Bio: Minje Kim is an assistant professor in the Dept. of Intelligent Systems Engineering at Indiana University. He leads his research group, Signals and AI Group in Engineering (SAIGE). He is also affiliated with Data Science, Cognitive Science, Dept. of Statistics, Center for Algorithms and Machine Learning, and Crisis Technologies Innovation Lab. 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 in ETRI, a national lab in Korea, from 2006 to 2011. Before then, he received his Bachelor’s and Master’s degrees in the Division of Information and Computer Engineering at Ajou University (with honor) and in the Department of Computer Science and Engineering at POSTECH (Summa Cum Laude) in 2004 and 2006, respectively. During his career as a researcher, he has focused on developing machine learning models for audio signal processing applications, such as speech enhancement, source separation, MPEG audio coding, music information retrieval, etc., stressing their computational efficiency in the resource-constrained environments or in the applications involving multichannel observations. He received Richard T. Cheng Endowed Fellowship from UIUC in 2011. Google and Starkey grants also honored his ICASSP papers as an outstanding student paper in 2013 and 2014, respectively. He is an IEEE Senior Member and also a member of the IEEE Audio and Acoustic Signal Processing Technical Committee. He is serving as an Associate Editor for EURASIP Journal of Audio, Speech, and Music Processing, and as a Consulting Associate Editor for IEEE Open Journal of Signal Processing. He also serves as a reviewer, program committee member, or area chair for major machine learning and signal processing venues, such as NeurIPS, ICML, AAAI, IJCAI, ICLR, ICASSP, ISMIR, IEEE T-ASLP, etc. He is on more than 50 patents as an inventor.
- CV: PDF
- Homepage: https://minjekim.com
- Google Scholar: link
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Bio: I’m in the Music track of the
Bio: I’m working on optimizing lightweight deep neural network-based end-to-end systems for speech/audio coding and enhancement. Some of these methods are found necessary to be integrated by domain knowledge in psychoacoustics and human auditory perception. Maintaining a good work-life balance has always been my target for my talent in table tennis, badminton, and opera singing. Life is complex — it has both real and imaginary parts.
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Bio: I am a
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Bio: I joined IU ISE and SAIGE in Fall 2019. I’m still new to the field but feel excited about signal processing with machine learning, especially its potential application on music analysis and generation. A crazy musical instrument learner and passionate runner. Love outdoor activities and non-fictional books.
- Mrinmoy Maity
- MS in Intelligent Systems Engineering (Spring 2019)
- Now at Microsoft as a Data Scientist