博士申请 | 帝国理工学院Stefan Vlask教授招收机器学习方向全奖博士生

2022 年 11 月 29 日 PaperWeekly

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帝国理工学院

帝国理工学院 （Imperial College London），简称帝国理工，成立于1907年，位于英国伦敦，是英国罗素大学集团成员、金砖五校之一、欧洲IDEA联盟成员，是一所享誉全球的世界顶尖高等学府。帝国理工与剑桥大学、牛津大学、伦敦政治经济学院、伦敦大学学院并称为“G5超级精英大学”，其研究水平被公认为英国大学的三甲之列，并以工程、医科专业、商学而著名。帝国理工学院QS 2022世界排名第7位，U.S. News 2022世界排名第20位，ARWU 2022软科世界大学学术排名中第23位。

导师简介

Stefan Vlaski is Lecturer in the Communications and Signal Processing Group within the Department of Electrical and Electronic Engineering, where he conducts research at the intersection of machine learning, network science and optimization with applications in signal processing and communications.

Stefan received the B.Sc. degree from Technical University Darmstadt, Germany, in 2013, and the M.S. as well as Ph.D. degree from the University of California, Los Angeles, USA, in 2014 and 2019, respectively. From 2019 to 2021 he was Postdoctoral Researcher with the Adaptive Systems Laboratory at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland.

He was recipient of the German National Scholarship at TU Darmstadt and the Graduate Division Fellowship at UCLA. His papers have been recognized at Best Student Paper contests at IEEE ICASSP 2016 and IEEE CAMSAP 2019, and his research has led to patents which have been assigned to UCLA and Amazon.

Website:

https://stefanvlaski.github.io/

招生信息

Our ability to design intelligent machines that learn models directly from data has led to tremendous progress across a plethora of scientific fields in recent years. Examples range from communications and signal processing to medical imaging, genetics, finance and social sciences. The objective of our research group at Imperial College London is to develop algorithms that enable provable and efficient learning across a variety of challenging environments.

We invite applications for a fully-funded PhD studentship at the intersection of optimisation, machine learning and network science. Projects can focus on any of the following areas:

• Distributed, federated, or decentralised learning for multi-agent systems

• Multi-task and meta-learning

• Robust, privacy- or communication-constrained learning

• Optimisation for machine learning (e.g., nonconvex landscapes, optimal algorithms)

• Information-theoretic concepts for (distributed) learning

The studentship covers funding for tuition (including international fees) as well as a stipend. Applicants will need to satisfy general entry requirements and apply through the departmental portal (https://www.imperial.ac.uk/electrical-engineering/study/phd/), listing Dr Stefan Vlaski as potential supervisor. Additionally, applicants are expected to demonstrate strong aptitude for analytical reasoning, excellent communication skills, and creativity.

申请方式

Applicants wishing to discuss details of their application are invited to email Dr Stefan Vlaski directly (s.vlaski@imperial.ac.uk). Details on research are also available online (https://stefanvlaski.github.io).