Minje Kim, Ph.D.

Bio: Minje Kim is an assistant professor in the Dept. of Intelligent Systems Engineering at Indiana University, where he leads his research group, Signals and AI Group in Engineering (SAIGE). He is also an Amazon Visiting Academic, consulting for Amazon Lab126. At IU, he is affiliated with various programs and labs such as Data ScienceCognitive ScienceDept. of StatisticsCenter 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 at 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. His journal article was selected for 2020 IEEE SPS Best Paper Award. He is an IEEE Senior Member and also an elected member of the IEEE Audio and Acoustic Signal Processing Technical Committee (2018-2020 and 2021-2023). 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. 

Ph.D. Candidates

Kai Zhen
(Computer Science and Cognitive Science)

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.
Email: zhenk-at-iu-dot-edu

Sunwoo Kim
(Intelligent Systems Engineering)

Bio: I am a PhD student studying in ISE. Before IU, I lived in Singapore and then studied Physics at UIUC. Currently, my research interests are in end-to-end systems, latent feature representations, and attention mechanisms. On my free time, I like to watch TV (too much), read books (epics), play board games, and go out to eat. I love meeting people and doing things, have nice conversations so please don’t hesitate to ask. 🙂
Email: kimsunw-at-indiana-dot-edu

Ph.D. Students

Aswin Sivaraman
(Intelligent Systems Engineering)

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.
Email: asivara-at-iu-edu

R. David Badger
(Intelligent Systems Engineering)

Bio: I am a PhD student at IU ISE. I received my BSEE from the University of Kentucky and MS in ECE from IUPUI. My research interests include applying ML algorithms to solve RF time/frequency domain problems as well as challenges in the audio spectrum. 
Email: rdbadger-at-iu-edu

Haici Yang
(Intelligent Systems Engineering)

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 in music analysis and generation. A crazy musical instrument learner and a passionate runner. Love outdoor activities and non-fictional books.

Email: hy17-at-iu-edu

Darius Petermann
(Intelligent Systems Engineering)

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.

Email: daripete-at-indiana-dot-edu


  • Sanna Wager
    • Ph.D. in Informatics (Spring 2021)
    • Dissertation: “A Data-Driven Pitch Correction Algorithm for Singing Voice”
      with the committee: Minje Kim (chair), Christopher Raphael (IU CS), Donald Williamson (IU CS), and Daniel McDonald (U. of British Columbia Statistics)
    • Now at Amazon as an Applied Scientist
    • Homepage:

  • Mrinmoy Maity
    • MS in Intelligent Systems Engineering (Spring 2019)
    • Now at Microsoft as a Data Scientist