11-631: Data Science Seminar - Syllabus

Course Learning Outcomes

This course introduces students to the breadth of data science—covering human-centered, analytic, and systems approaches—through exposure to a wide variety of research topics and literature. Emphasis is placed on developing core academic skills: reading, writing, presenting, critiquing, discussing, and researching in data science. Students will collaborate to analyze publications, synthesize ideas from diverse subfields, and effectively communicate their insights both individually and in groups.

• Gain exposure to the breadth of data science literature, including human-centered, analytic, and systems-oriented research, as well as relevant application areas, venues, and writing styles.
• Learn how to critically read, evaluate, and discuss data science publications, justifying academic assessments of specific works.
• Develop skills for writing academic papers and reviews, by synthesizing research content from multiple perspectives.
• Present research papers in a clear, comprehensive, and collaborative manner, connecting a given publication to related works and broader themes in the field.

Time & Location

TR 05:00PM- 06:20PM, TEP 1403

Course Format

In-Person. The course opens with an initial overview of the Data Science literature and tutorials on how to analyze and critique Data Science publications. The course also provides tutorials on preparing and presenting reviews of Data Science publications and related literature.

Course Organization

The main objective of the course is to get familiar with critically reading, reviewing, and presenting data science papers, to prepare you for the capstone courses. As such, the course will involve the following activities which each student is expected to participate in (N: number of times the activity occurs):

• Attend lectures and guest lectures (6 or 7)
• Read paper according to a specialist role, write a short summary, and discuss it with a group (3)
• Do a practice (1) and main (1) in-class presentation (individual or in pairs) for papers that you have read
• Attend in-class presentations by your colleagues, and ask a question for each presentation
• Write a paper review (1) and a literature survey (2) according to a theme of your choosing
• Write a constructive review of a capstone report (1) and of a capstone presentation (1)

Presentations

A main goal of the class is to learn how to clearly and effectively present research/papers to others, which is a core component of being a data scientist. There will be two opportunities to present papers in class:

• Practice presentations: Each student will present a paper to a smaller audience over Zoom, and will receive feedback from one of the TAs.
• Main presentations: Each student (individually or in pair) will present a paper to the entire class.

Presentation length: There will be 4 presentations per class, which means each presentation must be at most 15 minutes long, with 5 minutes for questions from the audience.

Audience questions: see below.

Selection of papers: For the practice presentations, you will be assigned the paper that you have to present. For the main presentations, we will ask for paper suggestions from all students, and then assign presentation papers taking into account who nominated what paper.

Presentation format and visuals: Please refer to this guide for tips on how to present your paper: # Presentation tips

Here are some general tips for presenting a research paper, though some of these may not apply to your paper.

Introduction

• Assume an adversarial crowd - assume they don’t care about your project
• Motivate your project by explaining: why it matters, what real-world problem it might solve, etc.
• Reel people in using a captivating example! Simplify your task and walk through an example so people really understand. High level data descriptions don’t give the listener a concrete idea of the data we’re looking at. Caveat: make sure the example is short; people shouldn’t be reading for more than 10-15 seconds.

Data description

• Include interesting examples if it’s data that people are unfamiliar with or if the data has interesting properties
• Include data statistics (with numbers and/or graphs)

Methodology

• Diagrams, flowcharts, drawings are much better than text! Often it takes a while to come up with a good visualization for your model, but it can create much more lasting impression than 5 equations.
• Keep equations at a minimum (and don’t put more than 1 or 2 equations in one slide)
• Also, using animation as you explain your models or algorithmic procedures can help people follow along as you are talking.
• Use intuitive labels or icons.
  • If it’s a vector, draw a narrow vertical rectangle
  • Use logos or cliparts for articles, stories, people, etc.

Experimental Set-Up

• Include what the train/dev/test split is.
• Include what objective function you are optimizing and what metrics you will be evaluating

Results

• Make visuals (tables and graphs) easy to follow with a clear takeaway message. Audiences should be able to look at tables and draw conclusions without having to interpret them on their own. You can also use bold font to make the best performing models more clear in tables.
• Replace all tables with graphs/other types of visualizations (you will very likely lose points if you screenshot a table from the paper). Check out these tips for data viz: http://guides.library.duke.edu/datavis/topten.
• Make sure to title and label tables/axis/legends correctly.
• Limit significant figures! p= 0.35 is much more legible than p = 0.346749362.
• Include short takeaways from results (plots/tables)
• Tell the audience what an ideal plot would look like to help understand the plot

General tips

• Limit the number of words per slide as much as you can Try writing out what you want to say first, then replace words with graphs/images/icons.
• Rehearse your talk fully at least once, it helps debug structural and technical issues and helps you figure out how you’re doing on the time limit. This is especially helpful if you’re co-presenting
• Make sure you look up at the audience and not your slides (especially if those are behind you). Using speaker notes is fine but make sure you’re not reading them out loud.
• If you’re pointing at something in the slide, try to highlight it either using a laser pointer or using animations (fade, red circles, etc).
• Content warning: if you’re tackling a problem space that contains sensitive topics, offensive language, etc. please use a content warning, and refrain from using actual examples (e.g., use emojis or blurred out text instead). Refrain from using slurs out loud or in your slides (e.g., fg, fggot, n**er, btch, n*gga, etc). Remember to keep your audience’s well being in mind.

Audience participation (main presentations)

During the main presentation phase, those who are not presenting that week must fill out an audience form with a question for each of the papers. If you presented that week, you do not need to ask a question (e.g., if you presented Tuesday, you do not need to ask a question on Thursday, and vice versa).

The instructor will randomly select a 1-3 students to ask their question out loud for each paper. You are not allowed to ask ChatGPT or other GenAI tools to produce a question for you.

Questions will be both graded as your attendance and for question quality. The reason for the latter is that it’s important to be able to ask good specific questions for new research.

Rubric for questions:

• 0 points: no question
• 1 point: low-effort or low-hanging fruit question (e.g., How does [newest OpenAI model] do on this? How would this work on other languages? Is there a scenario where [method/system] fails? How fast is the system? Can the system design be extended to other networks?)
• 2 points: good question

Summaries and Discussions

Another main goal of the class is to be able to critically read a paper and discuss it with others. There will be a total of three discussion lectures, which will go like this:

• You will be assigned a paper
• You will be assigned a specialist role, which will describe the lens through which you should present the paper
• Before the start of each discussion lecture, you will have to post a summary on Canvas that summarizes your talking points for the discussion.
• After each discussion lecture (or during), you will post a follow up comment on your own summary on Canvas with one thing you learned from the discussion.

Summary format & length: you will post the summaries to Canvas, and keep them between 400 and 600 words. Ensure that your summaries are readable to someone who has not read the paper. Avoid huge walls of text, and feel free to use bulleted lists or other formatting. Do not post images, charts, or tables.

The specialist roles are the following:

• Reviewer
• Archaeologist
• Researcher
• Industry Expert
• Social Impact Assessor

See the following link for descriptions on what each role should do:

Each student in a team is assigned an equally important role to discuss and critique a paper. Details and preparations for each role are provided below. Students should review these and ask for clarification if needed. While students can express role preferences to reflect their individuality and backgrounds, the instructor makes the final decision. Students may not always get their preferred roles but are encouraged to use this as a chance to explore new abilities. Due to class size, not all students will play every role.

Reviewer

Complete a full critical but not necessarily a negative review of the paper. Follow the guidelines for writing the paper review click here. Write between 400 and 600 words.

Archaeologist

The aim of this role is to show the originality and relevance of your research with respect to past and future methodology. Determine where this paper sits in the context of previous and subsequent work. Concretely, you must find and report on one prior paper that substantially influenced the current paper and one newer paper that cites this current paper. Briefly summarize each of these papers and their relationship to the current paper. Write between 400 and 600 words.

1. Identify Influential Prior Work:
   • Find a previous paper that substantially influenced the current paper. Summarize its key contributions and explain how it relates to and sets the foundation for the current research.
2. Locate Subsequent Citations:
   • Identify a newer paper that cites the current paper. Summarize this newer work and describe how it builds upon or diverges from the current research.
3. Contextualize the Research:
   • Place the current paper within the broader research landscape by comparing and contrasting it with the identified prior and subsequent works. Discuss its originality and relevance in advancing the field.
4. Write a Comprehensive Summary:
   • In 400 to 600 words, synthesize the summaries and analyses of the prior and subsequent papers, detailing their relationship to the current paper and its position within the ongoing research conversation.

Researcher

The aim of this role is to propose an imaginary follow-up project – not just based on the current but only possible due to the existence and success of the current paper. You must write the proposal with the following components:

• Motivation/introduction/problem statement: why is the proposed project important? Why is the general task or area important?
• Background: what do we already know in the field? What has been tried? What have we learned so far?
• Method and design: what is your proposed approach? How will you set up your experiments ? How will you evaluate success?
• Significance and conclusion: what would the results tell us, if your project was successful? What doors would that open for other researchers or practitioners?
• References: make sure to cite papers that you mention.

Write between 400 and 600 words.

Industry Expert

You must discuss how the method discussed in the paper can apply to one real-world scenario and propose a new application. Put yourself in the shoes of an employee proposing this application to their boss. Establish context (such as the industry you are working in, current solution and resources, etc.) and discuss the use-case, road map, and business advantage of utilizing the method for your application.

1. Identify the Problem:
   • Describe a problem that can be solved using the paper.
   • Propose a realistic solution based on the paper.
2. Establish Context:
   • Describe your employer’s context, including: industry relevance, current solution, available resources and funding, company data, business goals and objectives.
   • Make reasonable assumptions if data is unavailable.
3. Detail the Steps and Roadmap:
   • Outline background work before implementation.
   • Specify data and resource requirements.
   • Plan steps for complete solution implementation.
4. Discuss the Impact:
   • Argue why your solution is a worthwhile investment.
   • Highlight advantages and superiority over the current solution.
   • Discuss potential disadvantages, drawbacks, and costs.

Write between 400 and 600 words.

Social Impact Assessor

The Social Impact Assessor evaluates the potential societal implications of the paper’s findings and methodologies. This role involves analyzing how the research might affect various communities, considering both positive and negative consequences. The assessor examines ethical considerations, potential biases, and the broader societal context. By providing a critical perspective on the social ramifications, the assessor ensures that the discussion includes an understanding of the research’s impact beyond the technical aspects.

1. Identify Stakeholders:
   • Determine who will be affected by the research outcomes, including communities, industries, and specific demographic groups.
2. Analyze Positive and Negative Impacts:
   • Evaluate the potential benefits and drawbacks of the research on society, considering aspects such as privacy, equality, and access.
3. Examine Ethical Considerations:
   • Assess the ethical implications of the research methods and findings, identifying any biases or ethical concerns.
4. Provide Recommendations:
   • Suggest ways to mitigate negative impacts and enhance positive outcomes, ensuring the research promotes social good and minimizes harm.

Write between 400 and 600 words.

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Examples of summaries for different papers

PENDING

Review & literature survey

Another goal of this class is to prepare you for the capstone classes in the Spring and Fall, in which you will do a capstone research project of your choice. To prepare you for that project, you will have to work your way up to a literature survey in the topic area of your project, in a group of at most 4 students. This will encompass three steps:

• Team and topic choice: you will have to come up with a topic area, 4 relevant papers, and find your teammates.
• Paper review: you will each write a review of a paper, focusing on the remaining broad questions that the paper leaves open. Each teammate will get one of the papers you submitted. Write at most 1.5 pages of content.
• Draft literature survey: focusing on your task and topic area, summarize and compare your 4 related papers’ similarities and differences, as well as the remaining questions that the papers leave open. Write at most 3 pages, and at least 2 pages (excluding references).
• Literature survey: staying in the same topic area and task, you will choose an additional 4 papers, and discuss them together with the 4 papers from the draft literature survey (8 total) in a literature survey. Write at most 6 pages, and at least 5 pages (excluding references).

Format: LaTex with Bibtex, using the ARR style format (LaTeX templates, also available as an Overleaf template).

All assignments from this sequence can be found here: Full assignment: ## Finding teammates and selecting papers for review & literature survey

To prepare for the review and literature surveys, you must find 4 teammates, select a topic for your literature survey, and select 4 papers that are related. Write a short justification for why you chose this topic, and why you chose each paper. Also describe each of the teammates’ expertise.

Using the Google or Canvas form, give short explanations:

• What is the topic of your literature survey? Choose broadly,
• Write 4-5 sentences of introduction for this topic, including motivation (why it’s important as a subject of study).
• For each paper, write 2-3 sentence description of its relevance to the topic.
• For each team member, write a 2-3 sentence background of the expertise and interest of each teammate with respect to the topic of study.

We will randomly assign each team member a paper from the list.

Note, you are required to select recent (2020 or newer) and published (not arxiv-only) papers. Additionally, you cannot select survey papers or position papers. If you have doubts about the papers you select, please post on Piazza.

Paper review

The review should consist of (adapted from the ARR reviewing guide):

• A short summary of the paper (5-7 sentences), written as a neutral, dispassionate summary of the research question and findings/contributions. Make sure you acknowledge all the contributions that you believe the paper is making: experimental evidence, replication, framing of a new question, artifacts that can be used in future work (models, resources, code), literature review, establishing new cross-disciplinary connections, conceptual developments, theoretical arguments. A paper may make several contributions, and not all of them need to be equally strong. You should state in your own words what you see as contributions of the paper, rather than copy/paste it from the abstract.
• Strengths of the paper: even if you fundamentally disagree with a paper, it is important to accurately state all the best aspects of it. Once again, the strengths may come in many different forms: an engineering solution, framing an important issue, a literature review, a useful artifact (a model or a resource), a conceptual development, a reproduction. Performance improvements or complex math are by themselves neither necessary nor sufficient. It should be clear in what way the study advances the field: what did we learn from it that we did not know before? What can we do that we could not do before?
• Weaknesses of the paper: here, list the aspects of the paper that could be improved, of which there could be many.
  • There may be claims that are not actually supported by the evidence or by the arguments, but that are presented as conclusions rather than as hypotheses/discussion. The framing may be misleading. There may be obvious methodological flaws (e.g., only the best run results are reported), errors in the proofs, in the implementation, or in the analysis. There may be insufficient detail to understand what was done or how to reproduce the method and the results. There may be a lack of clarity about what the research question is (even if it is “Does system A work better than system B”?), what was done, why, and what was the conclusion. The paper should also make it clear in what way the findings and/or the released artifacts advance the field.
  • A common reviewer mistake is confusing “must-haves” (weaknesses) with “nice-to-haves” (often, possible follow up or alternative experiments). Any project has limited time and pages, and it is always possible to think of more follow-up experiments. As long as enough work was done to prove the claims that the authors are making, any extra experiments are in the “nice-to-have” category, and not a weakness as such.
  • Note, try to focus on the conceptual, technical, and methodological weaknesses. Do not overly focus on how the paper is written, terminology, or clarity issues, unless they really make the paper harder to read (this is somewhat less likely since the papers all got into conferences, so presumably they should be somewhat understandable). Clarity issues often fall in the “nice-to-have” category (i.e., you think the paper would be nicer if it were written differently, but that doesn’t make the structure a huge weakness).
• Future directions and remaining open questions: to get you started thinking about your capstone, we want you to think about remaining open questions with respect to the broader goal of the paper, as well as any future directions you can think of. These could include methodological changes or improvements to the method, adaptations to new domains, follow up experiments to run, etc. Make sure to mention follow up experiments that would shed important light onto the paper’s main research goals and why the follow up directions would be required,(e.g., avoid simply saying “they should try it on another dataset”, make sure to motivate why the paper’s main research question would benefit from another dataset).

You must turn in your review using LaTex with Bibtex, using the ARR style format (LaTeX templates, also available as an Overleaf template).

Practice literature review

The goal of this assignment is to develop your skills in reviewing and synthesizing academic literature in the your subfield of data science. You will select a task and topic area, choose 4 related recently published research papers, and summarize and compare their methodologies, findings, and contributions. Additionally, you will identify and discuss any gaps or open questions that remain in the research. The review should be comprehensive yet concise, spanning 2 to 3 pages.

1. Read and Analyze Your Chosen Papers:
   • Thoroughly read each paper, taking notes on key points such as:
     • Research objectives and questions.
     • Methodologies and techniques used.
     • Key findings and results.
     • Contributions to the field.
     • Limitations and future research directions.
2. Summarize the Papers:
   • Write a brief summary for each of the four papers. Each summary should include:
     • A concise overview of the research problem and objectives.
     • Description of the methods and techniques employed.
     • Summary of the main findings and conclusions.
     • Discussion of the paper’s contributions to the field.
3. Compare and Contrast the Papers Along Various Dimensions:
   • Analyze the similarities and differences among the papers in terms of the following dimensions:
     • Research questions and objectives.
     • Methodological approaches.
     • Dataset, domain, scope.
     • Key findings and results.
     • Contributions and impact on the field.
   • Identify common themes, patterns, and trends that emerge from the comparison.
4. Discuss Open Questions and Future Directions:
   • Highlight any gaps or open questions that the papers leave unanswered.
   • Discuss potential areas for future research based on the identified gaps.
   • Reflect on how addressing these questions could advance your subfield.
5. Write the Structured Review:
   • Organize your review into a coherent and logical structure, according to this structure:
     • Introduction: Introduce the topic area and the importance of the selected task within data science. Provide a brief overview of the four papers you will review.
     • Summaries: Provide individual summaries of the four papers, focusing on highlighting the details of each paper’s approach that you will focus on in the comparison part. Suggestion: avoid writing more than a 1/4 page (1/2 column) for each summary), and use the \paragraph command for each new paper.
     • Comparison: Compare and contrast the methodologies, findings, domains, and contributions of the papers. Suggestion: have one subsection for each of the dimensions.
     • Open Questions: Discuss the gaps and open questions that remain.
     • Conclusion: Summarize the main points of your review and suggest directions for future research.
6. Format and Submission:
   • The review should be between 2 to 3 pages in length, not including references.
   • Use LaTex with Bibtex, using the ARR style format (LaTeX templates, also available as an Overleaf template).
   • Proofread your review to ensure clarity, coherence, and correctness.
   • Submit your assignment by the specified deadline.

Some notes:

• You can and are encouraged to cite more than just the required papers, especially in the introduction (e.g., to motivate the existence of the research area) and future directions sections (e.g., to give ideas of how to address open gaps).
• You can include at most one figure and one table in your write-up. They must bring in useful information that isn’t better written in text.
• Example published literature reviews (which are useful to learn how to frame reviews, we do not expect as much work as these published reviews):
  • Human-centered/NLP: https://aclanthology.org/P19-1159.pdf
  • Analytics/NLP: https://aclanthology.org/2020.coling-main.247.pdf
  • Systems: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cdt2.12016
  • Data science/ML: https://arxiv.org/abs/2402.16827
  • LLMs/Systems: https://arxiv.org/abs/2312.03863

Rubric:

• 2 points - Formatting & turning in assignment: did the student turn in the assignment on time? Is it using Latex & fits within the page limit?
• 3 points - Coherent choice of papers: are the papers related to the same overall topic?
• 5 points - Introduction
  • Does the review introduce the topic area well (with examples)? Does it motivate why the task is meaningful or important? Does it give an overview of the literature review?
• 12 points - Summaries
  • For each paper: Does the review summarize the papers’ main task and overall topic? Does it summarize papers’ methodology, approach? Does it summarize the papers’ main results, findings, and takeaways?
• 20 points - Comparison. For each dimension:
  • Does the review appropriately compare the similarities the papers of the papers? Does the review contrast the papers appropriately? If some dimensions are not applicable, does the review mention it? Does the review go beyond simply restating what papers did, but instead tie the papers together thematically and relationally?
  • Dimensions: Research questions and objectives; Methodological approaches; Dataset, domain, scope; Key findings and results.
• 10 pts - Open Questions. Does the review appropriately outline open questions that are remaining? Does the review mention future directions for next research projects towards tackling the broader task? Does the review provide possible methods for tackling these future directions? Does the review mention more than 2 open remaining questions?
• 3 pts - Conclusion - Does the review appropriately summarize the task at hand, and papers examined? Does it briefly mention open questions or future work? Is the conclusion an appropriate length (~1/4-1/2 column)?

Total: 55 points

Literature review

The goal of this assignment is to further finetune your skills in reviewing and synthesizing academic literature in the your subfield of data science. You will build on top of the existing practice literature review, adding 4 more papers, and incorporating the comments from TAs. The final review should 5-6 pages.

You should find 4 related papers, preferably ones that are cited in your other 4 papers, or that cite one/some of the 4 papers. Same as for the review, you are required to select recent (2020 or newer) and published (not arxiv-only) papers. Additionally, you cannot select survey papers or position papers. If you have doubts about the papers you select, please post on Piazza.

Rubric:

• 2 points - Formatting & turning in assignment: did the student turn in the assignment on time? Is it using Latex & fits within the page limit?
• 3 points - Coherent choice of papers: are the papers related to the same overall topic?
• 5 points - Introduction
  • Does the review introduce the topic area well (with examples)? Does it motivate why the task is meaningful or important? Does it give an overview of the literature review?
• 24 points - Summaries
  • For each paper: Does the review summarize the papers’ main task and overall topic? Does it summarize papers’ methodology, approach? Does it summarize the papers’ main results, findings, and takeaways?
• 40 points - Comparison. For each dimension:
  • Does the review appropriately compare the similarities the papers of the papers? Does the review contrast the papers appropriately? If some dimensions are not applicable, does the review mention it? Does the review go beyond simply restating what papers did, but instead tie the papers together thematically and relationally?
  • Dimensions: Research questions and objectives; Methodological approaches; Dataset, domain, scope; Key findings and results.
• 20 pts - Open Questions. Does the review appropriately outline open questions that are remaining? Does the review mention future directions for next research projects towards tackling the broader task? Does the review provide possible methods for tackling these future directions? Does the review mention more than 2 open remaining questions?
• 6 pts - Conclusion - Does the review appropriately summarize the task at hand, and papers examined? Does it briefly mention open questions or future work? Is the conclusion an appropriate length (~1/4-1/2 column)?

Total: 100 points

Capstone project review

Finally, during the last two weeks of class, students will attend at least one final second-year capstone presentation and review one draft capstone report written by a second-year MCDS student team.

Extra credit assignments

ChatGPT red-teaming

Extra credit: Try asking ChatGPT/other AI platforms some questions related to a paper that someone presented in class, and assess the output’s correctness. Your goal is two-fold: (1) find an input question / prompt that will lead ChatGPT to produce something incorrect, and (2) explain what about the output is incorrect, and hypothesize why ChatGPT might have gotten it wrong.

You will get more points the more creative your input prompt/question is, and the better your explanation is for why it got it wrong. More details in the assignment on Canvas.

Science communication

Extra credit: Create a short piece of social media content (e.g., TikTok/Instagram Reels/YouTube shorts video, henceforth: content piece) on a concept related to data science. Along with your video, you will submit a short write-up that describes your process (e.g., daily journal log, production diary, challenges), contributions of each team member, and answers to the rubric questions (see requirements below). More details in the assignment on Canvas.

Attendance Policy

This course will be held in person. You are responsible for completing the work assigned and seeking clarification as needed. Late work is generally not accepted without prior arrangement or proper justification.

• Attendance is required for:
  • Lectures and guest lectures
  • Discussions (absence will incur a penalty)
  • Assigned presentation slots (practice and real)
• For presentation phase, on days you are not presenting:
  • You get 2 unexcused absences, i.e., you will get to be absent without telling the instructors beforehand.
  • For any further absences during presentation phase, you will not get attendance points for that lecture.
  • If you get caught posting questions without being in class, you will get 3 absences worth of penalty.
• In-advance excused absences:
  • If you have interviews or other pre-existing commitments, can ask for an exceptional absence (up to 2 more), not including medical reasons.

Grace day policy & scheduling presentations:

• You are allowed one homework grace day that you can use towards exactly one of the following assignments:
  • Paper review
  • Literature survey draft
  • Literature survey
  • Capstone report review
  • Capstone final presentation review
• For main presentations only, you are allowed to swap dates with another student exactly once. If you chose to swap main presentation slots (e.g., due to a job interview), you and your swapping partner must let the instructors know via email at least 3 days in advance (otherwise you will get a zero on the presentation assignment).
• There are no grace days for the discussion lectures (and corresponding summaries) or main presentation questions.

Assessment

AIV, Plagiarism, and GenAI Policy

Collaboration policy: For preparing each presentation and literature survey, you must only share work with your assigned teammates and no other students. Paper summaries, reviews, and capstone reviews are individual assignments. This course is intended to give you experience in autonomous research, so trying to delegate or shortcut preparation is a wasted learning opportunity. Acting against this rule will be considered an academic integrity violation and lead to reprimands, including possible dismissal from the program (see the MCDS Handbook).

Plagiarism and AIV policy: The presentation and related work survey emphasize a literature search and compare/contrast to other material. All material you find and use in any of the course deliverables must be explicitly and correctly referenced/cited. Notes:

• Directly copying text from the paper being summarized, and/or from author websites or other sources, without using “quotation marks” around everything that is a direct quote, followed by a reference to the source being quoted, is plagiarism.
• Text and/or slides copied directly from other sources without attribution in presentations is also considered plagiarism.

Here are some resources for learning what is and isn’t plagiarism:

• Paraphrasing and Direct Quotations
• Paraphrasing and Plagiarism
• Paraphrasing Spectrum

GenAI use policy: In this course, you are expected to do all the work that is required to satisfy the learning objectives. Use of generative AI (e.g., ChatGPT, Perplexity, etc.) to automatically do your assignments for you is not allowed: any assignment you turn in must be your own writing and content (i.e., no direct copy-pasting output from GenAI models, and no copy-pasting + manual or automatic paraphrasing).

You may use GenAI to enhance your understanding of papers and subjects (e.g., by asking questions about papers), helping find papers to include in your literature review (but beware of it may invent non-existent papers), and to ask for feedback on your writing flow (e.g., “can you indicate whether the ordering of paragraphs makes sense”). For grammar and writing improvement, we suggest using Grammarly instead of GenAI.

It is very easy to tell when a student is not actually familiar or has not actually understood the material. If we suspect or confirm that you turned in something AI generated or relied too heavily on AI for your assignments, we reserve the right to ask you to justify your turned-in assignment, waive all your grades in that homework category, or even report an academic integrity violation (AIV).