Ohio University

Learning & Intelligent Systems Lab

Reinforcement Learning and Autonomy

DRL

Autonomous Robots & Drones (sponsored by ODHE, AFRL, and Ohio Univ.)


  • Nagura, D., Bihl, T., Liu, J. (2025), Reinforcement Learning with Human Experience (RLHE) for Racing Car Games. 2025 ASEE Conference.
  • Zhang, Y., Liu, J. (2025), From O(n) to O(1): A Novel Learning-based Approach for Path Planning, 2025 ICRAS Conference. Accepted.
  • Nagura, D., Bihl, T., Liu, J. (2024) Boosting Race Car Performance Through Reinforcement Learning from Ai Feedback (RLAIF). [link] [bib]
  • Song, S., Saunders, K., Yue, Y., & Liu, J. (2022). Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning. IEEE ICMLA'22. [pdf]
  • Zhang, Y., Liu, J. (2023). Vertex-based Networks to Accelerate Path Planning Algorithms. IEEE Interntional Workshop on Machine Learning for Signal Processing (MLSP'23) (pp. 1-6), IEEE.
  • Song et al, Vision-based Collision Avoidance through Deep Reinforcement Learning, IEEE NAECON 2021 [pdf]

    • Neuromorphic Computing

    Neuromorphic Computing

    Spiking Neural Nets (sponsored by US Air Force and Ohio Univ.)


  • Abuhajar, N., Wang, Z, Yue, Y, Baltes, M, ... & Liu, J. (2025). Three-stage Hybrid Spiking Neural Networks Fine-tuning for Speech Enhancement. Frontiers in Neuroscience. [pdf]
  • Yue, Y., Baltes, M., Abuhajar, N., ... & Liu, J. (2023). Spiking neural networks fine-tuning for brain image segmentation. Frontiers in Neuroscience, 17. [pdf]
  • Baltes, M., Abujahar, Yue, Y., T., Smith, C. D., Liu, J. (2023). Joint ANN-SNN Co-training for Object Localization and Image Segmentation. IEEE ICASSP'23 [pdf]
  • Marc Batles, Hybrid ANN-SNN Co-Training for Object Localization and Image Segmentation, M.S. thesis, April 2023. [OhioLINK]
  • Yue, Y., Baltes, M., Abujahar, N., Sun, T., Smith, C. D., Bihl, T., & Liu, J. (2023). Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation. IEEE ISBI'23 [pdf]
  • Colton C. Smith, The Evaluation of Current Spiking Neural Network Conversion Methods in Radar Data, M.S. thesis, Aug. 2021. [OhioLINK]

    • Deep Learning

    Brain Analysis

    Brain Analysis (sponsored by Univ of Kentucky, NIH)

  • Yue, Y., Baltes, M., Abuhajar, N., ... & Liu, J. (2023). Spiking neural networks fine-tuning for brain image segmentation. Frontiers in Neuroscience, 17. [pdf]
  • Chen et. al, Hippocampus Segmentation through Multi-view Ensemble ConvNets, ISBI 2017 [link] [pdf]
    • Hobbs et. al, Quad-mesh based radial distance biomarkers for Alzheimer's Disease, ISBI 2016: [link] [pdf]
      • Chen et. al, Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble, MIML 2017 [link] [pdf]

        • Wang et. al, Ensemble of mUlti-sized FCNs to Improve White Matter Lesion Segmentation, MLMI 2018 [link] [pdf]

        • Sun et al., TraceCaps: A Capsule-based Neural Network for Semantic Segmentation [link] [pdf]

          • Digital Pathology

          Digital Pathology (sponsored by Charles River Labs)


        • Wang, J., Liu, J. (2024) Semi-Supervised Learning and Focal Masking for Vessel Segmentation in X-ray Coronary Angiography. IEEE ICMLA'2024. [link] [bib]
        • Wang, J., Liu, J. (2024) Semi-Supervised Learning and Focal Masking for Vessel Segmentation in X-ray Coronary Angiography. IEEE ICMLA'2024. [link] [bib]

        • Wang et. al, Residual Pyramid FCN for Robust Follicle Segmentation. ISBI 2019 [link] [pdf]

          • Zhewei Wang, Fully Convoultional Networks (FCNs) for Medical Image Segmentation, December, 2020, [OhioLINK]

            • Speech Enhancement

            Speech Enhancement (sponsored by Ohio Univ.)


          • Sun et al,, Boosting the Intelligibility of Speech Enhancement Networks Through Self-Supervised Representations), IEEE ICMLA'21, [[pdf]]

            • Abuhajar et al., Individualized Conditioning and Negative Distances for Speaker Separation. IEEE ICMLA'22 [pdf]

              • Abujahar et al, Network Compression and Frame Stitching for Efficient and Robust Speech Enhancement, NAECON 2021 [pdf]

                • Gong et al., Dilated FCN: listening longer to hear better, WASPAA’19 [link] [pdf]

                  • Radar signal processing

                  Radar signal processing (sponsored by USAF STTR Program)


                • Colton C. Smith, The Evaluation of Current Spiking Neural Network Conversion Methods in Radar Data, M.S. thesis, Aug. 2021. [OhioLINK]

                • McGee et. al, Network Fusion for Radar Emitter Detection, NAECON 2021 [link] [pdf]

                  • Smart Airport

                  Smart Airport, runway monitoring (sponsored by FAA)


                • Qin, X., Song, S., Brengman, J., Bartone, C., & Liu, J. (2025). Towards All-time, All-weather FOD dection through Generative AI. 2025 IEEE Conference on Image Processing (ICIP'25), Accepted.
                • Qin, X., Song, S., Brengman, J., Bartone, C., & Liu, J. (2024) Robust FOD Detection using Frame Sequence-based DEtection TRansformer (DETR) (2024). IEEE Conference on Artificial Intelligence. [link] [bib]
                • Song, S., Qin, X., Brengman, J., Bartone, C., & Liu, J. (2023). Holistic FOD Detection Via Surface Map and Yolo Networks. In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE. [pdf] [bib]

                  • Machine Learning

                  Metric Learning

                  Nonlinear Metric Learning (Sponsored by Univ. of Kentucky, NIH)


                • Shi et. al, Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis. Pattern Recognition, 2017 [link] [pdf]

                  • Shi, B., Liu, J. (2018). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Neurocomputing [pdf]

                    • Zhang, P., et al, (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. MICCAI 2017 [pdf ]

                    • Wang, Z., etl al, Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups, ICPR 2018 [pdf]