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Fall 2021

Introduction to Computer Science Research

(CS 3950 – CRN 14684 – 2 credits)

General Information

·      Instructor: Professor Tina Eliassi-Rad, Khoury College of Computer Sciences

·      Lectures: Mondays, 2:50 PM – 3:55 PM, Snell Library 123

·      Office hours: Mondays &Thursdays, 4:45 PM – 5:30 PM, Online via Zoom

o   Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [fa21 cs3950].

·      Course website on Canvas: https://northeastern.instructure.com/courses/86726

Overview

This 2-credit undergraduate course introduces the scientific method and provides an overview of research in computer science and related fields.

Format

Students will read papers and prepare annotated bibliographies for selected publications. The annotated bibliographies must be uploaded to Canvas before class in PDF format. The students will also research a grand challenge problem and prepare a presentation for class.

Prerequisites

CS 2500: Fundamentals of Computer Science 1.

Grading

·      Attendance & participation 40%

·      Homework assignments (30% = 3 * 10%)

·      Grand challenge research & presentation (30%)

Textbooks

This course does not have a designated textbook. The readings are assigned in the syllabus (see below). You are expected to have read the assigned material before each lecture.

Schedule (Evolving and Subject to Change)

Lec #

Date

Topic

Readings & Notes

1

Mon Sep 13

Overview & Logistics

Ganesh Mani et al. Artificial Intelligence’s Grand Challenges: Past, Present, and Future. AI Magazine 42(1), pp. 61-75, 2021.

Optional: National Academy of Engineering’s 2008 Grand Challenges for Engineering

2

Mon Sep 20

Computer Science Research

Peter J. Denning. Is Computer Science Science?. In Communications of ACM, April 2005.

Dean Keith Simonton. After Einstein: Scientific genius is extinct. Nature 493(602), January 2013.

3

Mon Sep 27

How to read (and write) a (good) research paper

Philip W.L. Fong. Reading a Computer Science Research Paper. SIGCSE Bull. 41(2), pp. 138-140, 2009.

S. Keshav. How to Read a Paper. SIGCOMM Comput. Commun. Rev. 37(3), pp. 83-84, 2007.

Michael Ernst. How to Write a Technical Paper, Last updated July 6, 2021.

Tim Roughgarden. Reading in Algorithms, Paper-Reading Survival Kit.

4

Mon Oct 4

Technology & Ethics

Guest lecturer:
Dr. Will Fleisher

Cathy O’Neil and Hanna Gunn. Near-Term Artificial Intelligence and the Ethical Matrix. In Ethics of Artificial Intelligence, edited by S. Matthew Liao, October 2020.

--

Mon Oct 11

No class

Indigenous People’s Day

5

Mon Oct 18

Unreasonable Effectiveness of …

Eugene Wigner. The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Communications in Pure and Applied Mathematics13(I), 1960.

Alon Halevy, Peter Norvig, and Fernando Pereira. The Unreasonable Effectiveness of Data. IEEE Intelligent Systems 24, pp. 8-12, 2009.

6

Mon Oct 25

Turing Test

Alan Turing. Computing Machinery and Intelligence. Mind, LIX (236), pp. 433-460, 1950.

7

Mon Nov 1

2008 Turing Award Topic: Programming Language & System Design

2008 Turing Award Winner: Barbara Liskov; Turing Lecture Video

Leah Hoffmann. 2009. Q&A: Liskov on Liskov. Communications of the ACM 52(7), pp. 120-ff, 2009.

Karen A. Frenkel. 2009. Liskov's Creative Joy. Communications of the ACM 52(7), pp. 20-22, 2009.

Andrew C. Myers, Barbara Liskov. A Decentralized Model for Information Flow Control. In Proceedings of the 16th ACM Symposium on Operating Systems Principles (SOSP '97), pp. 129-142, 1997.

8

Mon Nov 8

2011 Turing Award Topic: Causality

2011 Turing Award Winner: Judea Pearl; Turing Lecture Video

Judea Pearl. The Seven Pillars of Causal Reasoning with Reflections on Machine Learning. Communications of the ACM, 62(3): 54-60, 2019.

Leo Breiman. Statistical Modeling: The Two Cultures. Statistical Science, 16(3): 199-215, 2001.

9

Mon Nov 15

2018 Turing Award Topic: Deep Learning

2018 Turing Award Winners: Yoshua Bengio, Geoffrey E Hinton, Yann LeCun; Turing Lecture Video

Yoshua Bengio, Yann LeCun, Geoffrey Hinton. Deep Learning for AI. Communications of the ACM 64(7), pp. 58-65, July 2021.

Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep Learning. Nature 521(7553), pp. 436-444, 2015.

10

Mon Nov 22

Student Presentations

11

Mon Nov 29

Student Presentations

 

12

Mon Dec 6

Wrap-up

Notes, Policies, and Guidelines

·      You are expected to have read the assigned material before each lecture.

·      We will use Northeastern’s Canvas for announcements, assignments, and your contributions.

·      Late assignments are not accepted for credit. If you have a verifiable medical condition or other special circumstances, email eliassi [at] ccs [dot] neu [dot] edu as soon as possible.

·      When emailing eliassi [at] ccs [dot] neu [dot] edu about the course, begin the subject line with [fa21 cs3950].

·      Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!