Admissions Information

Learn more about the department's admissions process, including our opportunities for diverse students.

Graduate Admissions Details

Thank you for your interest in computer science graduate studies at CU Boulder. The following faculty members and degree programs are recruiting new MS and PhD students for fall 2024. Explore opportunities in computer science education and research at CU Boulder, and learn more about what student life is like in the department.    

We have computer science masters info sessions, CS PhD info sessions, guides to applying to PhD programs at CU Boulder, computer science research lab recruitment sessions where you can talk to lab members and understand more about their work, and computer science research talks where CU Boulder CS researchers explain what they're working on so you can better understand how you might fit into their labs and groups. 

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Graduate Recruitment Events

Degrees and Admissions

MS Degrees Overview

  • Host: Bridget Dooney or Sara Freix

Master's in CS at CU Boulder: General info sessions with

Join Sara Freix or Bridget Dooney to learn more about the Master of Science degrees offered in the department. 

 

 

PhD Degree Overview

  • Host: Rajshree Shrestha

Registration links coming soon:

  • Register for session on Wednesday, October 11 at 9 AM (MST)
  • Register for session on Wednesday, October 11 at 4 PM (MST)
  • Register for session on Tuesday, November 14 at 9 AM (MST)
  • Register for session on Tuesday, November 14 at 4 PM (MST)
  • Register for session on Wednesday, December 13 at 4 PM (MST

 

Learn more about pursuing a PhD in computer science at CU Boulder. 

Master's in Network Engineering Overview

  • Host: Sara Freix

Registration coming soon.

  • Monday, 10/9/2023, 9:00 AM
  • Monday, 10/9/2023, 4:00 PM

Join Network Engineering advisor Sara Freix to learn more about our master's in network engineering. 

Computational Linguistics (CLASIC)

CLASIC, a stand-alone Professional Master of Science degree, provides students with a solid foundation in both linguistics and computer science graduate course work as well as several courses focused on data-driven linguistics, computational linguistics, information processing and Natural Language Processing (NLP). 

 

Hear from Current PhD Students

  • Hosts: Ananya Ganesh and Michael Hoefer

Watch the Recording  View the Presentation Slides

Get advice on your PhD application, directly from two current computer science PhD students! 

What Are Professors Looking For?

  • Host: Dan Larremore

Watch the Recorded Session

Join computer science and BioFrontiers Assistant Professor Dan Larremore for a discussion of how to choose good research questions, as well as what faculty are looking for in PhD or postdoc applicants. 

PhD Application Tips

  • Host: Casey Fiesler 

Watch the Videos

Casey Fiesler, assistant professor in information science and computer science, shares her advice on how to choose the PhD program that's right for you, tips on writing a good statement of purpose and more in this YouTube playlist. 

Applying to the CS PhD

Watch the video

Learn what to consider when applying for a PhD at CU Boulder from fellow PhD students. 

Faculty, Labs and Research Groups

Wireless Interdisciplinary Research Group

Spectrum Sharing for a National Radio Dynamic Zone (NRDZ) video

IWR Lab Overview Video

The Computer Science Department Interdisciplinary Wireless Research Lab focuses on Proximity Wireless Network Communications fundamental and applied research with an emphasis on the 3GPP LTE/5G and IEEE 802.11 Wi-Fi protocols.

The application emphasis is for the support of space agency (NASA, ESA, JAXA, CSA, DLR, CNES, etc.) human exploration missions and for Public Safety. Activities include fundamental research in PHY layer transmission optimization for range and throughput, the application of AI/ML for multi-layer optimization, and the development of a general solution and architecture for Dynamic Spectrum Sharing (DSS). Applied research focuses on standards (CCSDS) specification development and interoperability testing.

Looking for students interested in spectrum engineering, RF sensing and wireless communications.

Hybrid Control Systems Lab

Working towards safer autonomous systems (2023)

Our lab primarily focuses on formal verification and control of (stochastic) hybrid systems. These systems consist of both continuous dynamics, capturing the behavior of physical systems, and discrete dynamics, capturing the behavior of computational devices.

Given the versatility and generality of this modeling framework, the methods employed for their analysis and design hold significant promise in numerous safety-critical applications. Notable examples of these applications encompass autonomous cars, autonomous airplanes, transportation networks, chemical processes, critical infrastructures, robotics, healthcare, and more.

CU Systems Lab

Computer Systems Lab video (2020)

The Computer Systems Lab at CU consists of about 14 faculty whose expertise generally lies in building practical computer systems.

Most of the Systems faculty are co-located in a single large lab with their students. Students and faculty receive funding for and work in areas including large scale network systems; cloud and edge computing; distributed systems; virtualization; wireless and mobile systems including IoT, drones, and sensors; privacy preserving networks; network security and anticensorship; computer architecture security; autonomous and software-defined networks; automotive and telematics systems; social computing; data analytics; distributed and embedded machine learning and computer vision; and cybersafety and cyberbullying. 

Emotive Computing Lab & Institute for Student-AI Teaming

Emotive Computing Lab Overview video

D'Mello's team is interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world tasks.

D'Mello's research is at the intersection of the cognitive, affective, computing, and learning sciences. The team applies insights gleaned from this basic research program to develop intelligent technologies that help people achieve to their fullest potential by coordinating what they think and feel with what they know and do. This research uses a range of techniques such as eye tracking, speech and language processing, physiological sensing, computer vision, time series analyses, discourse modeling, and machine learning. The interaction contexts include educational games, collaborative problem solving, classroom discourse, computerized reading, and workplace activities. Data is collected in the lab, online, in schools, and the workplace. We are always looking forward to working with motivated and dedicated researchers, students (at all levels), and postdocs.

We look for:

  1. a clear alignment of interests
  2. prior research experience in any area (ideally with publications)
  3. excellent collaboration and communication skills
  4. A “can do” attitude that exhibits confidence, enthusiasm (for research), and perseverance in the face of challenges.

If you would like to inquire about joining us, please send us a note with your CV/resume, brief statement of prior work, and how you see yourself contributing to our work. We do receive a lot of requests and only reply if the mutual interest seems to be sufficiently strong (hint: you can demonstrate this by reading some of our recent papers and looking over our current projects).

We are interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world tasks. We apply insights gleaned from this basic research program to develop intelligent technologies that help people achieve to their fullest potential by coordinating what they think and feel with what they know and do.

Autonomous Robotics and Perception Group

Autonomous Robotics and Perception Group at CU Boulder Video

Heckman's group studies probabilistic perception algorithms and estimation theory that enables long-term autonomous operation of mobile robotic systems, particularly in unknown environments.

Living Matter Lab

 Living Matter Lab Overview Video

The Living Matter Lab investigates the extent to which we can change healthcare to make it a personal process.

We are looking for students with backgrounds in Biology, Biochemistry and BioComputing/BioInformatics. The research focuses around microfluidic biochips, devices that enable direct interaction of humans with their microbiome for diagnosis purposes. So far, we have built systems based on biochips to serve as personal laboratories: small portable devices that people can own and use to develop customized bio-protocols ("bio-apps").

Physical Prediction, Inference, and Design Group

The Physical Prediction, Inference and Design group develops methods and community software for solving arising in physical and engineering sciences.

Much of this work has an element of high-performance/parallel computing and numerical analysis to design robust methods and implementations. The research is usually interdisciplinary and collaborative in the sense that group members take classes and engage significantly outside the CS department (e.g., Earth sciences, materials science, aerospace) so as to develop durable, sustainable solutions at appropriate levels of abstraction.

HIRO Group

​ Human Interaction and Robotics Group at CU Boulder

The HIRO group performs research at the intersection of human-robot interaction, artificial intelligence, and robot control & planning. 

 The goal of the group is developing robot technologies that enable close, natural, and extended cooperation with humans. As Director of the HIRO Group, Assistant Professor Alessandro Roncone seeks to lower the barriers to entry for humans to naturally interact with robots, and in doing so he designs robot systems that are able to work with and around people. With a large lab of undergraduate, graduate, and PhD researchers, HIRO Group alumni have gone on to work in industry at companies such as Microsoft, Facebook, Lumen, and Amazon.

GeoHAI

Morteza Karimzadeh is a spatial data scientist, with research and contributions cutting across geographic information retrieval, machine learning, geovisualization, and visual analytics.

Karimzadeh conducts integrative research that brings together data science with social/environmental science to inform best practices for a more sustainable and equitable society. His research primarily focuses on method development, spanning various domains including social media analytics, energy (resilience and production), crisis management, situational awareness, precision agriculture, and digital humanities and beyond.

Utility Research Lab

The Utility Research Lab is a highly interdisciplinary group that invents and investigates digital fabrication technology, tools, and techniques.

We develop hardware, software and materials that empower people to create with purpose. In tandem, we explore potential applications in the world around us—including through the creation of interactive systems, devices (sensors, actuators, and displays), and assistive/adaptive technology. We are looking for students who are interested in digital fabrication and/or have backgrounds in HCI, physical computing, computer graphics, or computational design/simulation.

Science of Science and Computational Discovery Lab

Daniel Acuña: Computer Science Pre-research session Fall 2022

The SOS+CD Lab works on understanding current practices in science and developing semi-automated methods to mine scientific knowledge from the vast, unstructured dataset of full-text publications, citations, and images.

We use various computational techniques, including deep learning, natural language processing, graph analytics, image processing, and causal inference. See a list of our projects and publications. The ideal student should have a good grasp of quantitative methods and be a good programmer. In addition, the ideal candidate should have an undergraduate or master’s degree in Computer Science, Engineering, Applied Statistics, Mathematics, or a similar quantitative field.

Abstract lab

I have been devoting my passion to the research of vulnerability exploitation (a.k.a., pwn in hacker community) and protection design for cyber-infrastructure systems (e.g., operating system kernel).

As white hat hackers, we not only explore new attack methods but also aim to build scientific foundations for vulnerability exploitation. As system defenders, we seek to build lightweight yet effective protection mechanisms that can be deployed in the real world. If you are interested in learning hacking methods or developing protection solutions for your daily-use software systems, feel free to apply and reach out!

Neurosymbolic Artificial Intelligence

Alvaro Velasquez Video

Alvaro Velasquez is seeking to integrate neural structures for pattern recognition and symbolic structures for reasoning to develop novel neurosymbolic artificial intelligence solutions.

Kissler Lab (Computational Epidemiology)

The Kissler Lab builds mathematical models to better understand, anticipate, and respond to infectious disease epidemics.

We have a special interest in co-circulating respiratory pathogens and their downstream effects (e.g. antibiotic prescribing and resistance). We are seeking students with a strong background in mathematics and/or statistics and who are enthusiastic about learning the biological underpinnings of infectious disease transmission.

Correll Lab

Nikolaus' research interests are spread across robotic manipulation and new "materials that make robots smart".

Such "robotic materials" tightly integrate sensing, actuation, computation, and communication to allow materials to sense their environment, and change their shape and appearance. Leading to novel tactile sensors, skins, and high-speed actuators that can augment existing robotic systems, such materials are also posing new challenges in manufacturing, in particular robotic assembly. 

BLAST: Boulder Language and Social Technologies

​ Overview video

At BLAST we work at the intersection of Natural Language Processing, Machine Learning, Social Computing and Computational Social Science. We aim to address societally relevant problems through the computational analysis of language.

SHINE Lab

Dr. Hirshfield leads the SHINE Lab (System-Human Interaction with NIRS and EEG), with lab research focused on investigating the use of non-invasive brain and physiological measurements to passively classify users’ social, cognitive, and affective states in order to enhance usability testing and adaptive system design.

SHINE Lab research is focused primarily on the use of functional near-infrared spectroscopy (fNIRS), a relatively new non-invasive brain imaging device that is safe, portable, robust to noise, which can be implemented wirelessly; making it ideal for research in human-computer interaction. In addition to the fNIRS device, students in the SHINE Lab also work with EEG, eyetracking, and other forms of physiological data (e.g., heartrate, electrodermal activity). Work in the SHINE Lab is focused on measuring and predicting social, cognitive, affective states using the lab’s sensors and building intelligent systems (back-end algorithms and front-end interfaces)that adapt in real-time to support human performance. The SHINE Lab also applies these measurements to the human-machine teaming domain, where autonomous systems can adapt to human team members based on their mental states in order to improve overall team performance. Topics such as neurophysiological measurements, machine learning, reinforcement learning, human-computer interaction, human-machine teaming, and AI are all areas of focus in the lab.

Software Evolution Research Group

Successful software undergoes constant change. Our research addresses two important questions: What changes occur most often in practice? How can we automate them to improve programmers' productivity and software quality?

Our current work has been pioneering refactorings and program transformations to improve the productivity of Python ML engineers. Prior to that we opened the field of refactoring in other cutting edge domains for enterprise software systems. The students joining our lab have (i) extensive professional software engineering experience evidenced by working on real-world software systems and (ii) are excited about making a practical impact on industry and moving their research into widely used IDEs such as PyCharm or IntelliJ. The students joining our lab also work closely with the industry members of the PPICenter.org, which is founded and led by Professor Dig. More information about our industry-university research center can be found on our webpage.

Larremore Lab

The Larremore Lab at CU Boulder’s BioFrontiers Institute & CS Department uses mathematical modeling, complex networks, and computational methods to identify important principles in infectious diseases and the science of science.

When the right methods do not exist, we develop them. When the necessary data are unavailable, we collaborate or collect them ourselves. And whenever possible, we aim for our scientific findings to drive technological development and policy change. Students with strong mathematical foundations, data analysis skills, and an interest in exploring topics in biology or social science would be a great fit for our interdisciplinary research aims.

Esther Rolf's Lab

Esther Rolf's Lab VIDEO

Pre-research Advising Session Video: Esther Rolf 

I study statistical and geospatial machine learning. My research blends methodological and applied techniques to study and design machine learning algorithms with an emphasis on usability, data-efficiency and fairness.

Current directions include developing algorithms and infrastructure for reliable environmental monitoring using machine learning, and understanding the multifaceted nature of representation in data and how that affects our ability to train fair and effective machine learning systems. I'm looking for students interested in statistical or geospatial machine learning methodology and tailoring those methods to the context of real world problems. Prospective students should be excited about working in and actively fostering a collaborative, supportive, and communicative research community in our lab doing exciting, innovative, and sometimes interdisciplinary research. Ideal background includes skills in statistics, optimization, linear algebra and python.

Lv Group

My group conducts research on full-stack data analytics, which integrates systems, algorithms, and applications for effective and efficient data analytics in ubiquitous computing and scientific discovery. Please check my lab's webpage to learn more.

             

Layer Lab

The Layer Lab develops algoritjms for large-scale genomic data exploration in a range of populations, from sick kids to wildlife populations.

Huck Bennett

My research interests are in algorithms, computational complexity, and some aspects of cryptography. I am especially looking for students to work on problems related to lattices and error-correcting codes.

 

Networking and Security Research (NSR)

My research introduces new systems, algorithms, and abstractions to enable a more manageable network and computing infrastructure.

This is rooted in the fact that a significant portion of security and reliability issues are often a result of limitations in the management of networked systems. My research has been enabling and capitalizing on a more dynamic and programmable computing and network infrastructure, via such technologies as virtualization/containerization, software-defined networking, and the movement toward cloud based services.

In general, my students are interested in systems programming (writing software, using and modifying open source code, etc.). Entrepreneurial interest is a bonus -- I believe systems research and entrepreneurship go hand-in-hand, and find it a great avenue for students to pursue.