Exploring Inventory optimization Through Small Business

Upon completion of the project the interns work will allow the partner organization to better understand
how inventory management is handled in small businesses. The project will also help understand what
inventory levels should be for small businesses based on predictive analytics.

Linguistic Data Science for the Development of a Business Corpus

This project is dedicated to the development of a new business corpus as a novel data for the company’s business intelligence. It focuses on linguistic pre-processing for the business domain using two types of collected corpora: text and speech. An automatic annotation of the pre-processed business corpus will be completed using labels related to sentiment analysis and emotion mining technologies. Specific rules will be used to strengthen these labels. Last, a cognitive social analysis on human behaviors and team dynamics will be completed within a business meeting.

Using Machine Learning to Optimize a Workflow Management System.

Workflow management frameworks support the creation of task dependencies and make efficient use of resources while running those workloads. Typically, these tasks can be long running processes like machine learning algorithms or access data from databases. Workflow management consists of mapping tasks to suitable resources and the management of workflow execution in a cloud environment. The goal of this project is to optimize the job scheduling algorithm using machine learning techniques in a workflow orchestration framework that manage workloads across a heterogeneous system.

Data Science Search Engine Optimization

Search is an important way people get the information they want. Whether we want to find more content about a specific topic, or get general information on a subject, search engines lie at the core of this process. At Flipp, search plays a crucial role in the overall user experience and drives relevant content to consumers. Consequently, improving search by assisting consumers in finding a larger volume of relevant products will be of growing importance to Flipp. The proposed project aims to improve Flipp’s search experience by achieving greater relevancy, volume and ease of use.

Visualization, understanding and engineering of machine learning models for entity recognition

Machine learning is a discipline of teaching computers repeatable tasks that humans do well but slowly. At Interdata we are on a mission to use Artificial intelligence to understand the data being stored by organizations and the relationships between those data assets. As such Darrell will be working on methodologies and tools to expand our understanding of the algorithms we develop in order to improve them. He will then use those methodologies and tools to engineer new algorithms to be used by the organization to categorize and tranform data.

Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics

As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data.

Longitudinal Weak Labeling for Lung Cancer Prognosis and Treatment Response Prediction

This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels (Hwang, 2016) can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images to a 3D context, studying labeling performance as the dataset size grows.

E-Community Health and Toxicity

Online communities abound today, arising on social networking sites, on the websites of real-world communities like schools or clubs, on web discussion forums, on the discussion boards of videogames, and even on the comment pages of news sites and blogs. Some of these communities are “healthy” and foster polite discussion between respectful members, but others are “toxic” and devolve into virulent fights, trolling, cyber-bullying, fraud, or worse even, incitation to suicide, radicalization, or the sexual predation and grooming of minors.

Speech recognition for older, pathological voices

Some diseases and brain injuries can seriously impair language. Patterns in an individual's speech can allow computers to describe these impairment with a high degree of accuracy. These techniques can be used to test large groups of people for drug trials and potentially replace pen-and-paper based testing methods. To fully automate this process, speech recognition systems can be used to automatically transcribe speech. Unfortunately, these technologies continue to perform relatively poorly for elderly speakers, or for individuals with speech disorders.

Comparative Analysis on Various Blockchain Technologies and How Can They Transform the Financial Services for Scotiabank

Blockchain is an emerging technology that has the potential to change the way financial participants transact with each other. It enables direct transfer of value and financial assets between participants over networks without the need for a central authority (internet of value). It does this by combining the functionality of different technologies - distributed systems, smart contracts, mutual consensus verification, and cryptography. Given its potential Scotiabank is investing in technical research and business application.