Michigan State University

What you'll study

This PhD is organised around original research supported by advanced coursework and departmental seminars. Program structure typically includes a combination of tailored coursework, qualifying exams, research rotations or practica, and a supervised dissertation that contributes new knowledge to the field.

• Core and advanced coursework — topics commonly include algorithms and theory, machine learning and artificial intelligence, databases and information retrieval, computer systems and networking, security and privacy, human–computer interaction, and software engineering.
• Research methods and seminars — students take seminars that expose them to current literature, present research progress, and develop skills in experimental design, reproducible computing and scientific communication.
• Specialisation areas — typical specialisms available through faculty supervision and research groups include data science and big data analytics, natural language processing, robotics and embedded systems, cybersecurity and trustworthy systems, human-centred computing and social computing, and high-performance computing.
• Doctoral milestone work — milestones usually comprise passing qualifying or preliminary examinations, proposing a dissertation topic, conducting original research, publishing in peer-reviewed venues and defending the dissertation.
• Teaching and professional development — many students gain experience through teaching assistantships, mentoring undergraduates and participating in workshops on grant writing, ethics and research communication.

Entry requirements

Admission is selective and seeks applicants with strong academic preparation and clear research potential. Typical requirements include:

• Academic qualifications — a relevant bachelor's degree is the minimum; a completed master’s in computer science, information science or a related discipline is commonly expected or advantageous. Exceptional applicants with a strong bachelor’s record and research experience may be considered.
• Transcripts — official records demonstrating strong performance in computing, mathematics and related coursework.
• Letters of recommendation — usually three references from academic or professional referees who can speak to research potential.
• Statement of purpose / research statement — a clear description of research interests, relevant experience and fit with faculty and research groups.
• Curriculum vitae — documenting academic background, publications, projects and relevant work experience.
• English language proficiency — for applicants whose first language is not English, an approved English language qualification is required; acceptable tests and minimum scores are set by the university and should be checked on the departmental pages.

Standardised test requirements (for example GRE) and funding guarantees can vary; consult the department for current guidance and information about financial support such as assistantships and fellowships.

Career prospects

Graduates of the PhD programme pursue a wide range of careers that leverage deep technical expertise and research training. Common pathways include:

• Academic careers — tenure-track faculty positions and postdoctoral research roles at universities and research institutions.
• Industry research and development — research scientist or research engineer roles in corporate labs and R&D groups working on AI, systems, data infrastructure and security.
• Data science and engineering leadership — senior data scientist, machine learning engineer or principal engineer positions in companies across finance, healthcare, manufacturing and technology sectors.
• Startups and entrepreneurship — founding or technical leadership roles in technology startups, often commercialising research outcomes.
• Government and policy — technical advisor or research roles in national laboratories, government agencies and organisations concerned with cybersecurity, privacy and public-interest technology.

Why study at Michigan State University

Michigan State offers an interdisciplinary environment with established research centres and collaborative networks that support computing and information research. Doctoral students benefit from access to experienced faculty across departments, state-supported research infrastructure and opportunities for cross-college collaboration.

• Active research groups — students can join research labs working in AI, data analytics, human-centred computing, systems and security, enabling publication and collaboration across disciplines.
• Research infrastructure — access to university computing resources and partnerships that support large-scale experiments, data-intensive research and high-performance computing needs.
• Funding and professional development — a range of assistantships, fellowships and training programmes help students gain teaching experience, publish work and prepare for academic and industry careers.
• Collaborative culture — opportunities for interdisciplinary projects with engineering, natural sciences, social sciences and business, reflecting the applied and societal impact of modern information science research.

Prospective applicants should consult the department's graduate admissions pages and contact potential supervisors to discuss fit and available research opportunities before applying.