Bio: I’m an assistant professor at Indiana University Bloomington. I’m with the Department of Intelligent Systems Engineering at the School of Informatics, Computing, and Engineering. I earned my PhD in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Before joining UIUC, I worked as a researcher in ETRI, a national lab in Korea, from 2006 to 2011. I received my 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. My research focuses on developing machine learning algorithms applied to audio processing, stressing their computational efficiency in the resource-constrained environments or in the applications involving large unorganized datasets. To this end, I developed some machine learning algorithms, such as manifold preserving topic models, deflation methods for nonnegative matrix factorization, hashing-based speed-up techniques for topic models, component sharing (or co-factorization) for collaborative audio enhancement from a massive set of crowdsourced recordings, and bitwise neural networks where all computations are carried out in an efficient bitwise fashion, etc. I received Richard T. Cheng Endowed Fellowship from UIUC in 2011. Google and Starkey grants also honored my ICASSP papers as the outstanding student papers in 2013 and 2014, respectively. I was selected as an outstanding teaching assistant for the course “Machine Learning for Signal Processing” in the fall 2015, too.
- Homepage: https://minjekim.com
700 N. Woodlawn Ave.
Luddy Hall Rm 4140
Bloomington, IN 47408
- Schedule a meeting with me: http://doodle.com/minje
Bio: I’m in my fourth year of the Music track of the PhD in Informatics and Computing at Indiana University. My adviser is Minje Kim. My undergraduate degree was in Bassoon Performance at Indiana University’s Jacobs School of Music, studying under Kathleen McLean. My undergraduate minor was math. I am interested in how technology influences the way people relate to music, and how we can use technology to develop our understanding of music. Music has been at the front end of technical innovation for thousands of years. Pythagoras described its deep connection to math, architecture, astronomy, and psychology; Beethoven worked with piano makers to develop instruments that could handle his compositions; conductor Herbert von Karajan was among the first to acquire the newest analog recording devices. I strive to promote interaction between the fields of acoustic music and computing so that the recent innovation in the digital world can be applied in the best possible way to music. I hope to 1) develop knowledge of music representation in technology, so that results are aesthetically convincing, 2) give musicians more general and easy access to training in music technology, and 3) encourage the development of music-related technology that helps the youngest generation connect to high-quality music as listeners and performers.
Bio: My research in general is on audio, speech and music signal processing in the current machine learning paradigm. I’m currently working on a psychoacoustically enhanced deep neural network with parameter quantization towards a compact and energy efficient speech denoising autoencoder.
Bio: I am a second year PhD student studying in CS/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 🙂
Bio: I am a first-year
Bio: I am a first year
Bio: Mrinmoy is a Master’s student in ISE at Indiana University Bloomington with a focus on Deep Learning. More specifically, he is interested in optimizing deep neural networks using less computations and limited storage spaces to enable on device operations on embedded devices in real-time. He mostly focuses on applications in audio domain although his research interests are generic enough to be applied to areas like automated driving and natural language processing. He also holds a broader interests in Artificial Intelligence in areas like Generative models and Reinforcement learning.
Bio: MiSuk Lee received the B.S. and M.S. degrees in electronics engineering from Hoseo University, Asan, South Korea, in 1991 and 1993, respectively, and the Ph.D. degree in electrical and electronics engineer- ing from the Korea Advanced Institute of Science and Technology, Daejeon, South Korea, in 2001. Since February 2002, she has been with the Electronics and Technology Research Institute, Dae- jeon, South Korea. Her current research interests include digital speech and audio coding and digi- tal audio signal processing techniques for immersive broadcasting.