2027 Computer Science Degree Automation Exposure Report: Which Career Paths Face the Most AI and Technology Disruption
Co-Founder and Chief Data Scientist
Computer science students are no longer choosing between "safe" and "automated" careers. They are choosing how much of their future work will be changed by AI. The U.S. Bureau of Labor Statistics reported a $105,990 median annual wage for computer and information technology occupations in May 2024, more than double the $49,500 median for all occupations.
This guide helps students, career changers, and degree planners compare automation exposure, salary context, growth outlook, resilient skills, and specializations so they can choose a path that benefits from AI rather than competes against it.
Key Things You Should Know
- Computer and IT occupations are projected by BLS to produce about 317,700 openings per year from 2024 to 2034, but routine coding, testing, reporting, and support tasks face the highest AI exposure.
- The most resilient computer science paths combine technical depth with judgment-heavy work, such as cybersecurity, AI systems, cloud architecture, data governance, human-centered product work, and safety-critical software.
- High automation exposure does not always mean poor career value. Software development remains well paid, with a May 2024 BLS median wage of $133,080, but graduates need AI-assisted development, systems design, security, and communication skills to stay competitive.
- Which Computer Science Career Paths Face the Greatest Risk of AI and Automation?
- Which Job Tasks Are Most Likely to Be Automated in Computer Science Careers?
Which Computer Science Career Paths Face the Greatest Risk of AI and Automation?
Automation exposure measures how much of a role's work can be performed, accelerated, or reshaped by software, AI models, scripts, low-code platforms, or monitoring tools. It is not the same as job loss risk. A career can be highly exposed to AI and still grow if demand rises, regulation increases, or employers need humans to supervise, secure, and integrate automated systems.
The table below ranks common computer science career paths by likely AI and technology disruption. Use it as a decision guide, not a prediction of whether a job will disappear, because exposure varies by employer, industry, project complexity, regulatory requirements, and the worker's skill level.
| Career path | Automation exposure | Why the work is exposed | What keeps the role valuable | Best fit for students who want |
| Entry-level programmer | High | AI coding assistants can generate boilerplate code, convert syntax, write simple scripts, and explain errors. | Debugging unfamiliar systems, understanding business requirements, code review, secure design, and production judgment. | A coding-heavy start, if they are willing to move quickly into architecture, domain expertise, or security. |
| Software quality assurance tester | High | Regression tests, test cases, bug summaries, and automated test scripts are increasingly generated or optimized by tools. | Risk-based testing, usability evaluation, compliance testing, and translating user behavior into test strategy. | Detail-oriented work that can evolve into quality engineering, DevOps, or product reliability. |
| Web developer | Moderate to high | Template systems, AI page builders, and code generation tools reduce demand for simple static sites and routine front-end work. | Accessibility, performance, security, complex integrations, UX judgment, and full-stack problem solving. | Visual and interactive software work, especially with strong design and user research skills. |
| Data analyst | Moderate to high | AI can draft SQL, create dashboards, summarize trends, and produce basic charts from structured data. | Data quality judgment, causal reasoning, stakeholder communication, privacy awareness, and business context. | Business-facing analytics, if they build statistical, domain, and data governance skills. |
| Systems analyst | Moderate | Documentation, process mapping, and requirements summaries can be accelerated by AI tools. | Stakeholder negotiation, workflow design, vendor evaluation, and translating messy organizational needs into systems. | A bridge between technology, operations, and business decision-making. |
| Software developer | Moderate | AI changes how code is written, tested, documented, and reviewed, especially for common application patterns. | System design, product judgment, security, scalability, maintainability, and team collaboration. | Strong earnings potential with continuous learning and AI-assisted engineering habits. |
| Cloud engineer or DevOps engineer | Moderate | Infrastructure-as-code, deployment scripts, monitoring alerts, and incident summaries can be automated. | Reliability engineering, cost control, security hardening, incident response, and architecture trade-offs. | Hands-on systems work tied to business-critical infrastructure. |
| Cybersecurity analyst | Moderate | Threat detection, log review, phishing analysis, and alert triage are increasingly AI-supported. | Adversarial thinking, incident response, governance, risk assessment, and human accountability. | A high-demand field where AI raises both the threat level and the need for defense. |
| AI or machine learning engineer | Moderate | Model building tools and automated machine learning can speed up experiments and feature engineering. | Model evaluation, deployment, risk management, data strategy, explainability, and domain-specific design. | Working directly with AI systems rather than being displaced by them. |
| Computer and information research scientist | Low to moderate | AI can support literature review, coding experiments, and simulation workflows. | Original research, mathematical reasoning, novel algorithms, scientific judgment, and long-horizon problem solving. | Advanced study, research, and work on unsolved technical problems. |
The highest exposure usually appears where tasks are repetitive, text-based, rule-based, or easy to verify. The lowest exposure appears where work involves ambiguity, responsibility, deep architecture, security consequences, human negotiation, or original research.
One common mistake is choosing a career based only on current salary. A high-paying role can still require aggressive reskilling if its entry-level tasks are being automated, while a lower-paid role can become more valuable if it sits close to regulation, security, or human decision-making.
Which Job Tasks Are Most Likely to Be Automated in Computer Science Careers?
Computer science careers are usually disrupted task by task before they are disrupted job by job. That distinction matters because a student can reduce risk by learning to supervise, validate, and improve automated workflows instead of performing only the tasks that tools are starting to handle.
The table below identifies common computer science tasks and how likely they are to be automated or AI-assisted. It also shows the human contribution that remains important when tools are introduced.
| Task | Automation likelihood | How AI or automation changes the task | Human value that remains |
| Writing boilerplate code | High | AI assistants can draft common functions, APIs, forms, scripts, and configuration files. | Knowing whether the generated code is secure, maintainable, and appropriate for the system. |
| Basic debugging | High | Tools can explain stack traces, suggest fixes, and identify common syntax or dependency problems. | Diagnosing production failures, edge cases, performance issues, and architecture-level causes. |
| Unit test generation | High | AI can create test cases from code and generate coverage for common scenarios. | Deciding what risks matter, testing real user behavior, and validating safety-critical cases. |
| Documentation drafts | High | AI can summarize code, write release notes, and translate technical concepts into simpler language. | Ensuring accuracy, completeness, compliance, and usefulness for the intended audience. |
| Dashboard creation | Moderate to high | Analytics tools can generate charts, summaries, and natural-language insights from data sources. | Interpreting whether the data is biased, incomplete, causal, or relevant to the decision. |
| Security alert triage | Moderate | AI can prioritize alerts, cluster suspicious activity, and summarize incident timelines. | Investigating adversarial behavior, making escalation decisions, and coordinating response. |
| System architecture | Low to moderate | AI can suggest patterns and produce diagrams, but cannot fully own trade-offs across cost, risk, scale, and users. | Design judgment, accountability, cross-team coordination, and long-term maintainability. |
| Product requirement discovery | Low to moderate | AI can summarize interviews and organize feature requests. | Understanding conflicting stakeholder goals, ethics, usability, and business priorities. |
| Incident response leadership | Low | Automation can surface logs and likely causes faster. | Decision-making under pressure, communication, accountability, and risk containment. |
The safest strategy is not to avoid automation-prone tasks entirely. Early-career workers still need fundamentals. The goal is to use those tasks as a foundation, then move toward higher-level work that requires judgment, verification, architecture, domain knowledge, and communication.
Students should be especially careful about portfolios that show only AI-generated output. Employers increasingly want evidence that a candidate can explain design choices, test assumptions, handle failure, protect data, and work with users, not just produce code quickly.
AI adoption affects computer science graduates differently depending on industry. A developer in a bank, a hospital technology vendor, a defense contractor, and a consumer app startup may all write code, but the pace of automation, risk tolerance, compliance burden, and hiring expectations can be very different.
The table below summarizes industries where AI adoption is especially important for computer science graduates. It focuses on how adoption changes opportunity and disruption rather than treating every industry as equally exposed.
| Industry | AI adoption pattern | Impact on computer science roles | Resilient career angle |
| Information technology and software | Fast adoption of AI coding, cloud automation, model deployment, and developer productivity tools. | Entry-level coding tasks are compressed, but demand rises for engineers who can build, integrate, secure, and monitor AI-enabled systems. | AI engineering, platform engineering, secure software development, and product-focused systems design. |
| Finance and insurance | Strong use of AI for fraud detection, risk modeling, customer service, compliance review, and trading infrastructure. | Routine analytics and reporting are exposed, while data governance, cybersecurity, explainability, and compliance-aware engineering become more valuable. | Fintech engineering, risk analytics, cybersecurity, model validation, and data privacy. |
| Healthcare technology | Growing AI use in clinical workflow tools, imaging support, scheduling, patient communication, and administrative automation. | Software work is shaped by privacy, safety, interoperability, and regulatory constraints, which keeps human oversight important. | Health informatics, secure data systems, medical software quality, and responsible AI implementation. |
| Manufacturing and logistics | Adoption centers on robotics, predictive maintenance, computer vision, supply chain analytics, and digital twins. | Traditional IT roles may blend with automation engineering, sensor data, edge computing, and operational technology security. | Industrial AI, robotics software, embedded systems, cloud operations, and cyber-physical security. |
| Government and defense | Adoption is more controlled because of security, procurement, privacy, and public accountability requirements. | Change may be slower, but demand can be strong for cybersecurity, data modernization, secure cloud migration, and mission-critical systems. | Cybersecurity, systems engineering, secure software, and compliance-heavy architecture. |
| Education and professional services | AI is used for tutoring, content generation, workflow automation, knowledge management, and client-facing tools. | Support and content tasks are exposed, while integration, privacy, learning analytics, and change management roles expand. | Edtech engineering, learning analytics, AI governance, and human-centered product development. |
A practical way to compare industries is to ask whether AI is mainly replacing repetitive work, increasing demand for technical infrastructure, or creating new compliance and oversight needs. The strongest opportunities often appear where all three are happening at once.
Students considering interdisciplinary paths should look beyond traditional tech employers. For example, health technology roles may appeal to students who like computing but want human-centered, regulated work. Those comparing tech with clinical graduate routes may also research an SLP online masters program to understand how different education paths connect to automation-resistant human services.
The most resilient computer science graduates are not necessarily the ones who know the most programming languages. They are the ones who can learn new tools quickly, understand systems deeply, evaluate risk, and work well with people who have different goals and constraints.
The table below separates skills that are easily assisted by AI from skills that usually become more valuable when AI tools are introduced. Students should build both, but long-term stability depends more on the right-hand column.
| Skill area | AI can assist with | Resilience-building version of the skill |
| Programming | Generating functions, translating code, explaining syntax, and drafting examples. | Designing maintainable systems, reviewing generated code, debugging complex failures, and understanding trade-offs. |
| Data analysis | Creating charts, writing queries, summarizing datasets, and drafting reports. | Question design, data quality assessment, causal reasoning, privacy protection, and decision support. |
| Cloud computing | Writing scripts, templates, deployment notes, and monitoring summaries. | Reliability design, cost governance, security architecture, incident response, and scaling strategy. |
| Cybersecurity | Scanning logs, summarizing alerts, drafting rules, and identifying known vulnerabilities. | Threat modeling, adversarial reasoning, response leadership, compliance, and risk communication. |
| Machine learning | Building baseline models, generating code, and comparing common algorithms. | Evaluation design, bias detection, model monitoring, explainability, and responsible deployment. |
| Communication | Drafting summaries, emails, documentation, and presentation outlines. | Persuasion, negotiation, stakeholder alignment, ethical judgment, and explaining uncertainty. |
Students can make their skill-building more practical by following a sequence that mirrors how modern teams work. This is more effective than collecting unrelated certificates without applying them. Follow these steps:
- Build a strong foundation in data structures, algorithms, databases, networking, operating systems, and software engineering practices.
- Use AI tools in projects, but document where they helped, where they failed, and how you verified the final result.
- Add one marketable technical depth area, such as cloud, cybersecurity, machine learning, full-stack engineering, embedded systems, or data engineering.
- Complete projects that include deployment, testing, monitoring, security review, and user feedback rather than only classroom-style code.
- Practice explaining technical decisions in plain language through project writeups, demos, peer reviews, and mock interviews.
Human-centered skills matter because technology adoption is also a people problem. Graduates who understand hiring, training, organizational design, and workplace change may find useful context by comparing CS-heavy roles with people operations programs such as the cheapest online human resources degree, especially if they want to work in HR technology, workforce analytics, or AI governance.
Specialization is one of the strongest ways to reduce automation risk, but not every specialization offers the same kind of stability. The best long-term choices usually sit near complex systems, high accountability, security, regulation, physical infrastructure, or rapidly growing demand.
The table below compares computer science specializations by stability factors. It is designed to help students choose a concentration, elective path, graduate focus, or project portfolio direction.
| Specialization | Long-term stability | Why it is resilient | Important caution |
| Cybersecurity | Very strong | AI increases both attack capability and defense needs, while security decisions require accountability and risk judgment. | Entry-level hiring can still be competitive; hands-on labs, networking knowledge, and certifications can help. |
| AI and machine learning engineering | Strong | Organizations need people who can build, evaluate, deploy, monitor, and govern AI systems. | Basic model training is becoming easier, so depth in data, evaluation, infrastructure, and domain context matters. |
| Cloud computing and DevOps | Strong | AI systems, applications, and analytics platforms depend on scalable, secure, cost-controlled infrastructure. | Automation is high, so workers must understand reliability, architecture, and incident response. |
| Data engineering | Strong | AI systems depend on trustworthy data pipelines, storage, governance, lineage, and quality controls. | Routine ETL scripting is exposed; resilient workers focus on architecture, scale, and governance. |
| Human-computer interaction and UX engineering | Moderate to strong | AI products still need usable, accessible, trustworthy interfaces and careful user research. | Simple design production is exposed; research, accessibility, and complex product judgment are stronger. |
| Embedded systems and robotics | Moderate to strong | Physical systems require hardware constraints, safety testing, real-time performance, and integration with sensors. | Some roles require specialized hardware knowledge and may be geographically concentrated. |
| General web development | Moderate | Complex applications still need skilled developers, but simple site-building is increasingly automated. | Students should add full-stack, accessibility, security, performance, or product depth. |
| General programming without specialization | Weaker | Foundational coding remains useful, but routine implementation is highly exposed to AI tools. | Graduates should quickly build a domain, platform, or advanced technical focus. |
The strongest specialization is not always the trendiest one. A student who dislikes statistics may struggle in machine learning but thrive in cybersecurity or cloud systems. A student who enjoys people and design may build a more durable career in UX engineering, product analytics, or responsible AI than in a narrowly technical role they do not enjoy.
When comparing specializations, ask whether the path develops transferable judgment. Skills tied only to one tool can age quickly, while skills tied to systems, security, data, users, and decision-making remain useful across tool changes.
AI can put pressure on salaries for routine entry-level tasks, but it can also raise compensation for workers who use AI to handle more complex, higher-impact work. The salary question is therefore not "Will AI lower computer science pay?" but "Which roles will reward people who can use AI responsibly and move up the value chain?"
The table below combines May 2024 BLS median wage data with automation exposure and growth context. Median wages are not starting salaries, and they vary by region, employer, experience, industry, and education level.
| Occupation | May 2024 median annual wage | BLS 2024-2034 outlook | AI exposure | Career advancement signal |
| Computer and information research scientists | $140,910 | Much faster than average | Low to moderate | Strong for students pursuing advanced study, AI research, algorithms, or complex technical innovation. |
| Software developers | $133,080 | Much faster than average | Moderate | Strong if developers build architecture, security, AI-assisted engineering, and product judgment. |
| Information security analysts | $124,910 | Much faster than average | Moderate | Strong because AI expands both cyber threats and defensive automation needs. |
| Database administrators and architects | $117,450 | Faster than average | Moderate | Strong when paired with data governance, cloud data platforms, privacy, and AI data readiness. |
| Computer systems analysts | $103,790 | Faster than average | Moderate | Solid for graduates who connect business needs, technical requirements, and workflow redesign. |
| Computer programmers | $98,670 | Declining | High | Riskier if the role remains narrow; better if it evolves into software engineering, systems, or domain specialization. |
| Web developers and digital designers | $95,380 | Faster than average | Moderate to high | Better for workers who add UX, accessibility, performance, security, or full-stack application skills. |
| Computer support specialists | $61,550 | About as fast as average | Moderate to high | Can be a useful entry point if it leads to systems administration, cloud, cybersecurity, or IT operations. |
The trade-off is clear: some high-paying roles also have high task exposure, especially if the work is narrow and repetitive. Long-term salary resilience usually comes from moving toward ownership of systems, risk, infrastructure, users, revenue impact, or compliance.
Students should also consider cost and time-to-value. A bachelor's degree may open broader recruiting pipelines, while certificates, internships, open-source work, and project portfolios can help students test a specialization before committing to graduate school. The best return on education usually comes when the credential matches a specific career direction rather than serving as a vague promise of job security.
AI is not only disrupting computer science careers; it is creating new ones. Many emerging roles are built around designing, integrating, testing, securing, explaining, and governing AI systems. These roles often favor graduates who understand both computer science fundamentals and the real-world context where AI is used.
The table below highlights opportunity areas that are growing because organizations need people to make AI useful, reliable, safe, and measurable.
| Emerging opportunity | What the role focuses on | Useful computer science preparation | Why AI creates demand |
| AI application developer | Building software that uses AI models through APIs, retrieval systems, and workflow automation. | Software engineering, APIs, databases, prompt design, evaluation, and security. | Employers want AI embedded into products and internal tools, not isolated experiments. |
| Machine learning operations engineer | Deploying, monitoring, retraining, and maintaining models in production. | Cloud, DevOps, data engineering, model monitoring, and automation. | AI systems need reliability and oversight after launch. |
| AI security specialist | Protecting models, data pipelines, prompts, APIs, and AI-enabled applications from misuse. | Cybersecurity, software security, threat modeling, and cloud architecture. | AI introduces new attack surfaces and data leakage risks. |
| Data governance analyst or engineer | Managing data quality, access, privacy, lineage, and compliance for AI-ready systems. | Databases, data engineering, privacy principles, documentation, and analytics. | AI output is only as trustworthy as the data and controls behind it. |
| Responsible AI analyst | Evaluating bias, explainability, model behavior, documentation, and policy alignment. | Statistics, ethics, human-computer interaction, data analysis, and communication. | Organizations need defensible decisions when AI affects customers, workers, or regulated processes. |
| AI product manager | Defining use cases, measuring value, coordinating teams, and managing risk for AI-enabled products. | Software fundamentals, analytics, user research, communication, and business strategy. | AI projects often fail when they lack clear problem definition and adoption planning. |
AI also expands hybrid careers. Students can combine computer science with healthcare, finance, law, education, operations, design, media, or the arts. For example, students interested in computer vision, imaging tools, creative software, or generative media may find it useful to compare CS coursework with creative programs such as photography colleges online to understand how technical and visual skills can reinforce each other.
The key decision is whether AI will be the object of your work, a tool inside your work, or a market force affecting your work. Computer science graduates who can answer that question clearly are better positioned to choose courses, internships, projects, and employers strategically.
Preparation should start before graduation. Students who wait until the job search to learn AI tools, cloud platforms, testing practices, or security basics may find that entry-level expectations have already moved ahead of their coursework.
The steps below provide a practical plan for building an AI-resilient computer science profile. They are ordered so students can move from fundamentals to specialization without skipping the basics employers still expect.
- Master the fundamentals first: data structures, algorithms, databases, networking, operating systems, version control, and software engineering practices remain the base for evaluating AI-generated work.
- Use AI tools openly and critically in personal projects, then document how you verified accuracy, handled errors, tested edge cases, and protected sensitive data.
- Choose one technical depth area by the middle of the degree, such as cybersecurity, cloud, data engineering, machine learning, full-stack systems, embedded software, or human-computer interaction.
- Build a portfolio with deployed projects, not just classroom assignments; include tests, documentation, architecture notes, monitoring, security considerations, and user feedback.
- Complete internships, research projects, hackathons, open-source contributions, or campus tech work that proves you can collaborate on real systems.
- Learn enough statistics, ethics, and data privacy to recognize when AI output is misleading, biased, incomplete, or inappropriate for a decision.
- Practice explaining technical trade-offs to nontechnical audiences through demos, presentations, project writeups, and behavioral interview stories.
- Track employer tool requirements in job postings every semester, then adjust electives, certificates, or projects based on what appears repeatedly.
Students should also avoid a few common mistakes. Do not assume AI will eliminate entire professions, do not avoid AI tools out of fear, and do not use AI in a way that prevents you from learning fundamentals. The strongest graduates use AI as a multiplier while still understanding what the tools are doing.
Another red flag is over-specializing too early in one vendor's tool. Cloud platforms, frameworks, and AI products change quickly. A durable learning plan focuses on concepts, architecture, security, data, and users, then applies those concepts across tools.
Students should evaluate computer science careers using a balanced framework: automation exposure, salary, growth outlook, education cost, personal fit, and adaptability. A role with high exposure may still be a good choice if it offers strong pay, strong demand, and a clear path into higher-value responsibilities.
Use the following questions when comparing career paths, concentrations, internships, or graduate programs. They help separate realistic risk assessment from hype-driven fear.
- Task exposure: Which daily tasks are repetitive, text-based, rules-based, or easy for AI to verify?
- Human judgment: Which parts of the role require accountability, stakeholder trust, ethical judgment, safety, security, or ambiguous decision-making?
- Growth path: Does the entry-level role lead toward architecture, security, data governance, product ownership, research, or leadership?
- Industry context: Is the employer in a regulated, security-sensitive, infrastructure-heavy, or rapidly digitizing industry?
- Skill transferability: Will the skills remain useful if a specific tool, framework, or platform changes?
- Portfolio evidence: Can you prove that you can build, test, deploy, explain, and improve systems with and without AI assistance?
- Education return: Does the degree, certificate, or graduate program connect clearly to roles with durable demand and realistic salary potential?
A strong decision rule is to avoid careers where you expect to perform only routine tasks for many years. Instead, look for roles where routine work is the starting point and the advancement path moves toward systems thinking, users, risk, strategy, or technical depth.
The best computer science career choice is rarely the one with the lowest AI exposure. It is usually the one where your interests, skills, and education plan position you to use AI better than others, solve problems that still require human judgment, and keep learning as the technology changes.
Employer expectations are shifting from "Can you code?" to "Can you solve problems with code, data, AI tools, security awareness, and business judgment?" For computer science graduates, this means the degree still matters, but a transcript alone is less persuasive than evidence of applied, responsible, and collaborative technical work.
In the AI era, employers increasingly look for candidates who can combine tool fluency with fundamentals. The most competitive graduates can explain how a system works, when AI output is unreliable, how to protect sensitive data, and how technical decisions affect users. Consider the following:
- AI-assisted productivity: Candidates should know how to use coding assistants, documentation tools, test generation, and data analysis tools without blindly trusting their output.
- Security-first thinking: Even non-cybersecurity roles now require awareness of credentials, vulnerabilities, data exposure, secure APIs, and software supply chain risk.
- Portfolio depth: Employers value projects that show architecture, testing, deployment, version control, documentation, and measurable problem solving.
- Communication: Graduates need to explain technical trade-offs to managers, users, clients, and cross-functional teams.
- Domain awareness: Candidates with finance, healthcare, logistics, education, manufacturing, or public-sector context can apply computing skills to higher-value problems.
AI is also changing advancement. A junior developer who only completes assigned tickets may face more pressure, while a junior developer who improves deployment reliability, reviews AI-generated code, documents risks, and communicates clearly can move into higher-responsibility work faster.
Some computer science graduates eventually move into product, operations, analytics leadership, or technology management. Students who want to combine technical foundations with executive decision-making may compare technical graduate options with executive MBA online programs, especially if they want to lead AI adoption rather than remain only in implementation roles.
Other Things You Should Know About Computer Science Degree Automation Exposure
Roles built around routine coding, basic testing, simple web production, repetitive reporting, and first-level support have the highest task exposure. The role itself may not disappear, but the entry-level work can become more automated and more competitive.
For many students, yes, especially if the program builds strong fundamentals, applied projects, AI tool fluency, cybersecurity awareness, and a clear specialization. The degree is less valuable if a student relies only on coursework and does not build evidence of practical skills.
Cybersecurity, AI engineering, cloud architecture, data engineering, computer systems, and responsible AI work tend to be more resilient because they involve risk, infrastructure, judgment, and accountability. No specialization is fully automation-proof, so continuous learning matters.
AI may reduce the value of narrow, repetitive implementation work, but it can increase the value of developers who design systems, review AI-generated code, secure applications, improve reliability, and connect software to business needs. Salary outcomes depend on skill depth, industry, region, and experience.
See What Experts Have To Say About Studying Computer Science
Read our interview with Computer Science experts
Elan Barenholtz
Computer Science Expert
Associate Professor
Florida Atlantic University
Kathleen M. Carley
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Professor of Computer Science
Carnegie Mellon University
Imed Bouchrika, Phd
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Professor of Computer Science
National Higher School of Artificial Intelligence
Derek Riley
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Professor, Program Director
Milwaukee School of Engineering
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