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)
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).
* most of courses from the SI5 at Polytech Nice Sophia have a short description
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Data science 1 | 6 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Elective courses From Polytech offer, that could be selected without time schedule conflicts (20-21 acad. year) for a total of at least: | 15 in total, from the two lists |
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| Elective courses From Master in Computer Science offer, that could be selected. Exams after Christmas break. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Innovation & Entrepreneurship Courses
| I & E 1 | Total 9 ECTS |
|---|---|
| Basics in I&E (spread in the empty slots, till end of january/february) | 3 coeff |
| Business Intelligence 1 (new course, TBC) |
3 coeff |
| 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 | ||||||||||||||||||||||||
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| Elective courses From Polytech offer, that could be selected without time schedule conflicts for a total of at least: | 9 in total, from the two lists | ||||||||||||||||||||||||
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| Elective courses From Master in Computer Science offer, that could be selected. Exams early June. | |||||||||||||||||||||||||
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Innovation & Entrepreneurship Courses
| I&E 2 (6 ECTS): choose two elective courses (Feb-May) among | Number of ECTS |
|---|---|
| EIINE812 Business Intelligence 2: Innovation management in large organizations (in creation 2020-21) |
3 |
| EIINE813 Complementary course 1: Digital Innovation in FinTech (in creation 2020-21) |
3 |
| EIINE814 Complementary course 2: Data science for business (in creation 2020-21) |
3 |
| Digital cities (shared with DS4H graduate school) (TBC for Spring 2021) | 3 |
| I&E 3 (9 ECTS) | Coefficient |
|---|---|
| Business Dev. Lab Part 2 (spread in the empty slots, from early march till mid of June) | 5 |
| Summer school : globally organized by EIT Digital, July-August | 4 |
Overall, the Basics in Innovation and Entrepreunership accounts for 6 ECTS, and the Business Development Lab for 8 ECTS.
Second Year (from 2019-2020)
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. And the ones from virtual KOM held online, Oct. 2020.
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 2020-21; 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 2020-21 | Schedule | |
|---|---|---|
| Management of massive data (mostly network streaming technologies) | Friday morning | Coefficient 2 |
Elective courses (12 ECTS)
| Elective courses list from 2020-21 | Topic | Schedule | Coeff |
|---|---|---|---|
| Statistical machine learning (see p2) (Period1) | Data modeling and analysis | Thursday afternoon, during period Sept Jan., 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 during period Sept Jan., 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 (updated with more Deep L, new title is : Applied A.I.) (Period 1) | Data modeling and analysis | Period 1, Monday afternoon | 2 |
| Distributed optimization and games (Period 1) renamed Advanced Machine Learning |
Data modeling and analysis | Period 1, Tuesday afternoon | 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, not opened in 20-21) | 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), / Machine Learning for image processing |
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, Friday 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 6 half days Sept./Oct period |
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/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 Binarization for Deep Neural Networks (summer Internship) |
Keros/Comred CNRS I3S/INRIA MinD / Spark CNRS I3S/INRIA |
Li Yang (ELTE) |
|
Génération d’emplois du temps d’infirmières. Annotation with ML tools of a film dataset (summer Internship) |
MC3 CNRS I3S SigNet CNRS I3S |
Zhaopeng Tao(KTH), Rui Zhang (TU/e) Rui Zhang (TU/e) |
| 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 (TU/e) |
| Comparison of the performance of scientific calculation codes | Keros/Comred CNRS I3S/INRIA | Lin Sinan(Aalto) |
| Juan Alvarez(KTH) | ||
| Thomas Van Dommelen (UPM) |
Master 2, Cohort 2019-20
| Project and 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 (UPM) |
| Bias mitigation | 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 (UPM) |
| Automatic modeling of critcal control systems for IT infrastructure - merging information with trust networks |
KTH, Stockholm | 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) |