Our research agenda covers the following areas and is conducted within the confines of projects executed with industry partners:
- Database and Big Data technology
- Data Mining, Statistics and Predictive Modeling
- Machine Learning and Graph Analytics
- Information Retrieval and Natural Language Processing
- Business Intelligence and Visual Analytics
- Data Warehousing and Decision Support
- Communication and Visualization of Results
- Privacy, Security and Ethics
- Entrepreneurship and Data Product Design
This list gets directly filled from ZHAW's project database. Not all projects may show up due to interlinkage aspects.
OSR4H – Open Set Recognition for Hematology
Development of a Proof of Concept for visual Open Set Recognition (OSR) algorithms applied to a Hematology task, the classification of white blood cells.
An experimental framework to allow evidence-based sustainability policymaking
This research project evaluates the feasibility of using a mathematical decision framework (based on the 2019 Nobel laureates in Economy who developed an experimental approach for improving policy in poverty through field experiments) in sustainability policy, and achieving a software-supported and data-driven ...
AC3T – AI powered CBCT for improved Combination Cancer Therapy
The project enables a novel, combined, adaptive cancer therapy combining tumor treating field and radiation therapy due to significantly improved static (3D) and time-resolved (4D) low dose Cone Beam Computer Tomography images based on artificial intelligence image reconstruction algorithms. ...
DISCAP – Digital Infrastructure for Sustainable Consumption and Production (UNEP)
The goal of this project is to lay the technological foundations for empowering various stakeholders to make fact-based decisions to achieve sustainable consumption and production. In particular, we will evaluate how various data science, machine learning and artificial intelligence methods can be used to extract ...
PRISM: Predicting Radicalization Events in Social Media User Timelines
The PRISM project focuses on detecting radicalization events in Social Media networks. Overall, we are interested in unveiling the mechanics that lead to the event of extremist ideology being transferred and incorporated into a social media user’s world view. Specifically, the proposed project aims to identify ...
DataInc – Intelligent Data Integration and Cleaning
Clean, reliable data is crucial to an increasingly digitized financial industry. We currently observe a lack of consistent, high-quality data across asset classes which requires costly and time-intensive human intervention. We propose an AI-driven solution to address this issue.
AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support
Monitoring diverse sensor signals of patients in intensive care can be key to detect potentially fatal emergencies. But in order to perform the monitoring automatically, the monitoring system has to know what is currently happening to the patient: if the patient is for example currently being moved by medical staff, ...
Interactive Data Visualization Made Easy
Interactive dashboards are a great tool to display research data, both for experts and the general public. Yet, many researchers lack the required skills to create them. We offer a solution by developing a simple framework enabling researchers to create and deploy such dashboards with their data. We will use the ...
End-to-End Low-Resource Speech Translation for Swiss German Dialects
This project investigates the application of recent findings in Speech Translation to Swiss German. Speech Translation (ST) is the task of translating spoken utterances in one language into written text in a different language. It serves as an essential tool for breaking down language barriers in various ...
Ecological and economic process optimization in cement production through machine learning
The goal of this Industry 4.0 project is to stabilize and optimize the clinker burning process in a cement plant using machine learning and improved process analytics. This significantly reduces emissions of carbon dioxide, ammonia and nitrogen oxides, improves clinker quality and reduces the consumption of thermal ...
One of the major promises of Big Data lies in the simultaneous mining of multiple sources of data. This is particularly important in life sciences, where different and complementary data are scattered across multiple resources. To overcome this issue, the use of RDF/semantic web technology is emerging, but querying these systems often proves to be too complex for most users—thereby hampering wide development and adoption of these technologies.