CS 860 (02): Reading Seminar on Algorithmic Gems (Winter 2024)

Lectures	Thursdays 1:30 PM to 4:20 PM in DC 2585
Instructor	Sepehr Assadi (userid: sassadi)
Instructor office hours	Mondays 10:00 AM (by appointment)
Prerequisites	Mathematical maturity, and a strong background in undergraduate-level probability theory, data structures, and algorithm design are all essential.
Textbook	There is no official textbook. Required materials will be posted on this webpage.
Course outline	The course outline will be posted here at some point before the start of the term. This page contains the highlights of course outline that can be updated as the term progresses.
Communication	The lectures will be delivered on the blackboard or via slides. This webpage contains all relevant information for this course. For specific concerns, questions, or comments regarding your experience in the course, you can directly email me @ (sassadi).

This course is a journey through several beautiful algorithmic ideas and results, many of which have already had a profound impact on theoretical computer science. We will cover a mix of classical and modern results. The class will be organized as a blend of lectures and a reading group. The first four weeks (topics 1 through 4) include guest lectures by our Dean's Distinguished Visitor, Prof. Sanjeev Khanna. During this period the students can sign up to present the remaining topics. This will be a great opportunity for us to collectively learn about exciting algorithmic results.

The following is a tentative list of the topics that we will cover in this course (not necessarily in this particular order):

Guest Lectures:

Week 1 : Karger’s Min Cut Algorithm and Cut Sparsifiers for Graphs

Random Sampling in Cut, Flow, and Network Design Problems, by Karger; STOC 1994.
A New Approach to the Minimum Cut Problem, by Karger and Stein; JACM 1996.
Randomized Approximation Schemes for Cuts and Flows in Capacitated Graphs, by Benczur and Karger; SICOMP 2015.

Week 2 : Fingerprinting: Equality testing, Matrix multiplication verification, Polynomial Identity testing, Schwarz-Zippel Lemma, Matchings

Week 3 : The PCP Theorem and the proof of the PCP(n^3,1) verifier

Week 4 : Small space computation: distinct elements sketch, ell_0 samplers, and its applications to graph connectivity in dynamic streams

Student Presentation Topics:

Submodular Function Minimization

The AKS Primality Testing Algorithm (Annals of Mathematics 2004).

Cut Matching Game and Sparsest Cut (JACM 2009)

Polylogarithmic-Time Deterministic Network Decomposition and Distributed Derandomization (STOC 2020)

Matching Size Estimation in Sublinear Time (FOCS 2021)

Nearly Optimal Communication and Query Complexity of Bipartite Matching (FOCS 2022)

Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow (FOCS 2022)

Additional Guest Lectures (Time-permitting):

The Multiplicative Weights Algorithms (MWU) and its Applications

Random walks in graphs: s-t connectivity, the Pagerank algorithm, matchings in regular bipartite graphs

The final grade for the course will be based on the following weights:

100% Class Presentation

Territorial Acknowledgement

The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations.

Students with Disabilities

You are encouraged to discuss with me any appropriate accommodations that we might make on your behalf following the guidelines of the AccessAbility Services.

AccessAbility Services, located in Needles Hall, Room 1401, collaborates with all academic departments/schools to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AccessAbility Services at the beginning of each academic term.

Statement of Inclusivity

I am committed to creating a learning environment in which all of my students feel safe and included, regardless of race, ethnicity, religion, gender or sexual orientation. Because we are individuals with varying needs, I rely on your feedback to achieve this goal. I invite you to let me know about what I can stop, start, or continue doing to make sure every one of my students feels valued and can engage actively in our learning community.

Faculty of Math Statement on Diversity: It is our intent that students from all diverse backgrounds and perspectives be well served by this course, and that students’ learning needs be addressed both in and out of class. We recognize the immense value of the diversity in identities, perspectives, and contributions that students bring, and the benefit it has on our educational environment. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In particular:

We will gladly honour your request to address you by an alternate/preferred name or gender pronoun. Please advise us of this preference early in the term so we may make appropriate changes to our records.
We will honour your religious holidays and celebrations. Please inform us of these at the start of the course.
We will follow AccessAbility Services guidelines and protocols on how to best support students with different learning needs.

Your Health & Well-Being

This is a challenging course with various advanced topics and not-so-easy assignments and exams. All of these are going to be time and energy consuming. But, this is also an elective course so one of my main goals is for you to enjoy taking this course and learning these cool materials as much as I enjoy teaching them. Part of making sure you have fun involves taking care of yourself. Do your best to maintain a healthy lifestyle and work-life balance this term by eating well, exercising, getting enough sleep, and taking some time to relax -- all of these will tremendously help you to achieve your goals in the course and to enjoy the process in the meantime.

You can find more resources to help you with your health & well-being with the Campus Wellness and Student Success Office -- they have tons of resources on helping you to succeed, including very good tips on time management techniques.

Faculty of Math Statement on Mental Health: The Faculty of Math encourages students to seek out mental health support if needed:

On-campus Resources:

Campus Wellness: here to help enhance and support your wellness in a safe, respectful, and inclusive environment.
Counselling Services: email counselling.services@uwaterloo.ca or phone 519-888-4567 ext. 32655
MATES: one-to-one peer support program offered by Waterloo Undergraduate Student Association (WUSA) and Counselling Services: mates@wusa.ca
Health Services: located across the creek from the Student Life Centre; phone: 519-888-4096.

Off-campus Resources:

Good2Talk (24/7): Free confidential help line for post-secondary students. Phone: 1-866-925-5454 (Ontario and Nova Scotia only)
Here 24/7: Mental Health and Crisis Service Team. Phone: 1-844-437-3247 (Waterloo Region only)
OK2BME: set of support services for lesbian, gay, bisexual, transgender, or questioning teens. Phone: 519-884-0000 extension 213 (Waterloo Region only)
EMPOWER ME: Phone 1-833-628-5589 for Cdn./USA; for other countries see here
EMPOWER ME in China: China North 108007142831 and China South 108001402851

Rights and Responsibilities

Every member of this class---instructor, TA, and students---has rights and responsibilities toward having a pleasant, fair, supportive, and free of discrimination and micro-aggression environment in this course, and we are all answerable to the University policies governing ethical behaviour (Policy 33).

In addition, academic dishonesty and plagiarism is considered a serious offense in this course. I expect that any assignment or exam you submit in this course will be your own product and follows the collaboration and external resources policies specified in the course outline. If an assignment is too hard, start earlier, ask for help, or simply do not answer the question --- academic dishonesty is never the right answer. If you have any concerns or questions about these policies, please discuss them with me.

Finally, a reminder that all the course content including lecture notes, presentations, and other materials prepared for the course, are the intellectual property (IP) of the instructor. These course materials are available to you to enhance your educational experience, and sharing them without permission and proper citation is a violation of intellectual property rights.

University Policies: It is your job to know the university policies that govern your behaviour in this course. Some pointers are:

Academic Integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research. The Office of Academic Integrity's website contains detailed information on UW policy for students and faculty. This site explains why academic integrity is important and how students can avoid academic misconduct. It also identifies resources available on campus for students and faculty to help achieve academic integrity in---and out---of the classroom.

Grievance: A student who believes that a decision affecting some aspect of their university life has been unfair or unreasonable may have grounds for initiating a grievance (Policy 70). When in doubt please be certain to contact the department/school’s administrative assistant who will provide further assistance.

Discipline: A student is expected to know what constitutes academic integrity to avoid committing academic offences and to take responsibility for their actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about "rules" for group work/collaboration should seek guidance from the course professor, academic advisor, or the undergraduate associate dean.
For information on categories of offences and types of penalties, students should refer to Policy 71. For typical penalties check Guidelines for the Assessment of Penalties.

Appeals: A decision made or penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes they have a ground for an appeal should refer to Policy 72, Student Appeals.

Intellectual Property: Students should be aware that this course contains the intellectual property of their instructor, TA, and/or the University of Waterloo. Intellectual property includes items such as:

Lecture content, spoken and written (and any audio/video recording thereof);
Lecture handouts, presentations, and other materials prepared for the course (e.g., PowerPoint slides);
Questions or solution sets from various types of assessments (e.g., assignments, quizzes, tests, final exams); and
Work protected by copyright (e.g., any work authored by the instructor or TA or used by the instructor or TA with permission of the copyright owner).

Course materials and the intellectual property contained therein, are used to enhance a student’s educational experience. However, sharing this intellectual property without the intellectual property owner’s permission is a violation of intellectual property rights. For this reason, it is necessary to ask the instructor, TA and/or the University of Waterloo for permission before uploading and sharing the intellectual property of others online (e.g., to an online repository).

Permission from an instructor, TA or the University is also necessary before sharing the intellectual property of others from completed courses with students taking the same/similar courses in subsequent terms/years. In many cases, instructors might be happy to allow distribution of certain materials. However, doing so without expressed permission is considered a violation of intellectual property rights.

Please alert the instructor if you become aware of intellectual property belonging to others (past or present) circulating, either through the student body or online. The intellectual property rights owner deserves to know (and may have already given their consent).

Use of Generative Artificial Intelligence: The following statement is prepared by the Office of Academic Integrity with input from the Centre for Teaching Excellence, Library, and consultations with Associate Deans and members of the Standing Committee on New Technologies, Pedagogy, and Academic Integrity (Last Updated: August 2023):

Generative artificial intelligence (GenAI) trained using large language models (LLM) or other methods to produce text, images, music, or code, like Chat GPT, DALL-E, or GitHub CoPilot, may be used for assignments in this class with proper documentation, citation, and acknowledgement. Recommendations for how to cite GenAI in student work at the University of Waterloo may be found through the Library.

Please be aware that generative AI is known to falsify references to other work and may fabricate facts and inaccurately express ideas. GenAI generates content based on the input of other human authors and may therefore contain inaccuracies or reflect biases. In addition, you should be aware that the legal/copyright status of generative AI inputs and outputs is unclear. Exercise caution when using large portions of content from AI sources, especially images. More information is available from the Copyright Advisory Committee.

You are accountable for the content and accuracy of all work you submit in this class, including any supported by generative AI.