Adithya Rajan is seeking a career in machine learning and big data. He has a Ph.D. in Electrical Engineering from Arizona State University with extensive coursework in machine learning, optimization, and statistics. He has over 3 years of industry experience as a data scientist and research engineer developing machine learning algorithms. His research focuses on applying statistical techniques like stochastic ordering and information theory to wireless communications and signal processing.

1. ADITHYA RAJAN
11925 W 109 ST APT 203, Overland Park, KS 66210 | adithya.rajan@gmail.com | (480) 297-9810
OBJECTIVE
Seeking a research and development career in machine learning and big data, where I may contribute to the team's
skill-value through my background in applied statistics, optimization and information theory.
EDUCATION
Ph. D., (Electrical Engineering) Arizona State University, Tempe, AZ. May 2014
 Thesis: “On the comparison of fading channels”
 GPA : 3.96 / 4.00
 Coursework included Machine learning, optimization, statistics, inference theory.
M.S., (Electrical Engineering) Arizona State University, Tempe, AZ. May 2010
 Thesis: “Diversity combining over fading channels with impulsive noise”
 GPA : 3.75 / 4.00
 Coursework included linear algebra, digital signal and image processing, algorithms.
B.E., (Electronics and Communications Engineering) Sathyabama University, India. Jul 2007
 Aggregate Score: 86/100
WORK EXPERIENCE
1. Garmin International – Olathe, KS Feb 2015 – Present
Data Scientist – Advanced solutions engineer
 Responsible for design and optimization of machine learning algorithms for wearable devices.
 Experience with business analytics and market segmentation.
 Big data manipulation using the Hadoop stack.
2. Arizona State University – Tempe, AZ Nov 2014 – Feb 2015
Researcher
 Worked on researching the applications of stochastic ordering to wireless system technology.
 Involved in writing journal publications and actively peer-reviewing other research papers.
3. Digital Compression Technology – Herndon, VA
Research & development staff March – November 2014
 Responsible for complete system design of back-haul system including analysis and simulation.
 Involved in the testing and troubleshooting of learning algorithms such as neural networks.
 Stability analysis of subsystems such as gradient descent equalizer, phase locked loops.
 Performed massively parallel simulations on multi-GPU cluster using MATLAB and CUDA.
4. Broadcom Corporation – San Diego, CA
Summer intern May – August 2012
 Proposed and tested a new algorithm for optimizing power amplifier stability in 802.11 WiFi devices
 Concepts from nonlinear system optimization and stability theory were utilized.
RELEVANT SKILLS
Software
 Java, C++, MATLAB, R
 Hadoop stack: Mapreduce, storm,
HBase, spark, Pig.
 Linux shell scripting, Perl scripting
Concepts
 Parallel computing/ algorithm parallelization
 Statistical inference and predictive analysis
 Machine learning – classification/clustering

2. RELEVANT PROJECTS
 Big Data
1. Market segmentation by clustering user demography and Garmin device usage data for Garmin’s fitness
and wellness users (over 2 million users).
2. Development of learning algorithms and predictive modeling for Garmin’s fitness devices to enhance user
experience.
 Distributed and parallel computing:
1. Developed a system for offline processing of fruit-fly embryo images to detect patterns of interest.
2. Parallelization of Monte-Carlo simulations for digital communication systems (Matlab/CUDA)
 Computer vision:
1. Implementation of real-time 3-dimensional scene reconstruction system using two cameras
2. Disparity estimation using images grabbed from two cameras
 Statistical estimation :
1. Implementation of statistical Bayesian detector for an author identification problem.
2. Implemented a classifier for anomaly detection in embryo images.
 Digital signal processing:
1. Implementation of compressive sensing algorithms using convex optimization techniques.
2. Implementation of adaptive filters for channel equalization in wireless channels.
3. Implementation of the Viterbi decoder and Belief propagation decoder for decoding of channel codes.
RELEVANT COURSES
 Linear algebra and Convex optimization
 Detection and estimation theory
 Machine Learning
 Random signal theory
 Digital signal processing
 Database management systems
 Data structures and algorithms
 Information theory
 Workshop on parallel computing
 Probability and statistics
RESEARCH EXPERIENCE
 Machine learning:
1. Designed and implemented a handwriting recognition and OCR employing neural networks (C++).
2. Worked on clustering techniques for image segmentation. This was applied toward automatic segmentation
of embryo images using MATLAB.
3. Implemented a complete real-time automatic face recognition engine using PCA and SVM classifiers.
 Information theory:
1. Pioneered a new sub-field in wireless communications, which draws a parallel between actuarial/financial
science and wireless communications. This technique, called stochastic ordering, is now used for wireless
system comparison and asymptotic performance analysis of wireless systems. (7 IEEE publications)
2. Established that the industrial standard in combining schemes for multi-antenna communication is highly
sub-optimal under RF interference, and proposed a new low-complexity implementable scheme which
performs almost optimally. (1 IEEE publication)
INTERESTS AND ACHIEVEMENTS
 Contributed to NSF research grant which was successfully awarded (2010-2014, Arizona state university).
 Recognized by Redhat inc., and awarded special recognition award for usability and localization of open-
source handwriting recognition project (2006).
 President of student volunteer group promoting Indian classical music in Arizona. Group of 10-20 students.
(2010-2014)
 Hardware hobbyist – I like to hack up circuits using platforms such as Raspberry-pi in my spare time.
 Developed open-source C++ library for neural networks.

3. PUBLICATIONS
Thesis publications
 “On the Ordering of Communication Channels”, Arizona State University, May 2014. (1 citation)
 “Diversity Combining over Fading Channels with Impulsive Noise”, Arizona State University, May 2010.
Journal Papers
 “A Unified Fading Model using Infinitely Divisible Distributions”, IEEE transactions on wireless
communications, Submitted Dec. 2015.
 “Stochastic Ordering of Fading Channels through the Shannon Transform”, IEEE transactions on information
theory, July 2014. (1 citation)
 “A Representation for the Symbol Error Rate Using Completely Monotone Functions”, IEEE transactions on
information theory, June 2013. (5 citations)
 “Applications of Stochastic Ordering to Wireless Communications”, IEEE transactions on wireless
communications, Sept 2011. (22 citations)
 “Diversity combining over fading channels with stable noise”, IEEE trans. on wireless communications, Sept
2010. (42 citations)
Conference Papers
 “A Representation for the Symbol Error Rate of Arbitrary Constellations under AWGN”, ASILOMAR
conference, Nov 2012.
 “Ergodic Capacity Ordering of Fading Channels”, International symposium on information theory, July 2012.
(4 citations)
 “On the Complete Monotonicity of Symbol Error Rates”, International Symposium on Information Theory,
July 2012. (6 citations)
 “Stochastic Ordering of Fading Channels”, Signal processing advances in Wireless Communications, May
2011. (1 citation)
 “Electrooculogram Based Instrumentation and Control System and its Applications for Severely Paralyzed
Patients”, International conference on biomedical and pharmaceutical engineering, Singapore, Dec 2006. (11
citations)