Steven Hicks

I'm a researcher and developer based in Oslo, Norway. My main research topics include artificial intelligence (AI), with a focus on deep learning. Currently, I hold a full-time position at SimulaMet as a research scientist and I serve as the CTO of Innsikt.AI, a new startup developing training software for police interview training focused on abused children. I have published several papers on AI across a diverse range of topics and am recognized in the Stanford's World's Top 2% Scientists ranking.

For inquiries or to discuss potential collaborations, please reach out to me at [email protected]. You can find a short version of my resume below, and the full PDF version is available here.

Conducted interdisciplinary research in the application of AI in medicine and education, with a focus on algorithmic transparency. Worked on projects to integrate AI technologies in practical healthcare solutions, enhancing patient outcomes and system efficiencies.

Directing the technological strategy and innovation at a leading startup dedicated to the development of virtual child avatars for sophisticated police training, aimed at sensitive interviews with abused children. Innovating in the application of AI and VR technologies to establish realistic and ethical training environments.

I am an associate professor at Oslo Metropolitan University (OsloMet), focusing on machine learning solutions for musculoskeletal disorders. Responsible for student supervision, development of novel machine learning algorithms, and conducting influential academic research.

Designed and implemented algorithms for automated clipping and highlight generation in soccer matches. Developed software for detecting and preventing match-fixing, enhancing the integrity of sports analytics.

Optimized table rendering processes and contributed to the development of applications geared toward efficient data access and management.

Architected and engineered a comprehensive loan management solution using the .NET framework for a U.S. mortgage provider. Worked across the entire stack development lifecycle, from database design and back-end logic to crafting dynamic front-end interfaces.

Focused on developing methodologies to improve transparency and interpretability in AI systems used in healthcare, contributing to the field of ethical AI. Collaborated with medical professionals to align technological advancements with practical healthcare needs.

Developed a system for automatic report generation of gastrointestinal procedures, using deep learning and explainable AI to provide results that are interpretable for medical doctors.

Developed a cross-platform mobile application for the Norwegian Seamen's Church using the Ionic Framework. This project demonstrated practical software development skills and provided a valuable service to the community, enhancing the accessibility and functionality of the Church's digital presence.

Developed a robust system for the acquisition and annotation of colonoscopy data from multiple healthcare centers across the United States. The platform aims to establish a comprehensive dataset to serve as a benchmark for future AI solutions in gastrointestinal care.

Collaborated with SEF and Unibet in designing and implementing a web-based application geared towards detecting match-fixing in soccer. The software features intricate statistics and data visualization modules to identify potential fraudulent activities.

Contributed to the first version of the Smittestopp application as a member of the data science team. Responsibilities included visualizing user trajectories, validating contact events, and devising algorithms for the classification of different modes of transport.

Partnered with Augere AS to create datasets and AI models for detecting gastrointestinal diseases. Implemented models in Python using TensorFlow and PyTorch, enhancing diagnostic accuracy and efficiency.

Designed and implemented algorithms for fish detection and classification to automate feeding procedures in aquaculture. The algorithms were developed in Python using TensorFlow and PyTorch.

Contributed to the initial development of Nyss, the Red Cross' disease surveillance system. This system includes a web-based application for visualizing disease outbreaks and a mobile application for field data collection, built using a React and Redux stack.

Created a financial analytics application that analyzes nettbank transactions to provide users with an organized overview of recurring expenses. The app also includes social features for sharing subscriptions with friends via Facebook, developed with a React and Redux stack using DNB's internal APIs.

Developed a web application to manage data storage within the DHIS2 ecosystem at the University of Oslo. The application was later integrated into the official DHIS2 platform, leading to part-time employment.

Contributed to shaping educational strategies and initiatives in AI across Norway as part of NORA's educational council. Played a key role in driving the agenda of integrating AI into broader educational frameworks nationally.

Organized and led multiple challenges across renowned workshops like ACM MultiMedia, ICPR, ImageCLEF, DLAMC, ICMR, and Nordic AI Meet. Developed challenge themes, managed participant outreach, submission evaluation, and ensured smooth operation of challenge sessions.

Actively involved in the central organization of MediaEval, an annual international multimedia workshop. Responsible for overseeing key aspects of the workshop, including program planning, speaker coordination, and participant engagement.

Actively involved in technical program committees for conferences like ACM MM, ACM MM Asia, CBMS, ISMICT, MultiMediaEval, ICME, Nature Scientific Reports, and Nature Scientific Data.

Contributed to organizing NAIS’s annual conference, focusing on advancing and showcasing the latest developments in AI in Norway.

Served as an editor for SIGMM Records, responsible for the interview section.

Initiated and coordinated a bi-weekly journal club for the AI Lab at OsloMet. Facilitated discussions on recent advancements and research in AI, promoting a culture of continuous learning and academic discourse.