Outstanding researchers in the fields of AI and cybersecurity are recognized and rewarded by Qualcomm Innovation Fellowship Europe
July 3rd, 2023, AMSTERDAM: Qualcomm Technologies, Inc., announced today the winners of the 14th edition of Qualcomm Innovation Fellowship (QIF) Europe: Atri Bhattacharyya (EPFL), Karsten Roth (University of Tübingen), Siwei Zhang (ETH Zürich) and Tycho van der Ouderaa (Imperial College London).
QIF is an excellence award through which Qualcomm Technologies recognizes and mentors some of the most innovative engineering PhD students across Europe, India, and the United States. The European program rewards researchers in the fields of artificial intelligence and cybersecurity with individual prizes of USD $40,000 and dedicated mentors from the Qualcomm Technologies team.
“Each year we are faced with the difficult decision of choosing between excellent research proposals that have the potential to positively impact the world” said Michael Hofmann, Director of Engineering at Qualcomm Technologies Netherlands B.V. “Our day-to-day technology can be enhanced with the great work done by the finalists in areas such as pose estimation, causality, advanced digital signatures, digital health, and many more.”
The twelve finalists are PhD candidates from ETH Zurich, Imperial College London, Tübingen University, University of Cambridge, University of Oxford, CISPA, and EPF Lausanne.
After careful review, the following four winners were selected for their outstanding proposals:
Atri Bhattacharyya
SecureCells: A Secure Compartmentalized Architecture
(Security Systems)
EPF Lausanne
In the world of software security, vulnerabilities like log4J and Heartbleed pose significant risks to the safety of our programs, particularly browsers. Existing protection methods have fallen short in meeting the demands of modern performance and security requirements. Atri proposes SecureCells to address this issue. By revolutionizing the way computer systems manage and protect their memory, SecureCells offers enhanced security for a wide range of programs, including those used on mobile devices, desktop computers, and servers. With minimal performance overhead, SecureCells ensures the safety of critical programs in today’s rapidly evolving technological landscape.
Karsten Roth
Effective Lifelong Adaptation of Vision-Language Foundation Models across Domains for Practical Deployment
(Machine Learning)
Tübingen AI Center
Open-vocabulary vision-language models are key for advancements in multimodal applications, but their usefulness diminishes as their training context becomes outdated. It is thus important to develop and understand mechanisms that allow us to continuously update these models without costly retraining. As a result, Karsten first proposes to benchmark current continual learning techniques and obtain insights on impact of various design choices considered in such techniques. The proposal then plans to leverage these insights and introduce extensions to improve performance of continual learning strategies. This work is expected to provide valuable insights into transferring continual learning research from small data and model regime to large-scale continuous adaptation in foundation models.
Siwei Zhang
Interaction-aware Learning of 3D Human Motions and Behaviors from Egocentric Views
(Computer Vision)
ETH Zürich
AI-powered AR/VR devices are transforming sectors like healthcare, education, and entertainment by understanding surroundings and human actions. However, current techniques are primarily limited to third-person view applications and rarely consider social interactions. Siwei proposes to address these limitations by advancing fundamental research in egocentric human behavior understanding in an interaction-aware manner. The proposal plans to investigate synthesizing human-scene and social interactions and egocentric motion capture and reconstruction.
Tycho van der Ouderaa
Learning Equivariances from Data
(Machine Learning)
Imperial College London
Large neural networks are increasingly used to solve real-world problems outperforming more classical machine learning models on many tasks. Inductive biases, such as invariance and symmetries in these models, play a key role in their overall performance and generalization abilities. Although recent works have allowed extensions to various symmetry groups and domains, the neural network architectures and group structures they use are fixed and need to be selected manually or through expensive cross-validation. Tycho proposes to instead learn equivariances and corresponding neural structures from training data through a combination of flexible parameterizations and an amenable objective function capable of learning symmetries. Solving this problem would make finding the architecture as simple as learning weights and pave the way for the automatic discovery of compute/parameter efficient architectures.
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