Research Capstone (CS 7980)

Graduate Course (MSCS), Khoury College of Computer Sciences Northeastern University, The Roux Institute

Course Description

This course offers students a culminating experience to demonstrate proficiency in key concepts learned throughout their programs in the core and elective courses. Designed to reinforce concepts in ethics and basic concepts in research, beyond an emphasis on the technical principles learned throughout the program.

Course Objectives

This advanced research course is designed to immerse students in the rapidly evolving field of computer science, with a specific focus on cutting-edge topics such as computer vision, medical imaging, data analytics, machine learning, artificial intelligence, and computer science education. Through a combination of theoretical understanding and practical application, students will aim to contribute to the body of knowledge in these areas by publishing at least one research poster, paper, or journal article, depending on their current progress and research maturity.

The goal is not only to produce tangible research outputs but also to equip students with a robust set of skills necessary for conducting thorough scientific inquiry. Participants will engage deeply with the process of conducting a literature survey to establish the context and necessity of their work. They will learn how to precisely define research problems and propose innovative methods to address these problems. Further, the course will guide students through the implementation of their methods and the critical evaluation of their results. Beyond research methodologies, the curriculum includes vital academic proficiencies such as reading and interpreting technical papers, mastering the art of technical writing, and developing effective research presentations. By the end of the course, students should be well-prepared to contribute to their chosen fields and present their findings to the scientific community.

Course Outcomes

• Exhibit proficiency in the design, implementation and testing of software.
• Apply algorithmic and theoretical computer-science principles to solve computing problems from a variety of application areas.
• Research and apply ethical considerations when solving computing problems.
• Read, discuss, and extract key ideas from research papers published at top-tier computing conferences.
• Demonstrate the conceptual and methodical competence to present research in the computing field.

Projects

• A list of project options will be provided at the beginning of the semester. There are two types of project offerings: industry (XN) projects and faculty projects.
• Students may also propose their own entrepreneurial or startup project, provided it is approved by the instructor. Each project must have a faculty member serve as the Principal Investigator (PI) to provide oversight and guidance. Projects should demonstrate innovation, feasibility, and potential real-world impact.

Course Schedule

Detail below is subject to change.

Week	Module	Assignment	Materials
1	Meet & Greet	Projects Discussion
2	Literature Review	Project Selection Day!
3	Literature Review II	Check-in I
4	Problem Introduction	Check-in II
5	Problem Statement	Check-in III
6	Your Algorithm	Check-in IV Panel Review I
7	Your Algorithm	Check-in V
8	State of Art Algorithms	Check-in VI
9	Midterm	Midterm Project Demo (Async Video)
10	Critiques and Responses	Check-in VII
11	Experiments, Threats to Validity	Check-in VIII
12	Results and Analysis	Check-in IX Panel Review II
13	Final Analysis	Check-in X
14	Final Day	Final Presentation Final Report

Any time our class time coincides with a holiday, we will not meet in person.

Course Assessment

Final grades will reflect students’ effort and performance. The course grade will be based on the following:

• 20% Project Check-ins (2% each - Good Execution - 2 points, Average - 1 point, Poor/None - 0 points)
• 15% Panel Review I (General Proposal Overview)
• 20% Midterm Demo (10% paper, 10% presentation)
• 20% Final Demo (10% paper, 10% presentation)
• 10% Demo Day (10% attendees)
• 15% Panel Review II (final paper)

Late submissions are subject to 10% penalty each day up to 100%. No regrading.

Grade Calculations

Grades will be calculated on an absolute basis: there will be no overall curving. The mapping of raw point totals to letter grades is given below. Please note that these grade boundaries may move slightly at the discretion of the instructor, but the grade boundary for A is unlikely to change.

Range	Grade
93.00 – 100.00	A
90.00 – 92.99	A-
86.00 – 89.99	B+
82.00 – 85.99	B
77.00 – 81.99	B-
73.00 – 76.99	C+
69.00 – 72.99	C
65.00 – 68.99	C-
0.00 – 64.99	F

Materials

• Code repository (e.g., GitHub)
• LaTeX editing environment (e.g., Overleaf)
• UML diagram tool (e.g., Lucidchart)
• Graphing software (e.g., Pgfplots, draw.io)
• Project Management (e.g., Trello)
• Large Language Models (e.g., Claude)