# Courses of Academic Year 2019/2020

A preliminar list of the didactic offer of the ICT doctorate follows. The list will be completed and updated during the year.Seminars on Bibliometrics and research Evaluation will be offered by UNIMORE for all the doctorate courses.

### Uncertainty Quantification with Applications in Science and Engineering

** Lecturer: ** Prof. Clemens Heitzinger (TU Wien)

Clemens Heitzinger received his master's degree (Dipl.-Ing.) in
mathematics and his PhD degree (Dr. techn.) in technical sciences with
honors both from TU Vienna. He was a visiting researcher in the
Department of Mathematics and Statistics at Arizona State University,
a research associate in the School of Electrical and Computer
Engineering at Purdue University, and a senior research associate in
the Department of Applied Mathematics and Theoretical Physics at the
University of Cambridge. In 2015, he returned to TU Vienna as an
Associate Professor in the Department of Mathematics. He is also an
Adjunct Professor in the School of Mathematical and Statistical
Sciences at Arizona State University. He was awarded the START Prize,
Austria's most prestigious award for young scientists, by the Austrian
Science Fund (FWF) in 2013. His research interests are stochastic
partial differential equations, uncertainty quantification, Bayesian
PDE inversion, and reinforcement learning with applications for
example in nanotechnology and medicine.

** Schedule: **

to be defined

** Program: **

In recent years, stochastic aspects have started to play an
increasingly important role in the modeling and simulation of physical
and engineering applications using partial differential equations
(PDE). These aspects, models, and methods are often subsumed under
the term uncertainty quantification. Applications include, for
example, semiconductor devices and nanoscale sensors, as random
effects have become essential at the nanometer scale. However, the
ideas and methods can be applied to many other applications.
In this lecture series, we start with the theory of deterministic PDE
for modeling transport phenomena. The Poisson and Poisson-Boltzmann
equations are the foundation for self-consistency. The Boltzmann
transport equation provides the basis for charge transport and
many-body problems, and other transport equations such as the
drift-diffusion-Poisson system can be derived from it. We discuss
existence and uniqueness of the solutions and introduce numerical
methods. Some deterministic and stochastic homogenization problems
stemming from nanotechnological applications will also be discussed.
Next, stochastic versions of the transport equations are introduced
and the multilevel Monte-Carlo method for the efficient solution of
stochastic PDE is explained. Various extensions are also discussed.
Optimal methods are also explained; this means that given a prescribed
total error, the optimal parameters of the method are determined such
that the total computational effort is minimized.
In the last part, Bayesian inversion for determining unknown
parameters in PDE models is discussed. Bayesian inversion makes it
possible to calculate unknown model parameters or parameters functions
by comparing the model with measurements. Bayesian inversion has the
advantage, compared to other, simpler methods, that it yields
probability distributions of the unknown parameters. From the
probability distributions, conclusions about how well nonlinear,
ill-posed inverse problems can be solved can be drawn immediately and,
e.g., confidence intervals are found. The theory of Bayesian PDE
inversion is discussed, practical algorithms are explained, and
applications to engineering problems are shown. Bayesian inversion is
considered an essential new tools for sensor devices.
Lecture notes (ca. 300 pages) will be distributed.

** Exam: **

Not yet defined

** CFD: **2

### Diritti & Doveri & Pubblicazioni: Incontri con gli studenti delle Scuole di Dottorato ICT e Industriale e del Territorio

** Lecturer: **Pola Michele e Simona Assirelli

** Schedule: **

lun 09/03 e merc 11/03 - ore 14.00 - 17.00

** Program: **

See program and schedule

** Exam: **

Not yet defined

** CFD: **2

### Biosensing with advanced electronic devices

** Lecturer: ** Prof. Muhammad Alam Ashraf (Purdue University, USA)

https://nanohub.org/groups/ashraf_alam/alambio

**Professor of Electrical and Computer Engineering**

Purdue University

School of Electrical and Computer Engineering

Hall for Discovery & Learning Research

207 S. Martin Jischke Dr.

West Lafayette, Indiana 47907-1971

**Muhammad Ashraful Alam **is a Professor of Electrical and Computer Engineering where his research and teaching focus on physics, simulation, characterization and technology of classical and emerging electronic devices. From 1995 to 2003, he was with Bell Laboratories, Murray Hill, NJ, where he made important contributions to reliability physics of electronic devices, MOCVD crystal growth, and performance limits of semiconductor lasers. At Purdue, Alam’s research has broadened to include flexible electronics, solar cells, and nanobiosensors. He is a fellow of the AAAS, IEEE, and APS and received the 2006 IEEE Kiyo Tomiyasu Award for contributions to device technology.

** Schedule: **

**Lectures (of 1.5 hours each) will start the week of February 24th (exam possible):**

Part 1: **Introduction to Nanobiosensors**

Lecture 1: What is nanobiosensors, anyway?

Lecture 2: Basic concepts: Biomolecules, Analyte density, diffusion distances

Lecture 3:Basic concepts: Types of biosensors, geometry of biosensing

Part 2: **Setting Time**

Lecture 4: Response time of classical nanobiosensors

Lecture 5: Response time of complex nanobiosensors

Lecture 6: Beating the diffusion limit by biobarcode sensors

Lecture 7: Beating the diffusion limit by Droplet Spectroscopy

Lecture 8: Beating the diffusion limit by analyte flow

Lecture 9: Settling time vs. first passage time

Part 3: **Sensitivity**

Lecture 10:Sensitivity and types of biosensors

Lecture 11:Potentiometric biosensors

Lecture 12:On charge screening of cylindrical sensors

Lecture 13:ISFET as a pH-meter

Lecture 14:Origin of charges in a biomolecule

Lecture 15:How to beat screening

Lecture 16:Amperometric Sensor – An introduction to glucose sensors

Lecture 17:Amperometric Sensors – Michaelis-Menton equation

Lecture 18:Beating the diffusion limit in an amperometric DNA sensors

Lecture 19: Elements of an Cantilever sensor

Lecture 20:Cantilever sensors and its nonideal response

Lecture 21:Nonlinear nanobiosensing by a Flexture-FET

Part 4:**Selectivity**

Lecture 22: Selectivity: energetics of molecular recognition

Lecture 23: Selectivity: Spatial distribution of random sequential absorption

Lecture 24: When all else fail: Tag, filter, and amplify

Lecture 25:An information theory perspective on selectivity

Lecture 26:Physics of Ion-selective fuel-cell based glucose sensors

Lecture 27:Physics of Ion-selective sweat-sensors

Part 5:**Putting them together**

Lecture 28:Genome sequencing by Ion Torrent – Part 1

Lecture 29:Genome sequencing by Ion Torrent – Part 2

Lecture 30:Introduction to Microfluidic and paper based sensors.

Lecture 31: Conclusions: Looking back and looking forward

** Program: **

The course will provide an in-depth analysis of the origin of extra-ordinary sensitivity, fundamental limits, and operating principles of modern nanobiosensors. The primary focus will be physics of biomolecule detection in terms three elementary concepts: response time, sensitivity, and selectivity. And, we will use potentiometric, amperometric, and cantilever-based mass sensors to illustrate the application of concepts for specific sensor technologies.
Roughly speaking Lectures 1- 5, 9, 10-15, 22, 23 cover the fundamental topics of sensing, the others complement the scenario with application examples.

** Exam: **

Not yet defined

** CFD: **12

### Data Science and Machine Learning: basics and applications to Health Care

** Lecturer: **Dr. Paolo Missier

** Schedule: **

Le lezioni di novembre (PART I) si tengono in sala master nei seguenti orari:

- Lunedì 25 dalle 14 alle 19

- Mercoledì 27 dalle 9 alle 13

- Giovedì 28 dalle 15 alle 19

PART II date e orari da definire, settimana 13-17 gennaio

** Program: **

PART I Fundamentals of Machine Learning methods, overview of Data Science in the Health Care

- Introduction to Data Science and the role of Machine Learning: Exploratory vs Predictive Data Analytics (EDA, PDA) ?
- EDA: Overview of common techniques with notebook examples ?
- Machine Learning: basic techniques, common pitfalls, and how to avoid them ?
- Expressive intelligible supervised learning models: General Additive models with pairwise interactions (GA2M). Shaply index ?
- Application. Insights from wearable activity trackers: Human Activity Recognition using a public benchmark dataset ?

PART II Health Applications and transition to Deep Learning

- The UK Biobank: opportunities for research: working with Electronic Health Records ?
- Genomics: From DNA sequencing to variant calling: a big data processing pipeline. ?
- Genome-wide Association studies (GWAS). ?
- GWAS using the Hail platform (Spark): a complete example from the Broad InstituteFrom GWAS to machine learning for Genome-Wide Association studies ?

- Introduction to Deep Neural Networks (Deep Learning) with applications to health care

Lab activities:

- The course takes a very practical, hands-on approach to illustrate key concepts, with the help of python programs that show popular data analytics and ML libraries at work in detail (Pandas and Scikit-learn). These are implemented as Jupyter notebooks and made available for students to work with throughout the course. Some understanding of python programming for scientific application is desirable, but not essential. ?

- At the start of the course, students will have the opportunity (optionally) to embark in a week-long data science experience using high frequency triaxial accelerometers (activity trackers) made available by Newcastle University. They will be able to collect their own activity data and pre-process them using open source third party SW, and then implement their owned hoc analysis algorithms to “make sense” of the activity traces. ?

Main references:

- Deep Learning book, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016. http://www.deeplearningbook.org/ ?
- Note: we do not cover Deep Learning in this course. We only use part I of the book: "* Part I: Applied Math and Machine Learning Basics" ?

- O’Reilly Scikit-learn book: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ ?
- scikit-learn user guide, Release 0.21.3 https://scikit-learn.org/stable/_downloads/scikit-learn-docs.pdf ?

** Exam: **

Not yet defined

** CFD: **8

### Corsi Di Formazione Complementare Per Dottorandi E Assegnisti Ediz. 2019

** Lecturer: **

** Schedule: **

18-21 November 2019

** Program: **

See program details .

La partecipazione all'evento sarà possibile previa iscrizione, entro e non oltre la data del 14.11.2018, tramite il modulo disponibile al link:
https://docs.google.com/forms/d/e/1FAIpQLSflg423UJTyqwsH5T0dl-tFvzQPXpWwVkm9bAZWvWlfmex01A/viewform?usp=sf_link

Le presentazioni illustrate in occasione del corso in oggetto sono disponibili a questo URL

** Exam: **

Not yet defined

** CFD: **6

### Project Management Simulation Workshop: SPATIUM

** Lecturer: **Professor David W. Parker
Businness School University of Queensland
Brisbane, Australia

Download the lecturer CV

** Schedule: **

September 26, 10:00-13:00. 14.30-16.30

Laboratory G1.3, Padiglione Tamburini, via Amendola 2, area San. Lazzaro di Reggio Emilia

** Program: **

Spatium simulation allows participants to conduct a complex mega-project; and while doing so, the consequences of key decision-making and strategies can be identified in the eventual outcome. All reading materials and full instructions will be supplied.
Spatium is a simulation model allows users to explore running a complex project and which requires consideration of differing approaches to project execution, risks management, stakeholder monitoring, plus numerous other factors that need to be identified.
The following organizations are just some of users of Spatium:
http://www.prendo.com/simulations/spatium/

** Exam: **

Not yet defined

** CFD: **2

### Project Management Simulation Workshop: SPATIUM

** Lecturer: **Professor David W. Parker
Businness School University of Queensland
Brisbane, Australia

Download the lecturer CV

** Schedule: **

September 25, 10:00-13:00. 14.30-16.30

Laboratory G1.3, Padiglione Tamburini, via Amendola 2, area San. Lazzaro di Reggio Emilia

** Program: **

The workshop is suitable for both participants with little knowledge or practical experience of project management, as well as those experienced or teaching managing projects. The workshop will cover theoretical aspects, cases, and group activities to illustrate and explore important rudiments of project management. Over 80 per cent of commercial projects fail to meet their planned time, quality or cost. Why? What unique features did successful projects have? Does lean, agile and flexible bring benefits?
The workshop will cover:
What is a project: Relationships among Portfolios, Programs and Projects; Operations, production and projects; Organizations and project management; Business value; Project management and managing a project: skills and competencies; Project Management and Body of Knowledge (PMBoK) and Projects in Controlled Environments (PRINCE); Influences on Project Life Cycle eg Organizational; Stakeholders and Governance; What is project success?; Project Life Cycle and Phases; Project Management Processes; Process Groups; Knowledge Areas; Project Integration Management; Project Scope; Work Breakdown Structure (WBS); Project Time Management and Scheduling; Project Cost Management; Project Quality Management; Project Teams and HR; Communication Management; Project Risk Management; Procurement Management; Closing Projects.

** Exam: **

Not yet defined

** CFD: **2