Microsoft Malware

Classification

https://github.com/DhirajReddy/MachineLearning/tree/master/MicrosoftMalwareDetection

Malware type classification using byte files and assembly files.

Run supervised learning algorithms like logistic regression, KNN, Random forest, GBDT with hyperparameter tuning to reduce multi class log loss. Self-Driving Car

https://github.com/DhirajReddy/MachineLearning/tree/master/NeuralNetworks/SelfDrivingCar

Using video from the Car’s dash board as input to predict the steering angle.

Train data on Nvidia’s CNN architecture to increase the accuracy of predicted angle. Netflix Movie

Recommendation System

https://github.com/DhirajReddy/MachineLearning/tree/master/NetflixMovieSuggestion

Given a user and movie predict the rating the user would choose.

Concatenate user-user and movie-movie similarity matrix with other baseline model outputs from Surprise library achieving a stacked recommendation system. Human Activity Recognition

https://github.com/DhirajReddy/MachineLearning/tree/master/NeuralNetworks/HAR

Given 30 human’s accelerometer & gyroscope readings along x, y and z axis for 30 days predict their activity viz. Sitting, Walking, Sleeping etc. Used in Fitbit.

Run RNN/LSTM on overlapping window filtered features from time series data to reduce categorical cross entropy.

New York City’s Yellow Cab

Demand Prediction

https://github.com/DhirajReddy/MachineLearning/tree/master/NYC

Extract features out of pickup data over 3 months using sliding window average and Fast Fourier Transform to predict demand at a given latitude and longitude in the next 10 minutes.

My Experiments with ML

https://github.com/DhirajReddy/MachineLearning

Projects include clustering, implementation of SGD on Amazon Food Reviews etc.

Python, Keras, Sklearn, Seaborn, Pandas, Numpy, Matplotlib.pytplot, xgboost

Recommendation systems, Neural Networks, CNN, RNN/LSTM

C#, WPF, Rx.NET, REST API, Moq, RhinoMocks, SpecFlow, TestStack.White. Skill Set

Equity Derivative Structured Products Pricing

Applied scatter plots and tSNE on historic trades to detect pattern among firm’s clients

Incorporated KMeans and DBSCAN for grouping clients with similar trading strategy

Improved spread accuracy through experimentation with decision trees using xgboost

Exploring ARIMA models and LSTM/GRU deep networks to predict the approximate number of structured products traded per day or per month

Increased trading velocity of frequently traded bespoke products by templatizing products through support for pricing for new products using WPF, C#, Rx.NET, REST Dhirൽൾ N V

(Advanced) Machine Learning /

Artificial Intelligence

(Advanced) .NET

Experience – 5 1 2 years

https://www.morganstanley.c

Vijeo/Blue (HMI)

Utilized WPF, C#, Caliburn.Micro, AvalonDock, WpfLocalizationFrameWork to implement Docking, Localization, Simulator's Navigation, Save/load project, UI from wireframes and other key infrastructure modules

Utilized effective presentation skills to Training new Hires on HMI, PLC, LUA scripts and organization tools for development.

https schneider-electric.co.in

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Data Scientist Machine Learning Engineer Software Developer ac8o9n@r.postjobfree.com dhiraj-n-v-2702665a +91-903******* Personal Projects

TIA Portal (Totally Integrated Automation)-S71500 and ET200AL

Integrated PLC modules into TIA portal by MDD - Siemen's Version of EDD

(Electronic Device Description) for supporting new S7 and ET200AL products

Alleviated laborious tasks such as version management of MDD files, Module tests generation, repeated TFS action etc. by developing frugal windows applications, visual studio plugins, batch files etc.

Education

Bachelors of Electronics & Communication Engineering

CGPA – 9.5

Undergraduate Coursework: Embedded Programming, Digital Signal Processing, Computer Architecture, Digital Logic, Control Systems, Electronic Circuits, Tele Communication.

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