20 Computer Science Research Topics for High School Students

Computer science is one of the most dynamic, versatile, and rapidly evolving fields of the 21st century. From artificial intelligence and data science to cybersecurity and ethics, it offers high school students a gateway to solve real-world problems and explore innovative technologies. As tech reshapes every industry—from medicine and education to transportation and entertainment—there’s never been a better time for students to engage in meaningful research that blends technical knowledge with social impact.

For students considering college applications, STEM competitions, or publication, research in computer science can also serve as a powerful differentiator. It not only sharpens problem-solving and analytical skills but also builds confidence in navigating complex ideas—whether you’re coding a model or evaluating algorithmic fairness.

If you’re just getting started or looking to deepen your understanding through an independent project or a program like Nova Research or Nova Patent, choosing the right topic is crucial. Below are 20 compelling and accessible computer science research topics tailored for high school students. Each offers a unique opportunity to explore, experiment, and contribute to critical conversations in the tech world.

1. The Ethics of Artificial Intelligence

Artificial intelligence is no longer a futuristic idea—it’s embedded in everyday life. AI systems now help make decisions about job hiring, credit scoring, criminal justice, and even medical diagnoses. While these systems promise efficiency and scale, they also raise complex ethical questions. Can we trust machines to make fair and transparent decisions? Who is responsible when AI makes a mistake?

Why It Matters:
AI doesn’t operate in a vacuum. It reflects the choices of its designers and the data it’s trained on. This means every decision—how to rank a resume, how to classify an image, how to recommend a product—carries ethical weight. For students interested in philosophy, social justice, or public policy, this topic offers a rich interdisciplinary challenge.

Research Pathways:

• Explore how different ethical frameworks (e.g., utilitarianism, which focuses on outcomes, versus deontology, which emphasizes moral duties) could guide AI behavior. For example, should a self-driving car prioritize passenger safety or minimize total harm?
• Analyze real-world examples like facial recognition systems used by law enforcement. These tools have been found to disproportionately misidentify people of color. What ethical obligations do developers and institutions have to address these disparities?
• Consider the role of transparency and explainability in AI. Should users have the right to know how a decision was made? Can algorithms be audited fairly?

This topic pushes students to consider not just what technology can do, but what it should do—and how we, as a society, define justice, fairness, and accountability in the digital age.

2. Data Privacy in the Age of Surveillance

In today’s hyperconnected world, personal data is collected constantly—from what you search and click to where you go and who you talk to. Corporations and governments alike use this data to track behavior, target advertisements, and optimize systems. But where should the line be drawn? What does it mean to have privacy in the digital age?

Why It Matters:
Many users agree to terms and conditions without reading them. Few understand how their data is stored, sold, or shared. This gap between what companies do and what users expect has sparked global debates over digital rights, consent, and surveillance ethics.

Research Pathways:

• Audit the privacy policies of commonly used apps like Instagram, TikTok, or Discord. How clear and transparent are they about data usage? Do they comply with standards like GDPR (in the EU) or CCPA (in California)?
• Use browser tools to visualize how cookies and trackers follow users across the web. Tools like Privacy Badger or Lightbeam can reveal how even seemingly harmless browsing can lead to large-scale data profiling.
• Investigate how location tracking is used by apps, and what happens when sensitive data is leaked or sold. How do companies balance personalization with privacy?

This topic encourages students to become not only informed researchers but also more conscious users of technology. It also connects computer science with law, ethics, and consumer advocacy—important intersections in our digital world.

3. Machine Learning for Medical Diagnostics

Machine learning (ML) is revolutionizing healthcare by helping doctors diagnose diseases faster and more accurately. Trained on large datasets of medical images or patient records, ML models can detect patterns that even experienced physicians might miss. But the promise of ML also comes with risks—especially when models are trained on biased or incomplete data.

Why It Matters:
From early cancer detection to predicting the risk of heart disease, ML is being integrated into real-world medical decisions. However, a poorly trained model can misdiagnose a patient, delay treatment, or miss warning signs altogether. Understanding how these models work—and when they fail—is essential for anyone interested in medical technology, bioethics, or AI safety.

Research Pathways:

• Use open datasets (e.g., the Breast Cancer Wisconsin Dataset or COVID-19 chest X-ray data) to train a basic classification model in Python using libraries like Scikit-learn or TensorFlow. Evaluate its performance using accuracy, precision, and recall.
• Study how the bias in training data (e.g., lack of diversity in patient demographics) can lead to disproportionate false positives or negatives. How might this impact underrepresented populations?
• Explore ethical concerns around data privacy in health AI. Should patients have a say in whether their medical records are used for training algorithms? What regulations govern this?

This topic combines practical machine learning skills with deep ethical reflection, giving students the chance to contribute meaningfully to one of the most impactful uses of AI today.

4. Cybersecurity and Ethical Hacking

As society becomes increasingly dependent on digital infrastructure, the protection of sensitive information—personal, financial, medical, and governmental—has become critical. Cybersecurity professionals play a crucial role in safeguarding data, but so do ethical hackers, who work proactively to detect and fix vulnerabilities before malicious actors can exploit them. This field merges technical knowledge with ethical reasoning and problem-solving, making it a standout research area for high school students interested in digital safety.

Why It Matters:
From ransomware attacks on hospitals to phishing scams targeting students and families, cyber threats are becoming more sophisticated. Ethical hacking helps organizations preemptively address weaknesses through authorized, controlled penetration testing.

Research Pathways:

• Choose one type of cyberattack—such as phishing (email-based deception), DDoS (overwhelming a server with traffic), or SQL injection (manipulating database queries)—and explore how it works, who it targets, and what makes systems vulnerable.
• Use virtual tools like DVWA (Damn Vulnerable Web App) or Kali Linux in a sandboxed environment to simulate how an ethical hacker might identify and patch weak points in a system.
• Investigate how multi-factor authentication, encryption protocols, or firewalls mitigate specific threats. Can you test the strength of different password protection strategies or simulate a basic brute-force attack (ethically and safely)?

This topic encourages students to take a hands-on approach while building awareness of cybersecurity law, digital ethics, and the ever-evolving nature of online threats.

5. Quantum Computing: Future or Fiction?

Quantum computing is often portrayed as a game-changer—but what does that really mean? Unlike classical computers that process bits as 0s or 1s, quantum computers use qubits, which can exist in superposition (multiple states at once). This unique property allows them to solve certain problems exponentially faster than their classical counterparts, potentially transforming industries like cryptography, drug discovery, and climate modeling.

Why It Matters:
While the full power of quantum computing is still years away from widespread use, students can explore its principles today. It’s an ideal research topic for students interested in physics, computer science theory, and the future of secure communication.

Research Pathways:

• Compare the performance of classical vs. quantum algorithms on computational problems. For example, contrast Shor’s algorithm (for integer factorization using quantum computing) with traditional factoring methods. What are the security implications for encryption methods like RSA?
• Use beginner-friendly simulators like IBM’s Quantum Experience or Qiskit to run basic quantum logic circuits. How do gates like Hadamard or Pauli-Z behave differently from classical logic gates?
• Explore how quantum supremacy might affect cybersecurity. What happens when quantum computers can crack encryption that currently protects sensitive data like bank records or health information?

By engaging with this cutting-edge topic, students not only develop a foundational understanding of quantum mechanics in computing—they also explore how theoretical breakthroughs today could become cybersecurity crises (or solutions) tomorrow.

6. Algorithmic Bias and Discrimination

Algorithms may appear objective, but they reflect the values and assumptions of their creators—and the data they’re trained on. When these inputs are flawed or unbalanced, the results can perpetuate and even amplify discrimination, particularly in fields like hiring, criminal justice, healthcare, and education. This topic is ideal for students who want to explore the intersection of computer science, ethics, sociology, and equity.

Why It Matters:
AI systems are being deployed to make decisions that affect people’s lives. However, tools trained on biased data can lead to discriminatory outcomes—for example, disproportionately denying loans to certain racial groups or misidentifying individuals in facial recognition software.

Research Pathways:

• Analyze datasets used to train popular AI models. For instance, use open datasets related to job applications, criminal justice (e.g., the COMPAS dataset), or health outcomes. Are there imbalances in gender, race, age, or income distribution?
• Use tools like Fairlearn or Aequitas to test and visualize algorithmic fairness. Explore how different fairness metrics (e.g., equal opportunity vs. demographic parity) influence model outcomes.
• Propose solutions to reduce bias, such as re-weighting training data, using fairness-aware algorithms, or incorporating auditing mechanisms at different stages of the AI pipeline.

This research empowers students to not only understand the mechanics of bias, but to contribute to broader conversations about fairness, accountability, and social justice in technology.

7. Human-Computer Interaction (HCI)

Human-Computer Interaction (HCI) is the study of how people engage with digital systems. It encompasses user interface (UI) design, user experience (UX), accessibility, and even the psychology of interaction. As technology becomes more integrated into daily life, designing systems that are intuitive, inclusive, and responsive to user needs is more important than ever.

Why It Matters:
A poorly designed app can frustrate users or exclude entire groups—such as people with disabilities or older adults. On the other hand, a well-designed interface enhances usability, increases adoption, and supports equity in technology access. HCI is a perfect research field for students interested in design, psychology, education, or assistive technology.

Research Pathways:

• Choose a commonly used app or website and redesign its interface for a specific user group (e.g., elderly users, people with low vision, children). Apply accessibility standards such as WCAG (Web Content Accessibility Guidelines).
• Conduct a usability study with real participants. Use observational methods, surveys, or heatmap tools to evaluate how users interact with the new design.
• Explore how cognitive load or color contrast impacts user performance, or analyze mobile apps that promote accessibility.

Students can showcase not only technical skills in prototyping and interface design but also empathy and a user-centered mindset—qualities highly valued in tech careers today.

8. The Role of Computer Science in Climate Modeling

Climate science is data-intensive. From simulating atmospheric patterns to projecting sea-level rise, computer models are essential for understanding the past, present, and future of Earth’s ecosystems. This topic allows students to bridge environmental science and computational analysis—two fields with urgent global relevance.

Why It Matters:
Accurate climate models inform international policy, guide disaster preparedness, and shape public perception of global warming. As climate misinformation spreads, understanding how models work—and what their limits are—is increasingly important.

Research Pathways:

• Use open-source modeling tools such as En-ROADS, SimClimate, or Python-based platforms to simulate carbon dioxide (CO₂) emissions under various policy scenarios (e.g., carbon taxes, deforestation rates, energy use).
• Evaluate how changes in variables—like global population growth or energy demand—affect the outcome of simulations. What do these models suggest about the urgency of action?
• Analyze how model uncertainty (e.g., due to incomplete data or resolution limits) is communicated to the public. Do news outlets or policy briefs accurately reflect the nuance of simulation-based predictions?

Students pursuing this topic gain valuable experience with data modeling, scenario analysis, and policy communication, all while contributing to one of the most pressing global issues of our time.

9. Natural Language Processing (NLP) and Language Translation

Natural Language Processing (NLP) is a branch of artificial intelligence focused on helping machines understand, interpret, and generate human language. From chatbots and voice assistants to real-time translation and sentiment analysis, NLP powers many tools we use every day. For students interested in linguistics, AI, or cultural communication, this field offers rich research opportunities.

Why It Matters:
Despite significant advances, NLP still struggles with nuance, sarcasm, idioms, and cultural context—particularly in multilingual or informal settings. This opens space for exploration around language accuracy, bias, and inclusivity.

Research Pathways:

• Investigate how different NLP tools (e.g., Google Translate, ChatGPT, or DeepL) translate phrases across languages. Choose idiomatic expressions or culturally loaded terms to test for fidelity and tone.
• Compare machine translations with human translations. Where do discrepancies arise, and what do they reveal about the limitations of current models?
• Analyze how sentiment analysis tools interpret ambiguous or sarcastic language. What happens when these tools are applied to tweets, product reviews, or news articles?

Students exploring this topic gain insight into how language, culture, and technology intersect—an increasingly important space in our global, digitally connected world.

10. Cryptography and Secure Communication

Cryptography is the foundation of digital privacy and security. From protecting financial transactions to securing personal messages, encryption algorithms make it possible to transmit data safely over public networks. Studying cryptography introduces students to a world where mathematics, computer science, and logic converge.

Why It Matters:
In an age where cyberattacks and data breaches are common, understanding how information is encrypted and decrypted equips students with skills essential to cybersecurity, ethical hacking, and data protection.

Research Pathways:

• Build a basic encryption algorithm such as the Caesar cipher (a simple shift cipher) or Vigenère cipher (a polyalphabetic cipher), and demonstrate how these classical methods work.
• Then compare these to modern encryption standards such as AES (Advanced Encryption Standard) or RSA, highlighting differences in key strength, computational efficiency, and vulnerability to attacks.
• Dive into the concept of public-key cryptography, where encryption and decryption keys are not the same. Explore how this model underpins secure web browsing (e.g., HTTPS) and digital signatures.

This topic allows students to explore both hands-on programming and the underlying mathematics of security—an excellent foundation for future work in software engineering, fintech, or information assurance.

11. The Impact of Social Media Algorithms

Social media platforms rely on complex recommendation algorithms to curate user experiences. These algorithms decide what content appears in your feed, what videos autoplay next, and what accounts are promoted. While they’re optimized to maximize engagement, they also raise questions about echo chambers, misinformation, and mental health.

Why It Matters:
The information students consume increasingly comes not from deliberate choices, but from algorithmic suggestion. Understanding how this works—and its influence on behavior—can lead to smarter digital citizenship and more ethical technology design.

Research Pathways:

• Conduct a small experiment tracking how likes, shares, watch time, or comments impact the visibility of posts on platforms like YouTube, TikTok, or Instagram. What trends do you notice?
• Analyze how algorithmic changes (e.g., a shift from chronological to interest-based feeds) affect user engagement and content diversity.
• Explore the psychological impact of curated content: How might endless positive content fuel comparison anxiety? How do algorithms unintentionally reinforce bias or misinformation?

This research blends computer science with psychology, ethics, and media studies—making it a great interdisciplinary project for students interested in technology’s social impact.

12. Autonomous Vehicles and Pathfinding Algorithms

Autonomous vehicles depend on real-time data processing and decision-making algorithms to navigate safely and efficiently. These systems must weigh factors like speed, distance, obstacles, and traffic laws—all while reacting in real time. Understanding the algorithms that power these decisions is essential for anyone interested in robotics, transportation, or artificial intelligence.

Why It Matters:
Self-driving technology is rapidly evolving and promises to reshape transportation—but it must be safe, ethical, and explainable. Exploring the logic behind these decisions helps students understand the technical and societal stakes of automation.

Research Pathways:

• Implement a pathfinding algorithm such as A* or Dijkstra’s algorithm in a 2D grid-based simulation using Python or a robotics platform like Arduino with obstacle detection.
• Evaluate the algorithm’s performance based on different priorities: shortest route, fastest time, or safest path. What trade-offs emerge?
• Discuss how self-driving systems incorporate sensor fusion (e.g., LiDAR, cameras, radar) and real-time updates. Explore ethical dilemmas, like how an autonomous vehicle should respond in a no-win crash scenario.

This project teaches not just algorithmic logic, but systems thinking and the complexities of integrating hardware and software in high-stakes environments.

13. Computer Vision and Image Recognition

Computer vision enables machines to “see” and interpret images, much like the human brain. It’s the technology behind facial recognition, augmented reality, security surveillance, and more. With access to public datasets and open-source libraries, students can dive into hands-on projects in this exciting domain of artificial intelligence.

Why It Matters:
Computer vision is reshaping fields like healthcare, retail, and autonomous systems. By learning how machines interpret visual input, students gain insight into one of the fastest-growing areas of machine learning.

Research Pathways:

• Use open datasets such as MNIST (handwritten digits) or CIFAR-10 (labeled images of everyday objects) to train a basic image classifier using Python libraries like TensorFlow or Keras.
• Evaluate how well the model performs under different conditions, such as noise distortion, angle shifts, lighting changes, or blurred edges.
• Explore applications of computer vision in accessibility tools (e.g., visual assistance for the blind), public safety (e.g., license plate recognition), or art (e.g., AI-generated illustrations).

This topic combines practical coding with visual creativity and opens doors to cutting-edge careers in AI, robotics, and interactive media.

14. Computational Thinking in K–12 Education

Computational thinking refers to the set of problem-solving skills used to design systems, break down complex problems, and automate solutions. While often associated with computer science, it’s increasingly viewed as a foundational skill for all learners, much like reading or math. This topic explores how computational thinking can be introduced early in education—and not just in STEM classes, but across disciplines like language arts, history, and the arts.

Why It Matters:
As the future of work continues to evolve, computational thinking equips students with transferable skills like pattern recognition, algorithmic thinking, and abstraction. These cognitive tools are not limited to programming—they enhance logical reasoning, creativity, and critical thinking across all subjects.

Research Pathways:

• Conduct a small-scale study or survey in a school setting to observe how integrating coding into elementary math instruction affects students’ logical reasoning and problem-solving confidence.
• Alternatively, perform a literature review of academic studies on K–12 computational thinking programs. Focus on how early exposure impacts long-term STEM engagement and academic performance.
• Explore case studies of interdisciplinary integration, such as using coding to model narrative structures in storytelling or to analyze historical events through simulations.

This project is ideal for students interested in education technology, psychology, curriculum design, or child development, and it allows for both qualitative and quantitative research approaches.

15. Gamification and Behavior Design

Gamification is the application of game elements—such as points, levels, rewards, leaderboards, or challenges—to non-game contexts. It’s widely used in education apps, fitness trackers, language-learning platforms, and employee productivity tools to influence user behavior and increase engagement.

Why It Matters:
Understanding gamification is not just about making things fun—it’s about applying principles of behavioral science, motivation, and design thinking. When used effectively, gamification can encourage healthy habits, boost learning, and improve task completion rates. But when poorly implemented, it can lead to burnout, manipulation, or shallow engagement.

Research Pathways:

• Design and prototype a simple app or web-based tool aimed at promoting a positive habit (e.g., daily reading, recycling, hydration, or mindfulness). Incorporate elements like progress bars, badges, or daily streaks.
• Conduct a usability study or behavior-tracking experiment with a small group of users over a period of time. Measure engagement, retention, and user feedback.
• Explore different gamification frameworks (e.g., Octalysis, self-determination theory) and evaluate what makes gamification ethical and sustainable. When does it truly motivate, and when does it simply manipulate?

This topic bridges computer science, psychology, user experience (UX) design, and behavioral economics—offering a rich opportunity for applied, interdisciplinary research.

16. Ethical Concerns in Deepfake Technology

Deepfake technology uses deep learning algorithms to generate hyper-realistic videos that depict people saying or doing things they never actually did. While the technology has creative and even educational applications—such as voice cloning for accessibility or film restoration—it also poses serious risks for misinformation, defamation, and loss of personal agency.

Why It Matters:
Deepfakes challenge our basic assumptions about truth in media. They can be used to spread political propaganda, fake evidence in court, or create non-consensual explicit content. Understanding how they work—and how to detect or regulate them—is increasingly important for digital ethics, journalism, and cybersecurity.

Research Pathways:

• Explore how deepfakes are created using generative adversarial networks (GANs) and the types of data required to train them.
• Investigate current deepfake detection methods—including AI-based tools like Microsoft's Video Authenticator or the Deepware Scanner—and evaluate their accuracy under different conditions.
• Study real-world incidents involving deepfakes and assess the social, legal, and psychological consequences. What should legislation cover? What role should platforms play in moderating deepfake content?

This topic empowers students to engage in pressing ethical conversations while applying technical knowledge to real-world problems.

17. The Digital Divide and Technology Access

The digital divide refers to the gap between individuals and communities that have access to modern information and communication technologies—and those who do not. It’s a complex issue affected by geography, income, education, and infrastructure, with far-reaching consequences for education, employment, healthcare, and civic engagement.

Why It Matters:
The COVID-19 pandemic underscored how deeply access to devices and the internet shapes opportunities. Students without stable internet or digital literacy often fall behind in school or lose access to key services, reinforcing cycles of inequality.

Research Pathways:

• Design and distribute a survey to local schools, libraries, or community centers to assess device access, broadband availability, and digital literacy.
• Analyze the findings to identify patterns across demographics such as age, income, or geographic location. Where are the gaps most severe?
• Propose practical, community-based solutions, such as mobile Wi-Fi hotspots, device recycling programs, or local tech literacy workshops. Evaluate the cost, impact, and feasibility of each.

This project blends computer science with social science, giving students the opportunity to apply technical knowledge to social justice and policy change.

18. Robotics and AI in Everyday Life

From robotic vacuum cleaners to AI-powered assistants like Alexa and Siri, robotics is no longer confined to factories and research labs. It's entering schools, hospitals, homes, and public spaces—changing how we live and work.

Why It Matters:
As robotics become more autonomous and widespread, questions arise about job displacement, accessibility, user trust, and privacy. For students interested in engineering, human-robot interaction, or ethics, this topic is rich with technical and societal angles.

Research Pathways:

• Build a basic robot using platforms like Arduino or Raspberry Pi, and program it to complete simple tasks—such as line following, object detection, or obstacle avoidance—using sensors and motors.
• Reflect on the social implications of automation: What tasks are being replaced, and who benefits or loses from this transition?
• Consider the design of robots for specific populations, such as elderly care, early childhood education, or individuals with physical disabilities. How does purpose influence design?

This topic allows for both hands-on engineering experience and reflective analysis of robotics’ role in modern life.

19. The Evolution of Programming Languages

Programming languages have evolved significantly since the days of FORTRAN and COBOL. Today, languages like Python, JavaScript, and Rust offer varied syntax, capabilities, and community ecosystems suited to everything from AI development to mobile apps. Studying this evolution offers insight into how computing adapts to human needs and how developers shape the tools they use.

Why It Matters:
Each programming language is designed with trade-offs in mind—simplicity vs. speed, abstraction vs. control, popularity vs. niche capabilities. By comparing languages, students learn not only syntax but also the philosophies that guide software development.

Research Pathways:

• Choose two popular languages (e.g., Python vs. JavaScript, or Rust vs. C++) and compare them across key categories: readability, speed, ease of debugging, and community support.
• Explore how language features affect developer productivity. For example, Python’s indentation-based structure may make code easier to read, while JavaScript’s flexibility can support fast web development.
• Survey developers or analyze open-source project repositories (e.g., on GitHub) to see which languages are most used for certain tasks and why.

This topic connects history, design, and software engineering in a way that’s both approachable and deeply insightful—especially for students thinking about future careers in programming.

20. Open Source Software and Collaborative Development

Open source software (OSS) is the backbone of the modern internet. From the Linux operating system to web browsers like Firefox and platforms like WordPress, open source projects are built and maintained by global communities of contributors who share their code freely. Participating in OSS exposes students to real-world development practices, collaborative problem-solving, and peer-driven innovation.

Why It Matters:
Unlike classroom assignments, open source development simulates the challenges of real-world software engineering—bug tracking, code review, version control, documentation, and community governance. It's also a way for students to build a public portfolio of contributions, learn from experienced developers, and gain valuable technical and communication skills.

Research Pathways:

• Choose a beginner-friendly open source project on platforms like GitHub, GitLab, or SourceForge (look for labels like “good first issue” or “help wanted”). Projects may range from websites to Python libraries or data visualization tools.
• Contribute by fixing a bug, improving documentation, or writing a simple feature. Document your workflow, including how you selected the project, navigated the codebase, used Git for version control, and interacted with the community through pull requests and issue threads.
• Analyze the collaborative dynamics of the project. How are decisions made? What coding conventions or project guidelines are enforced? What can this model teach us about democratic collaboration in digital spaces?

This topic blends technical development with community-based learning, offering students both skill-building and insight into how decentralized, global collaboration works in practice.

Why Computer Science Research Matters

Pursuing computer science research as a high school student is far more than a résumé enhancer—it’s a transformative experience that builds both intellectual maturity and practical capability. In a world increasingly shaped by algorithms, data, and automation, developing a strong foundation in computational thinking empowers students not just to consume technology—but to shape it.

Computer science research fosters a mindset of inquiry, innovation, and persistence. Whether you're training a machine learning model to detect patterns in health data, evaluating the social impact of recommendation systems, or prototyping an educational robot, each project invites you to engage deeply with problems that are both technical and human. You learn to ask better questions, navigate ambiguity, troubleshoot failures, and iterate toward solutions. These are the same skills that drive innovation across every modern industry—from medicine and education to climate science and global policy.

This process also cultivates resilience and self-direction. Unlike textbook exercises, research doesn’t come with answer keys. It challenges you to define your own goals, assess your results critically, and communicate your ideas with clarity. In doing so, you gain more than technical fluency—you build the confidence to lead, to learn independently, and to contribute meaningfully to the world around you.

For students in mentorship-based programs like Nova Research and Nova Patent, working one-on-one with scholars from Stanford, Harvard, MIT, Yale, and Princeton helps bring this process to life. Many turn their original research into journal publications, competition submissions, or even patentable technologies, while developing the academic maturity to thrive in future university-level inquiry.

But whether you have access to a formal program or are pursuing your project independently, the most important part is to start with curiosity. Let your questions lead you—about how systems work, why algorithms make certain choices, or how you can make technology more fair, efficient, or human-centered.

Choose a topic that excites you. Frame a question that matters. Follow it with tenacity, creativity, and heart. Because the future of computer science isn’t just in coding—it’s in the courage to explore the unknown and the commitment to make something better.