Computer Science Project
Resume:
Radu Florian
Office address: Home address:
Johns Hopkins University
Baltimore, MD 21218
Tel: 410-***-****, Fax: 410-***-****
Baltimore, MD 21218
Tel: 410-***-****
Education
Johns Hopkins University,
Baltimore MD
1996-present: PhD
Candidate, Computer Science
Expected thesis area:Statistical
Natural Language and Speech Processing
Teaching Fellowship
Bucharest University,
Bucharest Romania
Diploma de studii aprofundate, June 1996(equivalent
to M.S. degree), Computer Science, GPA 10.00/10.00;
Thesis: Wavelet-based transformations
- a survey;
Merit Fellowship;
Bucharest University,
Bucharest Romania
B.S.,June 1995, obtained with Diploma
de merit, (equivalent of magna cum laude) Computer Science,
GPA 10.00/10.00.
Thesis: Speech recognition and neural
networks;
Merit Fellowship;
Professional experience
Bucharest University,
Bucharest Romania
Teaching instructorIntroduction to Computer Programming: Responsible for designing and conducting laboratoryassignments
with the classResponsible for teaching recitations, designing part
of the final exam; all grading in class.Neural Networks:Responsible for teaching recitations, designing and conducting
laboratory experiments, partial grading.
Programming Languages
C++ (Borland, ANSI, gnu, Microsoft), C (gnu, Borland,
Microsoft), Pascal(Borland, Turbo, Delphi), Perl, Lisp, Scheme, Java, Prolog,
Smalltalk.
Operating Systems
Solaris, SunOS, Linux, MS-DOS, MS-Windows 3.1, MS
Windows95.
Projects
Speech
recognizer
As a part of my BS thesis, I have implemented a speech
recognizer in C++ It was developed from scratch, using the basic sound
functions implemented under Windows 3.1 (read utterance, open file and
read it chunk by chunk, play the file). The input file was a spoken utterance
in Microsoft WAVE format. The algorithm I investigated computed the mel-cepstral
coefficients from the input and used a dynamic time warping algorithm to
align them against a set of previously learned patterns. There were 3 patterns
for each word to be recognized; they were obtained from the training set
using a 3-NN clustering algorithm. The
system was a discrete-word, speaker-dependent recognition system. I trained
it on a data set of digits and commands, 16 words in total. The accuracy
was close to 80%.
Visualization
tool for regular grammars
As a course project, I have also implemented a visualization
tool for mapping regular grammars or regular expressions to a graphical
representation. One can associate every word of length n from the language
generated by a regular expression with a dot on a table. My project drew
that table in a window. This a method of generating fractals. I also designed
an algorithm for constructing the regular grammar that generates the same
language as a regular expression.
A
CASE Tool
As a team course project for an 1 year course in software
engineering, I have devised and implemented a CASE tool, including the
full set of stages of analysis, design, implementation, validation and
maintenance. The program was written in C++.
Courses attended:
Computer
Science:600.465 Natural
Language Processing600.476 Information
Extraction from Speech and Text600.466 Information
Retrieval and Text Understanding600.463 Algorithms
I600.421 Object
Oriented Systems600.439 Computational
Biology600.488 Computational
GeometryOperating SystemsNeural NetworksDistributed SystemsStatistical Methods in Pattern RecognitionArtificial IntelligenceParallel ProgrammingL-SystemsSoftware Engineering
Math
and Statistics courses:Mathematical (Real and Multidimensional) AnalysisComplex AnalysisGeometryNumerical MethodsFunctional AnalysisAlgebra I and IIDifferential EquationsPartial Differential EquationsInformation TheoryStatisticsSimulationAdvanced Statistical Methods
Topics of Interest
Natural
Language Processing and Understanding
Speech
Recognition
Artificial
Intelligence
Machine
Learning
Machine
Translation
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