No.: 8,950,555
      Issue Date: Feb 10, 2015
    • Title: "Method for Performing Image Processing Applications Using Quadratic Programming"
      Inventors: Brand, Matthew E.; Chen, Dongui
      Patent No.: 8,761,533
      Issue Date: Jun 24, 2014
    • Title: "Method for Solving Control Problems"
      Inventors: Brand, Matthew E.; Yao, Chen; Shilpiekandula, Vijay
      Patent No.: 8,554,343
      Issue Date: Oct 8, 2013
    • Title: "Method for Optimization Radiotherapy Particle Beams"
      Inventors: Brand, Matthew E.
      Patent No.: 8,492,735
      Issue Date: Jul 23, 2013
    • Title: "Motion Planning for Elevator Cars Moving Independently in One Elevator Shaft"
      Inventors: Brand, Matthew E.
      Patent No.: 8,424,651
      Issue Date: Apr 23, 2013
    • Title: "Motion Planning for Elevator Cars Moving Independently in One Elevator Shaft"
      Inventors: Brand, Matthew E.
      Patent No.: 8,424,650
      Issue Date: Apr 23, 2013
    • Title: "Content Aware Resizing of Images and Videos"
      Inventors: Brand, Matthew E.; Shamir, Ariel; Rubinstein, Michael; Avidan, Shmuel
      Patent No.: 8,380,010
      Issue Date: Feb 19, 2013
    • Title: "Method and System for Localizing in Urban Environments From Omni-Direction Skyline Images"
      Inventors: Ramalingam, Srikumar; Brand, Matthew E.
      Patent No.: 8,311,285
      Issue Date: Nov 13, 2012
    • Title: "Method for Temporally Editing Video"
      Inventors: Brand, Matthew E.
      Patent No.: 8,290,298
      Issue Date: Oct 16, 2012
    • Title: "Method for Editing Images and Videos"
      Inventors: Brand, Matthew E.
      Patent No.: 8,290,297
      Issue Date: Oct 16, 2012
    • Title: "Method for Determining a Location From Images Acquired of an Environment with an Omni-Directional Camera"
      Inventors: Ramalingam, Srikumar; Brand, Matthew E.; Bouaziz, Sofien
      Patent No.: 8,249,302
      Issue Date: Aug 21, 2012
    • Title: "Method and Apparatus for Touching-Up Images"
      Inventors: Brand, Matthew E.; Pletscher, Patrick A.
      Patent No.: 8,160,396
      Issue Date: Apr 17, 2012
    • Title: "Resource Allocation for Rateless Transmissions"
      Inventors: Brand, Matthew E.
      Patent No.: 8,155,048
      Issue Date: Apr 10, 2012
    • Title: "Method for Routing Packets in Wireless Ad-Hoc Networks withProbabilistic Delay Guarantees"
      Inventors: Molisch, Andreas F.; Brand, Matthew E.; Maymounkov, Petar B.
      Patent No.: 8,040,810
      Issue Date: Oct 18, 2011
    • Title: "Method for Routing Packets in Ad-Hoc Networks with Partial Channel State Information"
      Inventors: Molisch, Andreas F.; Brand, Matthew E.
      Patent No.: 7,822,029
      Issue Date: Oct 26, 2010
    • Title: "Method for Finding Minimal Cost Paths under Uncertainty"
      Inventors: Nikolova, Evdokia V.; Brand, Matthew E.
      Patent No.: 7,756,021
      Issue Date: Jul 13, 2010
    • Title: "Method and System for Determining Instantaneous Peak Power Consumption in Elevator Banks"
      Inventors: Brand, Matthew E.; Nikovski, Daniel N.
      Patent No.: 7,743,890
      Issue Date: Jun 29, 2010
    • Title: "Method for Finding Optimal Paths Using a Stochastic NetworkModel"
      Inventors: Mitzenmacher, Michael D.; Brand, Matthew E.; Nikolova, Evdokia V.
      Patent No.: 7,573,866
      Issue Date: Aug 11, 2009
    • Title: "System and Method for Scheduling Elevator Cars Using Pairwise Delay Minimization"
      Inventors: Nikovski, Daniel N.; Brand, Matthew E.; Ebner, Dietmar
      Patent No.: 7,546,905
      Issue Date: Jun 16, 2009
    • Title: "System and Method for Scheduling Elevator Cars Using Branch-and-Bound"
      Inventors: Brand, Matthew E.; Nikovski, Daniel N.; Ebner, Dietmar
      Patent No.: 7,484,597
      Issue Date: Feb 3, 2009
    • Title: "On-Line Recommender System"
      Inventors: Brand, Matthew E.
      Patent No.: 7,475,027
      Issue Date: Jan 6, 2009
    • Title: "Method for Generating a Low-Dimensional Representation of High-Dimensional Data"
      Inventors: Brand, Matthew E.
      Patent No.: 7,412,098
      Issue Date: Aug 12, 2008
    • Title: "Incremental Singular Value Decomposition of Incomplete Data"
      Inventors: Brand, Matthew E.
      Patent No.: 7,359,550
      Issue Date: Apr 15, 2008
    • Title: "Variable Multilinear Models for Facial Synthesis"
      Inventors: Brand, Matthew E.
      Patent No.: 7,133,048
      Issue Date: Nov 7, 2006
    • Title: "Method and System for Scheduling Cars in Elevator Systems Considering Existing and Future Passengers"
      Inventors: Brand, Matthew E.; Nikovski, Daniel N.
      Patent No.: 7,014,015
      Issue Date: Mar 21, 2006
    • Title: "Method for Determining Poses of Sensors"
      Inventors: Brand, Matthew E.
      Patent No.: 7,006,944
      Issue Date: Feb 28, 2006
    • Title: "Modeling Shapes, Motions, Flexions and Textures of Non-Rigid 3D Objects Directly from Video"
      Inventors: Brand, Matthew E.
      Patent No.: 7,006,683
      Issue Date: Feb 28, 2006
    • Title: "Method for Mapping High-Dimensional Samples to Reduced-Dimensional Manifolds"
      Inventors: Brand, Matthew E.
      Patent No.: 6,947,042
      Issue Date: Sep 20, 2005
    • Title: "Rendering Deformable 3D Models Recovered from Videos"
      Inventors: Brand, Matthew E.
      Patent No.: 6,873,724
      Issue Date: Mar 29, 2005
    • Title: "Analysis, Synthesis and Control of Data Signals with Temporal Textures Using a Linear Dynamic System"
      Inventors: Brand, Matthew E.
      Patent No.: 6,864,897
      Issue Date: Mar 8, 2005
    • Title: "Optimal Parking of Free Cars in Elevator Group Control"
      Inventors: Brand, Matthew E.; Nikovski, Daniel N.
      Patent No.: 6,808,049
      Issue Date: Oct 26, 2004
    • Title: "Method for Generating Realistic Facial Animation Directly from Speech Utilizing Hidden Markov Models"
      Inventors: Brand, Matthew E.
      Patent No.: 6,735,566
      Issue Date: May 11, 2004
    • Title: "Method and System for Dynamic Programming of Elevators for Optimal Group Elevator Control"
      Inventors: Brand, Matthew E.; Nikovski, Daniel N.
      Patent No.: 6,672,431
      Issue Date: Jan 6, 2004
    • Title: "Method for Acquiring Static and Dynamic Super-Resolution Texture Maps from Video"
      Inventors: Brand, Matthew E.
      Patent No.: 6,650,335
      Issue Date: Nov 18, 2003
    • Title: "Method for Designing Optimal Single Pointer Predictive Keyboards and Apparatus Therefore"
      Inventors: Brand, Matthew E.
      Patent No.: 6,646,572
      Issue Date: Nov 11, 2003
    • Title: "Method for Predicting Keystroke Characters on Single Pointer Keyboards and Apparatus Therefore"
      Inventors: Brand, Matthew E.
      Patent No.: 6,621,424
      Issue Date: Sep 16, 2003
    • Title: "Method for Inferring Target Paths from Related Cue Paths"
      Inventors: Brand, Matthew E.
      Patent No.: 6,459,808
      Issue Date: Oct 1, 2002
    • Title: "System for Having Concise Models from a Signal Utilizing a Hidden Markov Model"
      Inventors: Brand, Matthew E.
      Patent No.: 6,212,510
      Issue Date: Apr 3, 2001
    • Title: "Markov Model Discriminator Using Negative Examples"
      Inventors: Brand, Matthew E.
      Patent No.: 6,112,021
      Issue Date: Aug 29, 2000
    See All Patents for MERL