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
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
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
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
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
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 /
Experience – 5 1 2 years
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.
Data Scientist Machine Learning Engineer Software Developer email@example.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.
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.