Jinming Liang | Engineering | Best Researcher Award

Mr. Jinming Liang | Engineering | Best Researcher Award

Nanjing University of Aeronautics and Astronautics | China

Mr. Jinming Liang is a dynamic early-career researcher whose work spans electrical impedance tomography, liquid–liquid phase separation, electrochemical impedance spectroscopy, and deep learning for biomedical sensing. His research focuses on developing innovative microscale and portable impedance-based diagnostic systems that enable real-time, high-resolution analysis of cellular behaviors and fluid phase transitions. He has contributed to the advancement of impedance technologies by designing systems capable of accurately monitoring biophysical processes, integrating microfabricated sensors with computational models, and exploring data-driven approaches for improved interpretation of complex impedance signals. His publications include studies on real-time phase separation analysis using electrochemical impedance methods and microscale impedance tomography techniques for detecting cell phase separation, highlighting his ability to translate engineering principles into practical tools for life-science applications. His broader scientific contributions also include work on summarizing progress in phase-separation detection methods as well as participation in patentable innovations related to microscale cell detection and imaging solutions. Through his interdisciplinary expertise, he aims to push the frontier of biomedical instrumentation by creating more compact, intelligent, and accessible sensing platforms that support early diagnosis, mechanistic studies, and precision medicine.

Profile: Orcid

Featured Publications

Liang, J., Zhao, W., Liu, K., Sun, B., Zhu, C., & Yao, J. (2025). Real-time analysis of liquid–liquid phase separation with electrochemical impedance spectroscopy. Microchemical Journal, 116076.

Liang, J., Gao, B., Liu, K., Chai, X., Ji, J., Sun, B., & Yao, J. (2025). Microscale electrical impedance tomography method for cell phase separation detection. IEEE Nanotechnology Magazine.

You Zhang | Engineering | Best Researcher Award

Mr. You Zhang | Engineering | Best Researcher Award

University of Rochester, United States

Dr. You (Neil) Zhang is a Ph.D. candidate in Electrical and Computer Engineering at the University of Rochester, specializing in machine learning for speech, acoustics, and audio signal processing. His research focuses on spatial audio (HRTF personalization), speech anti-spoofing, singing voice deepfake detection, and audio-visual learning. He has held research roles at Dolby, Meta, Microsoft, Tencent, and Bytedance, contributing significantly to areas like perceptual HRTF modeling and audio-visual deepfake detection.

Profile:

🎓 Education:

  • Ph.D. in Electrical & Computer Engineering (Expected 2025)
    University of Rochester

  • M.S., University of Rochester

  • B.Eng., University of Electronic Science & Technology of China

  • Exchange Program, UC Berkeley

🧠 Research Interests:

  • Spatial Audio & HRTF Personalization 🎧

  • Speech Deepfake Detection & Audio Security 🔐

  • Multimodal Learning: Audio-Visual & Emotional Speech Synthesis 🎥🗣️

🏆 Honors & Fellowships:

  • IEEE SPS Scholarship (2024)

  • NIJ Graduate Research Fellowship (2023)

  • ICASSP Rising Star in Signal Processing (2023)

  • Open Scholarship Award @ UR (2025)

🧪 Research & Industry Experience:

  • Dolby Labs 🎶 – Sr. Researcher, Multimodal Spatial Audio

  • Meta Reality Labs 🧠 – HRTF Perceptual Learning

  • Microsoft, Tencent, ByteDance, IngenID 💼 – AI R&D Internships

  • Audio Information Research Lab, UR 🎙️ – Deepfake Detection, AV Speech, HRTF Neural Fields

📚 Selected Publications:

  • IEEE T-MM, SPL, ICASSP, Interspeech, NAACL

  • Co-organizer of SVDD Challenge at SLT 2024 & MIREX 2024

  • Contributor to Handbook of Biometric Anti-spoofing (Springer)

🎤 Talks & Tutorials:

  • Invited speaker at CMU, NII Japan, ISCA SPSC

  • Tutorials @ ASA, ICME, AES (Topics: HRTF, Deepfakes, ML for Acoustics)

🎓 Teaching & Mentorship:

  • TA for Machine Learning, Audio Signal Processing, Random Processes

  • Mentored 10+ undergrad and graduate students in UR, Tsinghua, UESTC

💼 Professional Service:

  • Reviewer for IEEE TASLP, TPAMI, ICASSP, Interspeech, CVPR Workshops

  • Member: IEEE, ASA, ACM, AES

  • DEI Committee @ UR ECE | Organizer of AR/VR Events

💻 Skills:

  • Programming: Python, MATLAB, C, Java

  • Tools: Git, Linux, PyTorch, Slurm

  • Languages: English 🇺🇸, Mandarin 🇨🇳

🏃 Hobbies & More:

  • Half-Marathon Finisher 🏅

  • Loves stand-up paddleboarding, travel, badminton 🌊✈️🏸

Google Scholar Citation Metrics:

  • Citations: 751 (All time) | 751 (Since 2020)

  • h-index: 12 (All time) | 12 (Since 2020)

  • i10-index: 14 (All time) | 14 (Since 2020)

Publication Top Notes:

  1. One-class Learning Towards Synthetic Voice Spoofing Detection
    Y. Zhang, F. Jiang, Z. Duan
    IEEE Signal Processing Letters, vol. 28, pp. 937–941, 2021.

  2. Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
    S.E. Eskimez, Y. Zhang, Z. Duan
    IEEE Transactions on Multimedia, vol. 24, pp. 3480–3490, 2021.

  3. UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021
    X. Chen, Y. Zhang*, G. Zhu*, Z. Duan
    ASVspoof 2021 Workshop, 2021.

  4. SingFake: Singing Voice Deepfake Detection
    Y. Zang, Y. Zhang*, M. Heydari, Z. Duan
    IEEE ICASSP, 2024.

  5. An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
    Y. Zhang, G. Zhu, F. Jiang, Z. Duan
    Interspeech, pp. 4309–4313, 2021.

  6. SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing
    S. Ding, Y. Zhang, Z. Duan
    IEEE ICASSP, 2023.

  7. A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification
    Y. Zhang, G. Zhu, Z. Duan
    Odyssey: The Speaker and Language Recognition Workshop, pp. 77–84, 2022.

  8. Global HRTF Personalization Using Anthropometric Measures
    Y. Wang, Y. Zhang, Z. Duan, M. Bocko
    Audio Engineering Society (AES) 150th Convention, 2021.

  9. Rethinking Audio-Visual Synchronization for Active Speaker Detection
    A. Wuerkaixi, Y. Zhang, Z. Duan, C. Zhang
    IEEE MLSP, 2022.

  10. CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
    Y. Zang, J. Shi, Y. Zhang, et al.
    Interspeech, pp. 4783–4787, 2024.

  11. VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music
    J. Shi, H. Shim, J. Tian, Y. Zhang, et al.
    NAACL (Demo Track), 2025.

  12. HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields
    Y. Zhang, Y. Wang, Z. Duan
    IEEE ICASSP, 2023.

  13. SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge
    Y. Zhang, Y. Zang, J. Shi, R. Yamamoto, T. Toda, Z. Duan
    IEEE SLT, pp. 782–787, 2024.

  14. DyViSE: Dynamic Vision-Guided Speaker Embedding for Audio-Visual Speaker Diarization
    A. Wuerkaixi, K. Yan, Y. Zhang, Z. Duan, C. Zhang
    IEEE MMSP, 2022.

  15. Predicting Global Head-Related Transfer Functions from Scanned Head Geometry Using Deep Learning and Compact Representations
    Y. Wang, Y. Zhang, Z. Duan, M. Bocko
    arXiv preprint, arXiv:2207.14352, 2022.

  16. Learning Arousal-Valence Representation from Categorical Emotion Labels of Speech
    E. Zhou, Y. Zhang, Z. Duan
    IEEE ICASSP, 2024.

  17. SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
    Y. Zhang, Y. Zang, J. Shi, et al.
    arXiv preprint, arXiv:2405.05244, 2024.

  18. ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
    X. Wang, H. Delgado, Y. Zhang, et al.
    Computer Speech & Language, 2025.

  19. Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions
    K. Zhou, Y. Zhang, S. Zhao, et al.
    arXiv preprint, arXiv:2409.16681, 2024.

  20. Mitigating Cross-Database Differences for Learning Unified HRTF Representation
    Y. Wen, Y. Zhang, Z. Duan
    IEEE WASPAA, 2023.

 

Yue-Der Lin | Engineering | Best Researcher Award

Prof. Yue-Der Lin | Engineering | Best Researcher Award

Professor Feng Chia University/Department of Automatic Control Engineering Taiwan

🎓 Dr. Yue-Der Lin is a Professor at the Department of Automatic Control Engineering, Feng Chia University, Taiwan. With extensive experience in biomedical engineering, he specializes in biopotential amplifier design, adaptive signal processing, and EEG signal analysis. His research bridges biomedical and electronic engineering, contributing significantly to advancements in biopotential measurement and bioinformatics.

Profile

Scopus

Google Scholar

Education

📘 Ph.D. in Electrical Engineering (Biomedical Engineering Program)
National Taiwan University, Taiwan (1992–1998)
📘 Master’s in Electrical Engineering (Biomedical Engineering & Electronic Circuit Programs)
National Taiwan University, Taiwan (1987–1989)
📘 Bachelor’s in Electrical Engineering (Control Program)
Chung Yuan Christian University, Taiwan (1983–1987)

Professional Experience

💼 Professor, Department of Automatic Engineering, Feng Chia University (2019–Present)
💼 Director, Department of Automatic Engineering (2016–2017)
💼 Director, Master’s Program of Biomedical Informatics and Biomedical Engineering (2014–2017)
💼 Visiting Scholar, Department of Electrical and Computer Engineering (2007)

Research Interests

🔬 Dr. Lin’s research focuses on:

  • Biopotential amplifier design
  • Adaptive signal processing
  • Biomedical signal analysis (EEG, EMG)
  • Bioinformatics systems
  • Advanced imaging techniques in biomedical engineering

Honors and Awards

🏆 Excellent Research Award, Feng Chia University (2005, 2007–2009, 2011–2019, 2021–2024)
🏆 Distinguished Scholar Award, National Science and Technology Council, Taiwan (2014–2015, 2018–2020, 2022–2024)
🏆 Albert Nelson Marquis Lifetime Achievement Award (2017–2018, 2020)
🏆 Marquis Who’s Who in the World (2008–2010, 2012–2020)
🏆 IBC Leading Engineers of the World (2008, 2013, 2015)

Publications Top Notes:

📚 Below are Dr. Yue-Der Lin’s selected publications with publication year, journal, and citation details:

Lin, Y.-D., Chong, F.-C., Sung, S.-M., et al. “The calculation of complexity in normal and apoplectic EEG signals.” Journal of the Chinese Institute of Engineers, Vol. 21, No. 5, pp. 585-594, 1998. Cited by 56

Lin, Y.-D., Wu, C.-P., et al. “An active comb filter structure for harmonic interference removal.” Journal of the Chinese Institute of Engineers, Vol. 21, No. 5, pp. 605-610, 1998. Cited by 40

Lin, Y.-D., et al. “Preamplifier with a second-order high-pass filtering characteristic.” IEEE Transactions on Biomedical Engineering, Vol. 46, No. 5, pp. 609-612, 1999. Cited by 95

Lin, Y.-D., et al. “An adaptive power-line interference removal technique for biopotential measurement.” Biomedical Engineering—Applications, Basis and Communications, Vol. 12, No. 1, pp. 24-32, 2000. Cited by 23

Lin, Y.-D., et al. “Comments on ‘Line patterns in the mosaic electric properties of human skin—A cross-correlation study’.” IEEE Transactions on Biomedical Engineering, Vol. 49, No. 3, pp. 274, 2002. Cited by 15

Use of acupressure to improve gastrointestinal motility in women after trans-abdominal hysterectomy

Aminoguanidine prevents the impairment of cardiac pumping mechanics in rats with streptozotocin and nicotinamide‐induced type 2 diabetes

A novel approach for decomposition of biomedical signals in different applications based on data-adaptive Gaussian average filtering