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<body>
<a href="./index.html" class="logo" title="Home"><span class="logo-initial">R</span></a>
<div class="special-menu">
<a href="#top" class="scroll-links special-link">Top</a>
</div>
<div class="scroll-menu">
<a href="#about" class="scroll-links">About</a>
<a href="#publications" class="scroll-links">Publications</a>
<a href="#projectsca" class="scroll-links">Computer Architecture</a>
<a href="#projectsml" class="scroll-links">Machine Learning</a>
<a href="#projectshpc" class="scroll-links">High Performance Computing</a>
<a href="#projectso" class="scroll-links">Projects (Others)</a>
<a href="#skills" class="scroll-links">Skills</a>
<a href="http://rajeshk.me/demos.html" class="scroll-links">Demos</a>
<a href="#contact" class="scroll-links last-link">Contact</a>
</div>
<section class="full-page" id="top">
<section class="left white wrapper">
<div class="container">
<div class="dummy">
<h1 class="uppercase">Rajesh Kumar</h1>
<h6 class="uppercase">Design Engineer 2 at Advanced Micro Devices, Bangalore</h6>
</div>
</div>
</section>
<section class="left black wrapper">
<div class="container">
<div class="dummy">
<img class="img-circle" src="images/me.png">
<div><a href="#portfolio" class="button white scroll-links" title="Academics & work experiences">Explore My Work</a></div>
<div><a href="http://rajeshk.me/demos.html" class="button white" title="Notes & blogs">Other Stuff</a></div>
</div>
</div>
</section>
<div style="clear: both;"></div>
</section>
<section class="content" id="portfolio">
<h1 class="heading" id="about" name="about">About me</h1>
<p>
Interested in "Computer Architecture for ML/AI" and "ML/AI for Computer Architecture". Experienced in Parallel/GPU computing, Computer Architecture, SoC Performance Analysis, Machine Learning. Like playing with Deep neural networks. I work at AMD, Bangalore as an SoC performance Analysis Engineer. Before joining AMD, I was pursuing MS by research in Computer Science & Engineering at <a href="http://iiit.ac.in" target="_blank" title="IIIT Hyderabad">IIIT Hyderabad</a> under the guidance of <a href="http://cstar.iiit.ac.in/~kkishore/" target="_blank" title="Dr. Kishore Kothapalli">Dr. Kishore Kothapalli</a> (<a href="http://cstar.iiit.ac.in/" target="_blank" title="CSTAR">CSTAR</a>). I have mostly worked in the areas of Parallel Algorithms and GPU computing. Before joining Masters, I worked at Infosys Ltd. as a Systems Engineer for over a year. Apart from high-performance computing, I love working on Machine Learning and Data Science problems. I love teaching (interacting with) school kids, reading Hindi literature and following Indian politics.
</p>
<h1 class="heading" id="publications" name="publications">Publications</h1>
<div class="item">
<div class="paper-detail"><i>[Publlished] </i><span class="authors">Rajesh Kumar, Suchita Pati, Kanishka Lahiri, </span><span class="paper-title"><b>Speeding up instruction tracing by hardware profiling AMD SimNow,</b> </span><span class="workshop-details"> to <b>AATC 2017</b> (AMD Asia technical conference)</span></div>
<div class="link">
<a href="" target="_blank">Available internally for AMD</a>;
</div>
</div>
<div class="item">
<div class="paper-detail"><i>[Publlished] </i><span class="authors">Rajesh Kumar, Suchita Pati, Kanishka Lahiri, </span><span class="paper-title"><b>DARTS: Performance Counter Driven Sampling using Binary Translators,</b> </span><span class="workshop-details"> to <b>IEEE-ISPASS 2017</b> (International Symposium on Performance Analysis of Systems and Software)</span></div>
<div class="link">
<a href="http://ieeexplore.ieee.org/document/7975281/" target="_blank">IEEE Explore Link</a>;
</div>
</div>
<div class="item">
<div class="paper-detail"><i>[Published] </i><span class="authors">Rajesh Kumar, Kishore Kothapalli, </span><span class="paper-title"><b>A Parallel Framework for Horizontally Local Dynamic Programming Problems,</b> </span><span class="workshop-details"> to <b>IEEE-IACC 2016</b> (International Conference on Advanced Computing)</span></div>
<div class="link">
<a href="http://ieeexplore.ieee.org/document/7544838/" target="_blank">IEEE Explore Link</a>;
</div>
</div>
<div class="item">
<div class="paper-detail"><i>[Published] </i><span class="authors">Rajesh Kumar, Kishore Kothapalli, </span><span class="paper-title"><b>A Novel Heterogeneous Framework for Local Dependency Dynamic Programming Problems,</b> </span><span class="workshop-details"> to <b>IEEE-IPDPS PLC 2015</b> (Programming Models, Languages and Compilers for Many-core and Heterogeneous Architectures)</span></div>
<div class="link">
<a href="http://ieeexplore.ieee.org/document/7284374/" target="_blank">IEEE Explore Link</a>;
</div>
</div>
<h1 class="heading" id="projectsca" name="projectsca">Projects (Computer Architecture)</h1>
<section class="grid-collection">
<div class="item">
<div class="project-title">Workload Sampling using Binary Translators and Machine Learning Techniques</div>
<div><code class="id_tags">Workload Analysis</code> <code class="id_tags">Comupter Architecture</code> <code class="id_tags">Machine Learning</code> <code class="id_tags">Instruction Tracing</code></div>
<div class="project-description"> We developed a new technique for workload sampling to drive architectural simulation that carefully integrates hardware performance counter monitoring, fast, dynamic binary translation for workload simulation and tracing, with standard machine-learning techniques. We exploit the observation that significant similarities exist between workload phase behavior as observed by host performance counters of (i) a native, or host-mode run of a workload, versus (ii) a guest-mode run, where it is run in a binary translating simulator. Using this methodology, we were able to generate a suite of traces that showed less than 2% geomean IPC error using cycle-level models of production x86 parts, and low errors on several other
key statistics, versus hardware measurements. This technique drastically reduces the effort and turnaround time required to generate such suites, cutting storage and computational requirements by over an order of magnitude compared to conventional approaches. This work is published in ISPASS 2017 (motivation) and AATC 2017 (Details).</div>
<div class="project-links">
<a href="http://ieeexplore.ieee.org/document/7975281/" target="_blank">Paper</a>;
</div>
</div>
<div class="item">
<div class="project-title">Workload (Client) Characterization and Tracing</div>
<div><code class="id_tags">Workload Analysis</code> <code class="id_tags">Comupter Architecture</code> <code class="id_tags">Machine Learning</code> <code class="id_tags">Instruction Tracing</code></div>
<div class="project-description"> We characterize and trace PC based (PCMark10, 3DMark, Skype for Business etc.) and gaming (DOTA2, League of Legends etc.) benchmarks. Based on the statistics gathered from this process, we project the performance of these benchmarks on future architectures. We use machine learning techniques on silicon and simulator statistics to achieve this.
</div>
</div>
<h1 class="heading" id="projectshpc" name="projectshpc">Projects (High Performance Computing)</h1>
<section class="grid-collection">
<div class="item">
<div class="project-title">Heterogeneous (CPU + GPU) Framework for Local Dependency Dynamic Pogramming Problems</div>
<div><code class="id_tags">CUDA</code> <code class="id_tags">OpenMP</code> <code class="id_tags">Parallel Programming</code> <code class="id_tags">GPU Computing</code> <code class="id_tags">Dynamic Programming</code></div>
<div class="project-description">We developed a heterogeneous framework for solving a class of dynamic programming problems called Local Dependency DP Problems. We classify LDDP problems into four categories and propose a parallel heterogeneous framework for solving these problems on multi-core CPUs and GPUs. We implement many GPU specific optimizations in the framework such as tiling (using shared memory), memory coalescing, overlapping computation with data transfers (using streams), and load balancing (between CPU and GPU). We have used CUDA and OpenMP for programming on GPU and CPU respectively. This work work is published in IEEE IPDPS-PLC 2015.</div>
<div class="project-links">
<a href="./pdfs/dp1.pdf" target="_blank">Paper</a>;
</div>
</div>
<div class="item">
<div class="project-title">Heterogeneous Framework for Horizontally Local Dynamic Pogramming Problems</div>
<div><code class="id_tags">C++</code> <code class="id_tags">CUDA</code> <code class="id_tags">OpenMP</code> <code class="id_tags">Parallel Programming</code> <code class="id_tags">GPU Computing</code> <code class="id_tags">Dynamic Programming</code></div>
<div class="project-description">We developed a heterogeneous framework for solving one more class of dynamic programming problems called HLDP Problems. We classify HLDP problems into appropriate categories and propose a parallel heterogeneous framework for solving these problems on multi-core CPUs and GPUs. We implement many GPU specific optimizations in the framework such as tiling (using shared memory), memory coalescing, overlapping computation with data transfers (using streams), and load balancing (between CPU and GPU). This work work is published in IEEE IACC 2016.</div>
<div class="project-links">
<a href="./pdfs/dp2.pdf" target="_blank">Paper</a>;
</div>
</div>
</section>
<h1 class="heading" id="projectsml" name="projectsml">Projects (Machine Learning)</h1>
<section class="grid-collection">
<div class="item">
<div class="project-title">Use Deep Learning to Clone Driving Behavior</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Computer Vision</code> <code class="id_tags">Deep Learning</code></div>
<div class="project-description">Built and trained a convolutional neural network for end-to-end driving in a simulator,using TensorFlow and Keras. Used optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/CarND-DeepLearning-ComputerVision/blob/master/CarND-Behavioral-Cloning-P3/writeup_template.md" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/CarND-DeepLearning-ComputerVision/tree/master/CarND-Behavioral-Cloning-P3" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Vehicle Detection and Tracking</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Computer Vision</code> <code class="id_tags">Deep Learning</code></div>
<div class="project-description">Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from a automotive camera taken during highway driving. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/CarND-DeepLearning-ComputerVision/blob/master/CarND-Vehicle-Detection-P5/writeup.md" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/CarND-DeepLearning-ComputerVision/tree/master/CarND-Vehicle-Detection-P5" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Train a Smartcab to Drive</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Autonomous Vehicles</code> <code class="id_tags">Reinforcement Learning</code></div>
<div class="project-description">Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/blob/master/Project-4/smartcab/project_description.md" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/tree/master/Project-4/smartcab" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Traffic Sign Classification</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Autonomous Vehicles</code> <code class="id_tags">Deep Learning</code></div>
<div class="project-description">Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/blob/master/Project-5/project_report.md" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/tree/master/Project-5" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">POS Tagging and CYK Parsing for Indian Languages</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Natural Language Processing</code> <code class="id_tags">Machine Learning</code></div>
<div class="project-description">We implemented POS taggers for Hindi, Tamil and Telugu using supervised (Hidden Markov Model) and unsupervised (clustering) approaches. For parsing, we used CYK algorithm on datasets of these languages. </div>
<div class="project-links">
<a href="./pdfs/pos_tagging.pdf" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/POS-Tagging-and-CYK-Parsing-for-Indian-Languages" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Eigen Faces based Face recognition</div>
<div><code class="id_tags">MATLAB</code> <code class="id_tags">Image Processing</code> <code class="id_tags">Machine Learning</code> <code class="id_tags">SVM</code> <code class="id_tags">PCA</code></div>
<div class="project-description">We implemented a face recognition system using PCA (Principle Component Analysis) and SVM (Support Vector Machines). PCA reduces the high dimensional image to a low dimensional space (referred to as eigen space). Computing the eigen vectors of scatter matrix (of the training samples) and selecting Top k eigen vectors are two key tasks in this process. We have implemented the above techniques on standard datasets maintained by Yale University, CMU University and also on a real time dataset generated during CSE471(Statistical Methods in AI) class at IIIT Hyderabad. We also performed validation, verification experiments on these datasets with the above mentioned techniques and recorded results and inferences.</div>
<div class="project-links">
<a href="./pdfs/eigen_faces.pdf" target="_blank">Report</a>;
<a href="https://github.com/rajesh-iiith/Face-Recognition-using-Eigen-Faces" target="_blank">Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Predicting Boston Housing Prices</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Regression</code> <code class="id_tags">Supervised Machine Learning</code> <code class="id_tags">Data Analysis</code> </div>
<div class="project-description">The goal of this project was to predict the selling price of future clients’ home in Greater Boston Area. The project includes statistical exploration of Boston Housing dataset followed by application of Decision Tree Regressor, grid search, cross validation and prediction. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/tree/master/Project-1" target="_blank">Report & Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Building a Student Intervention System</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Classification</code> <code class="id_tags">Supervised Machine Learning</code> <code class="id_tags">Data Analysis</code> </div>
<div class="project-description">The goal of this project was to identify the students who might need early intervention. The project starts with preparation and statistical exploration of Student dataset. We then apply and evaluate the performance of various classifiers (SVM, Decision Tree, and Gaussian) along with cross validation. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/tree/master/Project-2" target="_blank">Report & Code</a>
</div>
</div>
<div class="item">
<div class="project-title">Creating Customer Segments</div>
<div><code class="id_tags">Python</code> <code class="id_tags">Clustering</code> <code class="id_tags">Unsupervised Machine Learning</code> <code class="id_tags">Data Analysis</code> </div>
<div class="project-description">The goal of this project is to best describe the variation in the different types of customers (Wholesale customers dataset from UCI Repository) that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer. We perform data exploration, feature scaling, feature transformation, outlier detection followed by application of clustering algorithms like k-means and Gaussian Mixture Model. </div>
<div class="project-links">
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/tree/master/Project-3/customer_segments" target="_blank">Report & Code</a>
</div>
</div>
</div>
</section>
<h1 class="heading" id="projectso" name="projectso">Projects (Others)</h1>
<section class="grid-collection">
<div class="item">
<div class="project-title">Self Driving Car Projects to be uploaded in some days. Visit my github for code.</div>
</div>
</div>
<h1 class="heading" id="skills" name="skills">Skills (With sample proof of concepts)</h1>
<h4>Clicking on a skill will take you to a sample proof_Of_Concept page corresponding to that skill.</h4>
<p class="contacts">
<h2>Programming Languages</h2>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/blob/master/Project-5/Traffic_Sign_Classifier.ipynb">Python</a>
<a href="https://github.com/rajesh-iiith/SensorFusionAndLocalization" target="_blank">C++</a>
<a href="https://github.com/rajesh-iiith/Read-Write-Through-Servers" target="_blank">Java</a>
<a href="https://github.com/rajesh-iiith/Face-Recognition-using-Eigen-Faces" target="_blank">MATLAB</a>
<h2>Parallel Programming</h2>
<a href="https://github.com/rajesh-iiith/Parallel-Frameworks-for-Locality-Based-Dynamic-Programming-Problems/blob/master/LDDP/hp/tiling/hpCaseTwoTiledGpu.cu">CUDA</a>
<a href="https://github.com/rajesh-iiith/Parallel-Frameworks-for-Locality-Based-Dynamic-Programming-Problems/blob/master/LDDP/dp/hybrid/dpCpu.cu" target="_blank">OpenMP</a>
<h2>Machine Learning/ Deep Learning APIs/ Frameworks</h2>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree/blob/master/Project-5/Traffic_Sign_Classifier.ipynb">Tensorflow</a>
<a href="https://github.com/rajesh-iiith/CarND-DeepLearning-ComputerVision/tree/master/CarND-Behavioral-Cloning-P3" target="_blank">Keras</a>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree">sklearn</a>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree">Pandas</a>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree">Numpy</a>
<a href="https://github.com/rajesh-iiith/Machine-Learning-Nanodegree">Matplotlib, Seaborn</a>
</p>
<h1 class="heading" id="contact" name="contact">Contact</h1>
<p class="contacts">
<div>
<a href="mailto:[email protected]">Email me</a>
<a href="https://github.com/rajesh-iiith" target="_blank">Github</a>
</div>
</p>
</section>
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