Hi! I am Shubhi, currently working as a Researcher and Data Scientist in a German MNC in Germnay. I have completed my Ph.D. in Information Technology at the University of Stuttgart, Germany. I am presently mixing data analytics with Machine Learning to solve real-life problems with machine advancement and human intelligence. Machine Learning, Visual Analysis, Scientific Computing, and Remote Sensing are the areas whose specialization I have done — absorbed much of the existing knowledge in the above domains so that I could propose some new solutions. I am looking for “Opportunities to apply in daily life problems in academic industries, business, research,” and to explore more within my discipline. Currently, I am working with the data analytics and Visualization domain for Personal Care and Nutrition. In the past I have used environmental data (real time multiple sensors), suggested some air quality improvement solutions for Stuttgart (as a case study) used my skills of Machine learning (ML), Data visualization, and Visual Analytics.

Professional and research history

I am a researcher \& data scientist, working for Personal Care and Nutrition in a German multinational chemical company (BASF, Germany). I had the opportunity to work for the Data Science for Material (DSM) in BASF too. I have worked as a Researcher, Ph.D. student in Information Technology at the University of Stuttgart, Germany, where I focused on temporal predictions and visual analyses related problems and developed some novel data-driven methods based on Machine Learning and reduce-ordered modeling. Also worked as a researcher in the University of Applied Sciences, Stuttgart (HFT Stuttgart), Germany. I have designed some deep neural network, recurrent neural network, and reservoir computing models, where I heavily rely on Python, MATLAB, Dash, Tensor flow, Keras, and associated libraries. I have worked on some projects in Visual analysis of geospatial sensor data streams, where my prime aim was to develop data-driven models for wind flow prediction in a new-type windmills and sensor installations for future power plant establishment.

Meteorological data and its effect have been the researchers’ attention of the smart city planning for thorough utilization and management of resources, that help in effective government management, convenient public services, and sustainable industrial development. Renewable energy sources like wind and solar are being integrated into city planning to improve environmental quality. Wind energy is utilized through wind turbines and requires foreknowledge of wind parameters like speed and direction. I developed a framework to predict dominant wind speed and direction for a time-series wind dataset that could be incorporated into city planning for selecting suitable sites for wind turbines. Moreover, I worked on some classification based models and then explored the machine learning-based methods, including multiple CNN, decision tree, random forest, and deep neural network. My research work was not only limited to temporal prediction model development. Moreover, I have also performed interactive visual data analytics for the high-fidelity temporal measurements collected by the meteorological data simulation and sensors. During the data analytics part, I developed different toolkits within Tableau, MATLAB, and Python to perform a variety of tasks such as probability analysis, eigenvalue decomposition, correlations, and statistical analysis.

I obtained a Master of Technology (M.Tech) degree from IIT Kanpur, India, in Geoinformatics, where I had some great opportunities to dive deep into the development and applictions of information science infrastructures to address the problems of geography, cartography, geosciences, and engineering. I learned energy resources modeling from a technical, economic, and environmental perspective. I have completed my Bachelor of Technology (B.Tech) in Information Technology from Uttarakhand Technical University, India.