- CV: PDF
- Homepage: https://minjekim.com
- Google Scholar: link
700 N. Woodlawn Ave.
Luddy Hall Rm 4140
Bloomington, IN 47408
- Extracurricular activities (some music and photos)
Bio: Minje Kim is an associate professor in the Dept. of Intelligent Systems Engineering at Indiana University, where he leads his research group, Signals and AI Group in Engineering (SAIGE), and is affiliated with Luddy AI Center, Data Science, Cognitive Science, Statistics, Center for Machine Learning, and Crisis Technologies Innovation Lab. He is also an Amazon Visiting Academic, working at Amazon Lab126. He earned his Ph.D. in the Dept. of Computer Science at the University of Illinois at Urbana-Champaign. Before joining UIUC, He worked as a researcher at ETRI, a national lab in Korea, from 2006 to 2011. Before then, he received his Master’s and Bachelor’s degrees in the Department of Computer Science and Engineering at POSTECH (Summa Cum Laude) and in the Division of Information and Computer Engineering at Ajou University (with honors) in 2006 and 2004, respectively. During his career as a researcher, he has focused on developing machine learning models for audio signal processing applications. He is a recipient of various awards, including NSF Career Award (2021), IU Trustees Teaching Award (2021), IEEE SPS Best Paper Award (2020), Google and Starkey’s grants for outstanding student papers in ICASSP 2013 and 2014, respectively, and Richard T. Cheng Endowed Fellowship from UIUC in 2011. He is an IEEE Senior Member and also a member of the IEEE Audio and Acoustic Signal Processing Technical Committee (2018-2023). He is serving as Senior Area Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing, Associate Editor for EURASIP Journal of Audio, Speech, and Music Processing, and Consulting Associate Editor for IEEE Open Journal of Signal Processing. He is General Chair of IEEE WASPAA 2023 and also a reviewer, program committee member, or area chair for the major machine learning and signal processing venues, such as NeurIPS, ICML, AAAI, IJCAI, ICLR, ICASSP, Interspeech, ISMIR, IEEE T-ASLP, IEEE SPL, etc. He is on more than 50 patents as an inventor.
Bio: I am a PhD student at Indiana University (IU) hailing from the Chicago suburbs. I received my BS in Electrical Engineering at the University of Illinois at Urbana-Champaign (UIUC). My research focuses on machine learning applications to music and speech processing applications, addressing tasks like noise reduction, dereverberation, style transfer, and diarization. Using deep learning approaches, I incorporate prior knowledge about digital audio and digital signal processing in order to compress neural networks or to boost model efficiency.
Bio: I joined IU ISE and SAIGE in Fall 2019. I feel excited about signal processing with machine learning, especially its potential application to coding, music analysis, and generation. A crazy musical instrument learner and a passionate runner. Love outdoor activities and non-fictional books.
Bio: I am a dual major in Computer Science and Computational Linguistics programs at IU. My research is focused on self-supervised representation learning with applications in speech enhancement and speech recognition. I am passionate about creating speech processing systems for low-resource languages and other resource constricted tasks.
Bio: I am a PhD student as part of the ISE department. I am first and foremost passionate about the branch of machine learning applied to traditional audio signal processing problems. Pursuing my undergraduate and graduate studies in the field of Music Technology has led me to focus my research on audio signals. The bulk of my work has been on audio source separation, content analysis, and information retrieval. I am interested in investigating the design and research of new autonomous machine audition models, including but not limited to speech enhancement and audio source separation ones. With my background in audio engineering and auditory perception, I aspire to bring meaningful contributions to the understanding of sound, from machine listening-related problems to their application toward novel audio processing tools. On my free time, I enjoy bowling as well as spending time with my dog.
Bio: I am a Ph.D. student who joined SAIGE at Indiana University (IU) in 2022. My research interests include transfer learning, metric learning, and causal inference in machine learning. As a student of the ISE department, I am keen on learning modern machine learning algorithms and incorporating them into conventional speech and audio tasks, such as speech enhancement, dereverberation, and synthesis.
- Ph.D. in Informatics (Spring 2021) and minor in Statistics
- Dissertation: “A Data-Driven Pitch Correction Algorithm for Singing Voice” (pdf)
- The committee: Minje Kim (chair), Christopher Raphael (IU CS), Donald Williamson (IU CS), and Daniel McDonald (U. of British Columbia Statistics)
- Homepage: http://homes.sice.indiana.edu/scwager/
- Work at SAIGE: Dr. Sanna Wager began to work with Minje in fall 2016, and joined the group officially in spring 2017. During her Ph.D. study, she wrote five conference papers on various research topics including tensor decomposition-based multhchannel speech dereverberation, robust speech recognition, semi-supervised methods for collecting a large-scale singing performance dataset, and neural pitch correction algorithms for singing voice. She interned at various companies, such as Google, Smule, Spotify, and Amazon. Her dissertation research on neural pitch correction algorithms for singing received extensive media coverage, including BBC, The Times, Daily Mail, the New Scientist Magazine, etc. She is now at Amazon as an Applied Scientist.
- Ph.D. (dual degree) in Computer Science and Cognitive Science (Spring 2021)
- Dissertation: “Neural Waveform Coding: Scalability, Efficiency, and Psychoacoustic Calibration” (pdf)
- The committee: Minje Kim (chair), Robert Goldstone (IU Cognitive Science), Donald Williamson (IU Computer Science), and Shen Yi (U. of Washington, Speech and Hearing Sciences)
- Homepage: http://www.kaizhen.us
- Work at SAIGE: Dr. Kai Zhen joined the lab in fall 2017 and led multiple neural waveform coding projects, which pioneered a new research area. His papers were published in leading signal processing and speech processing conferences and journals, such as Interspeech, ICASSP, IEEE Signal Processing Letters, and IEEE T-ASLP. The Cognitive Science Program at IU recognized his research by awarding the Outstanding Research Award in 2021. During his Ph.D. study, Dr. Zhen interned at LinkedIn (2018 and 2019) and Amazon (2020). During his internship at Amazon, his work on efficient neural ASR systems was selected as one of the 17 best poster presentations out of 180 internship projects. He is now at Amazon as an Applied Scientist.
- Ph.D. in Intelligent Systems Engineering (Spring 2022) and minor in Computer Science
- Dissertation: “Model Compression for Efficient Machine Learning Inference” (pdf)
- The committee: Minje Kim (chair), Peter Todd (IU Cognitive Science), Christopher Raphael (IU Computer Science), and Fan Chen (IU ISE)
- Homepage: https://www.kimsunwoo.com
- Work at SAIGE: Dr. Sunwoo Kim joined the lab in Fall 2017 and has focused on developing efficient machine learning models, such as bitwise neural networks, boosted hashing methods, and knowledge distillation. Out of various papers he published, his ICASSP 2020 paper was recognized as the Best Student Paper runner-up. He interned at Qualcomm (2019) and Amazon (2020 and 2021). He is now at Amazon as an Applied Scientist.
- Ph.D. in Intelligent Systems Engineering (Spring 2022)
- Dissertation: “Open-Source Classification Systems for Frequency-Domain RF Signals: Robust Physical Layer Multi-Sample Rate Processing” (pdf)
- The committee: Minje Kim (chair), Lei Jiang (IU ISE), Lantao Liu (IU ISE), and Ariful Azad (IU ISE)
- Work at SAIGE: Dr. R. David Badger joined the lab in Fall 2018. He has worked on RF signal processing, with a focus on signal compression and classification. He has open-sourced his dataset and deep learning-based RF classification system, which was the first of its kind that aims at multi-class RF signal classification using neural networks. He is now at Naval Surface Warfare Center Crane Division.
- MS in Intelligent Systems Engineering (Spring 2019)
- Now at Microsoft as a Data Scientist