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.

 

Jamal Alotaibi | Engineering | Best Researcher Award

Assist. Prof. Dr. Jamal Alotaibi | Engineering | Best Researcher Award

Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Dr. Jamal Alotaibi is an accomplished researcher and educator in the field of Computer Engineering. With expertise in IoT, AI, and security, he has contributed significantly to the advancement of Smart Transportation and Vehicle-to-Vehicle (V2V) communication. Currently serving as the Head of the Computer Engineering Department at Qassim University, his work focuses on secure and efficient computing frameworks for the Internet of Vehicles (IoV).

Profile

Google Scholar

Education 🎓

  • Ph.D. in Computer Engineering (2018 – 2022) – Wayne State University, USA

  • M.Sc. in Electrical and Computer Engineering (2016 – 2017) – Wayne State University, USA

  • B.Sc. in Computer Engineering (2008 – 2013) – Qassim University, KSA

Experience 👨‍🏫

  • Qassim University (2022 – Present) – Assistant Professor, now Head of the Computer Engineering Department (2024–Present)

  • Wayne State University (2016 – 2022) – Research Assistant in IoT and Security Labs

  • STC Company (2013) – Network Engineer

  • Consultations:

    • Ford Motor Company (2020 – 2022) – Embedded Systems Consultant for Electric Vehicles

    • Verizon Company (2021–2022) – V2V Infrastructure Consultant

    • City of Detroit (2021–2023) – IoV Consultant

Research Interests 🔬

  • Internet of Vehicles (IoV) and Fog Computing

  • Software-Defined Networking (SDN) for Smart Transportation

  • Blockchain-based Security Solutions

  • Machine Learning for Secure Communication Systems

Awards 🏆

  • Head of IoT Research Lab – Wayne State University

  • Head of Research Committee – Qassim University (2023 – Present)

Publications Top Notes: 📚

SAFIoV: A Secure and Fast Communication in Fog-Based IoV Using SDN and Blockchain

IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021

Read Here

A Lightweight and Fog-Based Authentication Scheme for Internet-of-Vehicles

IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEM-CON), 2021

Read Here

PPIoV: A Privacy-Preserving Framework for IoV-Fog Using Federated Learning and Blockchain

IEEE World AI IoT Congress, 2022

Read Here

Insight into IoT Applications and Common Practice Challenges

Insight Journal, 2022

Read Here

A hybrid software-defined networking approach for enhancing IoT cybersecurity with deep learning and blockchain in smart cities

SDN-Enabled Efficient Resource Utilization in a Secure, Trustworthy and Privacy Preserving IOV-Fog Environment

Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

🧑‍🏫 Dr. Jingyang Mao is a dedicated lecturer at the School of Electrical and Electronic Engineering, Faculty of Intelligence Technology, Shanghai Institute of Technology. With a Ph.D. in Control Science and Engineering from the University of Shanghai for Science and Technology (2022), he specializes in cutting-edge research on networked control systems and cyber-physical systems. His academic journey also includes a visiting scholar tenure at Louisiana State University, USA (2019–2021). Dr. Mao’s work bridges theoretical innovations with practical applications in modern engineering systems.

Profile

Orcid

Education

🎓 Ph.D. in Control Science and Engineering (2022)

  • University of Shanghai for Science and Technology, Shanghai, China

✈️ Visiting Scholar (2019–2021)

  • Department of Electrical and Computer Engineering, Louisiana State University, USA

Experience

👨‍💻 Lecturer (2022–Present)

  • School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Focus: Cyber-physical systems, networked control, and adaptive filtering

Research Interests

🔍 Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems 🌐
  • Multi-rate systems ⏱️
  • Joint recursive filtering 🔄
  • Unknown input estimation
  • Adaptive event-triggered mechanisms ⚙️

Awards

🏆 Award Nomination: Best Researcher Award
Recognized for groundbreaking contributions to the theory and application of cyber-physical systems.

Publications Top Notes:

📄 “Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms”

Event‐based reduced‐order H∞$H_{\infty }$ estimation for switched complex networks based on T‐S fuzzy model

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Event-Based Distributed Adaptive Kalman Filtering With Unknown Covariance of Process Noises

Brahim El Boudani | Engineering | Best Researcher Award

Dr. Brahim El Boudani | Engineering | Best Researcher Award

Lecturer London South Bank University United Kingdom

🎓 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

📚 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

💼 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

🔬 Dr. El Boudani’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

🏆 Dr. El Boudani’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: 

📄 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.

 

RISHITEJ CHAPARALA | Engineering | Best Researcher Award

Mr. RISHITEJ CHAPARALA | Engineering | Best Researcher Award

Assistant professor Pragati engineering college India

👨‍🎓 Rishitej Chaparala is an Assistant Professor and Ph.D. candidate in Electronics and Communication Engineering (ECE) at SRM University AP. With 6.5 years of teaching experience, he has been an integral part of institutions like TKR College of Engineering, BVC Engineering College, and Kallam Harnadhreddy Institute of Tech & Engineering. Rishitej’s primary research interest lies in plasmonic waveguide and antenna design, spanning the RF to THz spectrum. He is an active member of OPTICA, IEEE, and IETE and has filed multiple patents in the field of sensor systems.

Profile

Scopus

Google scholar

Education 🎓

Rishitej holds a Ph.D. (Full-time) in ECE from SRM University AP, with his synopsis submitted in October 2024. He completed his M.Tech in VLSI Design from Sasi Institute of Technology and Engineering (2013-2016) and B.E. in ECE from GGR College of Engineering, Vellore, in 2011.

Experience 💼

With over 6.5 years in teaching and 3 years in research, Rishitej has held positions in institutions like TKR College of Engineering, BVC Engineering College, Dhanekula Institute of Engineering & Tech, and currently at Pragati Engineering College, Rajahmundry. He has also worked as a Quality Analyst at SERCO Pvt Ltd for a year.

Research Interests 🔬

His research focuses on Plasmonic Waveguide/Antenna Design across the RF to THz spectrum. He explores the enhancement of spoof surface plasmon polariton (SSPP) waveguides and their applications in sensor-based technologies, including glucose detection for medical diagnostics and dielectric constant measurement for material analysis.

Awards 🏆

Rishitej’s work has been recognized at national and international platforms. His research on SSPP waveguides has earned him multiple accolades in conferences and journals. Notable recognitions include contributions to IEEE Sensors Journal, Optical Engineering, and Progress in Electromagnetics Research M.

Publications Top Notes: 📚

Chaparala, R., Shaik Imamvali, & Sreenivasulu Tupakula. “Enhancement of spoof surface plasmon polariton waveguide performance through modified groove width.” Optical Engineering, 63.5 (2024): 055102-055102. DOI: 10.1117/1.OE.63.5.055102

Chaparala, R., Shaik Imamvali, & Sreenivasulu Tupakula. “Spoof surface plasmon polaritons based feeder for a dielectric rod antenna at microwave frequencies.” Progress in Electromagnetics Research M, Vol. 129, 23-32, 2024.

Chaparala, R., et al. “Optimization of Dielectric Rod Antenna Performance Using Spoof Surface Plasmon Polaritons.” Sensors MDPI (Accepted, IF: 3.4).

Imamvali, S., Chaparala, R., Tupakula, S. “Spoof surface plasmon polaritons based detection of glucose in blood phantom for medical diagnosis.” IEEE Sensors Journal (2024, IF: 4.3).

Kolli, V. R., Chaparala, R., Tupakula, S., & Talabattula, S. “A high-sensitive integrated optic serially coupled racetrack ring resonator based pressure sensor.” Optical Materials (2024, Q1, IF: 3.1).