Producing the Foundation of a Publishable Paper

Computer Science Research Practicum

COACH

DR. MICHAEL LITMAN, PH.D.

Master Computer Scientist

A practicum is the first half of a thesis. You will not study research from a distance — you will do it, and walk out holding the foundation of a publishable paper: a real problem, a literature review, a reproducible design, measured results, a drafted paper, and a road to a venue.

START WEEK 1 SEE THE SYLLABUS
16
Weeks
1
Conference-style paper
13
Domains to choose from
8+8
Frame → Execute

A 16-week, master's-level research practicum in applied computer science and AI. Instead of sixteen weeks of method lectures ending in a proposal, you produce the foundation of a publishable paper: a defined problem, a systematic literature review, a reproducible experimental design, preliminary results, a conference-style draft, and a publication roadmap. You choose a domain — AI, security, networking, software engineering, databases, HCI, graphics, AR/VR, robotics, cloud, HPC, architecture, or data science — and investigate how modern AI advances it.

— SYLLABUS

Part One

Week 1
Ch 1 — What Is Research, Really?
The scientific method; basic vs applied research; research vs development; what makes work publishable; the kinds of computer-science research — and reading three recent conference papers to see the shape of the thing
P1Read Like a Scientist
Week 2
Ch 2 — Finding a Problem Worth Solving
Gap analysis; industry- vs academic-driven research; turning an observation into a research question; three candidate areas and ten candidate questions
P2Research Opportunity Report
Week 3
Ch 3 — Finding the Literature
Google Scholar, the ACM Digital Library, IEEE Xplore, arXiv, Semantic Scholar, Connected Papers; building and managing a bibliography in Zotero; forward/backward citation chasing
P3Annotated Bibliography (20+ sources)
Week 4
Ch 4 — Reading, Synthesis, and the Comparison Matrix
Reading papers efficiently; extracting methodologies and datasets; identifying trends and limitations; building a comparison matrix that surfaces the gap your work will fill
P4Literature Review Draft
Week 5
Ch 5 — AI Across the Computer Science Domains
Modern LLMs, agents, retrieval-augmented generation, fine-tuning, computer vision, reinforcement learning, and multimodal AI; matching an AI method to a domain problem in security, software engineering, databases, networking, and more
P5Domain-Specific AI Integration Proposal
Week 6
Ch 6 — Research Questions, Hypotheses, and Variables
Formulating a falsifiable hypothesis; independent and dependent variables; controls; threats to validity (internal, external, construct, conclusion)
P6Question, Hypothesis, and Variables
Week 7
Ch 7 — Designing a Reproducible Experiment
Benchmarks, baselines, control groups, ablations, and reproducibility; train/validation/test discipline; the design that another researcher could rerun and confirm
P7Experimental Design Document
Week 8
Ch 8 — The Proposal — and Defending It
Assembling problem, literature review, methodology, and expected outcomes into a 10-15 minute proposal talk; the peer-review session; revising under critique; plus the cumulative midterm on research methods
P8Proposal Presentation + Revised Proposal (MIDTERM)

Part Two

Week 9
Ch 9 — Building the Research Environment
Data collection and tool selection; reproducibility from the start; Git and version control; environment pinning, seeds, dataset documentation, and the research portfolio
P9Reproducible Experiment Setup
Week 10
Ch 10 — Pilot Experiments
A debugging methodology for experiments; validating your assumptions before you scale; sanity-checking metrics; measuring performance on a small run
P10Pilot Results Report
Week 11
Ch 11 — Full Experimental Execution
Running the primary experiments; collecting performance metrics, statistical results, and observations; recording faithfully what you saw, not what you hoped, with provenance for every number
P11Raw Results Dataset
Week 12
Ch 12 — Analyzing the Data
Statistical significance and effect size; confidence intervals and multiple-comparison correction; visualization; error analysis; Python/Jupyter and R — and the discipline of an honest measure
P12Results Section Draft
Week 13
Ch 13 — Discussion: Making Meaning
Comparing your findings against prior work; explaining what you found; handling unexpected outcomes honestly; the line between what the data supports and what you wish it said
P13Discussion Section
Week 14
Ch 14 — Writing the Research Paper
The standard structure — abstract, introduction, related work, methodology, results, discussion, future work; the rhetorical moves of an abstract; the ACM and IEEE conference templates in LaTeX/Overleaf; assembling the full draft
(no new project — the full draft + writing drills)
Week 15
Ch 15 — Peer Review and Revision
The formal peer-review process; reviewing for novelty, methodology, and writing quality; reading reviews of your own work; revising and writing a response
(no new project — the formal review + revision)
Week 16
Ch 16 — The Research Symposium
A conference-style presentation; the finished conference-style paper; and a concrete publication roadmap — choosing a venue (workshop, conference, or journal), reading its call for papers, and mapping the path from draft to submission
P14Final Paper, Talk, and Publication Roadmap (FINAL)