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Qualcomm Innovation Fellowship awards researchers in the field of cyber security and artificial intelligence


The 13th edition of the QIF has 4 winners.

​In a statement Qualcomm Technologies Inc announced the winners of the 13th edition of its Qualcomm Innovation Fellowship (QIF) Europe program.

“Patrick Jattke (ETH Zurich), Stratis Markou (University of Cambridge), Francisco Vargas (University of Cambridge), and Dingfan Chen (CISPA Helmholtz Center for Information Security)” have been selected as the winners of the QIF.

QIF is an annual program that focuses on recognizing, rewarding, and mentoring the most innovative engineering PhD students across India, Europe, and the United States.

The program rewards young researchers in the fields of artificial intelligence and cybersecurity with individual prizes of $40,000, dedicated mentors from the Qualcomm Technologies team as well as the opportunity to present their work in person to an audience of technical leaders at the company’s HQ in San Diego.

“At QIF Europe, each year we are delighted by the insightful and forward-looking proposals we receive, and the 2022 cohort was no exception. The innovative researchers are set to impact important technology areas such as, interpretable AI, data privacy, fair and generalizable algorithms, and more,” said Jilei Hou, vice president and head of AI research, engineering, Qualcomm Technologies, Inc. “We are proud to support their novel research and look forward to seeing the winners thrive in the field of AI and cybersecurity in the coming years.”

Following a careful review of their work, the following four winners were selected for their outstanding proposals:

Patrick Jattke, from ETH Zurich, supervised by Kaveh Razavi, has been selected for his proposal “Rowhammer Meets AI: Leveraging Deep Learning for Building a General Rowhammer Testing Methodology”.

Patrick’s proposal searches for a new methodology to find Rowhammer bit flips. Rowhammer is a hardware bug in modern memory and has been a concern for the semiconductor industry for almost a decade. Details of existing solutions are often not disclosed, making it hard to assess their security assurances. Patrick proposes to combine two state-of-the-art Rowhammer analysis techniques to automatically discover weaknesses in new devices.

Stratis Markou from the University of Cambridge, supervised by Carl E. Rasmussen and advised by Richard E. Turner, has been selected for his proposal: “Systematic Design of Neural Process Models for Probabilistic Meta-Learning”.

Meta-learning, part of machine learning concerns itself with “learning how to learn”. While currently there are still significant limitations that affect its applicability in the real world, Markou’s proposal aims to answer whether we can build meta-learning models which quantify their uncertainties well and have small test-time resource requirements, and whether we can reduce the amount of data collected necessary to train these models.

“Stratis will investigate improving the class of models of Gaussian Neural Processes (GNPs) and moving beyond the Gaussian assumption within NPs. He will then investigate how to develop efficient equivariant NPs, which can improve performance with fewer parameters and easier training”.

Francisco Vargas from the University of Cambridge, supervised by Neil Lawrence, has been selected for his proposal: “A Unifying Framework for Sampling, Inference and Transport via Schrödinger Bridges”.

Francisco takes us on a journey through the Schrödinger Bridge problem, in which one tries to find the stochastic evolution between two probability distributions. He sets out to unify the work that has been done on this problem in the machine learning community using the Sinkhorn algorithm, relating variational inference, sampling, and optimal transport together.

Dingfan Chen from CISPA Helmholtz Center for Information Security, supervised by Mario Fritz, has been selected for her proposal: “Unifying Deep Generators for Privacy-preserving Data Sharing”.

Data sharing could intrude on privacy, and depends on the nature of data or corresponding regulations, which hinders technological progress. Differentially private (DP) data publishing, where only a sanitized form of the data, with rigorous privacy guarantees, is created using generative models, provides a potential solution. The challenge is, existing methods struggle to generate high-fidelity data that is useful for real-world applications. The proposal aims at providing a unified view of the design space for private generators and its systematic exploration to derive novel methods that cater to different use cases.