Prospective Students

The Center for Machine Learning at Indiana University brings together over 10 faculty working in areas including theory, reinforcement learning, statistical learning, speech and audio processing, robotics, planning and control, medical image processing, graphical models, computational neuroscience, computer vision, case-based reasoning, and deep learning. 

Together we have at least 10 PhD positions available for Fall 2022 admission. Topics include:

  • Algorithms for ML: scalable (e.g., parallel, distributed round/communication-efficient) algorithms for reinforcement learning, online learning, clustering.
  • Probabilistic ML: graphical models, efficient inference algorithms, applications, and computational/statistical learning theory.
  • AI: probabilistic planning, unsupervised learning, memory augmentation, reinforcement learning, and the connections between planning and probabilistic inference.
  • Robotics: planning, decision-making, and learning methods for autonomous robotic systems, and coordination approaches for distributed multi-robot or swarm systems.
  • Computer Vision: object and action recognition, 3D reconstruction, egocentric computer vision, deep learning, and graphical model inference.
  • ML and DL for speech/music/audio processing: speech enhancement, source separation, privacy and security for speech/audio applications, speech/audio coding, music information retrieval and music signal processing

For all areas ideal candidates should have demonstrable strong math and theory skills and/or excellent programming and system building skills. Background in the relevant areas listed above is of course desirable.

Applicants should have received a Bachelor’s degree in computer science, engineering, statistics, or related fields. 

The Center brings together faculty from different departments across the university, including Computer Science, Intelligent Systems Engineering, and Statistics. Each faculty has a home department (listed on the website above) but can advise students in other departments. Please mention the faculty with whom you would like to work in your Statement of Purpose. You may apply to multiple programs. For best consideration, please submit applications by these dates:

  • Computer Science: Priority deadline: December 1, 2021, Second round: January 1, 2022
  • Intelligent Systems Engineering: Priority deadline: December 1, 2021, Second round: January 1, 2022
  • Statistical Science: Priority deadline: January 15, 2022

For detailed information about the programs and application process, please visit:

Email inquiries: 

  • For information about the admission process or requirements, please write
  • For information about the Center for Machine Learning, please contact
  • For information about specific positions or projects, please write the faculty member directly.

Research Assistant Positions

Medical Image Analysis for Precision Health

As science moves into the big data era, new imaging modalities that can generate large-scale and comprehensive information have emerged. For example, whole slide imaging (WSI) allows for the digitization of an entire tissue specimen composed of tens of thousands of cells in 1 minute, even with a 40X objective; magnetic resonance imaging (MRI), on the other hand, can give us detailed information on the organ/body-level. With these images, we are aiming to develop (multiple-) AI-based analysis systems to help clinicians with automatic, consistent, and accurate decisions. For this position, students are expected to work collaboratively with not only IU medical school but also external medical centers. 

Faculty Contact: Prof. Xuhong Zhang

Neural Speech and Audio Coding

Speech/audio coding has traditionally involved substantial domain-specific knowledge such as speech generation models and psychoacoustics. If you haven’t heard of this concept, don’t worry, because you might be using this technology in your everyday life, e.g., when you are on the phone, listening to the music using your mobile device, watching television, etc. The goal of speech/audio coding is to compress the input signal into a bitstream, whose bitrate is, of course, smaller than the input, and then to be able to recover the original signal out of the code. The reconstructed signal should be as perceptually similar as possible to the original one. In this project, we tackle this area by incorporating scalability, efficiency, and knowledge about human perception (a.k.a. psychoacoustics) into various deep learning systems.

Faculty Contact: Prof. Minje Kim (minje at indiana dot edu)

For more information:

Personalized Speech Enhancement

One of the keys to success in machine learning applications is to improve each user’s personal experience via personalized models. A personalized model can be a more resource-efficient solution than a general-purpose model, too, because it focuses on a particular sub-problem, for which a smaller model architecture can be good enough. However, training a personalized model requires data from the particular test-time user, which are not always available due to their private nature. Furthermore, such data tend to be unlabeled as they can be collected only during the test time, once after the system is deployed to user devices. One could rely on the generalization power of a generic model, but such a model can be too computationally/spatially complex for real-time processing in a resource-constrained device. In this project, we develop algorithms that overcome the lack of labeled personal data in the context of speech enhancement. Our machine learning models will require zero or few data samples from the test-time users, while they can still achieve the personalization goal. Because our research achieves the personalization goal in a privacy-preserving and resource-efficient way, it is a step towards a more available, affordable, and fair AI for society.

Faculty Contact: Prof. Minje Kim (minje at indiana dot edu)

For more information:

Associate Instructor (Teaching Assistant) Positions