Discover Graduate Programs & Active Faculty Recruitment - Fall 2027
Explore faculty recruiting in Fall 2027 by research area
- AI - COGNITIVE SCIENCE
- AI - GENERATIVE
- AI - MACHINE LEARNING
- AI - NATURAL LANGUAGE PROCESSING
- AI - ROBOTICS
- COMPLEX SYSTEMS
- COMPUTATIONAL BIOLOGY
- COMPUTATIONAL SOCIAL SCIENCE
- DISTRIBUTED & NETWORK SYSTEMS
- HUMAN CENTERED COMPUTING
- NUMERICAL & SCIENTIFIC COMPUTING
- PROGRAMMING LANGUAGES
- QUANTUM COMPUTING
- SYSTEMS & NETWORKING
- THEORY OF COMPUTING
- VERIFICATION
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Information sessions on various graduate degrees and admissions
Join live virtual events where faculty researchers showcase their cutting-edge projects, advisors outline application strategies, and current students discuss the doctoral research experience at CU ÀÖ²¥´«Ã½.
You can register for one or more of these sessions through the links below.
- - Hosted by Professor Daniel Acuna
- Ìý- Hosted by Professors Fruzsina Agocs and Evan Chang
- Ìý- Hosted by Assistant Professor Paul Krogmeier
- Ìý- Hosted by Assistant Professor Mark Zhao.
- Ìý- Hosted by Professor Theodora Chaspari
- Ìý- Hosted by Professor
- Ìý- Hosted by Professor Majid Zamani
Discover our new AI-focused curriculum as faculty introduce specialized courses and research projects, advisors share targeted application advice, and current students discuss hands-on AI experiences.
You can register for one or more of these sessions through the links below.
Join live virtual events where faculty outline the core curriculum and research opportunities, advisors offer essential application insights, and current students share hands-on experiences and career outcomes.
You can register for one or more of these sessions through the links below.
Watch our pre-recorded degree presentation to learn about the Computer Science Master’s program options, admissions requirements, and application process! (,Ìý)
Learn more about the MS in network engineering.
Join live virtual gatherings where network engineering faculty outline the specialized curriculum and lab opportunities, advisors provide key application guidance, and current students share hands-on project experiences and career insights.
You can register for one or more of these sessions through the links below.
Watch our pre-recorded degree presentation to learn about the Network Engineering Master’s program, admissions requirements, and application process! (,Ìý)
Explore faculty recruiting in Fall 2027 by research area
The HIRO Group works at the intersection of robotics, AI, and human-robot collaboration, with two complementary thrusts: (1) embodied intelligence and sensorimotor learning—treating physical contact as structured information for grounded manipulation, and (2) social intelligence—enabling robots to coordinate with humans in dynamic, mixed-initiative settings through shared task models and legible behavior.
We seek PhD students with strong fundamentals in CS, robotics, or a related quantitative field, and interest in one or more of: machine learning for manipulation, vision-language-action models, human-robot interaction, cognitive-science-inspired robotics, or contact-rich control. Prior hands-on experience with real robotic systems (not just simulation) is a plus but not required.
- Prospective students are encouraged to email a brief note describing which of our thrusts resonates most with their background and interests.
We focus on creating cutting-edge computational solutions for visual data analysis. ÌýWe create fully-automated methods, human-centric approaches, and hybrid human-machine partnerships for interpreting images, videos, and multimodal data, with an emphasis on meeting societal needs.Ìý
Our work is relevant for a diversity of applications, including to improve upon the status quo for accessibility, (bio)medical data analysis, and privacy preservation. ÌýWe frequently collaborate with partners from industry, medical campuses, and other academic institutions, while sharing our expertise with the broader community by publishing our findings and hosting workshops.Ìý
For relevant background, the following areas of expertise are greatly valued in our group: computer vision, machine learning (especially deep learning), and crowdsourcing. ÌýBeyond that, please review publications from our group to learn about what we have been exploring.
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.
Our Work:
The ACME Lab (Creativity Machine Environment) at the ATLAS Institute and Computer Science Department focuses on the intersection of human-computer interaction (HCI), physical computing, and creative technology. We design, build, and study novel interactive systems, including sketching interfaces, tangible interaction, augmented and virtual reality (AR/VR), and physical AI agents (with computer vision and robotic platforms) that expand human creativity and expression.
Student Background:
We are looking for self-motivated PhD students with a strong interest in human-centered design and project-based building. Ideal candidates should possess a background in computer science, HCI, engineering, or design computing, with strong prototyping skills—whether in software (graphics, AR/VR development, machine learning) or hardware (robotics, digital fabrication, physical computing). A passion for building tools that empower people to create, play, and learn is highly valued.
The Utility Research Lab invents and investigates digital fabrication machines, tools, and materials to tackle real-world challenges in sustainability, advanced manufacturing, and human-computer interaction.
We are looking for students interested in digital fabrication, 3D printing, AI / generative design in CAD tools, robotics, electromechanical design, and technical human-computer interaction.
We build computer systems to train and deploy state-of-the-art machine learning (ML) models. We focus on improving the scalability, efficiency, and security of systems across the end-to-end ML pipeline. Our interests span the entire systems stack — including distributed/operating systems, databases, computer architecture, and security — and their intersection with machine learning.
- The GEM (Geospatial and Environmental Machine learning) Lab conducts research in geospatial machine learning and AI. Our research blends methodological and applied techniques to design machine learning algorithms and architectures with an emphasis on usability, data-efficiency, and impact for science and policy. When I am reviewing applications, I look for 1) mathematical foundations for machine learning -- several or all of the following: linear algebra, calculus, statistics, optimization, and 2) demonstrated interest in research questions that align with GEM lab's focus (e.g. through the individual statement).
My work seeks to integrate deep learning and symbolic AI to achieve the best of both worlds. Examples of symbolic AI include knowledge graphs, logics, and automata. We are also researching application domains for this novel integration, including the discovery of new molecules for drug design and depolymerization and the optimization of organ exchanges.
The HIRO Group works at the intersection of robotics, AI, and human-robot collaboration, with two complementary thrusts: (1) embodied intelligence and sensorimotor learning—treating physical contact as structured information for grounded manipulation, and (2) social intelligence—enabling robots to coordinate with humans in dynamic, mixed-initiative settings through shared task models and legible behavior.
We seek PhD students with strong fundamentals in CS, robotics, or a related quantitative field, and interest in one or more of: machine learning for manipulation, vision-language-action models, human-robot interaction, cognitive-science-inspired robotics, or contact-rich control. Prior hands-on experience with real robotic systems (not just simulation) is a plus but not required.
- Prospective students are encouraged to email a brief note describing which of our thrusts resonates most with their background and interests.
Our Work:
The ACME Lab (Creativity Machine Environment) at the ATLAS Institute and Computer Science Department focuses on the intersection of human-computer interaction (HCI), physical computing, and creative technology. We design, build, and study novel interactive systems, including sketching interfaces, tangible interaction, augmented and virtual reality (AR/VR), and physical AI agents (with computer vision and robotic platforms) that expand human creativity and expression.
Student Background:
We are looking for self-motivated PhD students with a strong interest in human-centered design and project-based building. Ideal candidates should possess a background in computer science, HCI, engineering, or design computing, with strong prototyping skills—whether in software (graphics, AR/VR development, machine learning) or hardware (robotics, digital fabrication, physical computing). A passion for building tools that empower people to create, play, and learn is highly valued.
The Utility Research Lab invents and investigates digital fabrication machines, tools, and materials to tackle real-world challenges in sustainability, advanced manufacturing, and human-computer interaction.
We are looking for students interested in digital fabrication, 3D printing, AI / generative design in CAD tools, robotics, electromechanical design, and technical human-computer interaction.
My current focus is on the analysis of time-series data from complex adaptive nonlinear systems. I use a variety of approaches, ranging from the traditional ones from nonlinear dynamics (delay reconstruction, Lyapunov exponents, etc.) to information theory and topological data analysis.
The Larremore Lab focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in infectious diseases and computational social science. We try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems in two key areas:
1. Infectious Diseases. The lab develops data-informed mathematical models for infectious disease surveillance and countermeasures, including testing, vaccination, and seroepidemiology, primarily for respiratory pathogens such as RSV, flu, and SARS-CoV-2. Past work has also focused on the malaria parasite P. falciparum and its rapid recombination to evade the human immune system. Our goal is to use models and computation to improve the study of pathogens and ultimately decrease the burden of disease. We are a member of the CDC's Insight Net and the epiENGAGE consortium.
2. The Scientific Ecosystem. The lab analyzes and models the patterns and processes that define the ecosystem of scientific research and discovery. Our goal is to combine rigorous computation, ecological theory, and social science to understand how the scientific community works, and how it can be made more equitable and more productive. Here, we continue to build on a decade-old collaboration with the Clauset Lab.
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.
Our Work:
The ACME Lab (Creativity Machine Environment) at the ATLAS Institute and Computer Science Department focuses on the intersection of human-computer interaction (HCI), physical computing, and creative technology. We design, build, and study novel interactive systems, including sketching interfaces, tangible interaction, augmented and virtual reality (AR/VR), and physical AI agents (with computer vision and robotic platforms) that expand human creativity and expression.
Student Background:
We are looking for self-motivated PhD students with a strong interest in human-centered design and project-based building. Ideal candidates should possess a background in computer science, HCI, engineering, or design computing, with strong prototyping skills—whether in software (graphics, AR/VR development, machine learning) or hardware (robotics, digital fabrication, physical computing). A passion for building tools that empower people to create, play, and learn is highly valued.
The Larremore Lab focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in infectious diseases and computational social science. We try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems in two key areas:
1. Infectious Diseases. The lab develops data-informed mathematical models for infectious disease surveillance and countermeasures, including testing, vaccination, and seroepidemiology, primarily for respiratory pathogens such as RSV, flu, and SARS-CoV-2. Past work has also focused on the malaria parasite P. falciparum and its rapid recombination to evade the human immune system. Our goal is to use models and computation to improve the study of pathogens and ultimately decrease the burden of disease. We are a member of the CDC's Insight Net and the epiENGAGE consortium.
2. The Scientific Ecosystem. The lab analyzes and models the patterns and processes that define the ecosystem of scientific research and discovery. Our goal is to combine rigorous computation, ecological theory, and social science to understand how the scientific community works, and how it can be made more equitable and more productive. Here, we continue to build on a decade-old collaboration with the Clauset Lab.
My work seeks to integrate deep learning and symbolic AI to achieve the best of both worlds. Examples of symbolic AI include knowledge graphs, logics, and automata. We are also researching application domains for this novel integration, including the discovery of new molecules for drug design and depolymerization and the optimization of organ exchanges.
The Larremore Lab focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in infectious diseases and computational social science. We try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems in two key areas:
1. Infectious Diseases. The lab develops data-informed mathematical models for infectious disease surveillance and countermeasures, including testing, vaccination, and seroepidemiology, primarily for respiratory pathogens such as RSV, flu, and SARS-CoV-2. Past work has also focused on the malaria parasite P. falciparum and its rapid recombination to evade the human immune system. Our goal is to use models and computation to improve the study of pathogens and ultimately decrease the burden of disease. We are a member of the CDC's Insight Net and the epiENGAGE consortium.
2. The Scientific Ecosystem. The lab analyzes and models the patterns and processes that define the ecosystem of scientific research and discovery. Our goal is to combine rigorous computation, ecological theory, and social science to understand how the scientific community works, and how it can be made more equitable and more productive. Here, we continue to build on a decade-old collaboration with the Clauset Lab.
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.
The HIRO Group works at the intersection of robotics, AI, and human-robot collaboration, with two complementary thrusts: (1) embodied intelligence and sensorimotor learning—treating physical contact as structured information for grounded manipulation, and (2) social intelligence—enabling robots to coordinate with humans in dynamic, mixed-initiative settings through shared task models and legible behavior.
We seek PhD students with strong fundamentals in CS, robotics, or a related quantitative field, and interest in one or more of: machine learning for manipulation, vision-language-action models, human-robot interaction, cognitive-science-inspired robotics, or contact-rich control. Prior hands-on experience with real robotic systems (not just simulation) is a plus but not required.
- Prospective students are encouraged to email a brief note describing which of our thrusts resonates most with their background and interests.
We focus on creating cutting-edge computational solutions for visual data analysis. ÌýWe create fully-automated methods, human-centric approaches, and hybrid human-machine partnerships for interpreting images, videos, and multimodal data, with an emphasis on meeting societal needs.Ìý
Our work is relevant for a diversity of applications, including to improve upon the status quo for accessibility, (bio)medical data analysis, and privacy preservation. ÌýWe frequently collaborate with partners from industry, medical campuses, and other academic institutions, while sharing our expertise with the broader community by publishing our findings and hosting workshops.Ìý
For relevant background, the following areas of expertise are greatly valued in our group: computer vision, machine learning (especially deep learning), and crowdsourcing. ÌýBeyond that, please review publications from our group to learn about what we have been exploring.
We investigate the complex interplay between thoughts and feelings while people engage in complex real-world activities individually or in groups. Our approach emphasizes use-inspired research, a discovery orientation to science, and theoretical and methodological pluralism. Our research involves theoretical, methodological, and translational integration of the computing, cognitive, affective, social, and learning sciences. We're an AI for cognitive science lab - while we leverage AI and develop AI models - our primary interest is in advancing cognitive science.
The Utility Research Lab invents and investigates digital fabrication machines, tools, and materials to tackle real-world challenges in sustainability, advanced manufacturing, and human-computer interaction.
We are looking for students interested in digital fabrication, 3D printing, AI / generative design in CAD tools, robotics, electromechanical design, and technical human-computer interaction.
My current focus is on the analysis of time-series data from complex adaptive nonlinear systems. I use a variety of approaches, ranging from the traditional ones from nonlinear dynamics (delay reconstruction, Lyapunov exponents, etc.) to information theory and topological data analysis.
The CompTools group works at the intersection of scientific computing, numerical analysis, and computational physics to design tools for modelling physical phenomena described by ordinary and partial differential equations (ODEs and PDEs).
The focus is on adaptive, efficient, and high-order accurate methods for the sake of user-friendliness and robustness even for badly conditioned problems. We emphasize building open-source software implementations of these methods.
The group is looking for students with a strong mathematical background (whether that is from engineering, physics, or applied math courses; particular emphasis on calculus, real and complex analysis, linear algebra, numerical ODEs/PDEs), some coding experience (for scientific computing), and an open mind to acquire new skills/learn new methods.
I work on the formal verification, synthesis, and control of cyber-physical and autonomous systems, with a particular focus on developing correct-by-construction and certified-by-construction methods for safety-critical autonomy. My research combines ideas from formal methods, control theory, optimization, and machine learning to provide rigorous guarantees for complex systems such as autonomous vehicles, robotic systems, and networked control systems.
I am looking for PhD students with strong mathematical and computational backgrounds who are interested in one or more of the following areas: formal methods, control theory, certified machine learning for autonomous systems, verification and synthesis, optimization, and cyber-physical systems. Prior research experience is helpful but not required. Strong applicants should be motivated to work on theoretically grounded methods with potential applications to trustworthy autonomous systems.
I am looking for students with a background in theoretical computer science/algorithms who are able to work on problems involving differential equation models and symbolic reasoning over them.
The Quantum Architecture, Systems, and Applications Research (QUASAR) Lab conducts research on the foundations of scalable quantum computing, with a primary focus on quantum computer architecture and systems. Our work spans hardware-software co-design, programming frameworks, compilers, runtime environments, reliability, and performance. We also explore the intersection of quantum computing, machine learning, and optimization, as well as applications in healthcare, drug discovery, and scientific computing.
We are looking for highly motivated and passionate PhD students who are excited about advancing the future of computing. Successful candidates come from a wide range of backgrounds, including computer architecture, computer systems, software engineering, machine learning, algorithms, mathematics, physics, and related fields. Prior experience in quantum computing is welcome but not required. We value curiosity, creativity, strong technical foundations, and a desire to tackle challenging research problems.
My research is focused on designing efficient fault-tolerant quantum computing (FTQC) systems. I have broad interests in systems-level questions for FTQC, including quantum error correction (QEC) optimization, QEC decoding and other real-time classical systems for FTQC, and quantum software and compilation. My work often studies FTQC systems through the co-design of QEC with both quantum hardware and applications. As a result I enjoy engaging in interdisciplinary collaborations with hardware-level experimentalists and application-level theorists.Ìý
I'm looking for students with an interest in software-driven systems research and an excitement for learning about the growing field of fault-tolerant quantum computing. A background in either computer systems or quantum computing is helpful but not required.
The Quantum Architecture, Systems, and Applications Research (QUASAR) Lab conducts research on the foundations of scalable quantum computing, with a primary focus on quantum computer architecture and systems. Our work spans hardware-software co-design, programming frameworks, compilers, runtime environments, reliability, and performance. We also explore the intersection of quantum computing, machine learning, and optimization, as well as applications in healthcare, drug discovery, and scientific computing.
We are looking for highly motivated and passionate PhD students who are excited about advancing the future of computing. Successful candidates come from a wide range of backgrounds, including computer architecture, computer systems, software engineering, machine learning, algorithms, mathematics, physics, and related fields. Prior experience in quantum computing is welcome but not required. We value curiosity, creativity, strong technical foundations, and a desire to tackle challenging research problems.
My research is focused on designing efficient fault-tolerant quantum computing (FTQC) systems. I have broad interests in systems-level questions for FTQC, including quantum error correction (QEC) optimization, QEC decoding and other real-time classical systems for FTQC, and quantum software and compilation. My work often studies FTQC systems through the co-design of QEC with both quantum hardware and applications. As a result I enjoy engaging in interdisciplinary collaborations with hardware-level experimentalists and application-level theorists.Ìý
I'm looking for students with an interest in software-driven systems research and an excitement for learning about the growing field of fault-tolerant quantum computing. A background in either computer systems or quantum computing is helpful but not required.
We build computer systems to train and deploy state-of-the-art machine learning (ML) models. We focus on improving the scalability, efficiency, and security of systems across the end-to-end ML pipeline. Our interests span the entire systems stack — including distributed/operating systems, databases, computer architecture, and security — and their intersection with machine learning.
- I work in Theoretical Computer Science on problems involving aggregating information and making decisions with strategic agents.
- I am looking for students who are excited about theory, math, and proofs in Computer Science and game-theory applications.
I work on topics in algorithms, computational complexity, and cryptography. My main focus is on lattices, error-correcting codes, algebraic and geometric problems, and fine-grained complexity.
I am looking for students with a background in theoretical computer science/algorithms who are able to work on problems involving differential equation models and symbolic reasoning over them.
My work seeks to integrate deep learning and symbolic AI to achieve the best of both worlds. Examples of symbolic AI include knowledge graphs, logics, and automata. We are also researching application domains for this novel integration, including the discovery of new molecules for drug design and depolymerization and the optimization of organ exchanges.
I work on the formal verification, synthesis, and control of cyber-physical and autonomous systems, with a particular focus on developing correct-by-construction and certified-by-construction methods for safety-critical autonomy. My research combines ideas from formal methods, control theory, optimization, and machine learning to provide rigorous guarantees for complex systems such as autonomous vehicles, robotic systems, and networked control systems.
I am looking for PhD students with strong mathematical and computational backgrounds who are interested in one or more of the following areas: formal methods, control theory, certified machine learning for autonomous systems, verification and synthesis, optimization, and cyber-physical systems. Prior research experience is helpful but not required. Strong applicants should be motivated to work on theoretically grounded methods with potential applications to trustworthy autonomous systems.
I am looking for students with a background in theoretical computer science/algorithms who are able to work on problems involving differential equation models and symbolic reasoning over them.
October 5th
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Register for the BASIL Lab’s Virtual Recruitment Week session.
