2014 | 2015
The bio’s for students from the 2015 cohort are listed below. For further details about what our 2015 students are working on, please see this document.
I am a computer science graduate with excellence from Aristotle University of Greece. My PhD research area is concerned with software reliability and multi-version execution. I had 4 years work experience starting at the GRID as a software engineer, then multiple internships at CERN and Google (Summer of Code) and finally 2 years at CERN as a full time employee. I have worked on various distributed system and Cloud solutions like OpenStack, Apache Hadoop, Kubernetes and Apache Hama, where I contributed various features and fixes. My current PhD researchresearch focuses on scaling dynamic analysis tools like Valgrind and LLVM Sanitizers through multi-version execution.
My name is Davide, and I have an MSc in Computer Engineering from Italy. After my work on software dependability assessment and software testing, I entered the HiPEDS programme to start a project on formal methods for specification of embedded systems.
My main interests are in the fields of Statistics and Machine Learning.
Daniel Coelho de Castro
My name is Daniel and I come from Rio de Janeiro, Brazil. I did my Bachelor’s in Computer Engineering at the Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio). I also went on a double-degree programme for 2.5 years in France at the École Centrale Paris (ECP), from where I got my Diplôme d’Ingénieur (Master’s in Engineering). As a member of the Biomedical Image Analysis group at Imperial, I am currently investigating ways to help experts make informed decisions from large medical image databases using machine learning. Fun fact: my surname means “rabbit” in Portuguese!
I completed my MEng in Electrical and Electronic Engineering at Imperial College London in 2015. I am in the 3rd year of the HiPEDS program, supervised by Pantelis Georgiou in the Centre for Bio-Inspired Technology. My research is using CMOS integrated circuits to create ultra-low power wearable devices to monitor physiology of patients and athletes. The focus is to use electrochemical measurements of bio-fluids to obtain physiological information non-invasively and more continuously than a blood test, and to use energy harvesting and wireless power transfer to enable such devices to be battery-less. I recently presented demonstrations of my work at BioCAS 2017 in Turin and IEEE Sensors 2017 in Glasgow.
I am Giannis and I am a Ph.D. student at Imperial College London. I hold a B.Sc. in Computer Science from the University of Cyprus (2014). During 2014, I was a research assistant at DMSL. I also hold an M.Sc. in Web Science and Big Data Analytics from the University College London (2015) and a M.Res. from Imperial College London (2016). My main research interests include Distributed Systems & Databases and Big Data Management and specifically Spatial Data Management. I currently work on spatial data management and spatial indexing.
I did my undergraduate degree in Electronics and Telecommunications from University of Pune (2008), India, after which I worked in Telecom (2008-2010) and high energy physics (2011). I obtained my MSc in Information and Communication Engineering from TU Darmstadt, Germany (2013) and then worked as a Marie Curie research fellow in University of Twente, Netherlands before joining HiPEDs.
For my PhD I am focusing on statistical machine learning methods for robotics and control. I am interested in using Gaussian Process based methods for non-parametric data modelling.
I graduated from the National Research University of Informational Technologies Mechanics and Optics (ITMO), Russia, with a Bachelor degree in Automa-tion Control (2012) and a Masters degree in Intelligence Systems in Robotics (2014). After completing the first year with distinction, I have progressed to my first year of PhD. My research is focused on application of multi-dimensional decompositions to big biomedical data.
Outside of the Imperial London College, I am a referee at the professional Elite Ice Hockey League (EIHL) of the UK and I teach Maths to children in a Saturday school.
I am a first year PhD student in the Multicore group, having completed the HiPEDS MRes last year. My interests are in testing and verification on heterogeneous devices (usually GPUs). I have three workshop publications and have completed an internship at Microsoft Research in Bangalore.
My main research interest is large-scale graph mining perceived from the systems perspective. Specifically, I am interested in the construction and design of scalable distributed systems for efficient processing of real-world graphs and complex networks. My previous research experience includes application and design of complex network frameworks for natural language processing. I am also an open source enthusiast with a long-time passion for programming and exploration of UN*X systems’ internals.
I am a second year HiPEDs CDT PhD student who is part of the computational optimisation group in the department of computing at Imperial College London. My advisor is Dr Ruth Misener with whom I have a journal paper completed. My research focuses on the integration of propositional logic into optimisation of industrial problems, e.g. bin and strip packing. Currently I’m looking at satisfiability modulo theories, an approach to assessing satisfiability of constraints, to develop new ways of solving packing problems to global optimality in collaboration with Professor Michael Huth.
I obtained both my bachelor and master degrees (summa cum laude) in computer engineering at University of Naples Federico II. My interests lie in the statistical theory of machine learning. In particular, both in the formalization of constrained adversarial environments for learning and in the design of robust algorithms whit provable guarantees. My competences are in machine learning, statistics and probability.
I am part of BioMedIA group at Imperial, which specialises in Medical Image Analysis. My interest lies in applying deep learning techniques to transform and extract clinically useful information from the images. This includes topics such as image denoising, image synthesis, segmentation and super-resolution. Last year I looked into how deep learning can applied to MRI image reconstruction problem, in hopes of reducing the time required to obtain the MRI scans. This year I am looking into how deep learning technologies can benefit from solving several related problems jointly.
My first degree was in Physics. After graduation I worked in the finance industry for 6 years, before coming to Imperial to study for an MSc in Computing. During my MSc, I got to know my current supervisor, prof Paul Kelly, who introduced me to the area of high-performance computing. I was drawn to the exciting projects and decided to pursue a PhD degree in this research area. I have just completed an internship at Codeplay in Edinburgh.