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        <h1>EN.601.727 Machine Programming</h1>
        <p>Johns Hopkins University &mdash; Fall 2026</p>
        <p><strong>Instructor:</strong> Ziyang Li &nbsp; | &nbsp; <strong>Email:</strong> <a href="mailto:ziyang@cs.jhu.edu">ziyang@cs.jhu.edu</a></p>
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        <section>
            <h2>Oral Presentations</h2>
        </section>

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        <section>
            <h3>Format</h3>
            <p>
                <strong>Six lectures</strong> in the second half of the semester are dedicated to
                <strong>student-led presentations</strong> &mdash; see the
                <a href="index.html#calendar">course calendar</a> for the dates.
                Each presentation topic includes <strong>three related research papers</strong>,
                with <strong>two students assigned to each paper</strong> &mdash; so each session has six presenters.
            </p>
            <p>
                Each paper is led by <strong>two students</strong>, who split the discussion between them:
            </p>
            <ul>
                <li>
                    The <strong>Defender</strong> (10 min) presents the paper&rsquo;s main ideas,
                    methodology, and key contributions.
                </li>
                <li>
                    The <strong>Attacker</strong> (10 min) critically evaluates the paper, discusses its
                    limitations, and raises questions from a reviewer&rsquo;s perspective.
                </li>
            </ul>
            <h3>Sign-Up</h3>
            <p>
                The sign-up form is now <strong>open</strong>!
                Please complete it to indicate your <strong>topic preferences</strong>, your
                <strong>preferred role</strong> (Defender/Attacker), and your <strong>availability</strong>.
                Topic and role assignments will consider your preferences; in case of conflicts,
                <strong>earlier submissions will receive priority</strong>, so sign up early.
                We will announce the assignments after collecting everyone&rsquo;s responses.
            </p>
            <p>
                <strong>Sign-up Form:</strong>
                <a href="https://docs.google.com/forms/d/1Bng2SpKeA4unzy0gQthO-sjHimNOsdlq-S2O8o9aklg/viewform" target="_blank">EN.601.727 Oral Presentation Sign-Up</a>
            </p>
            <p><strong>Deadline:</strong> Tuesday, October 20, at 11:59 PM</p>
            
            <h3>Grading Policy</h3>
            <p>
                This oral presentation is <strong>graded on a completion basis</strong>, contributing
                <strong>10%</strong> of your final course grade:
            </p>
            <ul>
                <li><strong>2%</strong> for submitting the sign-up form before the deadline</li>
                <li><strong>8%</strong> for successfully completing your oral presentation</li>
            </ul>
            <p>
                <strong>Important:</strong> If you do <strong>not</strong> submit the form by the
                deadline, you’ll be <strong>automatically assigned</strong> to a topic and presentation slot
                at the instructor’s discretion. Failure to present in your assigned slot without prior approval
                will result in a <strong>zero for the presentation component</strong>.
            </p>
            <h3>Attendance Notice</h3>
            <p>
                Attendance during the student-led sessions will be <strong>recorded strictly</strong>.
                If you must miss a session due to external circumstances, please notify
                <strong>the instructor or the TA in advance</strong>.
            </p>
        </section>

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        <section id="paper-topics">
            <h2>Paper Presentation Topics</h2>
            <p>
              The following eight topics explore different aspects of Machine Programming, with a focus on
              how AI can assist, automate, and advance programming and software development.
              These descriptions provide examples of research directions within each topic rather than an
              exhaustive list. The specific presentation papers will be assigned by the course staff.
            </p>

            <h3>Topic 1. LLM Training, Fine-Tuning, and Adaptation for Programming</h3>
            <p>
              How can we train and adapt AI models to better understand and generate code? This topic explores
              pre-training and fine-tuning on code-related data, reinforcement learning with coding feedback,
              and techniques such as LoRA, quantization, and knowledge distillation to improve the efficiency
              and effectiveness of models for programming tasks.
            </p>

            <h3>Topic 2. Multi-Agent Systems for Programming</h3>
            <p>
              How can multiple AI agents collaborate to accomplish complex programming tasks? This topic explores
              how coding agents divide responsibilities (e.g., planning, implementation, testing, and reviewing),
              how different organizational structures and communication topologies affect their performance, and
              how agents coordinate through communication protocols to develop software collaboratively.
            </p>

            <h3>Topic 3. Recursive Self-Improvement for Machine Programming</h3>
            <p>
              Can AI systems improve their own programming capabilities? This topic explores how AI agents can
              iteratively improve their coding workflows and harnesses, automatically search for better model or
              agent architectures, and conduct machine learning research by generating, executing, and evaluating
              code with limited human intervention.
            </p>

            <h3>Topic 4. AI Applications in Software Engineering</h3>
            <p>
              How can AI automate different stages of the software development lifecycle? This topic covers
              AI-powered coding and computer-use agents, program understanding and transformation (e.g.,
              decompilation, transpilation, and reverse engineering), and automated software testing, static and
              dynamic analysis, debugging, and program repair.
            </p>

            <h3>Topic 5. AI Applications in Cybersecurity</h3>
            <p>
              How can AI assist with programming tasks in cybersecurity? This topic explores AI-driven
              vulnerability detection and code analysis, automated generation and testing of exploits, and
              security agents that write and execute scripts for penetration testing, red teaming, and
              vulnerability remediation.
            </p>

            <h3>Topic 6. AI Applications in Cyber-Physical Systems</h3>
            <p>
              How can AI help develop programs that control and interact with physical systems? This topic
              explores AI-assisted generation, adaptation, and testing of software for autonomous vehicles,
              robots, and humanoid systems, including generating robot control code, planning executable actions,
              and validating programs through simulation and real-world feedback.
            </p>

            <h3>Topic 7. AI Applications in Mathematics and Scientific Computing</h3>
            <p>
              How can AI assist with programming for mathematical reasoning and scientific discovery? This topic
              explores AI-assisted formalization and theorem proving, generation of programs for scientific
              modeling and simulation (e.g., Navier&ndash;Stokes equations), and automated optimization of
              numerical algorithms and high-performance computing code.
            </p>

            <h3>Topic 8. AI Applications in Human-Computer Interaction (HCI)</h3>
            <p>
              How can AI make programming more accessible and interactive for human users? This topic explores
              natural-language, visual, and graphical programming, AI-assisted development interfaces, human-AI
              collaborative coding, educational systems for STEM students, and systems that help users create,
              understand, and modify programs with minimal programming expertise.
            </p>
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