

Rohit Philip, Ph.D.
COMPUTER VISION ENGINEER
Postdoctoral Research Associate  I
Dept. of Medical Imaging

Coursework

Fall 2005
 ECE 503 
Random Processes for Engineering Applications.
[Instructor: Prof. Michael W. Marcellin]
Probability, random variables, stochastic processes,
correlation functions and spectra with applications to communications, control, and computers.
 ECE 532 
Digital Image Analysis.
[Instructor: Prof. Jeffrey J. Rodríguez]
Digital image analysis, including feature extraction, boundary detection, segmentation, region analysis,
mathematical morphology, stereoscopy and optical flow.
 ECE 579 
Principles of Artificial Intelligence.
[Instructor: Prof. Jerzy W. Rozenblit]
Provides an introduction to problems and techniques of artificial intelligence (AI).
Automated problem solving, methods and techniques; search and game strategies,
knowledge representation using predicate logic; structured representations of knowledge;
automatic theorem proving, system entity structures, frames and scripts; robotic planning;
expert systems; implementing AI systems.
Graduatelevel requirements include additional assignments.

Spring 2006
 ECE 507 
Digital VLSI System Design. (Audit).
[Instructor: Prof. Janet M. Roveda]
This course covers the fundamental techniques for the design, analysis and layout of digital CMOS circuits and systems.
Major topics include: MOSFET basics (structure and behavior of a MOSFET, CMOS fabrication, and design rules),
detailed analysis of the CMOS inverter (static behavior, ratioed vs. ratioless design, noise margins,
computing rise and fall times, delay models, resistance and capacitance estimation, design and layout of static CMOS logic gates,
dynamic CMOS logic design, sequential circuit design (static and dynamic sequential circuit elements, clocking schemes
and clock optimization), CMOS data path design.
Graduatelevel requirements include additional homework and term projects.
 ECE 529 
Digital Signal Processing.
[Instructor: Prof. Nathan A. Goodman]
Discretetime signals and systems, ztransforms, discrete Fourier transform, fast Fourier transform, digital filter design.
Graduatelevel requirements include additional homework and a term project.
 ECE 535 
Digital Communication Systems  I.
[Instructor: Prof. Bane Vasic]
Digital modulation for the additive white Gaussian noise channel, emphasizing optimal demodulation, and analysis of error rates.
 ECE 638 
Wireless Communications.
[Instructor: Prof. Shuguang Cui]
This course will cover advanced topics in wireless communications for voice, data, and multimedia.
It begins with a brief overview of current wireless systems and standards.
It then characterizes the wireless channel, including path loss for different environments, random lognormal shadowing
due to signal attenuation, and the flat and frequencyselective properties of multipath fading.
Next it examines the fundamental capacity limits of wireless channels and the characteristics of the
capacityachieving transmission strategies. The next focus will be on practical digital modulation techniques and
their performance under wireless channel impairments. The next part of the course is spent investigating techniques
to improve the speed and performance of wireless links, which includes the design and performance analysis of
adaptive modulation and diversity techniques to compensate for flatfading. Three techniques to combat
frequencyselective fading are then examined: adaptive equalization, multicarrier modulation, and spread spectrum.
A significant amount of time will be spent on multiple antenna techniques: MIMO channel model, MIMO channel capacity,
and Space Time coding. The course concludes with studying various multiple access schemes in wireless systems and
with an introduction of crosslayer design for networks under hard constraints..

Fall 2006

Fall 2013

Spring 2014
 ECE 508 
AgentBased Simulation.
[Instructor: Prof. Miklos N. Szilagyi]
This course will introduce the student to: the concept of agents and multiagent systems;
the main issues in the theory and practice of multiagent systems; the design of multiagent systems;
contemporary platforms for implementing agents and multiagent systems; artificial life, artificial societies,
Nperson games. Upon completing this course, the students will understand: the notion of an agent;
how agents are different from other software paradigms; the key issues associated with constructing agents,
building and implementing models; the main approaches to developing agentbased simulation systems; the types
of multiagent interactions possible in such systems; the main application areas of agentbased simulation.
Most importantly, they will be able to develop meaningful agentbased systems.
Graduatelevel requirements include completion of more sophisticated projects than undergraduates.
 MATH 574M 
Statistical Machine Learning and Data Mining.
[Instructor: Prof. Hao Helen Zhang]
Basic statistical principles and theory for modern machine learning, high dimensional data analysis,
parametric and nonparametric methods, sparse analysis, shrinkage methods, variable selection, model assessment,
model averaging, kernel methods, and unsupervised learning.

Spring 2015
 ECE 542 
Digital Control Systems.
[Instructor: Prof. Hal S. Tharp]
Modeling, analysis, and design of digital control systems;
A/D and D/A conversions, Ztransforms, time and frequency domain representations,
stability, microprocessorbased designs.
Graduatelevel requirements include additional homework and a term project.
 MATH 546 
Theory of Numbers.
[Instructor: Prof. Susan Durst]
Divisibility properties of primes, congruences, quadratic residues, numbertheoretic functions, primality,
factoring, applications to crytopgraphy, introduction to algebraic numbers.
Graduatelevel requirements include more extensive problem sets or advanced projects.
 MATH 566 
Theory of Statistics.
[Instructor: Prof. Hao Helen Zhang]
Sampling theory. Point estimation. Limiting distributions. Testing Hypotheses. Confidence intervals.
Large sample methods.

Fall 2015
 ECE 501B 
Linear Systems Theory.
[Instructor: Prof. Hal S. Tharp]
Mathematical fundamentals for analysis of linear systems. The course develops the theory behind maps and
operators in finite and infinite dimensional linear vector spaces, metric spaces, and innerproduct spaces.
The course provides an introduction to representation theory, eigensystems, spectral theorems,
singular value decomposition, continuity, convergence, separability, and SturmLiouville theory.
 MATH 571A 
Advanced Statistical Regression Analysis.
[Instructor: Prof. Walter Piegorsch]
Regression analysis including simple linear regression and multiple linear regression.
Matrix formulation and analysis of variance for regression models. Residual analysis, transformations,
regression diagnostics, multicollinearity, variable selection techniques, and response surfaces.
Students will be expected to utilize standard statistical software packages for computational purposes.

