PhD in DATA ANALYTICS AND DECISION SCIENCES - 35th cycle · 2020. 4. 17. · Mara Tanelli [email protected] +39 02 2399 3621 Prof. Simone€Cinquemani [email protected]

PhD in DATA ANALYTICS AND DECISION SCIENCES -

35th cycle

Number of scholarship offered 8

DepartmentDIPARTIMENTO DI ELETTRONICA, INFORMAZIONE EBIOINGEGNERIA

Description of the PhD Programme

La grande quantità di dati raccolta attraverso i nuovi media e la disponibilità a memorizzarli edelaborarli a un costo relativamente contenuto hanno dato una forte spinta al settore del dataanalytics, che negli ultimi anni ha visto una crescita esponenziale. Questo ha fatto nascere ilbisogno di esperti con conoscenze approfondite (ben oltre la laurea magistrale) in diversi settori,in grado di lavorare in un ambito altamente interdisciplinare, adattandosi ai rapidi cambiamenti delsettore. Questo dottorato nasce per formare figure di senior data scientist attraverso uno studioapprofondito delle discipline e la partecipazione a ricerche di punta nell¿ambito della data scienceintegrando conoscenze di diversi settori disciplinari. Queste figure dovranno essere in grado diacquisire conoscenza dei fenomeni in gioco, di modellarli e realizzarli adeguatamente, dianalizzare criticamente i risultati prodotti per eventualmente guidare un processo di trasferimentotecnologico che le analisi potrebbero suggerire. Il dottorato si articola in tre anni, il primo dedicatoai corsi proposti e i successivi al lavoro di tesi. I corsi sono progettati per fornire una solidapreparazione interdisciplinare nelle diverse aree di ricerca che fanno parte della data science. Unperiodo obbligatorio di studio all¿estero e la partecipazione a scuole dottorali, conferenzeinternazionali, consente agli studenti di sviluppare una rete di relazioni che ne facilita la crescitaprofessionale.

Stampato il 23/04/2019 1 / 1


35th cycle

Research Field: DATA ANALYTICS AND DECISION SCIENCES

Monthly net income of PhDscholarship (max 36 months)

€ 1300.0In case of a change of the welfare rates during the three-year period, the amount could be modified.

Context of the research activity

Motivation and objectives of the researchin this field

The PhD program in Data Analytics and DecisionSciences aims at breeding the next generation of datascientists who will tackle the challenges and theopportunities created by the increasingly larger availabilityof data, cheap storage, and computer power. Datascientists who can capture the most relevant aspects ofphenomena in play, develop adequate models, supervisethe development of analytic pipelines, critically analyzethe result, and support the technological transfer that theresults might enable.

Methods and techniques that will bedeveloped and used to carry out theresearch

The research aims at developing novel approaches fordata analytics and decision science including novelstatistical methods for complex data, new econometricsetups, methods of advanced machine learning, as wellas technological solutions to integrate massive amount ofdata from heterogeneous data sources.

Educational objectives

The research will be carried out in collaboration with theteam of applied statisticians, economists, computer andmanagement engineers from three departments, theDepartment of Management, Economics and IndustrialEngineering, the Department of Mathematics, and theDeparment of Electronics, Information andBioengineering. Within this lively and stimulatingacademic and research environment, the doctoral studentwill be educated to become a skilful researcher in DataScience.

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Job opportunities

The profile of Data Scientist and the technological andmethodological solutions proposed are of interest for abroad range of actors, including International institutions,policy makers, statistical and research centres, largecorporations that aim to analyse heterogeneous datacoming from various datasources.

Composition of the research group

7 Full Professors7 Associated Professors2 Assistant Professors4 PhD Students

Name of the research directors Any faculty member

Contacts

PhD CoordinatorProf. Pier Luca LanziEmail: [email protected]: +390223993472

Additional support - Financial aid per PhD student per year (gross amount)

Housing - Foreign Students

1st year 2nd year 3rd year1500.0 €

per student1000.0 €


per student

max number of financial aid available: 1, given in order of merit (onlyfor students with scholarship)..

Housing - Out-of-town residents(more than 80Km out of Milano) --

Additional information: educational activity, teaching assistantship, computer availability, desk availability,any other information

Teaching AssistanshipThere are various forms of financial aid for activities of support to the teaching practice. The PhDstudent is encouraged to take part in these activities, within the limits allowed by the regulations.

Educational Activities

2nd year: max 1370,00 euro per student•

3rd year: max 1370,00 euro per student•

Computer Availability

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1st year: individual use•

2nd year: individual use•

3rd year: individual use •

Desk availability

1st year: individual use•

2nd year: individual use •

3rd year: individual use•

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35th cycle

Research Field: DATA ANALYTICS FOR IMPACT EVALUATIONS IN HEALTHCARE





The research focuses on analysis of socio-economicimpacts of health policies and health treatments. Theresearch combines data analytics methods, econometrictechniques and economic concepts and relies on theintegration of administrative, epidemiological, clinical andsocio-economic data to study i) efficiency and productivityconditions in the delivery of health services, ii) the real-world impact of therapeutic treatments.


The research relies on the combination between methodsin statistical learning and econometrics, with a focus oncausal analysis and impact evaluation in real world data.Successful candidates are expected to develop and dealwith advanced statistical models in healthcare, in order tomake impact and scenario analyses that may supportdecision making in pharmacoeconomic and public healthscenarios. Knowledge of statistical software (e.g., R,python or similar) is required.


The research will be carried out in collaboration with theteam of statisticians, economists, computer andmanagement engineers working in the Center for AnalysisDecision and Society of the Human Technopole. Withinthis lively and stimulating academic and researchenvironment, the student will be educated to become askillful data scientist in the field of health economics.

Job opportunities The profile of data scientist and the applications proposed1 / 2

in this project are of interest to of a broad range of actors,including (but not limited to): public and private institutionsdealing with healthcare, clinical and pharmaceutical data,as well as international institutions and research centersaiming at assessing performances of healthcareproviders, and policy makers in charge with healthcaregovernance.



Name of the research directors Prof. Fabio Pammolli and Prof. Francesca Ieva

Contacts

Fabio [email protected]

Francesca [email protected]






per student




There are various forms of financial aid for activities of support to the teaching practice.The PhDstudent is encouraged to take part in these activities, within the limits allowed by the regulations.

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35th cycle

Research Field: DIAGNOSTIC AND PROGNOSTIC HEALTH MONITORING METHODS FOR

SAFETY CRITICAL TRANSPORT SUB-SYSTEMS BASED ON DATA ANALYTICS IN

PRESENCE OF MODEL UNCERTAINTIES





Interdisciplinary PhD Grant The PhD research will be carried out in collaboration withresearch groups of the PhD programme in "MechanicalEngineering" (Ingegneria Meccanica). Seehttp://www.dottorato.polimi.it/index.php?id=242&L=1 forfurther information.

In transport systems, a failure in critical components canindeed pose severe and fatal threats. Thus, suchcomponents are most often endowed with direct orindirect monitoring systems to estimate future mechanicaland dynamic behaviours. Such a monitoring is effectiveonly if failures can be predicted in advance from earlywarnings of potential mis-functioning, thus allowing toavoid the failure itself or to switch the system to a safemode. However, if not properly designed, healthmonitoring systems may lead to cost increase and safetyhazards. It is then of utmost importance that such systemsenable an active and correct health monitoring of theunderlying physical system and of its components. Theproposed PhD research will address this very relevantindustrial problem proposing a novel methodologicalapproach for the design of data-driven approaches fordamage detection and residual-life prediction of safety-critical components in transport systems, consideringdifferent case studies, namely Automotive, Railway and

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Helicopter applications.


It is clear from the above discussion that the design ofeffective HMSs for safety-critical systems requires amultidisciplinary approach. In particular, the work will 1)combine mechanical system dynamics with data-analytics; 2) design effective algorithms that optimallyblend signal processing and Machine-Learningcompetences with context-based knowledge; 3) deal withbig-data inputs coming from experimental contexts. Notethat the considered applications ask for very high-performance levels, reachable only with a true integrationof the aforementioned competences. This will lead tonovel results of high scientific value, with strongrepercussions on the reference industrial sectors, thusenabling a valuable technological transfer.Expertise inprogramming languages for data analysis (Matlab andPython) is required. Preferable also to have some domainknowledge in the dynamics of mechanical systems and insignal processing topics.


The PhD is expected to develop multidisciplinaryexpertise in the fields of diagnostic and prognostic fordynamical systems, predictive mainteinance andadvanced competencies of data-analytics methods forthese field of application, with a specific regard toAutomotive, Railway and Aerospace applications. Thisresearch will be supervised by experts of these disciplinesat Politecnico di Milano, and the student will be involved inthe collaborations with corporate partners. Within thislively and stimulating academic and researchenvironment, the student will be educated to become askillful data analyst for the active monitoring of mobilitysystems and their components, with a rich range of real-life application in order to optimally blend methods andtools in the fields of interest.

Job opportunities

The profile of data scientist and the applications proposedin this project are of interest to of a broad range of actors,including (but not limited to): public and private institutionsdealing with mobility topics, automotive and aerospace

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companies, as well as international institutions andresearch centers working in mobility and predictivemaintenance, as well as policy makers in charge withsafety aspects of transportation and mobility governance.



Name of the research directors Prof. Mara Tanelli and Prof. Simone Cinquemani

Contacts

Mara [email protected]+39 02 2399 3621

Prof. Simone [email protected]+39 02 2399 8454






per student




--

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35th cycle

Research Field: HEALTH ANALYTICS FOR REAL WORLD DATA AND PRECISION

MEDICINE





Health analytics is concerned with the study of the hugeamount of data and information produced by the clinicaland/or administrative datawarehouses, as well as by theElectronic Health Records (HER), arising from healthcarecontext. In this huge amount data lies an immensepotential for statistical learning to become an usefulsupport to clinical practice. Recent advances in datacollection, as well as the availability of Real World Data,call for new statistical models and approaches to dataanalysis, especially for the application to different domainsof healthcare research.


The research will develop novel statistical methods (i)provide risk-stratified scenario analyses and new insightfulmethods for outcome research; (ii) measure and assess(cost)-effectiveness of procedures and protocols ofhealthcare paths; (iii) make pharmacoepidemiologicalanalyses. These methods and models will exploit complex(structured and not structured) data coming from clinicalregistries, secondary databases and HER, and willsupport decision making. Expertise in programminglanguages for statistics (R, python, SAS or similar) isrequired.

Educational objectivesThe PhD is expected to develop multidisciplinaryexpertise in the fields of Statistical Learning and HealthData Science, as this research will be supervised by a

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data science expert at Politecnico di Milano. The researchwill also involve clinicians and researchers of HumanitasUniversity and Center for Analysis Decisions and Society(CADS). Within this lively and stimulating academic andresearch environment, the student will be educated tobecome a skillful health analyst, i.e., a data scientist withdeep expertise in statistical modeling for the healthcaresetting.

Job opportunities

The profile of data scientist and the applications proposedin this project are of interest to of a broad range of actors,including (but not limited to): public and private institutionsdealing with healthcare, hospitals, clinical andpharmaceutical companies, as well as internationalinstitutions and research centers working in healthcareresearch, and policy makers in charge with healthcaregovernance.



Name of the research directors Prof. Francesca Ieva

Contacts

Francesca [email protected]






per student





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35th cycle

Research Field: HEALTHCARE RESEARCH AND BIOSTATISTICS BASED ON REAL

WORLD DATA





Big data analytics in healthcare context is concerned withthe study and the analysis of the huge amount of data andinformation produced by the clinical and administrativedatawarehouses. In this huge amount data lies animmense potential for statistical learning which aims tosupport public decision making. If properly collected,exploited and modeled, the use of big data in this contextmay leads to a great innovation in evaluation,assessment, and improvement of healthcare system. Ifcombined with suitable data visualization tools, healthanalytics helps managers operate better by providing real-time information that can support decisions.


The research will develop novel statistical methods forintegrated, big and complex data to, for example, (i)create more effective and efficient policies; (ii) measureeffectiveness of procedures and protocols of healthcarepaths; (iii) ensure greater transparency and trust in publicdecision, (iv) allow to create timely, error-free, appropriateand cost-effective public services (v) assess healthcareproviders performances. Expertise in programminglanguages for statistics (R, SAS or similar) is required.


The research will be carried out in collaboration with theteam of applied statisticians, economists, computer andmanagement engineers working in the Center for AnalysisDecision and Society of the Human Technopole. Within

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this lively and stimulating academic and researchenvironment, the student will be educated to become askilful data scientist.

Job opportunities

The profile of data scientist and the applications proposedin this project are of interest to of a broad range of actors,including (but not limited to): public and private institutionsdealing with healthcare, clinical and pharmaceutical data,as well as international institutions and research centresaiming at assessing performances of healthcareproviders, and policy makers in charge with healthcaregovernance.



Name of the research directors Prof. A.M. Paganoni and Prof. C. Masella

Contacts

Prof. Anna Maria [email protected]

Prof. Cristina [email protected]






per student





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35th cycle

Research Field: MACHINE LEARNING FOR DATA SCIENCE IN HEALTH ANALYTICS





Nowadays, healthcare systems generate large amount ofdata which include clinical data (e.g., medical imaging,physiological signals), administrative data and electronichealth records. Health analytics aims at exploiting thehuge potential that lies in these data to support severalclinical practices such as early diagnosis, design ofpersonalized treatments, prognostic outcome, andassessment of procedures and protocols.


The research will focus on the design and development ofnovel data analytics frameworks based on machinelearning methods, such as deep learning and ensemblemethods, proved successful in several domains. Inparticular, machine learning methods will be applied to (i)classify and extract features from physiological signalsand images; (ii) build prediction models from clinical dataand health records; (iii) identify either patterns oranomalies from a large population of samples.


The PhD is expected to develop multidisciplinaryexpertise in the fields of Machine Learning and HealthData Science, as this research will be supervised by adata science and machine learning expert at Politecnico diMilano. The research will also involve clinicians andresearchers of Humanitas University and Center forAnalysis Decisions and Society (CADS).

Job opportunities The profile of data scientist and the applications proposed1 / 2

in this project are of interest to of a broad range of actors,including (but not limited to): public and private institutionsdealing with healthcare, hospitals, clinical andpharmaceutical companies, as well as internationalinstitutions and research centers working in healthcareresearch, and policy makers in charge with healthcaregovernance.



Name of the research directors Prof. Pier Luca Lanzi

Contacts

Prof. Pier Luca [email protected]






per student





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35th cycle

Research Field: OBJECT ORIENTED DATA ANALYSIS FOR PRECISION MEDICINE





Precision medicine will require the analysis of massiveand complex data generated by different andheterogeneous sources. The aim of the project is todevelop new statistical methods and algorithms forprediction, classification and regression, together withproper quantifications of uncertainty, within the frameworkof Object Oriented Data Analysis.


Specific goals of the project are the development ofstatistical models and methods for: (i) decoupling joint andindividual variability for Hilbert data, (ii) supervisedlearning for non-Euclidean data, either Riemannian datalike covariance or correlation matrices, input/outputmatrices or non-Riemannian data like networks or texts,(ii) eliciting spatial and temporal dependence.


The research will be carried out in collaboration with theteam of statisticians, computer scientists, economists andmanagement engineers cooperating with the Center forAnalysis Decision and Society of the HumanTechnopole. Within this lively and stimulating academicand research environment, the doctoral student willbecome a skilful data scientist exposed to theoretical andapplied statistical problems that are moving the frontier ofthe discipline.

Job opportunities As Data Sciences are increasingly adopted by allcompanies in all industrial and business sectors, the

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demand for data scientists and analytics expertscontinues to outpace supply and dominates both the USand the European job market.



Name of the research directors P. Secchi, L. Tanca, G. Azzone

Contacts

Prof. Piercesare [email protected]

Prof. Letizia [email protected]

Prof. Giovanni [email protected]






per student





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35th cycle

Research Field: PRECISION POLICIES AND IMPACT ANALYSIS





Data Science (DS) is expected to deliver value for publicgovernance. In a number of studies, strong claims havebeen made about the potential of big data and dataanalytics and there are now several cases showing theirapplication in service delivery and organizationadministration. The role of DS in designing an effectiveand an efficient policy and in measuring its impact has, onthe contrary, still been explored only marginally. Theresearch will explore the issue, dealing with the problemsarising from its greater complexity and its distinctive inter-organizational boundaries.


The research will be based on the analysis of large databases and on field interactions with decision makers.


The research will provide the PHD candidate theopportunity to deepen its competence on Data Scienceand on the processes require to apply its results inpractice

Job opportunities

PHD graduates operating in this field can find careeropportunities in both research and consultancy, as datascientist or project managers.



Name of the research directors Prof. Giovanni Azzone and Prof. Piercesare Secchi

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Contacts

Giovanni [email protected]

Piercesare [email protected]






per student





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35th cycle

Research Field: SUPPORTING PRECISION MEDICINE THROUGH DEEP KNOWLEDGE

EXTRACTION





The introduction of powerful Deep Learning techniquesover the past few years has brought with it a step changein our ability to mine useful knowledge from unstructureddata like text and images. The project will developtechniques to extract clinically relevant and actionableknowledge from patient records, socio-economic datastores, and other structured and unstructured datasources.


The project will make use of machine learning and relatedtechniques to analyse unstructured data such as patientrecords and other patient-related information in order toderive insights for disease prevention, diagnosis, orquality of care assessment. (See this recent survey onmining medical healthrecords: https://arxiv.org/pdf/1702.03222.pdf)


The research will be carried out in collaboration with theteam of applied statisticians, economists, computer andmanagement engineers working in the Center for AnalysisDecision and Society of Human Technopole. Within thislively and stimulating academic and researchenvironment, the doctoral student will be educated tobecome a skilful researcher in Data Science.

Job opportunities The profile of Data Scientist and the technological andmethodological solutions proposed are of interest for a

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broad range of actors, including International institutions,policy makers, statistical and research centres, largecorporations that aim to analyse heterogeneous datacoming from disparate data sources.



Name of the research directors Prof. Letizia Tanca and Prof. Mark Carman

Contacts

Prof. Letizia [email protected]

Prof. Mark [email protected]






per student





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Documents

PhD in DATA ANALYTICS AND DECISION SCIENCES - 35th cycle · 2020. 4. 17. · Mara Tanelli [email protected] +39 02 2399 3621 Prof. Simone€Cinquemani [email protected]