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:

๐ŸŽ“ 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

๐Ÿง  Research Interests:

  • Spatial Audio & HRTF Personalization ๐ŸŽง

  • Speech Deepfake Detection & Audio Security ๐Ÿ”

  • Multimodal Learning: Audio-Visual & Emotional Speech Synthesis ๐ŸŽฅ๐Ÿ—ฃ๏ธ

๐Ÿ† Honors & Fellowships:

  • IEEE SPS Scholarship (2024)

  • NIJ Graduate Research Fellowship (2023)

  • ICASSP Rising Star in Signal Processing (2023)

  • Open Scholarship Award @ UR (2025)

๐Ÿงช Research & Industry Experience:

  • Dolby Labs ๐ŸŽถ โ€“ Sr. Researcher, Multimodal Spatial Audio

  • Meta Reality Labs ๐Ÿง  โ€“ HRTF Perceptual Learning

  • Microsoft, Tencent, ByteDance, IngenID ๐Ÿ’ผ โ€“ AI R&D Internships

  • Audio Information Research Lab, UR ๐ŸŽ™๏ธ โ€“ Deepfake Detection, AV Speech, HRTF Neural Fields

๐Ÿ“š 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)

๐ŸŽค Talks & Tutorials:

  • Invited speaker at CMU, NII Japan, ISCA SPSC

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

๐ŸŽ“ Teaching & Mentorship:

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

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

๐Ÿ’ผ 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

๐Ÿ’ป Skills:

  • Programming: Python, MATLAB, C, Java

  • Tools: Git, Linux, PyTorch, Slurm

  • Languages: English ๐Ÿ‡บ๐Ÿ‡ธ, Mandarin ๐Ÿ‡จ๐Ÿ‡ณ

๐Ÿƒ Hobbies & More:

  • Half-Marathon Finisher ๐Ÿ…

  • Loves stand-up paddleboarding, travel, badminton ๐ŸŒŠโœˆ๏ธ๐Ÿธ

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. 937โ€“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. 3480โ€“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. 4309โ€“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. 77โ€“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. 4783โ€“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. 782โ€“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.

 

Brahim El Boudani | Engineering | Best Researcher Award

Dr. Brahim El Boudani | Engineering | Best Researcher Award

Lecturer London South Bank University United Kingdom

๐ŸŽ“ Dr. Brahim El Boudani is a passionate researcher and educator specializing in deep learning, virtual reality, and 5G technologies. With a robust technical background in programming and data analysis, he has dedicated his career to academic excellence and innovative research. Currently, a sessional lecturer at London South Bank University, Dr. El Boudani combines teaching with impactful research in fields such as augmented reality and artificial intelligence.

Profile

Google Scholar

Education

๐Ÿ“š Dr. Brahim El Boudani earned his Bachelor of Arts in Modern Letters from Hassan II University, Morocco, and later pursued advanced studies at London South Bank University. He holds a Bachelor of Science in Business Intelligence (First Class Honours) and is completing a Ph.D. on deep learning applications in 5G localization, expected by Summer 2023. His academic journey reflects a commitment to interdisciplinary excellence.

Experience

๐Ÿ’ผ Dr. El Boudani has diverse professional experience, including roles as Workshop Supervisor at Camara Education LTD and Business Intelligence Analyst at London South Bank University. Since 2017, he has been a sessional lecturer, excelling in teaching deep learning, mobile computing, and software development while mentoring students and leading innovative extracurricular projects.

Research Interests

๐Ÿ”ฌ Dr. El Boudaniโ€™s research focuses on integrating deep learning with 5G IoT networks, indoor 3D positioning, and VR applications. His interests also include data warehousing, big databases, and the interplay of AI in mobile computing, highlighting his drive to innovate and address real-world challenges.

Awards and Achievements

๐Ÿ† Dr. El Boudaniโ€™s exemplary academic performance, including achieving First Class Honours in Business Intelligence, has earned him accolades. His impactful research, particularly on deep learning architectures for 5G networks, has positioned him as a thought leader in his domain.

Publications Top Notes:ย 

๐Ÿ“„ Dr. El Boudani has contributed significantly to peer-reviewed journals and conferences. His publications include:

Implementing deep learning techniques in 5G IoT networks for 3D indoor positioning (Sensors, 2020) – Cited by 50 articles.

Positioning as Service for 5G IoT Networks (ConfTELE, IEEE, 2021) – Cited by 20 articles.

SO-KDN: A Self-Organised Knowledge Defined Networks Architecture for Reliable Routing (International Conference on Information Science, 2021) – Cited by 25 articles.