Statistical machine learning for urban transportation system

In general, the goal of project is to investigate the train travel data and figure out the main factors affecting train travel time. Moreover, we will use machine learning algorithms to predict their arrival time to stations and forecast when delays will happen. Specifically, to figure out what factors are affecting train travel times, we will investigate several possibilities according to prior empirical knowledge. Among them, useful factors will be chosen from the data exploration process and undergone statistical significance tests.

Industrial Safety Management using Control Theory and Machine Learning

Optimizing plant processes is of prime importance now more than ever. With stricter infrastructures being placed on safety, environmental effect, and corporate social responsibility, more complex systems that optimize these factors are needed. These complex systems with advanced algorithms are intended to further streamline the existing process while mitigating issues leading to a safer workplace, and environment, while creating cost savings potentially in the millions. Two of the biggest problems that are hindering this growth are process optimization and management of alarm systems.

A Deep Learning Approach to Soft Sensor Design and Process Optimization for an Industrial Nickel Extraction Process

The objective of this project is to use artificial intelligence (AI) approaches to solve complex industrial problems. The two biggest advantages of AI-based approaches are the ability to continuously learn and also learn adequately from historical data. Traditionally, many process information are unmeasurable during live operations because of instrumentation limitations. Also, plants are not sufficiently optimized to maximize production quality, while minimizing waste.