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.
Be aware of the great opportunity by studying as an ENTRY student at UCA => you will be registered to two UCA degrees, mainly the UCA Polytech Nice Sophia engineering degree in Sciences Informatiques (Computer science), and the Master in computer science of UCA. The engineering degree is well-recognized through two labels: by the French CTI and the European EUR@ACE, still it requires you to commit to extra requirements, especially a French proficiency level at least A2 at the end of (or at worst 2 years after) your studies as an UCA engineering student (you may only graduate with the Master degree at the end, if some requirements get not fulfilled).
Sorry, but this opportunity cannot apply to EXIT students, as the number of studying semesters in the premises of UCA Polytech engineering school is too low (only 1..., should be at least equal to 2 to get opportunity to graduate with a CTI labelled engineering degree) => you can only graduate with the Master degree of UCA.
Check here for more information about studying at Sophia Tech campus of UniCA as international student.
First Year (4th year SI MUNDUS) EIT Digital track in Data Science
In the sequel, MAM4 means 4th year of Maths Appliquées (MAM) department of Polytech Nice Sophia. SI5 means 5th year of Sciences Informatiques (SI) of this same engineering school. MAM5 SD corresponds to the 5th year' option track entitled "Sciences des Données" (SD). 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 Module | Total number of ECTS |
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| Data science fundemental courses Semester 1 | 6 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Data science complementary courses Semester 1 , that could be selected if no time schedule conflicts, for a total of at least: | 15 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Innovation & Entrepreneurship courses
Semester 2Technical courses
Innovation & Entrepreneurship courses
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Second Year EIT Digital track in Data Science
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 | Schedule | |
|---|---|---|
| Panorama of Big Data technologies | Monday morning | Coefficient 2 |
| Data Science | Monday afternoon | Coefficient 2 |
| Period 2 Mandatory courses | Schedule | |
|---|---|---|
| AI engineering | Tuesday morning | Coefficient 2 |
Elective courses (12 ECTS)
| Elective courses list 2023-24 | Topic | Schedule | Coeff |
|---|---|---|---|
| Advanced Optimization (Period1 and 2) | Data modeling and analysis | From mid Sept till mid Nov., Sciences faculty campus NICE, Valrose, | 2+2 |
| Fundamentals of ML (Period 1 and 2) | Data modeling and analysis | From mid Nov till mid Feb., Sciences faculty campus NICE, Valrose | 2+2 |
| Deep learning (Period 1) | Data modeling and analysis | Period 1, Tuesday afternoon | 2 |
| Advanced Data mining (Period 2) | Data modeling and analysis | Period 2, Tuesday afternoon | 2 |
| Advanced topics in deep learning (Period 2) (more Maths oriented) | Data modeling and analysis | Period 2, Monday afternoon | 2 |
| Advanced deep learning (Period 2) (more CS oriented) | Data modeling and analysis | Period 2, Monday afternoon | 2 |
| Graph algorithms and combinatorial optimization (Period 1) | Data modeling and analysis | Period 1, Tuesday afternoon | 2 |
| Machine learning for image analysis (Period 1) | Application of data science, in particular on multimedia content and data on the web | Period 1, Wed morning | 2 |
| Machine learning for networks (Period 2) | Application of data science, in particular on multimedia content and data on the web | Period 2, Tuesday morning | 2 |
| Text mining and NLP (Period 2) | Application of data science, in particular on multimedia content and data on the web | Period 2, Monday morning | 2 |
| Web of linked Data (Period 1) | 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 |
| Knowledge Engineering (Period 1) | Application of data science, in particular on multimedia content and data on the web | Period 2, Monday morning (prerequisite: web of data) | 2 |
| Réalité virtuelle (Period 2, in French? TBC) | Application of data science, in particular on multimedia content and data on the web | Period 2, Wed morning | 2 |
| Security and privacy 3.0 (Period 2) | Data processing supporting technologies | Period 2, Tuesday afternoon | 2 |
| Sécurité des applications web (Period 2, in French) | Application of data science, in particular on multimedia content and data on the web | Period 2, Tuesday morning | 2 |
| Blockhain and privacy (Period 2) | Data processing supporting technologies | Period 2, Monday morning | 2 |
| Peer to Peer (Period 1) | Data processing supporting technologies | Period 1, Monday morning | 2 |
| Virtualized infrastructures in cloud computing (Period 2) | Data processing supporting technologies | Period 2, Friday afternoon | 2 |
| Large Scale Distributed Systems (Period 2) | Data processing supporting technologies | Period 2, Tuesday afternoon | 2 |
| Multimedia networking (Period 2) | Data processing supporting technologies | Period 2, Friday 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, Monday afternoon | 2 |
| Full stack software engineering for the Internet of Things (Period 1) | Data processing supporting technologies | Period 1, Monday morning | 2 |
| French as a Foreign Language. On top of program. (Period 1) | Period 1 |
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 early march, 4 weeks, almost full time in feb | 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 coordinator in I&E and spans the whole October-Early March period once per two weeks approximatively starting early october. 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 and the local ecosystem.
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 internship is 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 (next: ELTE) |
| Deep Learning as applied to medical diagnostics | Masai-Sparks/CNRS I3S/INRIA | DhivinJoshua Nelson (UT) Zhijie He (next: AALTO) |
| Classification of Spiking Neural Networks Prediction | Sparks CNRS I3S | Thuany Stuart(KTH) |
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) |
| Development of Spark-Kafka Applications for Data Retrieval concerning the Migration of the Cluster from IBM BigInsights to Cloudera 7.1 |
Reply S.p.A. Milano | Gabriele Zambra (UPM) |
| Bug report classification: A comparison of machine learning approaches | Ericsson | Yazan Salti (UPM) |
Master 1, Cohort 2021-22
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| Mixture of linear regressions for clustering of trajectories | SIS CNRS I3S | Mattia Arsendi (AALTO) |
| Online hate speech detection | Wimics-Sparks/CNRS I3S/INRIA | Lynda Attouche (Erasmus POLIMI) |
| Transformers Explainability | Masai Sparks/CNRS I3S/INRIA | Lynda Attouche (Erasmus POLIMI) |
| Descriptive and Inference Analysis of the ACQUA Dataset | DIANA INRIA | Lenny Klump (TU/e) |
| Crypto Bot and Alerting System | UCA | Ilaria Enache (UT) Taylor Lucero (UTrento) |
| Analysis of a research paper about Numerical study of distortion approximation of a compressed softmax layer |
SIS CNRS I3S | Zeyneb M'HAMEDI (UCA local student) |
| Stereo vision, Event camera | Sparks CNRS I3S | Yazaman Pazhoolideh (KTH) |
Master 2, Cohort 2021-22
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| Semi-automated labeling of Time Series for anomaly detection |
EZAKO, Sophia | Abetayeva Anar (AALTO) [now at Ezako] |
| Assessing and improving Data Quality using Machine Learning and control charts |
TIP Trailer Services | Stefano D'Angelo (POLIMI) [now at TIP] |
| NLP for startup industry classification | Novable, Brussels | Giuseppe Dimonte (POLIMI) [now ATA Italy] |
| AI-Assisted for Modeling Multitasking Driver | Aalto Univ. school of science | Hossein Firooz (AALTO) [now Sony AI Zürich] |
| Web development | Fortinet, Sophia Antipolis | Otto Makinen (UCA) [now Fortinet Sophia] |
| Automated insights extraction from a text with Amazon Web Services: Pastlab |
AWS Madrid | Arturo Pinar (UPM) [now Ibermatica, Spain] |
| Detecting Body Languages from interaction videos | INRIA STARS | Akos Tánczos (UPM) [now Bosch, Hungary] |
| Batch effect correction for Toxicogenomics data using Machine Learning and Deep Learning approaches |
Univ of Laval, Québec | Ivan Zabrodin (UCA) [now zz] |
Master 1, Cohort 2022-23
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| Human performance assessment of downscaled event data | Sparks, CNRS UCA I3S | Bueno Garcia Marina (UNITN) Kupczyk Eva (UPM) Trillo Carreras Lucia (UNITN) |
| yyy | xxx | Ciesielski Tymoteusz (UPM) |
| yyy | xxx | Malatinski Zsombor (UTurku) |
| Human body parsing | Masai Sparks/CNRS UCA I3S/INRIA | Raspanti Frederico (TU/e) |
| yyy | xxx | Van der Heijden Niels (UTwente) |
Master 2, Cohort 2022-23
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| ve-NeRF: Velocity and Pose Parameterized Event Generating NeRF for anomaly detection |
CNRS UCA I3S, Sophia-Antipolis | Abdikharim Aziz (UPM) [now at xxx] |
| Evaluation of Explainability Methods for Neural Natural Language Processing | LAMSADE, Univ Paris Dauphine | Attouche Lynda (Erasmus at POLIMI) [now at xxx] |
| Efficient 3D model retrieval for Industrial Metaverse applications | Siemens AG | Di Domenico Alessio (POLIMI) [now xxx] |
| Modeling, development and improvements on B2B banking Data Analysis Processes | Management solutions | Freyre Gomez Eduardo (UPM) [now xxx] |
| Deep active visio-tactile human pose estimation for intelligent interactive interior | BMW research and technology house | Fumelli Chiara (POLIMI) [now xxx] |
| Application of transformers in scientific literature mining | Rays of space Oy | Isavnina Kseniia (ELTE) [now xxx] |
| Data science and ML | Nyris Gmbh | Kubelka Laurin (UPM) [now xxx] |
| Aide à l’orientation dans le supérieur : visualisation d’une forêt aléatoire | CNRS UCA I3S, Sophia-Antipolis | M'hamedi Zeyneb (UCA) [now xxx] |
| Data analyst | Airbus Operations Gmbh | Martin Lesmes Rafael (UPM) [now xxx] |
| Privacy preserving machine learning | DEIB, Politecnico de Milano | Pazzi Riccardo (POLIMI) [now xxx] |
Workflow aware Access Control for the Industrial Metaverse in a Multi party environment |
Siemens | Pizzamiglio Giacomo (POLIMI) [now xxx] |
| Digital transformation | Loro Piana S.P.A. | Ruaro Giovanni (POLIMI) [now xxx] |
| Operations Stragegy and Analytics | Hellofresh SE | Schweiker Marcel (UPM) [now xxx] |
Master 1, Cohort 2023-24
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| Argumentation et contre argumentation dans le contexte de la guerre en Ukraine | Wimmics, CNRS UCA I3S/INRIA | Charafeddine Achir (Local EIT student) |
| Analysis of Arguments in US Presidential Election Debates from 1960 to 2020 Leveraging NLP Techniques | Wimmics, CNRS UCA I3S/INRIA | Alex Orlandi (KTH) |
| Car crashes analysis: Cincinnati | xxx | Andrea Nappi (UTurku) |
| Federated learning | Coati CNRS UCA I3S/INRIA | Jiayi Qi (KTH) |
Master 2, Cohort 2023-24
| Project or InternshipTopic/Title | Team | Student(s) |
|---|---|---|
| Data Science for Fraud Detection in Insurance Companies |
Shift Technology, Spain | Araluce Goded Inigo (UPM) [now at Shift Technology] |
| Leveraging LLM’s Capabilities To Enhance Complaint Handling Processes |
Italgas/BlueDigit, Italy | D'Agostino Fabio (POLIMI) [now at BlueDigit, Italy] |
| Anomaly detection in time series using generative methods | Inditex, Spain | Guerrero Jaime (UPM) [now Leroy Merlin, Spain] |
| Forecasting of waste composition of clients (using hierarchical time series data and conformal inference) |
Renewi, Netherlands | Faikaterini Vasilonikolidaki (TU/e) [now xxx] |