Multimodal Representation Learning for Healthcare Data

The abundance of electronic health record (EHR) data has accelerated the adoption of data-driven methods to automate various tasks ranging from patient care to resource management in hospitals around the world. The use of specific types of data such as X-ray images, doctor’s note and others have been used individually to implement machine learning models […]

Read More
Barrelfish: Smart recruiting solution using artificial intelligence

The proposed research would use AI to create an application called Barrelfish that will help in the recruitment process. The project focuses on lowering the time it takes to discover data, screen applicants, and retrieve recommendations. Clients will be able to select employees based on their skills, abilities, and experiences, as well as conduct virtual […]

Read More
Exploring Deep Learning Models for Understanding Consumer Language

With the ultimate goal of enhancing Nexxt Intelligence’s market research SaaS platform, inca, this project will create a robust, scalable algorithm for clustering consumer utterances into groups which are useful to ad-hoc market research objectives, and interpretable via natural language descriptions to market researchers. Due to the multifaceted and nuanced nature of consumer opinions and […]

Read More
Community Based Participatory Research Strategies for Combining Creativity with Sustainability in the Arts and Beyond

The proposed Creativity and Sustainability post-doctoral fellowship will be situated at Mass Culture (MC), and executed in cooperation with University of Toronto Scarborough’s (UTSC) Urban Just Transitions (UJT). Over the years, MC and the scholars involved in UJT have experimented with various forms of community-engaged methods in order to generate impactful research that will inform […]

Read More
Automatic Optical Character Recognition Preprocessing for Custom Gameplay Text

Computer Games are one of the key use cases of graphics cards of AMD. To ensure highest quality and performance, extensive testing of graphics hardware and software is required. However, much of this gameplay testing is manual and requires significant efforts due to varying styles in games and their versions. In this context, an open […]

Read More
Natural Language Processing for automatically checking novelty of ideas

XLScout uses Natural Language Processing (NLP), Machine Learning (ML), and Innovation/Scientific principles to deliver actionable intelligence and accelerate innovation by analyzing large patent and research databases. The company is eliminating the pain of manually going through document and quickly providing relevant information to support data-driven strategic decisions. Presently XLScout hosts a data vault of over […]

Read More
Model-based Reinforcement Learning with Structured Representation

Recent advancements in deep reinforcement learning (RL) have enabled incredible breakthroughs on a wide variety of problems in which computer systems are required to learn through interacting with the environment with no or minimal human intervention. An example of this is DeepMind’s AlphaGo agent, which taught itself to play Go at a superhuman performance. Deep […]

Read More
Deep Learning Based Approaches to Synthetic Data Generation

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this project, we propose a multi-stage framework called SynC […]

Read More
Build and improve image embedding models of cellular phenotypes

The over-arching goal of the project is to explore the use of several recently developed self-supervised image representation learning methods in an attempt to improve performance across several biologically relevant benchmarking tasks at Recursion. At recursion, deep-learning based models are used to generate feature embeddings for our imaging data and these embeddings to generate downstream […]

Read More
Information Extraction from Data Visualizations

Regulatory agencies publish several documents that outline the approval process of drugs. These contain valuable information on a drug’s safety, efficacy, etc. along with the feedback of reviewers from the agencies. Current technologies apply machine learning techniques to extract and categorize the unstructured text found in these documents. However, it does not accurately capture information […]

Read More
Smart Textiles for Monitoring Aerobic Function using Artificial Intelligence

Physical activity is a crucial part of cardiovascular disease and prevention. Some of the most important clinical measurements relate to how effectively the body is able to consume and use oxygen to fuel muscles. However, these clinical measurements require complex technologies that make it only feasible in a laboratory environment. New technologies will be needed […]

Read More
Cloud platform of machine learning

Surgical Safety Technologies Inc. is expanding upon its existing OR Black Box® platform, which will allow users of the platform to build a personalized, user-created library of surgical videos in the cloud. There are many people and groups around the world who will use this video library to make sure that performance evaluations are fair […]

Read More