Homogenize biography

content/seminars/2019-05-15.md

@@ -5,4 +5,5 @@ category: seminars
 
 Air transportation systems are exposed to daily disruptions, which have significant impact on operations causing not only monetary loss, but also customer dissatisfaction. Airlines operate tight schedules to maximise resource utilisation. However, the lack of sufficient buffers often result in the domino effect, where a delay of a single flight can delay many other dependent flights. Due to the complexity of air transportation systems the task of identifying the cause of a delay is not trivial. In this paper, we propose a framework for automatic detection of root-causes of delays and their propagation effects using airline historical data. The framework is composed of the following: 1) delay propagation model to create connection network, 2) delay network algorithm to find delay networks, and 3) community detection algorithm to identify root-causes and impact of disruptions. We test our framework on historical data of an airline, and show that the airline under study is prone to delay propagation through passenger connections. Additionally, majority of their delays are related to airport capacity, resource allocation, and passengers, and mainly originate from the hub.
 
-Short bio: Dr Vaggelis Giannikas is an Associate Professor at the School of Management, University of Bath where he also directs the engineering management teaching portfolio. He is studying the development and evaluation of intelligent logistics systems with applications in manufacturing, warehousing, inventory management and airline networks. A significant part of Vaggelis's research has been conducted in collaboration with corporations in Europe, USA and China. Prior to joining the University of Bath, Vaggelis served as a research associate at the Institute for Manufacturing, University of Cambridge where he was also the associate director of the Cambridge Auto-ID lab. He holds a PhD in Operations Management and Technology from the University of Cambridge and a BSc in Management Science and Technology from the Athens University of Economics and Business.
+#### Biography
+Dr Vaggelis Giannikas is an Associate Professor at the School of Management, University of Bath where he also directs the engineering management teaching portfolio. He is studying the development and evaluation of intelligent logistics systems with applications in manufacturing, warehousing, inventory management and airline networks. A significant part of Vaggelis's research has been conducted in collaboration with corporations in Europe, USA and China. Prior to joining the University of Bath, Vaggelis served as a research associate at the Institute for Manufacturing, University of Cambridge where he was also the associate director of the Cambridge Auto-ID lab. He holds a PhD in Operations Management and Technology from the University of Cambridge and a BSc in Management Science and Technology from the Athens University of Economics and Business.

content/seminars/2022-12-19.md

@@ -5,7 +5,7 @@ category: seminars
 
 Discussions about trustworthy and responsible AI have become central across multiple communities in recent years -  machine learning, law, social sciences, among others. A key challenge regarding trust in AI - also considered important by regulators as part of transparency for some AI applications - is to understand why “black boxes” may be making specific predictions. As a result, explainable AI (XAI) has been a growing topic of research. In this talk, I will discuss some potential drawbacks XAI may have - including the potential to erode safety in practice - and also present some work that takes into account behavioural aspects researchers and practitioners may need to consider when developing XAI.
 
-### Speaker bio
+#### Biography
 Theos Evgeniou is a professor of Decision Sciences and Technology Management at INSEAD and director of the INSEAD Executive Education program on Transforming your Business with AI.
 
 He has been working on Machine Learning and AI for the past 25 years, on areas ranging from AI innovations for business process optimization and improving decisions in Marketing and Finance, to AI regulation, as well as on new Machine Learning methods. His research has appeared in leading journals, such as in Science Magazine, Nature Machine Intelligence, Machine Learning, Lancet Digital Health, Journal of Machine Learning Research, Management Science, Marketing Science, Harvard Business Review magazine, and others.

content/seminars/2024-01-10.md

@@ -13,7 +13,7 @@ academia and in industry. This talk will present the main concepts,
 will introduce the key advances and will discuss open challenges and
 future research directions around mutation testing.
 
-### Speaker bio
+#### Biography
 Mike Papadakis is an Associate Professor at the
 University of Luxembourg where he leads the SERVAL (SEcurity,
 Reasoning and VALidation) research team. His research interests

content/seminars/2024-02-22.md

@@ -16,7 +16,6 @@ Preprint:
 https://discovery.ucl.ac.uk/id/eprint/10124292/1/api_repair_tools_study_camera_ready_submitted.pdf
 
 
-Short bio:
-
+#### Biography
 Dr Maria Kechagia is a Research Fellow at University College London. Previously, she was a postdoctoral researcher at the Delft University of Technology. She obtained a PhD degree from the Athens University of Economics and Business and an MSc degree from Imperial College London. Her research interests include static and dynamic analysis, automated program repair, software analytics, and software optimisation (e.g., energy efficiency). She has been a programme committee member of the research track of top software engineering venues including ICSE, ASE, ISSTA, MSR, ICSME, and SANER, and a reviewer for top software engineering journals including TSE, TOSEM, EMSE, and JSS.
 

content/seminars/2024-04-10.md

@@ -6,5 +6,5 @@ category: seminars
 Smartphones are ever-present in our daily lives and handle a wealth of sensitive information like text messages and photos. Malicious applications can obtain access to such data and leak them to third parties with potentially grave consequences (i.e., theft, blackmail, etc.). Therefore, the study of malware, with the goal of identifying such malicious applications, has attracted significant research interest in recent years, especially focusing on Android applications. Malware research based on a combination of static and dynamic approaches has been shown to be effective in identifying a range of typical malware types like example keyloggers and ransomware. Moreover, it has been shown that applications that are not malware in the traditional sense, e.g. social networking applications or even over-privileged system applications, might leak significant amounts of data without user consent or notification. Therefore, this talk will provide an overview of trends in research regarding static and dynamic analysis for Android applications with the goal of identifying such malicious or information-leaking behaviors. We will discuss different approaches based on taint analysis, system call analysis, provenance tracking, network traffic analysis and more. The talk will also cover the extended Berkeley Packet Filter (eBPF) and how it can be useful for tracing and dynamic analysis.
 
 
-Short bio:
+#### Biography
 Simon Althaus is a Research Associate at the Telecooperation Lab at Technical University of Darmstadt. He obtained a MSc degree from Technical University of Darmstadt working on botnets. Simon is currently researching in the field of Android security. His research interests include dynamic analysis, eBPF, provenance tracking, and privacy-enhancing technologies.

content/seminars/2024-06-12.md

@@ -9,5 +9,5 @@ hS is a new system that exploits the potential for non-linear execution in shell
 To achieve this, hS introduces a new system-call monitor that collects ordering and effect constraints, a lightweight container that controls the order and application of side effects, a formally verified streaming scheduler that executes components within a window of speculation while respecting their ordering constraints, and several runtime optimizations for speculation and application of side effects.
 Applying hS to a large and diverse set of shell scripts yields a ~2x speedup for free (i.e. without any extra code or annotations).
 
-## Biography
+#### Biography
 Georgios Liargkovas will be pursuing a PhD in OS scheduling and cloud computing, with a focus on distributed and serverless architectures, at Columbia University, advised by Prof. Kostis Kaffes. He graduated with a Bachelor's degree in Software Engineering and Data Science from the Department of Management Science and Technology at Athens University of Economics and Business. Since 2020, he has been a research assistant at BALab, concentrating on empirical software engineering and mining software repositories studies. He is also an affiliate researcher at Brown University’s Atlas Systems Group, engaged in advancing shell-script parallelization. His research interests include system design and optimization, particularly through the application of machine learning techniques. Outside academia, he is passionate about long-distance running, cycling, and music.