Jun Qi is a research assistant professor of computer science at Hong Kong Baptist University and an affiliated associate professor in the Department of Electronic Engineering at Fudan University. He earned his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2022. Before academia, he was a Research Software Development Engineer in the Deep Learning Technology Center at Microsoft Research, following a Master's in Electrical Engineering from the University of Washington in 2018. His research focuses on Quantum Machine Learning Theory and Tensor Network Optimization for Machine Learning Systems, supported by research grants from the Hong Kong Research Grants Council and the Hong Kong Research Impact Fund. Dr. Qi's first-authored publications have been published in prestigious Nature Portfolio journals and top-tier IEEE Transactions, with over 1800 citations on Google Scholar. His academic achievements include winning first prize in the Xanadu AI Quantum Machine Learning Competition in 2019 and having a signal processing letter paper nominated as a candidate for the best IEEE Signal Processing paper award in 2024 and 2025. Additionally, he has chaired special sessions on Quantum Machine Learning at ICASSP'23, ICASSP'24, and QCE'24, and delivered tutorials on Quantum Computing and Machine Learning at IJCAI'21, ICASSP'22, ICASSP'24, and QCE'24. He was also an invited keynote speaker at the IJCAI workshop on Quantum Machine Intelligence in 2024.
Hao Luo is pursuing his Master's in Electronic Engineering Department at Fudan University and is a visiting research assistant in Computer Science at Hong Kong Baptist University. He earned his Bachelor's in Electronic Information Engineering from the University of Electronic Science and Technology of China in 2024, where he researched quantum computing and wireless communications. His current research primarily focuses on the intersection of quantum computing and artificial intelligence, particularly leveraging quantum algorithms to explore potential quantum advantages for real-world problems.
Dr. Huck Yang is a Senior Research Scientist at Nvidia Research, focusing his research on parameter-efficient adaptation and aligning large pre-trained models. He serves as a special session co-chair for ICASSP 2024 on "In-Context Learning for speech processing". He has given tutorial presentations at ICASSP 2024, 2023, and 2022, as well as ASRU 2023 and Interspeech 2023, focusing on parameter-efficient adaptation for speech processing and acoustic modeling. He obtained his Ph.D. and M.Sc. from the Georgia Institute of Technology, with a Wallace H. Coulter Fellowship, in Atlanta, GA, USA, under the advisement of Prof. Chin-Hui Lee. He received his B.Sc. from National Taiwan University. Previously, he has worked as a research intern at Google Bard, DeepMind, Amazon Alexa, Hitachi Central Lab, and TSMC, and received Best Reproducible System awards and Judge's awards in DCASE challenges in 2020 and 2021, as well as a Best Student Paper Award Nomination at Interspeech 2023.
Prof. Sabato Marco Siniscalchi
Prof. Sabato Marco Siniscalchi is a prominent researcher in speech processing, deep learning, and quantum machine learning. His work has significantly advanced robust speech recognition through neural network adaptation techniques and speech attribute detection using hierarchical models. He has recently explored quantum neural architectures for speech applications, bridging the gap between classical deep learning and quantum computing. With academic appointments at the University of Palermo and NTNU, and prior industrial experience at Apple’s Siri team, he combines theoretical rigor with practical impact. His leadership in the IEEE speech community and collaborations with global researchers further underscore his influence in the field.
Prof. Javier Tejedor received a B.Sc. degree in computer engineering and M.Sc. and Ph.D. degrees in computer and telecommunication engineering from Universidad Autónoma de Madrid in 2002, 2005, and 2009, respectively. Since 2023, he has been an Associate Professor at Universidad San Pablo CEU, Madrid, Spain. His main interests are speech indexing and retrieval, spoken term detection, large vocabulary continuous speech recognition, and pattern recognition applied to pipeline monitoring systems and biomedical signals.