Course Logistics


Instructor: Prof. Emma Tosch (she/her) -

During class, I prefer for you to address me as "Prof. Tosch." "Dr. Tosch" and "Prof. Emma" are also fine, but the former feels overly formal to me and latter a little cute. It is normal for graduate students in computer science to refer to graduate faculty by first name, but due to the mixed enrollments of this class, I would prefer the more formal "Prof. Tosch."

Please do not refer to me as "Ms. Tosch," and especially not "Mrs. Tosch." While the rules for address in academia are complex and political, refraining from these two is one of the few near-universal norms.

TA(s): There are no TAs, nor course assistants for this course.


Meeting times and location

MWF 12pm-12:50pm, Innovation E204

Student hours (Office hours)

I will be in my office after class on Mondays and Wednesdays from 1-5pm and welcome students dropping by. I will always have Teams on, so if you need to join from another location, you can. I will also announce additional students hours on an as-needed basis.

  • Hybrid: Mondays, Innovation Hall E456 and MS Teams, 1-2pm and 3-5pm
  • Hybrid: Wednesdays, Innovation Hall E456 and MS Teams, 1-5pm

I am also available over MS Teams chat (may not be synchronous), and by appointment with 24hrs notice. Unless you wish to discuss a sensitive issue, please post any questions to the appropriate Teams channel; it is likely that more people will be interested in your question or observation than you realize!

Note: I will be working from home on Tuesdays and Thursdays.


This course provides in-person instruction only. In the event that we move to online instruction by university mandate, we will use the provided MS Teams Team. Please be advised that MS Teams is not fully functional on *Nix operating systems. Please ensure you familiarize yourself with the available technical support for students. If you need a suitable device for fully participating during a stay-at-home order, please contact UVM IT and your advisor for help acquiring a suitable device.

Platforms and Software

This course will be taught entirely in-person; participation via discussion will be a central feature. We will be using the following tools and technologies:

  • Blackboard For graduate students, Blackboard will be used as a portal for important links (i.e., those listed below), for displaying grades, and for occasional polling. Undergraduate students will use Blackboard to hand in their scribed notes.
  • Hotcrp (pronounced “hot crap”) will be used for paper bidding, reviews and discussion. Hotcrp is a real-world conference management system used widely in the systems community. Hotcrp is also approved for educational use. If you do not have a hotcrp account before the end of the first week of classes, please email to be added.
  • The course website will hold the schedule and a copy of the information in this syllabus. Copies will live on Blackboard and Teams, but this website should be considered the most up-to-date.
  • A Github organization will be used to manage projects.
  • MS Teams will be used for group chats. If the university mandates remote instruction, we will meet on Teams.

Mask Policy

All students are required to wear masks during class, regardless of vaccination status or state/university policy, until notified otherwise. This follows standard masking protocol.


Hard pre-requisite

Graduate standing or evidence of computing maturity; I will grant an override to any interested undergraduate in good academic standing.

Soft pre-requisites

Many of the readings for this course are drawn from the systems, programming languages (PL), software engineering (SE), and databases (DB) literature. Therefore, a background in one of these areas (e.g., a course in PL, SE, operating systems, distributed systems, database systems, applied formal methods, or some professional software development experience) may aid in understanding course readings and allow the student to spend fewer hours on reading and responses. The focus of this course is applying methods from these background areas to pressing data collection problems. Therefore, an interest in domain-specific problems in more data-intensive fields (data science, machine learning, computational social science) can be just as beneficial as a background in the technical methods.

I expect any motivated student to be able to excel in this course

Attendance Policy and Classroom Environment Expectations

This course follows standard attenance policy guidelines. Participation will be not be graded, but all students are strongly encouraged to ask questions and engage with the material during class time. Students may also engage with reviews through the commenting feature in hotcrp.

Course Content

Course Learning Objectives

After completing this course, students should have a holistic view of the modern data collection pipeline for the purpose of knowledge discovery. Students will be able to identify the major open research problems in this domain.

This course will also introduce students to the professional standards and tools of the computer science systems community.

Finally, this course will double as a research methods course, meaning that we will include discussion of what constitutes a research question, falsifiable hypotheses, and the various methods for evaluating systems that process both machine- and human-generated data.

Readings and Reviews

Readings must be done by 11:59pm the night before classes are due. This will give me sufficient time to read your reviews and comment on them. Some readings may take significant time, so please plan accordingly. You may want to start your readings the weekend before they are due.


Students will be assigned to present 1-2 papers. Nearly every career path requires strong communication skills, and public presentations are one way to develop these skills. You should take extra time to research the background of the paper you are presenting. We will discuss the qualities of a good presentation during class.

Undergraduates may swap their presentations, so long as they inform me ahead of time. Graduate students must present at least once.

Grading Criteria/Policies and Assessments

Undergraduate Credit

Undergraduates will be graded on a points-based system:

Letter Grade Points
A 93-100
A- 90-92
B+ 87-89
B 83-86
B- 80-82
C+ 77-79
C 73-76
C- 70-72
D+ 67-69
D 65-66
F Below 65

There are more than 100 points total available:

  • There will be 20-30 readings throughout the semester. Students will enter reviews each week. Each review is worth up to 3 points. Students will enter reviews into hotcrp.
  • Students will be required to present at least one, but no more than two, papers per semester. Students may choose to swap assignments and should let me know at least six hours before class. One student may negotiate with another to present their assigned reading, resulting in more than two presentations, so long as both parties consent. Each presentation is worth up to 10 points.
  • Undergraduates may volunteer to scribe lectures (at most 2 scribes/lecture) and project workshops. Scribing is more than note-taking; each document is worth up to 3 points and you will have the opportunity to improve your notes for more points. Notes should be typed up in LaTeX.
  • Undergraduates may join a graduate project, however joining a project will result in no numeric credit.

Thus, there are two ways to earn an A+:

  1. Earn over 100 points.
  2. Join and contribute to a graduate project AND earn “A” grade via the points system (i.e., 93 + project = A+)

Graduate Credit

The goal of the graduate section of this course is to provide structure to guide early-stage research projects and lay the foundation for a future publication. In most computer science domains, this means you will be doing preliminary work for a top-tier conference submission (equivalent to a top journal submission in other fields). My hope is that by the end of the semester, you have enough work for an initial workshop paper (or conference, if your subfield ranks journals higher). Therefore, graduate students will be evaluated thusly:

Component Percentage
Project 50%
Presentations 30%
Reviews 20%

My goal is to ensure that this course is not overly onerous for graduate students and that you are able to make research progress. During my first year of graduate school, Hanna Wallach gave me (and other new graduate students) this paraphrased advice:

Aim for the minimum passing grade, plus some epsilon correlated with how much you want to impress the professor. If you consistently earn higher grades, people will wonder why you weren't spending more time on research.

Unfortunately, this advice is hard to take in practice, so I will make things easier for you: I want to be able to give you all As.1 Show up, participate, put them time in and be good citizens, and you will pass the course with flying colors. If you are having a hard time keeping up, communicate issues early and often. This class will certainly be work, but it should not be a distraction.

That said, sometimes other courses are overly demanding and this creates an arms race for your time. Therefore, if I must provide a breakdown of how you will be graded for the presentations and reviews, it would shake out like this:

  • You will be graded on your presentation using the same criteria as the undergraduate students.
  • Graduate students who present more than once can have their second presentation substitute in for three reviews.
  • Reviews will be graded pass/fail. Passing critieria is equivalent to the criteria to be used for undergraduates achieving 3/3 points on their reviews.
  • Your total review grade will be the percentage of "passed" reviews.

How would I prefer to grade things? I'd prefer if most people had high engagement most of the time. I'd prefer to not have to police the occasional late review and instead assume that everyone is participating in good faith, and that folks who miss a review will make it up later. Things happen; it's not the end of the world. However, if everyone fails to engage, the quality of the course will drop. Therefore, I will track graduate engagement and let the class know when things are slipping.

Course Evaluation

All students are expected to complete an evaluation of the course at its conclusion. Evaluations will be anonymous and confidential, and the information gained, including constructive criticisms, will be used to improve the course.

  1. I will not be giving out A+s to graduate students.