Nine Veni grants for AI researchers
The Dutch Research Council (NWO) has awarded nine Veni grants to researchers involved in groundbreaking AI research. The recipients can use the grants – up to a maximum of 280,000 per researcher – to further develop their research ideas over the next three years. ICAI wants to congratulate these colleagues for their achievements!
The AI recipients of a Veni grant:
- Continual Learning under Human Guidance
dr. E.T. Nalisnick (M), Universiteit van Amsterdam
Artificial intelligence (AI) systems need to adapt to new scenarios. Yet, we must ensure that the new behaviours and skills that they acquire are safe. The researcher will develop AI techniques that allow autonomous systems to adapt but to do so cautiously, under the guidance of a human.
- Intelligent interactive natural language systems you can trust and control
dr. V. Niculae (M), Universiteit van Amsterdam
Artificial intelligence agents are seemingly approaching human performance in natural language tasks like automatic translation and dialogue. However, deployed in the wild, such systems are out of control, learning to produce harmful language even unprompted. Using recent machine learning breakthroughs, the researcher rethinks language generation for trustworthiness and controllability.
- Efficient AI with material-based neural networks
dr. H.C. Ruiz Euler (M), Universiteit Twente
The unprecedented success of artificial intelligence (AI) comes at the price of unsustainable computational costs. This project will research the potential of a novel technology for highly efficient AI hardware: “material-based neural networks”. This technology will enable the next generation of efficient AI systems for edge computing and autonomous systems.
- Helping computers say what they mean to say
dr. J.D. Groschwitz (M), Universiteit van Amsterdam
When a computer talks to us, for example when answering a question, it must translate that answer from its inner computer representation to fluent human language. This project combines linguistics and state-of-the-art machine learning to create a language generation system in which the output text expresses exactly what the computer meant to say.
- The life and death of white dwarf binary stars
dr. J.C.J. van Roestel (M), Universiteit van Amsterdam
Double white dwarf stars are a rare but important type of binary star. They are potential supernova progenitors, some merge to form massive rotating white dwarfs, and they also emit gravitational wave radiation. I will combine data from the Dutch BlackGEM telescope with multiple other telescope surveys and use novel machine learning methods to uncover the population of short-period eclipsing white dwarf binary stars across the entire sky. By comparing the observed population and characteristics with binary population synthesis models, I will determine how these double white dwarfs end their life.
- Personalizing radiotherapy with Artificial Intelligence: reducing the toxicity burden for cancer survivors
Lisanne van Dijk PhD, University Medical Center Groningen (UMCG)
Many head and neck cancer patients suffer from persistent severe toxicities following radiotherapy. As survival rates increase, toxicity reduction has become more pivotal. This project uses Artificial Intelligence techniques to predict toxicity trajectories, which can facilitate personalized decision-support to guide physicians in finding optimal strategies to reduce these severe toxicities.
- Towards realistic models for spatiotemporal data
dr. K. Kirchner (V), Technische Universiteit Delft
Many environmental factors, such as temperature or air pollution, are recorded at several locations and dates. Because of limited computing power, a realistic analysis of the resulting large datasets is often unachievable. This project develops computational approaches which enable efficient accurate data analysis and reliable forecasts for phenomena with uncertainty.
- Explainable Artificial Intelligence to unravel genetic architecture of complex traits
Gennady Roshchupkin PhD, Erasmus MC Medical Center
While we have learned that most diseases have a genetic component, we are still far away from understanding the underlying processes. Using Artificial Intelligence, I will investigate the complex relationship between DNA mutations and human health. This will be the basis for development of novel diagnostic, prognostic and therapeutic tools.
- Neural networks for efficient storage and communication of information
dr. J. Townsend (M), Universiteit van Amsterdam
The brain is an extremely efficient system for storing and communicating information. This research will study the use of artificial neural networks, inspired by the mechanisms in the brain, for data compression, enabling faster internet communication and more efficient storage of computer files.