Data Science Entry and exit program

DataScience
DataScience

Since september 2015, UCA runs an entry point in DSC. The exit point running since 2016 offers a specialization entitled Multimedia and Web Science for Big Data.

First Year (from 2020-2021, subject to modification)

In the sequel, MAM4 means 4th year of Maths Appliquées and Modelisation (MAM) department of Polytech Nice Sophia. SI5 means 5th year of Sciences Informatiques (SI) of this same engineering school. MAM5/SI5 SD corresponds to the 5th year' option track entitled "Sciences des Données" (SD). This SD track already starts from the MAM4 second semester curriculum (eg. MAM4 option SD). Of course, all courses listed are taught in English except if not specifically mentioned (FR).
Some old description of some of the course can be found here (reset of the full web site after the fire at OVH data center hosting former Polytech UCA web site is still on-going...)

Semester 1

For courses from the 5th year offer, time schedule is split in two periods (quarters of consecutive 8 weeks including last one for the written exam).

Technical courses

Name of the Module Total
number of ECTS
Data science 1 6
Subject Coeff. Shared with
Modelisation & optimisation in machine learning 3 MAM4; Till Christmas break;
Technologies for massive data 3 MAM5/SI5 SD; Monday Morning, Period 1
Elective courses , that could be selected without time schedule conflicts for a total of at least: 15
Subject Coeff. Shared with
Personal or in group project in Data science * 3 N/A, starting ASAP, early October till end of January
Refresher in Maths, Probas & Stats 3 Blocked full days early september
Processus stochastiques (FR) 3 MAM4; Till Christmas break;
Equations aux dérivées partielles (FR) 3 MAM4; Till Christmas break;
Interpolation Numérique (FR) 3 MAM4; Till Christmas break;
Data science include seminars from industrial local partners 3 MAM5/SI5 SD; Friday Afternoon Period 1
Data mining 3 MAM5/SI5; Tuesday Morning Period 2
Data mining for networks 3 MAM5/SI5; Thursday Afternoon Period 2
Gestion de données multimedia (Management of massive data (mostly network streaming technologies)) 3 MAM5/SI5 SD; Friday Morning Period 2
Introduction to Security 3 MAM5/SI5 TBD
Blockchain and privacy 3 MAM5/SI5; Thursday Morning Period 2
Virtualized cloud computing 3 MAM5/SI5; Monday Morning Period 2
Large scale distributed systems 3 MAM5/SI5; TBC Period 2
Peer to peer 3 MAM5/SI5; Tuesday Morning Period 1
From shallow to deep learning for multimedia data 3 MAM5/SI5; Wednesday Morning Period 2
Applied A.I. 3 MAM5/SI5; Monday Afternoon Period 1
Full stack software engineering for the Internet of Things 3 MAM5/SI5; Tuesday Morning Period 2
Content distribution in wireless networks/Multimedia networking 3 MAM5/SI5; Wednesday Morning Period 1
Evolving internet 3 MAM5/SI5; Friday Morning Period 1
Web of data (also online as Coursera EIT Digital course) 3 MAM5/SI5; Tuesday Morning Period 1
Semantic Web (prerequisite Web of data) 3 MAM5/SI5; Tuesday Afternoon Period 2
Ingénierie des connaissances 3 MAM5/SI5; Tuesday Afternoon Period 1
Traitement automatique du texte en IA (shared with M1 Info.) 3 N/A
Advanced programming (shared with M1 Info.) 3 Starts mid of october
Traitement automatique du texte en IA (TATIA) (FR) 3 Starts mid of october
Parallelism 3 Starts mid of october
Computer networks 3 Starts mid of october
BD vers Big Data (partly FR) 3 ?? and partly MOOC, Starts mid of september

Innovation & Entrepreneurship Courses

I & E 1 Total 9 ECTS
EMEIE101 Basics in I&E (spread in the empty slots, till end of january/february) 3 coeff
EMEIE103 Business Intelligence 1: Project management (spread in the empty slots, until jan.) 3 coeff
EMEIE102 Business Dev. Lab Part1 (spread in the empty slots, till end of january) 3 coeff

Semester 2

Technical courses

Name of the Module Total number of ECTS
Data science 2 6
Subject Coeff. Shared with
Data valorization 3 MAM4 SD from early Feb till mid of May
Computer vision and machine learning 3 MAM4 SD from early Feb till mid of May
Elective courses , that could be selected without time schedule conflicts for a total of at least: 9
Subject Coeff. Shared with
Personal or in group project in Data science (can be continuation of sem. 1)* 3 N/A
Artificial Intelligence 3 SI4 from early Feb till mid of May
Réalité augmentée (FR) 3 MAM4/SI4 SD from early Feb till mid of May
Optimisation(FR) 3 MAM4 from early Feb till mid of May
Time series 3 MAM4 from early Feb till mid of May
Programmation parallèle 3 SI4 from early Feb till mid of May
Réseaux avancés et middleware 3 SI4 from early Feb till mid of May
Combinatorial optimization 3 M1 Info
Operational research 3 M1 Info
Software engineering 3 M1 Info
Internet of the future 3 M1 Info
Communication & concurrency 3 M1 Info

Innovation & Entrepreneurship Courses

I & E 2 : choose 2 elective courses (March-June) among Total 6 ECTS
Business Intelligence 2: Data science for business 3 coeff
I&E Complementary course 1: Digital innovation in Fintech. 3 coeff
I&E Complementary course 2: Innovation management in Large scale organizations 3 coeff

I & E 3 Total 9 ECTS
Business Dev. Lab Part 2 (spread in the empty slots, from early march till 21st of June) 5 coeff
Summer school (globally organised EIT Digital in the July-August period) 4 coeff

Overall, the Basics in Innovation and Entrepreunership accounts for 6 ECTS, and the Business Development Lab for 8 ECTS.

Second Year (from 2020-2021, subject to modification)

For updated information, consult the slides presented at the Master school Kick Off Meeting, Trento University, Oct 2019, for committed and prospective exit point students in DSC.

Semester 3

Mandatory as elective courses are most of the times spread on either of the two periods (quarters of consecutive 8 weeks including last one for the written exam). The semester lasts from early september up to early march.

Compulsory courses (6 ECTS)

Period 1 Mandatory courses 2019-20; Majeure SD Schedule
Panorama of Big Data technologies Monday morning Coefficient 2
Data Science: include seminars from industrial local partners Friday afternoon Coefficient 2

Period 2 Mandatory courses 2019-20 Schedule
Management of massive data (mostly network streaming technologies) Friday morning Coefficient 2

Elective courses (12 ECTS)

Elective courses list from 2019-20 Topic Schedule Coeff
Statistical machine learning (see p2) (Period1) Data modeling and analysis Thursday afternoon, 1:30 pm to 4:30 from early Sept till early Dec., Sciences faculty campus NICE, Valrose, 2+2
Statistical computational methods = CART and random forests for high-dimensional data (see p3) (Period 1 and 2) Data modeling and analysis Thursday afternoon 1:30 pm,to 4:30 from late Oct. till early Feb. Sciences faculty campus NICE, Valrose 2+2
Fouille de données (Period 2) (basic data mining) Data modeling and analysis Period 2, Tuesday morning 2
Applied A.I. (Period 1) Data modeling and analysis Period 1, Monday afternoon 2
Machine learning: theory and algorithms (Period 2) Data modeling and analysis Period 2, Wednesday morning 2
Graph algorithms and combinatorial optimization (Period 1) Data modeling and analysis Period 1, Monday afternoon 2
From shallow to deep learning for multimedia data (Period 2) Application of data science, in particular on multimedia content and data on the web Period 2, Wednesday morning 2
Data mining for networks (Period 2) Application of data science, in particular on multimedia content and data on the web Period 2, Thursday afternoon 2
Web of Data (Period 1), also online as Coursera EIT Digital course Application of data science, in particular on multimedia content and data on the web Period 1, Tuesday morning 2
Semantic Web (Period 2) Application of data science, in particular on multimedia content and data on the web Period 2, Tuesday afternoon (prerequisite: web of data) 2
Security and privacy 3.0 (Period 2) Data processing supporting technologies Period 2, Wednesday afternoon 2
Sécurité des applications web (Period 1, in French) Application of data science, in particular on multimedia content and data on the web Period 1, Thursday morning 2
Blockhain and privacy (Period 2) Data processing supporting technologies Period 2, Thursday morning 2
Peer to Peer (Period 1) Data processing supporting technologies Period 1, Tuesday morning 2
Virtualized infrastructure in cloud computing (Period 2) Data processing supporting technologies Period 2, Monday morning 2
Large Scale Distributed Systems (Period 2) Data processing supporting technologies Period 2, TBD 2
Multimedia networking (Period 1) Data processing supporting technologies Period 1, Wednesday morning 2
Evolving internet (Period 1) Data processing supporting technologies Period 1, Friday morning 2
Techniques modernes de programmation concurrente (Period 1, in French) Data processing supporting technologies Period 1, Tuesday afternoon 2
Knowledge Engineering (Period 1) Application of data science, in particular on multimedia content and data on the web Period 1, Tuesday afternoon (prerequisite: web of data) 2
Full stack software engineering for the Internet of Things (Period 2) Data processing supporting technologies Period 2, Tuesday morning 2
Machine learning for image analysis (Period 1), Application of data science, in particular on multimedia content and data on the web Period 1, Thursday morning 2
Réalité virtuelle (Period 2, in French) Application of data science, in particular on multimedia content and data on the web Period 2, Thursday afternoon 2
Interagir dans un monde 3D (Period 1, in French) Application of data science, in particular on multimedia content and data on the web Period 1, Wednesday morning 2
Advanced topics in deep learning (Period 1, in French) Application of data science, in particular on multimedia content and data on the web Period 2, Monday afternoon 2
French as a Foreign Language (beginner or intermediate). On top of program whenever timeschedule allows it. (Period 1) Period 1, Wednesday afternoon
Refresher in Maths, Probas and Stats. On top of program Blocked 4 to 5 full days early Sept.

Project Fin d'Etudes in Data science (6 ECTS)

Project Fin d'Etudes Schedule
Personal research and/or development project in Data Science, individual or in small teams (up to 4 people) Starts september till end of februrary, 4 weeks almost full time mid nov till mid december 6 ECTS

Innovation and Entrepreunership module (6 ECTS)

Besides, the student must develop a mandatory Innovation and Entrepreneurship (I&E) work of 6 ECTS, as mandated by EIT Digital I&E common specification of masters. This work is coached by the UCA and EURECOM local coordinator(s) in I&E and spans the whole October-February period once per two weeks approximatively starting mid of october in general. The goal is to reuse on-line I&E material from the Moodle, common to all EIT Digital masters, and apply this to selected business cases. These business cases are most of the time proposed by the various EIT Digital Action Lines partners.

Semester 4

Internship/Master thesis (30 ECTS, 4 to 6 months max)

This internship can be done either in our partner research institutions teams at I3S, LJAD, INRIA even if for EIT Digital students, industrial internships are the preferred choice. We provide support and guidance to this aim. Including for outside the Nice - Sophia-Antipolis Technology park. The evaluation of the internship work encompasses three aspects: work achieved as measured by the internship supervisor, written thesis submitted at the end of August and evaluated by the university supervisor, oral defense organized early September (can happen in visio conference mode) and evaluated by a jury of professors. Positions as employee in a company can also be turned as the mandatory period for preparing the master thesis, as soon as the content is approved by the track local coordinator.

Project subjects and internships of former students


This is one way to let known who are the students that have studied one year in Data Science at UCA. This includes the name of their exit point, or respectively entry point. And what are the topics that they have been able to be in touch with. For Master 2 students, is also indicated the title of their master thesis, where they prepared it, and if available, where they are now employed.

Master 1, Cohort 2015-16

Project Topic/Title Team Student(s)
Car Embedded Raspberry PI implementation of machine learning algorithms MinD/Sparks CNRS I3S Ivan Lopez Moreno (TUB), Fabi Eitel (UPM), Diego Burgos Sancho (UPM)
Computing continuous SPARQL queries using Bloom filters over RDF streams on Storm and Grid'5000 platforms Scale/Comred CNRS I3S Usman Younas (TUB), Sander Breukink (TU/e)
Optimized hyper-parameters for deep architectures MinD/Sparks CNRS I3S Joana Iljazi (TU/e)
Deep Neural Style Adaption in Music MinD/Sparks CNRS I3S Fabi Eitel (UPM)
Data analytics applied on tweets Alcmeon Start-up Ivan Lopez Moreno (TUB), Diego Burgos Sancho (UPM)
SecTracks: a cybersecurity company that uses Open Data to predict threats as exposed by a tool that predicts attacks BDL project (including POC) in collaboration with SAP labs, Sophia-Antipolis Henny Selig (KTH), Joana Iljazi (TU/e), Zhanjie Zhu (TU/e)
Air tickets price optimization using social networks data BDL project (including POC) in collaboration with Amadeus, Sophia-Antipolis Sander Breukink (TU/e), Bo Li (TUB), Diego Burgos Sancho (UPM)
Optimising the maintenance of city bikes BDL project (including POC) in collaboration with Antibes municipality Ivan Lopez Moreno (TUB), Fabi Eitel (UPM), Siqi Li (TU/e), Usman Younas (TUB)

Master 1, Cohort 2016-17

Project Topic/Title Team Student(s)
Observatoire du discours politique français MinD/Sparks CNRS I3S and BCL UNS laboratory Veeresh Elango (KTH), Nazly Santos Buitrago (TU/e), Luis Galdo (TU/e), Juan Gonzales Huesca (TU/e)
A pruning method to compress deep network MinD/Sparks CNRS I3S Tongtong Fang (KTH)
A middleware to support continuous RDF data querying Scale/Comred CNRS I3S Ma Xin (TU/e), Hamed Mohammadpour (KTH)
Security solutions for big data systems (research papers study) Sparks CNRS I3S Marcos Bernal (UPM)
Jathagam: a solution for finding cyber security for big data BDL project in collaboration with SAP research lab (Sophia-Antipolis) Veeresh Elango (KTH), Nazly Santos Buitrago (TU/e), Luis Galdo (TU/e), Juan Gonzales Huesca (TU/e)
Smart Grid Security (SGS) Solutions BDL project in collaboration with SAP research lab (Sophia-Antipolis) Ma Xin (TU/e), Hamed Mohammadpour (KTH), Tongtong Fang (KTH), Marcos Bernal (UPM)

Master 2, Cohort 2016-17

Project and Internship Topic/Title Team Student(s)
Principal Component Analysis: theoretical properties in high dimension LJAD CNRS Yang Song (TU/e)
Development of a JSON Library for satisfaction surveys on mobile devices Zenith INRIA Ignacio Uya Lasarte (UPM)
Play Outside own group, startup project Argan Veauvy (UPM), Loic Lavillat (UPM), Justin Vailhere (UPM)
RGB-D store navigation through immersive techniques and sensor Lagadic INRIA Yolanda De La Hoz (UPM)
In-depth understanding of deep learning MinD/Spark CNRS I3S Hausmane Issarane (UPM)
CREDIT SCORE PROJECT DEVELOPMENT BASED ON QUNAR’S USER FEATURES Qunar.com, Being China Yang Song (TU/e)
DESIGN AND IMPLEMENTATION OF PARALLEL OPERATORS FOR A DISTRIBUTED QUERY ENGINE Leanxcale, Mardid, Spain Ignacio Uya Lasarte (UPM)
Large Scale Video Description on
YouTube-8M dataset
Mind/Spaks I3S CNRS Yolanda De La Hoz (UPM)
Creation of Data Science tools for Natural Language Processing and data visualization internal displacement monitoring centre (idmc), Geneve, Switzerland Hausmane Issarane (UPM) [now as EIT Digital post doc]
Operational Customer exPerience Air France KLM, Sophia-Antipolis Argan Veauvy (UPM) [now in ADP, Roissy]

Master 1, Cohort 2017-18

Project Topic/Title Team Student(s)
Deep Learning Spell Check MinD/Sparks CNRS I3S and BCL UNS laboratory Eliane Birba (KTH), Upsana Biswas (TU/e), Ponathipan Jawahar (TU/e)
Deep MNREAD Biovision INRIA & MinD/Sparks CNRS I3S Adriana Janik (KTH)
Geo Detection SAP Labs (Mougins) Carlos Callejo (Aalto)
Multiple Instance Learning for segmenting video content based on audio information Widmoka (Sophia Antipolis) & MinD/Sparks CNRS I3S José Diaz Mendoza (TU/e)
Analysis of adverse drug events in the French primary care database PRIMEGE MinD/Sparks CNRS I3S & CHU Nice Jacqueline Neef (UPM) [now IBM Madrid]
Computing images similarity using deep learning MinD/Sparks CNRS I3S & Bentley Antoine Lain (Aalto) [now PhD candidate, Edimburgh]
Deep learning for counting in crowds MinD/Sparks CNRS I3S Ion Mosnoi (Aalto)
Labeled topic classification Meritis Lab Guo Lei (Aalto) [now BMW, Munich]

Master 2, Cohort 2017-18

Project or Internship Topic/Title Team Student(s)
Action elasticity compensation in video classification MinD/Spark CNRS I3S Emily Söhler (UPM), Dane Mitrev (UPM), Luca Coviello (UPM), Antonio Paladini (POLIMI)
Joconde Learn Wimics-MinD/Spark CNRS I3S/INRIA Dina Mohamed Mahmoud (UPM), Sara Zanzottera (POLIMI)
Labeled topic classifier Meritis Labs Jaime Boixados (UPM)
Tensor and matrix factorizations in Python: application to recommender systems SIS CNRS I3S Claus Jungblut (UPM), Miguel Zaballa Pardo (TU/e)
Assessment of the quality and relevance of automatic tests Amadeus Lorenzo Frigerio (POLIMI), Alessandro Polenghi (POLIMI), Ivan Vigorito (POLIMI)

Long term digital data storage on DNA

SIS CNRS I3S Jose Luis Contreras (UPM), Riccardo Lo Bianco (POLIMI)
Multi-language topic classifier Meritis Lab (Sophia-Antipolis) Jaime Boixados (UPM) [now at Meritis, Paris]
Semantic segmentation on satellite imagery with
Deep Learning
Amazon Web Service (Berlin) Jose Luis Contreras (UPM) [now at AWS, Berlin]
Deep Neural Networks and Precision Agriculture for Grape Yield Estimation Fondazione bruno kessler (Trento) Luca Coviello (UPM)
Data anonymization through Generative Adversarial Networks in the differential Privacy scenario SAP Labs (Mougins) Lorenzo Frigerio (POLIMI)
Business intelligence for e-commerce Otto (GmbH & Co KG) (Hamburg) Claus Jungblut (UPM) [Virtuagym, a Columbia/NL company]
Clustering techniques for DNA signal reconstruction I3S CNRS Riccardo Lo Bianco (POLIMI)
End-to-end Multiple Object Tracking with
Convolutional Recurrent Neural Networks
Renault Software Labs (Sophia-Antipolis) Dane Mitrev (UPM)
Machine Learning in Transportation Data Analytics SWVL company (Cairo) Dina Mohamed Mahmoud (UPM) [CIB, Cairo)]
End-to-end Models for Lane Centering in Autonomous Driving I3S CNRS & Renault Soft. Labs Antonio Paladini (POLIMI)
Test quality analyses Amadeus (Sophia-Antipolis) Alessandro Polenghi (POLIMI)
Machine learning techniques for the detection and prediction of Multiple Sclerosis Berlin Center for Advanced Neuroimaging (Berlin) Emily Söhler (UPM)
Power Forecasting Models for Renewable Energy Sources Edison (EDF Group), Milano Ivan Vigorito (POLIMI) [Edison]
Live Data Sampling for Analytics Amadeus (Sophia-Antipolis) Miguel Zaballa Pardo (TU/e)
Evaluation of User Interface Technologies for Accelerator Controls CERN (Geneva) Sara Zanzottera (POLIMI)

Master 1, Cohort 2018-19

Project Topic/Title Team Student(s)
Detection of actions in Sports Videos using Multiple Instance Learning MinD/Sparks CNRS I3S Sherly Sherly (KTH)
Deconvoluted and distributed Deep CNNs for ECG classification MinD/Sparks CNRS I3S Ziqing DU (TU/e), Yaowei LI (Aalto)
Counting people in the tram MinD/Sparks CNRS I3S Jinrui LIU (KTH/Aalto/VCC)
Machine learning for healthcare Computing biomarker and pathway for cancer diagnostic using metabolic data Mediacoding/SIS CNRS I3S & Medecine faculty Chenchen HE (UPM)
Machine learning for biomedical: Localizing atrial flutter circuits using machine learning techniques on electrocardiograms (premilinary) CNRS LEAT laboratory Alessio MOLINARI (KTH)
Mobility for Seniors Invent@UCA Cristina RIOS IRIBARREN (UPM)
TrustSpace Invent@UCA Ignacio SANCHEZ (TU/e)
Deep learning for non linear regression problems (very preliminary) MinD/Sparks CNRS I3S Ignacio GARCIA MARTIN (Aalto)
DRL for communication networks (premilinary) Signet/SIS CNRS I3S Dana TOKMURZINA (Aalto)

Master 2, Cohort 2018-19

Project or Internship Topic/Title Team Student(s)
A Bio-Inspired Approach to Image Recognition LEAT CNRS Luca Comoretto (POLIMI)
Deep Reinforcement Learning for Solving Network Optimization Problems Signet/SIS CNRS I3S Mickaël Bernard (UPM)
Analytics of the Spain basket league results and performances Own project creation, ACB Analytics Ricardo Garcia Garcia (UPM), Rodrigo Rosado Gonzalez (UPM), Francisco Santos (UPM)
Nouvelles expériences clientes grâce à des modèles d'IA Accenture SAS France, Sophia-Antipolis Mickaël Bernard (UPM) [now PhD Candidate Univ Nebraska Lincoln, USA]
Machine learning demos/POC and action recognition learning Atos integration, Sophia-Antipolis Nasseredine Bajwa (UPM)
From payment transactions to a structured network: Key companies Identification, Risk Assessment and Graph Mining UniCredit, Wien (Austria) Comoretto Luca (POLIMI) [now ECB Frankfurt]
SAP Predictive maintenance IECISA (Spain) Alvaro Gallego (UPM)
Machine learning for analysis of multimedia content Pragsis Technologies, Madrid Ricardo Garcia Garcia (UPM)
DNA FOR COLD DATA ARCHIVING: USING MACHINE LEARNING FOR ROBUST DECODING Mediacoding/SIS CNRS I3S Eva Gil San Antonio (UPM) [now PhD candidate UCA]
Audit Logs and Metadata Pragsis Technologies, Madrid Francisco de Borja Gonzalez Conzalez (UPM)
Design of a toolbox allowing the recognition / detection and prediction of one or more behaviors on a client system in real time Alten, Sophia-Antipolis Rodrigo Rosado Gonzalez (UPM) [Alten]
Intelligent drone tracking system: design and development of IDTS cloud platform IDTS (Spain) Francisco Santos (UPM)
Deep Speech Recognition in noisy environment NXP semiconductors, Sophia-Antipolis Frederico Ungolo (UPM)
Hotel big data smart cache prototype Amadeus, Sophia-Antipolis Yirui Wang (UPM)

Master 1, Cohort 2019-20

Project Topic/Title Team Student(s)
NAMB Scale/Comred CNRS I3S Seyed Farzam Mirmoeini (UPM)
Deep learning networks as Process networks Keros/Comred CNRS I3S/INRIA Li Yang (ELTE)
Génération d’emplois du temps d’infirmières. MC3 CNRS I3S Zhaopeng Tao(KTH), Rui Zhang (TU/e)
Towards an evaluation of the carbon footprint of digital activities Sparks CNRS I3S Md Nurain Haider (TUB)
N/A UCA Yidan Cai (TU/e)
N/A UCA Juan Alvarez (KTH)
Thomas Van Dommelen (UPM)
Comparaison des performances de codes de calculs scientifiques : C++ versus Java Keros/Comred CNRS I3S/INRIA Lin Sinan(Aalto)

Master 2, Cohort 2019-20

Project or InternshipTopic/Title Team Student(s)
A DSL for safety validation of Autonomous Vehicles Keros/Comred CNRS I3S/INRIA Rafael Mosca (POLIMI)
What next? When next? Amadeus Sophia-Antipolis Jacek Wachowiak (KTH)
Bias Mitigation MinD/Sparks CNRS I3S Victoria Hedenmalm (KTH)
Prediction of hospital readmission MinD/Sparks CNRS I3S Lorenzo Foà (UPM)
Medical Text Analysis MinD/Sparks CNRS I3S Puneet Beri (TU/e)
NAMB- A High-Level Benchmark Generator for Stream Processing Platforms Scale/Comred CNRS I3S Ankita Pillay (KTH)
Spiking neural networks for event-based stereovision Sparks CNRS I3S Rafael Mosca (POLIMI)
Clustering of investment funds‘ business models and characteristics ECB Frankfurt Jacek Wachowiak (KTH)
Automatic Modeling of Critical Control Systems for IT Infrastructure
Merging Information with Trust Networks
KTH and FOI Victoria Hedenmalm (KTH)
Customer behaviour analytics JAKALA spa -Milano Lorenzo Foà (UPM)
Detection of Motion Artifacts in Thoracic CT Scans THIRONA Nijmegen, NL Puneet Beri (TU/e)
Energy Evaluation For Buildings Myrspoven, Stockholm Ankita Pillay (KTH)

Master 1, Cohort 2020-21

Project or InternshipTopic/Title Team Student(s)
Synaptic delays for temporal pattern recognition Sparks CNRS I3S Nicolas Arrieta Larazza (UT)
Nerea Ramon Gomez (UT)
Ivan Zabrodin (UCA)
Detecting Offensive Posts in Social Media Wimics-Sparks/CNRS I3S/INRIA Mariia  Solodiankina (ELTE)
Deep Learning as applied to medical diagnostics Masai-Sparks/CNRS I3S/INRIA DhivinJoshua Nelson (UT)


Master 2, Cohort 2020-21

Project or InternshipTopic/Title Team Student(s)
Comprehensive Study of Neuromorphic Computing
and Spiking Neural Networks
Sparks CNRS I3S Gabriele Zambra (UPM)
Yazan Salti (UPM)
Junior data scientist Reply S.p.A. Milano Gabriele Zambra (UPM)
Machine learning and java development Ericsson Yazan Salti (UPM)