Develop a Content Platform for publishers in Germany from scratch

1. Authentication and authorization for users
2. Adapters for aggregation of data from different sources
3. Building data lake using event sourcing and queues
4. APIs to post, update, delete and read
5. Implementing search endpoint by API users using Elastic Search indices
6. Aggregated Logging for each events using Cloudwatch, Lambda and Elastic Search
7. Alerting and Monitoring using Grafana and Cloudwatch
8. Building infrastructure as code using serverless framework
9. Code Styling using ES Lint and Unit Tests using Jest
10. Scrum Master Role for Sprint Meetings
11. Team Size : 7

Building proxy meta service for better advertising

1. Building proxy meta service to query Hyscore - a meta service provider for 10 million users visiting https://focus.de daily to serve better advertisements
2. Implementation of APIs and cache using Redis ElastiCache
3. Alerting and Monitoring using Grafana and Cloudwatch
4. Building infrastructure as code using serverless framework
5. Code Styling using ES Lint and Unit Tests using Jest
6. Team Size : 2

Semantic Labeling of Vehicles

1. Applying machine learning to categorize cars as sports, family or others
2. Research on vehicle types
3. Team Size : 3

View Point Estimation of Cars

1. Classification of view points of cars in order to sort listing images automatically according to their labels
2. Transfer Learning with VGGNet and ResNet Model
3. Team Size : 2

Integrate Amazon​ ​Alexa​ ​as​ ​Assistant ​for Endoscopy Operations

1. Development of Alexa Skills in nodejs and deployment in AWS
2. Designing of Architecture to communicate with API’s
3. Documentation of work
4. Team Size : 3

Deep​ ​Learning​ ​Solutions​ for​ ​Predictive​ ​Maintenance​ ​of​ ​Batteries​ ​in​ ​Cars

1. Application of Sequence Learning to represent driving profiles and understand patterns that leads to failures in batteries in cars
2. Algorithms - Recurrent Neural Network (Grid LSTM)
3. Implementation in Keras and Tensorflow using Python
4. Deployment in Apache Spark (pySpark) and AWS
5. Documentation of Work
6. Team Size : 3

Deep​ ​Traffic​ ​Sign​ ​Detection​ ​on​ ​a​ ​Driving​ ​Simulator

1. Build a tool chain for integration of deep learning model for traffic road sign detection in a simulation environment
2. Training the model with dataset from real world images for object detection and classification and evaluation of model on images from simulator
3. Algorithms - Region based Fully Convolutional Network
4. Implementation in Caffe using Python
5. Team Size : 4

Robust​ ​Verification​ ​of​ ​Autonomous​ ​Cars​ ​with​ ​respect​ ​to​ ​Uncertain​ ​Parameters

1. Linear Parameter Varying Modeling of Autonomous cars
2. Ascertaining relationship between dynamics of cars with respect to uncertain parameters like wind, rains, etc.
3. Team Size : 2

Virtual​ ​Flight​ ​Simulation

1. Creating ROS plugins for X-Plane flight simulator and corresponding nodes to record simulation flight data
2. Creating Dashboards with QlikView/QlikSense to visualize simulated flight data and near real time flight parameter tracking and warnings
3. Image Processing using Convolutional Neural Networks
4. Implementation of Autonomous Landing and Cesium 3D Air Traffic Display
5. Setting up of simulation environment in datacenter at Airbus Defense & Space, Manching, Germany
6. Team Size : 5

Pollution​ ​and​ ​Weather​ ​Monitor

1. An android app that monitors the air pollution through out the world by using sensors in cars
2. Use pollution sensors, gas sensors, flame sensors, temperature sensors, precipitation sensors in order to get the real time weather and pollution data at each node of a city
3. Pollution Routing
4. Technologies - Java, Android, MQTT, Python
5. Team Size : 4

Tool​ ​Cloud-​ ​Cross-company​ ​Lifecycle​ ​Management​ ​for​ ​Tools​ ​in​ ​the​ ​Cloud

1. Requirement Analysis
2. Database Designing
3. Web and Android Applications Development and Maintenance
4. Technologies: Java, Industry 4.0, Android, Web Services, JQuery, JavaScript, EPCIS
5. Team Size : 2

Industry​ ​4.0​ ​-​ ​Industrial​ ​Automation

1. Project​ ​Details​ ​-​ ​https://www.academia.edu/20924531/Industry_4.0_Prototype
2. Building and implementing architecture for the project
3. Build and develop all the required functionalities to adapt to all industrial automation standards for developing an automation solution to interact with Bluetooth, OPC-UA and Wifi enabled sensors and actuators
4. Predicting Tool Wear with data obtained from sensor
5. Integrating prediction results in the architecture and issuing warnings to GUI
6. Research of relevant literature and implementation of machine learning algorithms
7. Technologies used - WiFi, OPC-UA, Android, Rapid Miner, Apache Splunk, Eclipse Kura, AngularJS, Java, OSGi, MQTT, Bluetooth
8. Team Size : 4

Cloud​ ​Monitoring

1. Analysis & Implementation of appropriate Mining & Machine Learning methods for monitoring data from Cloud services
2. Design & implementation of user interfaces for capturing security requirements
3. Implementation of additional functionalities of the existing prototypes
4. Technologies: Machine Learning, Java, OpenVas, ZAP, W3af
6. Team Size : 2

Yelp​ ​Geo-Spatial​ ​Dataset​ ​Challenge​ ​2015

1. Data Preprocessing, Data Transformation and Visualization
2. Descriptive Mining and Predictive Mining
3. Infer Seasonal Trends and Categories, Predicition of Stars from User Reviews using Sentiment Analysis
4. Location Mining & Urban Planning
5. Technologies - Rapid Miner, Weka, R, Python, Streaming APIs, Ngram
6. Team Size : 5

Twitter​ ​Mining

1. Analysis of data collected from Twitter with the objective of finding interesting information and patterns
2. Data Cleaning and Preprocessing
3. Extracting Dense Sub-graphs from the filtered data
4. Analyzing the sub-graphs to find interesting information
5. Algorithm - Page Rank and Hadoop Map Reduce
6.Technologies - Java, JGraphT, Streaming APIs
7. Team Size : 4

Delta​ ​Polaris​ ​​(Delta​ ​Airlines)

1. Designing and developing applications with MVC2 Web framework, using the front controller design pattern like Struts 2
2. Designing the logical and physical data model, generated DDL scripts, and writing DML scripts for Oracle 9i database
3. Tuning SQL statements and Hibernate mapping to improve performance, and consequently met the SLAs
4. Creation of various database objects like procedures, triggers, functions etc
5. Creating functional and technical documents
6. Built and deploying Java applications into multiple environments
7. End to end support of work.
8. Team Size: 30

Web​ ​Based​ ​Learning​ ​Management​ ​System

1. Designing and developing a web based learning management system akin to Moodle
2. Research on similar web based learning management systems
3. Technologies - .Net Framework and Microsoft SQL Server
4. Documentation of Work
5. Team Size : 4

1. Machine Learning

Three types of Machine Learning algorithm:

1. Supervised Learning : Classification and Regression
2. Unsupervided Learning : Clustering
3. Reinforcement Learning

Supervised Learning

Classification : Identify what category new information belongs in.
Regression : Forecast the future estimating relationship between variables.

Unsupervised Learning

Clustering : Separate similar data points into intuitive groups.

Reinforcement Learning

Reinforcement Learning : Reward Based Learning

2. Deep Learning

1. Hierarchical method of learning representations or abstractions of data.
2. Recent upsurge in popularity of neural networks : scale of data and scale of computation.

3. Convolutional Neural Networks

Regular Neural Networks don’t scale well to full images.

1. Convolutional Neural Network take images as input.
2. Three main types of layers to build Convolutional Network architectures: 
Convolutional Layer, Pooling Layer, and Fully-Connected Layer.
3. Layers transform image volume into an output volume through differentiable function.