Programs

IGERT CURRICULUM

​The IGERT Core Curriculum consists of the following:

  • 3 skills courses (first two sections of E6915 course offered in Fall 2015, and CBS B8799 course in Fall 2016);
  • 4 studio courses (including E6898 offered in Spring 2016);
  • The Distinguished Speaker Series in the Fall or Spring semester

 Trainees may fulfill 3 of the 4 studio courses required at any time during their PhD career.  However, the first studio course, E6898 “From Data to Solutions”, must be taken in the first year of the program.  IGERT Trainees are strongly urged to fulfill the 3 skills courses during their first two years entering the IGERT program. Students cannot take more than one Machine Learning course to fulfill the core requirements. All IGERT Trainees must attend all of the Distinguished Speaker Series. Please speak with Diana Kim or with your IGERT faculty advisor if you have any questions or concerns regarding the educational program requirements.​

Spring 2016

Coms E6998 Topics in Computer Science Probablistic Mod Discrete Data (David Blei)

Psych G4495: ETHICS, GENETICS, & BRAIN (Frances Champagne)

EECS E6898: TOPICS – INFORMATION PROCESSING: From Data to Solutions (Julia Hirschberg, Shih-Fu Chang, Noemie Elhadad)
*Studio Course
Friday 12:10-2:00p.m. 
Location: CSB 453

COMS W4111 Introduction to Databases

COMS W4705 Natural Language Processing (Dragomir R. Radev)

COMS W4721 Machine Learning for Data Sci (Daniel Hsu)

COMS W4761 Computational Genomics (Itshack Pe'er)

COMS W4771 Machine Learning (Satyen Kyle)

COMS W4772 Advanced Machine Learning (Aurelie C. Lozano)

IEOR 8100-001: Learning and optimization for sequential decision making (Shipra Agrawal)

ECBM 6040: Neural Networks and Deep Learning (Aurel Lazar)

ECBM 9070: Bio-Inspired Computation (taught by IGERT faculty Nima Mesgarani)

EECS 6870: Speech Recognition (Michael Picheny, Stanley Chen, Bhuvana Ramabhadran, Markus Nussbaum-Thom)

Real-time Storytelling (Journalism School) (Link forthcoming) (Susan McGregor)
6:10 - 8:00 Wednesdays (Tentative)

Seminar on real-time data streams for insight and story creation We all know that data is at the heart of both exciting innovation and heated debate, as it continues to revolutionize everything from business to Internet governance to information security. But the data landscape is not what it used to be: today, the largest, most complex and most compelling data sets are not stored in a data center, but stream in real-time from consumer devices, activities and sensors all over the world.

Making sense of these data streams requires a new kind of thinking about data structures, storage, and storytelling. Through the lens of journalistic storytelling, this course will cover everything from the infrastructure needed to process real-time data streams to the ethics of collecting and publishing the data produced - sometimes incidentally - by real people. Topics include: identifying data sources, software design for processing data streams, case studies in steam-based storytelling, implications of unreliable sensors, and the ethics of data collection and publication.

Prerequisites: COMS 3133/4/7/9 (Data Structures) or equivalent programming ability in at least one systems or scripting language (C++, Java, Python)

Fall 2015

STATISTICS G6509 Section 001 (David Blei)

Natural Lang Processing & Psy - 29061 - C SC 87100 - 0 (Rivka Levitan)
Thursdays at 11:45 am - 1:35 pm (Aug 27, 2015 - Dec 23, 2015)
*This course is taught at CUNY Graduate Center*

W4771 Section 001 Machine Learning (Tony Jebara)

COMS 4772: Advanced Machine Learning COMS 4772 (Dan Hsu)

ECBME 4090: Brain Computer Interface Lab (Nima Mesgarani)

CS E6915: Academic Writing and Great Presentations (Janet Kayfetz)
Section 1 Academic Writing
Section 2 Great Presentations

EECS E6892: Topics in Information Processing: Bayesian Models for Machine Learning (John Paisley)

EECS E6893: Big Data Analytics (Ching-Yung Lin)

STAT W4400: Statistical Machine Learning (John Cunningham)

COMS W4705 Natural Language Processing (Dragomir R. Radev)

Spring 2015

EAS/CBS B8799: Intellectual Property for Entrepreneurs (Orin Herskowitz, Jeff Sears)

CS E6915: Tech Writing for CS and Engineers (Janet Kayfetz)
Section 1 Academic Writing
Section 2 Great Presentations

W4771 Section 001 Machine Learning (Daniel Hsu)

EECS E6893: Big Data Analytics (Ching-Yung Lin)

W4400 Regulatory Genomics (Dana Pe’er)

CUNY course: Natural Language Processing, Machine Learning and the Web (Andrew Rosenberg)

EECS 6998: Computational Models of Social Meaning (Smaranda Muresan)

COMS W4721: Machine Learning for Data Science (John Paisley)

STAT W4400: Statistical Machine Learning (John Cunningham)

Fall 2014

EECS E6898: TOPICS – INFORMATION PROCESSING: From Data to Solutions (Julia Hirschberg, Shih-Fu Chang, Noemie Elhadad)
*Studio Course
Friday 1:10-3:00p.m. 
Location: Mudd 825

CS E6915: Tech Writing for CS and Engineers (Janet Kayfetz)
*Skill Course
Section 001: Academic Writing;

*Skill Course
Section 002: Great Presentations;

Section 003: Writing and Presentations 1:1

ECBM E4090: Brain-Computer Interface (Nima Mesgarani)
*Studio Course
Section 001
T 10:10am-12:40pm
Call Number: 73749

STATISTICS G6509
*Studio Course
Section 001 (David Blei)
Mon Wed 1:10pm-2:25pm Loctaion TBA
Call Number: 92247

Journalism J6002 SEMINAR & PRODUCTION (Jonathan Stray)
*Studio Course
Section 001 FRONTIERS COMPUT JOURN
F 12:00pm-3:00pm
Call Number: 90800
Points: 6

Spring 2014

EECS6891: Replicating Computational Results course (Dan Ellis and Brian McFee)

CBS: Intellectual Property for Entrepreneurs and Managers with (Orin Herskowitz and Jeff Sears)

COMSE 6998: Digitally Mediated Storytelling course with (David Elson)

Fall 2013

COMS W4737: Biometrics (Peter Belhumeur)

COMS E6737: Biometrics (Peter Belhumeur)

EECS E6898: TOPICS – INFORMATION PROCESSING: From Data to Solutions (Shih-Fu Chang and Noemie Elhadad)
Friday 1:10-3:00p.m. Room 453 (Conference Room) in Computer Science Building

Psychology G4275: CONTEMP TPCS-LANG/COMMUNICATN (Michelle Levine)
Section 001 CONTEMP TPCS-LANG/COMMUNIC
Wednesday 10:10am-12:00pm in 405 Schermerhorn

CS E6915: Tech Writing for CS and Engineers (Janet Kayfetz)
Section 001: Academic Writing; Section 002: Great Presentations; Section 003: Writing and Presentations 1:1

CUNY-Queens course: Natural Language Processing, Machine Learning and the Web (Andrew Rosenberg)

Spring 2013

CS E6915: Tech Writing for CS and Engineers (Janet Kayfetz)
Section 001: Academic Writing; Section 002: Great Presentations; Section 003: Writing and Presentations 1:1

EECS E6891: Topics - Information Processing (Victoria Stodden and Dan Ellis) 

Fall 2012

EECS E6898: From Data to Solutions (Julia Hirschberg and Shih-Fu Chang)

DEPARTMENTAL REQUIREMENTS AND ELECTIVES

Advisors and trainees will meet at the start of each semester to identify other courses that will constitute the Trainee’s overall program.