Rohit Philip, Ph.D.


Postdoctoral Research Associate - I
Dept. of Medical Imaging

   Biography                Research                     Publications                 Coursework             CV/Contact

Fall 2005

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. Graduate-level requirements include additional homework and term projects.
  • ECE 529 - Digital Signal Processing. [Instructor: Prof. Nathan A. Goodman]
    Discrete-time signals and systems, z-transforms, discrete Fourier transform, fast Fourier transform, digital filter design. Graduate-level 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 log-normal shadowing due to signal attenuation, and the flat and frequency-selective properties of multipath fading. Next it examines the fundamental capacity limits of wireless channels and the characteristics of the capacity-achieving 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 flat-fading. Three techniques to combat frequency-selective 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 cross-layer design for networks under hard constraints..

Fall 2006

Fall 2013

Spring 2014

  • ECE 508 - Agent-Based Simulation. [Instructor: Prof. Miklos N. Szilagyi]
    This course will introduce the student to: the concept of agents and multi-agent systems; the main issues in the theory and practice of multi-agent systems; the design of multi-agent systems; contemporary platforms for implementing agents and multi-agent systems; artificial life, artificial societies, N-person 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 agent-based simulation systems; the types of multi-agent interactions possible in such systems; the main application areas of agent-based simulation. Most importantly, they will be able to develop meaningful agent-based systems. Graduate-level 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, Z-transforms, time and frequency domain representations, stability, microprocessor-based designs. Graduate-level requirements include additional homework and a term project.
  • MATH 546 - Theory of Numbers. [Instructor: Prof. Susan Durst]
    Divisibility properties of primes, congruences, quadratic residues, number-theoretic functions, primality, factoring, applications to crytopgraphy, introduction to algebraic numbers. Graduate-level 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 inner-product spaces. The course provides an introduction to representation theory, eigensystems, spectral theorems, singular value decomposition, continuity, convergence, separability, and Sturm-Liouville 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.
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