I'm Naums Mogers

a PhD student in AI

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I am a first year PhD student in the CDT Pervasive Parallelism at the University of Edinburgh. My PhD is supervised by Dr Christophe Dubach and co-supervised by Dr Michel Steuwer; I am a member of the Lift research group.

My research interests are deep neural networks (DNN), optimising compilation and optimisation of DNN for portable performance. I have worked on these topics within my PhD project, as a research intern at Microsoft Research and in a yearlong collaboration with Huawei. My research benefits from the diverse technical background I have: my past projects include a Scala and OpenCL framework for deep learning acceleration (PhD project), OpenCL and Python-based neural network-accelerated ant swarm simulation (Bachelor’s project) and two microcontroller-driven robots (funded projects for use in teaching).

In my studies and hobby projects, I worked with FPGAs, Raspberry Pi, Arduino and Mbed microcontrollers; in my research, I have worked with NVIDIA and ARM GPUs. My research includes extensive use of Caffe (desktop and Android) and Tensorflow; in the six internships I did, I worked on an antivirus core in C, C++, Python and Bash, wrote websites using PHP, JavaScript and MySQL and developed accounting software in Visual Basic. I participate in teaching of algorithms, machine learning, Java and cognitive science. For more information, see my talk slides, research poster and project page.

Bio highlights










Courses tought


2017 – 2020

PhD in Optimising Compilation of Machine Learning Models for Heterogeneous Hardware

Supervised by Dr Christophe Dubach @ University of Edinburgh

The focus of my research is rewrite rules-based compilation of the functional domain-specific language Lift for DNNs into OpenCL. The goal is to abstract DNNs from hardware without losing neither device-specific optimisations, nor performance-preserving portability. My approach involves expressing DNNs functionally, encoding parametrised optimisations as rewrite rules and exploring a huge search space of optimisations and their parameters. In addition to NVIDIA GPUs, I targeted ARM Mali GPUs for the yearlong collaboration with Huawei, where I cross-compiled DNN-code for HiKey.
2016 – 2017

MSc by Research in Optimisation of CNNs Using A Functional Data-Parallel Language

Supervised by Dr Christophe Dubach @ University of Edinburgh

For this project I expressed a CNN in the functional language Lift and explored the optimisational space of data tiling and grouping and weighting sequentialization, exploiting coalesced memory accesses and data locality. I presented a poster on this work at the Google PhD Summit in Munich.
2015 – 2016

MSc in Artificial Intelligence

Supervised by Dr Christophe Dubach @ University of Edinburgh

Dissertation title: ‘Expressing Artificial Neural Networks In A Functional Data-Parallel Language For GPU Acceleration’. The curriculum included courses in Machine Learning, Robotics and Cognitive Science.
2011 – 2015

BEng in Computer Science (with a year in industry)

Supervised by Dr Simon O’Keefe @ University of York

Dissertation title: ‘Memory In Simulated Swarms’. The curriculum was focused on system programming (schedulability analysis, embedded software, real-time systems, compilers) and artificial intelligence (neural computing, search algorithms, multi-agent systems, swarm intelligence).

Research Internships & Collaborations

Aug 2018 — Oct 2018

Research intern

Microsoft Research Cambridge

I worked on the project BrainWave, extending the functional data-parallel Lift compiler to a specialized machine learning accelerator. This work included extensive changes to all parts of the compilation chain including type checking, memory management, rewriting and code generation.

Sep 2017 — Aug 2018

Collaboration with Huawei

University of Edinburgh

Within this yearlong collaboration with Huawei, I focused on optimising CNNs for embedded devices, which included porting Caffe to the Android OS on the Huawei Kirin 960-based HiKey 960 board with ARM Mali G71 GPU.

Jul 2015 – Sep 2015

Research Intern

York Centre for Complex Systems Analysis

I designed a biologically inspired cellular model for a Wireless Sensor Network of Arduinos, capturing such biological properties as adaptivity, self-organization and fault-tolerance. Research topics covered include Artificial Epigenetic Regulatory Networks, Genetic Programming and Cell Signalling.


IBM and Swiss Re first prize and the hackathon finalist

September 2016, HackZurich

My team won in the IBM & Swiss Re challenge against 20 teams in the largest European hackathon HackZurich. Additionally, we became one of 25 finalists out of 152 teams in the main hackathon challenge. We developed a Machine Learning app for risk prediction using home IoT sensors and IBM analytic technologies.

YouTube Demo

York Award

July 2015, the University of York

The York Award is a programme of personal and skills development offered by the University of York in partnership with leading public, private and voluntary sector organisations. The York Award certificate is awarded alongside the degree in recognition of achievement of a significant programme of activity and, in particular, the adoption of an analytical and reflective approach to learning and competence in a broad range of skills.

Best poster

March 2016, National Student Research Conference

Mogers N, Trefzer, M, Lagos, D, The Sensor Organism (2015)

Poster (PDF) Project blog

Best Poster

October 2015, York Doctoral Symposium

Mogers N, Trefzer, M, Lagos, D, The Sensor Organism (2015)

Poster (PDF) Project blog

Teaching Experience

2016 – 2018

Algorithms, Data Structures And Learning

Teaching Assistant / Marker, University of Edinburgh

2017 – 2019

Object-Oriented Programming

Teaching Assistant, University of Edinburgh

2017 – 2018

Introductory Applied Machine Learning

Marker, University of Edinburgh

2008 – 2018

Machine Learning; Microcontrollers

Tutor, Facultative School For Talented Children

I wrote and tought two courses from scratch on Neural Networks and Decision Trees; assisted in teaching during the academic year and worked as a counsellor in six yearly summer camps.
In 2016, I tought a workshop on Arduino-based auto-aiming catapult design.


Software Testing

Tutor, University of Edinburgh


Compiling Techniques

Demonstrator, University of Edinburgh


Processing Formal And Natural Languages

Marker, University of Edinburgh


Raspberry Pi / Raspbian / Windows 10 IoT

Workshop tutor, Microsoft Student Partners


CDT Pervasive Parallelism

2016 – 2020, University of Edinburgh

Four-year scholarship sponsored by the Engineering and Physical Sciences Research Council.

Raspberry Pi project funding

2014, University of York

I responded to a competitive call for proposals and acquired funding to design and develop a Raspberry Pi-related project that helps promote technology in schools. The project included microcontroller programming in Python and Scratch, electric circuit design and use of actuators. I conducted a Raspberry Pi workshop for schoolteachers and participated in technology exhibition.

YouTube Demo


Jan 2019

Towards Mapping Lift to Deep Neural Network Accelerators


Workshop on Emerging Deep Learning Accelerators (HiPEAC), Valencia, Spain.

Apr 2018

Lift: Performance Stencil Code Generation with Lift


International Symposium on Performance Analysis of Systems and Software (ISPASS), Belfast, UK

Dec 2017

Optimisation of Neural Computations Using a Functional Data-Parallel Language


Google PhD Summit, Munich, Germany

Oct 2017

Computational Optimisation of CNNs Using a Functional Data-Parallel Language

Invited Talk

Glasgow Systems Seminar, University of Glasgow, UK

Jun 2017

Optimisation of Neural Computations Using a Functional Data-Parallel Language


The Scottish Informatics and Computer Science Alliance, University of Dundee, UK

Attended Academic Events

Google PhD Summit Participant

December 2017, Google Munich

Selected by Google to join the Compiler and Programming Language Summit 2017, where I presented a poster on my research project. Participated in the Round Tables where Google engineers shared highlights of Google’s latest research in the area of programming language implementation and how this research is applied to compilers and language tooling at Google.

Google Inside Look Participant

August 2017, Google London

Selected by Google for their exclusive Inside Look Program, offering top Technical students a fully sponsored two day program of tech talks, workshops, and other technical development content. Awarded to only 31 students out of thousands of applicants from Europe, Middle East and Africa.

Facebook PhD London Tech Talk Participant

October 2018, Facebook London

Selected by Facebook for a PhD Open House event for a talk and panel about research at Facebook.

Work Experience

Sep 2016 – Jun 2017

News Annotator

Thomson Reuters

As a part of a research project in natural language processing at Thomson Reuters R&D department, I annotated news articles with semantic tokens that aid automatic learning for a machine learning system.

Jul 2013 – Jul 2014

Engineering Intern


My responsibilities included antivirus engine development in C/C++/Python, manual and automated testing, code reviews, debugging, documentation maintenance and software release preparation. My team employed Agile Development practices including pair programming and daily planning meetings.

Jul 2012 – Sep 2012

IT and Digital Summer Intern

EDF Energy

Full-time internship at R&D department in C++ OCR software development and IT support.

Feb 2012 – Aug 2012

Web Designer Intern

Stockholm Environment Institute York

Part-time internship in web development.

Jun 2009 – Dec 2014

System Administrator / Software Developer

M2 Ltd

Part-time during the academic year and full-time during summers, my responsibilities included PHP and Visual Basic development, and IT support.



C, OpenCL, Scala, Python, Visual Basic
Caffe, MATLAB, Matplotlib, LaTeX
Java, JavaScript, PHP, CSS, MySQL
Raspberry Pi, Arduino


Cohort Representative

Since 2016, I am representing my cohort in the Centre for Doctoral Training (CDT) staff-student meetings.

PGR Student Representative

Since 2017, I am representing my programme (CDT) in the department-wide Staff-student liaison committee.


Research poster #3


Research poster #3


Naums Mogers (January 2019),Towards Mapping Lift to Deep Neural Network Accelerators, HiPEAC 2019 conference. Valencia, Spain.

Millenium Falcon Model


Millenium Falcon Model


Completed a 1/72 model of Millenium Falcon consisting of 904 pieces. Gosh, what a pleasure that was!

More close-ups in Google Photos.

Research poster #2


Research poster #2


This is my poster for the 2017 CDT Pervasive Parallism Student Showcase event. The poster briefly explains my current research direction.

Research poster #1


Research poster #1


This is my poster for the 2016 EPSRC CDT PPar & CDT HiPEDS Industrial Engagement Event. The poster briefly explains my current research direction.

Auto-aiming catapult


Auto-aiming catapult


In 2016, I organized a workshop in a scientific summer school, where we built four auto-aiming catapults with a group of 16 years-old students.

The catapults are based on Arduino Uno, Tamiya Twin-Motor Gear Boxes and HC-SR04 ultrasonic sensor; the frame is made of plywood.

The goal of the workshop was to learn how to operate the wood workshop tools, and learn the mechanisms of motors, microcontrollers and ultrasonic sensors.


Pololu 3pi robot


Pololu 3pi robot


The second year of the Bachelor’s course, University of York, 2013.

The group assessment was to program the Mbed microcontroller so that robot can follow a wall and avoid obstacles using IR proximity sensors, follow a line using bottom illuminance sensors and navigate on a floor using USB optical mouse tail.

Image source: www.hobbytronics.co.uk

A GPU-Accelerated stigmergic BCMM swarm


A GPU-Accelerated stigmergic BCMM swarm


This is my diploma project called “A GPU-Accelerated stigmergic BCMM swarm”. In short, it is a Python maze solver, that simulates a swarm on GPU using OpenCL. Swarm agents use neural networks to navigate in an unknown environment.

The project was developed using Python 3.4.2, OpenCL 1.2 (the code should be 1.0-compatible), PyOpenCL, Pygame, Numpy and FFmpeg. The platform is 64-bit Windows 7 and AMD Radeon HD 7870.

This work is distributed under MIT License: you are free to use and change it with appropriate references to the source.

Bachelor’s, the University of York, 2015.

Simulated Baxter robot


Simulated Baxter robot


This is a V-Rep 3D simulation of Baxter robot reaching targets and comfortable poses from different initial positions.

Produced as a part of a homework assignment for the Robot Learning & Sensorimotor Control course at the University of Edinburgh.

Master’s, the University of Edinburgh 2016.

Raspberry Pi project


Raspberry Pi project


I participated in the York Raspberry Jam & Maker Event at the National STEM Centre. Together with other CS students we presented our raspberry projects, which we delivered for the “Raspberry Pi in the classroom” event — the idea was to design projects which could be repeated in schools with children learning to use Scratch on the Pi.

The video above is a demo of my project called “Magnetic checkers” – the goal was to move metallic checker pieces around the field using an electromagnet. The machine ended up resembling a simple 3D printer: 3 stepper motors, one moving base in axis X, two moving the head (magnet) in Y and Z axes. Software allows user to control machine manually and give simple tasks for automatic control (i.e. “move piece from A2 to E7”).

Software: Scratch for high-level control, Python for sending high-frequency PWM signal to GPIO pins.

Hardware: Meccano structural parts, x3 NEMA 17 motors, x3 A4988 Pololu drivers, 12V 6W electromanget, Raspberry Pi, 3 PSUs.

York, 2014.

The Muse


The Muse


A 3D-printed model designed in Blender.

Pololu 3pi robot simulation


Pololu 3pi robot simulation


The individual part of the Pololu 3pi robot group assessment: it is a Matlab simulation, which interacts with robot’s C program by feeding it sensor readings in exchange for motor commands.

Bachelor’s course, the University of York, 2013.


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