Notes for AI/ML and Research Advising Conversation

Course and Research Orientation

• Speaker emotions and motivations:
  • Wants to pursue research and teaching with passion: "I love research. I love teaching."
  • Initially hoped the experience would be pleasant or easy with the current group; describes past optimism about this time.
  • Another participant also values teaching and notes it should be boring, but acknowledges enjoyment in teaching generally.
  • Discusses career trajectory: interested in moving from GTA to GRA eventually; has concerns about being a GTA.
• Course logistics and expectations mentioned:
  • Midterm format confirmed as pencil-and-paper: "for the midterm, can we expect that it will be entirely sort of pencil paper?" → Answer: yes, no programming required.
  • Course code inferred from the discussion around GenAI: appears as something like CS 690 (or 6-90) evaluating GenAI; suggests focus on GenAI within the course.
  • Mentions of class vibe: expressed that the class feels like a solid introduction to AI/GenAI topics.
• Social and campus context cues:
  • There are social groups referred to as "peach tea suites"; at least three suites are present, with some ambiguity about counts (three, possibly four).
  • A first-year student and a July intake are mentioned; indicates new students and onboarding dynamics.
• Advising and professor interaction context:
  • A casual reference to briefly speaking with a potential research adviser last year while looking for guidance.
  • Mentions interaction with security-related or cryptography-related topics in the context of building a research portfolio.
  • The speaker is contemplating talking to many professors (roughly 20) to explore opportunities; emphasizes the importance of making genuine inquiries and understanding researchers’ current work.
  • Specific professor named: Hakan Aden; reference to professor and security topics; indicates ongoing efforts to identify a suitable adviser.
  • Mentions a current research collaborator/experience with someone nicknamed "Billboard" (likely a project mention) focused on security topics; unclear application so far.
  • Indicates that the research project or task has not started yet and there is some ambiguity about how and when it will begin.
• Research areas and practical interests:
  • Security and cryptography surface as concrete current interests; practical/theoretical security is highlighted.
  • Quantum computing is identified as an upcoming area of interest for future work and study.
  • AI and machine learning are recognized as central topics of interest but with uncertainty about foundational understanding.
• Understanding AI/ML and knowledge gaps:
  • Expresses a need to grasp the basics of what AI actually is and what machine learning actually is, to be able to ask meaningful questions.
  • Acknowledges a gap between basic ML concepts and the broader discourse under the umbrella of data science.
  • Perceives AI/ML as hype but with a belief that it will significantly impact future markets and coding practices.
  • Recognizes that current AI knowledge may be insufficient to engage in advanced discussion; seeks a foundational understanding to ask good questions.
• Self-assessment and learning goals:
  • A shared sentiment: not knowing much about AI but wanting to learn enough to participate in conversations and research effectively.
  • Intends to align learning with practical questions about how AI systems work and how they generate results.
• Logistics and campus life notes touched on minor details:
  • Microphones in the room may or may not be functioning; alternative mics are available behind the screen.
  • Some comments about cash-only vending machines and incidental campus amenities (water access, Chick-fil-A) hint at everyday logistical constraints.
  • Brief, casual remarks reflect a typical first-year student experience navigating campus resources and environmental factors.
• Summary of key attitudes and themes:
  • A strong drive toward authentic engagement with research, teaching, and real-world applications.
  • A pragmatic approach to course assessments (midterm format) and expectations about programming requirements.
  • A proactive, exploratory mindset for research placement: reach out to multiple professors, assess fit, and pursue topics spanning security, cryptography, physics of quantum information, and AI/ML foundations.
  • Recognition that AI/ML is both a hot topic and a source of questions; the goal is to establish a baseline understanding to enable meaningful inquiry and critique.
• Immediate implications for study and planning:
  • Prepare for a pencil-paper midterm; review foundational AI/ML concepts in parallel with security/cryptography basics.
  • Start an outreach plan to potential advisers: prepare a concise message outlining interests, questions, and how you could contribute; target around 20 professors; tailor inquiries to their current work.
  • Map out a learning roadmap for AI/ML fundamentals: definitions of AI vs ML, differences between models, understanding what it means for systems to generate output, and how to evaluate performance.
  • Consider the timing and scope of future courses (e.g., an AI course mentioned as a potential follow-up) to build a coherent sequence from basics to advanced topics.

Midterm and Course Logistics (Direct Details)

• Midterm format: pencil-paper only; no programming; no reliance on computer-based tasks.
• Course focus: introductory exposure to GenAI and related technologies; practical implications for research and coding practices.
• Classroom tools: microphones in the room; alternative mics behind the screen if issues arise.
• Communication with peers: informal dialogue about course experiences and current topics in AI/security.

Research Advising Landscape and Strategies

• Adviser search dynamics:
  • Early-stage exploration for a research adviser; conversations with multiple professors; emphasis on genuine alignment of interests.
  • Past interaction: brief meeting with an adviser last year during the search process.
• Practical steps for securing a project/adviser:
  • Ask about what the faculty member is currently doing; use this as a basis to propose collaboration.
  • Be explicit about your own interests (security, cryptography, theory) and how you can contribute.
  • Build a portfolio of potential supervisors (roughly 20 professors considered), recognizing that not all will be available or a good fit.
  • Communicate clearly that you are in the early stage (second year in the CSH program) and are seeking meaningful, authentic collaboration.
• Current project landscape and examples:
  • Ongoing work with a focus on security topics; mentioned areas include theoretical security and cryptography.
  • A potential engagement with a project/team referred to as "Billboard" for security work; precise nature and application remain unclear.
  • Mention of quantum as a future area to explore and prepare for.
• Year and onboarding context:
  • First-year students are present in the group; there is ongoing community formation and onboarding into groups (e.g., peach tea suites).
• Key takeaways for students seeking advisers:
  • Be proactive and genuine in outreach.
  • Demonstrate curiosity and a baseline understanding to ask informed questions.
  • Cast a wide net (target many potential advisers) but focus on fit and shared research interests.

AI/ML Fundamentals: What to Learn and Why

• Core questions to address:
  • What exactly are AI and machine learning? How do they differ?
  • What are the basic machine learning algorithms and how do they work at a high level?
  • How do data and models interact to produce outputs?
  • What are the typical limitations and error modes of AI systems (e.g., bias, robustness)?
• Gaps and context:
  • There is a recognized gap between basic ML concepts and the broader data-centric discourse; this gap can hinder forming precise questions.
  • The speaker believes understanding foundational concepts is essential to being able to ask meaningful, technically grounded questions about AI systems.
• Practical implications for research and coding:
  • A better foundational understanding will enable more effective critique and inquiry into AI-generated outputs and the algorithms behind them.
  • The expectation that AI will influence how we write code in the foreseeable future, underscoring the need to understand core mechanisms.
• Attitude toward AI hype and realism:
  • Acknowledgement that AI research and industry discourse often overstate capabilities; need to maintain a grounded, critical perspective.
  • Despite hype, practical impact on markets and software development is anticipated; thus, education should be forward-looking.
• Personal learning goals:
  • Build a concise, practical baseline of AI/ML knowledge to facilitate intelligent questions and collaborations.
  • Gain enough understanding to participate in AI-related discussions and evaluation without being a domain expert yet.

Practical Next Steps and Action Plan

• Short-term actions:
  • Prepare for the pencil-and-paper midterm by reviewing course materials and notes on GenAI basics.
  • Draft outreach emails to potential advisers; outline your interests (security, cryptography, theory) and questions you want to explore.
  • Plan to talk to around 20 professors to maximize chances of finding a good match; prioritize authenticity and alignment of interests.
• Learning roadmap for AI/ML basics:
  • Clarify definitions: AI vs ML vs deep learning; what constitutes a model and training data.
  • Learn about common ML algorithm families (supervised, unsupervised, reinforcement) at a high level.
  • Understand the workflow of AI systems: data collection, model training, evaluation, deployment, and monitoring.
  • Explore fundamental concerns: data bias, model robustness, interpretability, and security implications.
• Long-term goals:
  • Establish a research project that integrates AI/ML fundamentals with security/cryptography themes.
  • Build the capability to ask informed, technically grounded questions about AI systems to guide collaborations.
  • Track and align with upcoming topics (e.g., quantum, security) to expand research horizons.
• Campus and resource considerations:
  • Be mindful of practical campus logistics (e.g., cash-only vending machines, water access, cafeteria options) that affect daily routines.
  • Use informal campus networks (peers, advisers, first-year groups) to stay informed about opportunities and resources.
• Ethical and philosophical considerations:
  • Reflect on the societal and market implications of AI/ML advances on coding practices and employment.
  • Consider how research proposals and questions align with responsible AI development and security priorities.

Quick Reference: Key Names and Concepts from the Transcript

• People/roles:
  • Professor Hakan Aden (mentioned as a potential adviser)
  • A person or project referred to as "Billboard" (involved in security work; exact role not clarified)
• Topics and domains:
  • Security and cryptography (theoretical security focus mentioned)
  • Quantum computing (anticipated future area)
  • AI and machine learning basics (foundational learning goals)
• Terms and phrases:
  • GTA (Teaching Assistant role)
  • GRA (Research Assistant role)
  • GenAI (Generative AI) focus of the course
  • CSH (program/track context for the speaker)
  • Peach tea suites (campus groupings for new students)

Notes on Gaps and Clarifications Needed

• Some dialogue remains ambiguous or informal (e.g., exact counts of peach tea suites, specific course codes, and the identity of certain names).
• The exact expectations for the current or upcoming AI-related coursework beyond the midterm format are not detailed in the transcript.
• The timeline for starting a research project/adviser relationship is not defined beyond indicating that the process has not yet begun.