Transcript Agenda - NYU Computer Science Department

Computer Sciences at NYU
Open House
January 2004
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Graduate Study at New York University
The MS in Computer Sciences
The MS in Information Systems
The MS in Scientific Programming
The PhD in Computer Science
Questions and answers
Reception with faculty
Why study at NYU
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Largest C.S. department in the area
Many areas of strength
High caliber, high quality program
All courses taught by faculty (regular,
visiting, or industrial adjuncts)
Possibility to get involved in research -biocomputing, graphics, algorithms….
Great campus location!
Why study Computer Science
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Monetary: media, finance, communications,
information technology, new industries.
Intellectual: Improve skills and ability to
acquire new skills
Personal: It’s fun.
Subject Matter of the M.S.
program
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BAs program; M.S. s design; PhDs do
research. (Note: Most profs also program.)
You learn not only languages, you learn how
to design languages, similarly for databases,
operating systems etc.
Programming in the large (by small groups)
Research environment
Evening classes to accommodate working
professionals
Deeper Understanding
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“...Everything is on the surface, you don’t
read the rule book, you do it by tinkering.
The danger is that this sort of tinkering
becomes a model for all understanding”
(Sherry Turkle, Sci.Am. April 1998)
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An MS allows you to understand the
details under the hood (and build your
own engines when needed).
The field of Computer Science
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Foundations
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basic sciences (algorithms, programming
languages, operating systems, compilers)
Advanced technology
– staying current (cryptography, Java/XML,
distributed computing, networking,
animation, verification, visualization,
biocomputing ...)
Foundation Subjects
The scientific bases of computing:
– Algorithms (also complexity and theory of
computation)
– Programming Languages
– Operating Systems
– Compilers
Technological Subjects
Software design methodologies
 Graphics, animation, and visualization
 Artificial Intelligence, NLP, Pattern Rec
 Numerical computing, Time Series Anal
 Secure file systems/Cryptography
 Databases, Distributed systems
 Internet programming & Multimedia
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Requirements of MS in CS
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36 credits (12 courses)
– typically 2-3 years (must be completed in five)
Core Examination on Foundations
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Specialization Area
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Possibility of internships/independent
studies/interdisciplinary courses.
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Privileges of Grad students
Numerous seminars (10 a week)
 Libraries
 Coles Sports facility
 Meeting future colleagues
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Some Research Projects
Multimedia and user interfaces
 Robust distributed computation
 Performance of parallel systems
 Image recognition in industry and medicine
 Computational genomics
 Fluid dynamics and airfoils
 Motion capture/Query by humming.
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The PAC Program
For students with some professional
experience (power user) but no
undergraduate degree in CS
 Reasonable math background
 Begins each Fall semester
 Adds 1 year, 8 credits
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Some Entry Stats
General GREs > 700 in quantitative and 4.0
or better in analytic.
 Strong grades
 Strong specific recommendations (from
work and/or academia)
 Relevant experience, knowledge and
desires.
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Master’s in Information
Systems
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The MS in Information Systems
– with the Stern School of Business
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Roughly half computer science and half
business courses + capstone projects course
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Aim is to train Chief Information Officers.
The Project course
Centerpiece of MSIS program.
 Offers students real-world experience
 Recent projects with The Gertrude Stein
Repertory Theatre and Bell Labs, HBO,
ILX Systems, Inc. , InterWorld , The
Hypertext Neurological Knowledgebase
(THyNK), etc.
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Master’s in Scientific
Computing
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The MS in Scientific Computing is a joint
program with the Courant Math Department
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Goal is to train designers of mathematical
programs in science and finance.
The Ph.D. Program
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72 Credits (24 courses)
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Normally, 2 to 4 years more than M.S.
Certification of practical and theoretical
skill.
 Oral preliminary exams
 Thesis
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Proposal
Submission
Defense
QUESTIONS
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check also
www.cs.nyu.edu
or write to [email protected]
Some choices:Applications
Programming
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Graphics
Data Communications & Networks
Advanced topics in data communications
Advanced topics in Operating Systems
User Interfaces
Real-Time programming
Unix tools
Groupware
Staying Current
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Quick-learning from fad to fad is not
enough (“No, but I’ve heard of it…”)
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Needs solid scientific / technical basis to
recognize and adapt to innovation.
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How: foundation courses + timely courses
from leading researchers and practitioners.
Some Areas of Specialization
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Software Engineering
Applications Programming
Databases and distributed Computing
Numerical Analysis
Artificial Intelligence
Computer Architecture
Graphics
Internet technologies and Multimedia