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EIT Digital DSC track

 

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.

To confort our strong link with the industrial partners, sponsors are invited: the 2015-16 cohort' sponsor has been Amadeus Head of Innovation, Landry Holi. The 2016-17 as in 2018-19 cohorts sponsor has been IBM Global Industry Solution center Nice-Paris, Jean Michel Corrieu. The 2017-18 cohort sponsor has been SAP Labs France located in Mougins, whose head is Ms Olena Khushakowska. In 2019-20,  Therapixel, an SME in AI applied to biomedical applications, head person Pierre Fillard, is being the cohort' sponsor.

 First Year Program (from 2018-2019 onwards, 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).

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 ModuleTotal
number of ECTS
Data science 1 6
SubjectCoeff.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 From Polytech offer, that could be selected without time schedule conflicts (19-20 acad. year) for a total of at least: 9
SubjectCoeff.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
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; Friday Afternoon Period 1
Analysis and indexing of images & videos in big data systems 3 MAM5/SI5; Wednesday Morning Period 2
Compression, analysis and visualisation of multimedia content (update in progress with more Deep L) 3 MAM5/SI5; Monday Afternoon Period 1
Middleware for the Internet of Things 3 MAM5/SI5; Tuesday Morning Period 2
Content distribution in wireless networks 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
 
Elective courses From Master in Computer Science offer, that could be selected. Exams after Christmas break. Choose two courses at least, for a total of at least: 6
SubjectCoeff.Time schedule
Computational linguistics (DS4H minor) 3 N/A
Advanced programming 3 Starts mid of october
Project development 3 ?? many slots spread, mostly on tuesday morning and wednesday morning
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
Logic (for AI) 3 Starts mid of october
Resolution de problèmes: Introduction (FR) 3 Starts mid of october
AI Game programming 3 Starts early october
BD vers Big Data (partly FR) 3 ?? and partly online, Starts mid of september

 

Innovation & Entrepreneurship Courses
I & E 1Total 9 ECTS
Basics in I&E (spread in the empty slots, till end of january/february) 3 coeff
Digital business (SKEMA Business course DS4H EUR shared) 3 coeff
Business Dev. Lab Part1 (spread in the empty slots, till end of january) 3 coeff
 

Semester 2

Technical courses

 

 
Name of the ModuleTotal number of ECTS
Data science 2 9
SubjectCoeff.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
Temporal series 3 MAM4  rom early Feb till mid of May
 
Elective courses From Polytech offer, that could be selected without time schedule conflicts  for a total of at least: 3
SubjectCoeff.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
 
 Innovation & Entrepreneurship Courses

 

I & E 2Total 6 ECTS
Entrepreunership (SKEMA Business course DS4H EUR shared) 3 coeff
Digital IP and Law (Law Faculty DS4H EUR shared).  3 coeff

 

I & E 3Total 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 2019-2020, 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 Scienceinclude seminars from industrial local partners Friday afternoon Coefficient 2


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


Elective courses (12 ECTS)
Elective courses list from 2019-20TopicSchedule 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
Compression, analysis and visualization of multimedia content (update in progress with more Deep L) (Period 1) Data modeling and analysis Period 1, Monday afternoon 2
Distributed optimization and games (Period 2) Data modeling and analysis Period 2, Wednesday morning 2
Graph algorithms and optimization (Period 1) Data modeling and analysis Period 1, Monday afternoon 2
Analysis and indexation of images and videos in big data systems (from shallow to deep learning) (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 1) Data processing supporting technologies Period 1, Friday afternoon 2
Content distribution in wireless networks (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
Middleware for the Internet of Things (Period 2) Data processing supporting technologies Period 2, Tuesday morning 2
Advanced image processing (Period 1), evolution in progress with more Machine Learning content 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
Ingénierie 3D (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. More details will follow (from here).


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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(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/TitleTeamStudent(s)
NAMB Scale/Comred CNRS I3S Seyed Farzam Mirmoeini (UPM)
MDA/MDE -TBA Keros/Comred CNRS I3S/INRIA Li Yang ()
Génération d’emplois du temps d’infirmières. MC3 CNRS I3S Zhaopeng Tao(KTH), Rui Zhang ()
Towards an evaluation of the carbon footprint of digital activities Sparks CNRS I3S Md Nurain Haider(TUB)
Deep Reinforcement Learning for Cloud Networks Signet SIS CNRS I3S Yidan Cai ()
Comparaison des performances de codes de calculs scientifiques : C++ versus Java Keros/Comred CNRS I3S/INRIA Lin Sinan()



Master 2, Cohort 2019-20 (on-going)

Project or InternshipTopic/TitleTeamStudent(s)
A DSL for safety validation of Autonomous Vehicles Keros/Comred CNRS I3S/INRIA Rafael Mosca (POLIMI)
TBA Amadeus Sophia-Antipolis Jacek Wachowiak (KTH)
TBA MinD/Sparks CNRS I3S Victoria Hedenmalm (KTH)
TBA 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)
TBA TBA Victoria Hedenmalm (KTH)
Data analyst JAKALA spa -Milano Lorenzo Foà (UPM)
Data analyst THIRONA Nijmegen, NL Puneet Beri (TU/e)
Data analyst Myrspoven, Stockholm Ankita Pillay (KTH)