Distinguished Lecturer Series 2018

The Department of Information Engineering is proud to present the Distinguished Lecturer Series 2018 The Distinguished Lecturer Series, active since 2004, is an annual program of high impact lectures where internationally renowned scholars are invited to discuss cutting-edge research in ICT and neighboring disciplines. While aiming at scientific excellence, our lectures are typically open to the general public as  well.

We are looking forward to meeting you in our upcoming events.

www.dei.unipd.it/distinguished

 

The Road to a New Medicine: Interfaces Between Engineering, Biology and Pharmacology in Drug Development
Friday, May 11, 2018 - 11:00
Aula Magna
A. Lepschy, Dept. of Information Engineering

Paolo Vicini
Senior Director, Translational Sciences, MedImmune, Cambridge, UK
Affiliate Associate Professor, Department of Bioengineering, University of Washington, Seattle, USA
Short bio & Abstract


Future Wireless Services satisfying requirements in “Health” and “Smart City” applications
Thursday, May 31, 2018 - 14:00
Aula Magna A. Lepschy, Dept. of Information Engineering


Leo P. Ligthart
Professor at FIEEE, FIET

Short bio & Abstract


Probabilistic and Deep Learning Techniques for Mobile Robots
Wednesday, September 26, 2018 - 14:00

Aula Magna A. Lepschy, Dept. of Information Engineering

Wolfram Burgard
Professor at University of Freiburg

Short bio & Abstract


 

 

SHORT BIOS and ABSTRACTS


The Road to a New Medicine: Interfaces Between Engineering, Biology and Pharmacology in Drug Development
Friday, May 11, 2018 - 11:00
Aula Magna
A. Lepschy, Dept. of Information Engineering

Paolo Vicini
Senior Director, Translational Sciences, MedImmune, Cambridge, UK
Affiliate Associate Professor, Department of Bioengineering, University of Washington, Seattle, USA

Short Bio: Paolo Vicini is Senior Director of Clinical Pharmacology, Pharmacometrics and DMPK (Drug Metabolism and Pharmacokinetics) at MedImmune, a wholly owned subsidiary of AstraZeneca. Previously, Paolo was a Research Fellow with Pfizer Worldwide Research and Development in San Diego and a Bioengineering faculty member at the University of Washington, Seattle, where he continues to be an Affiliate Associate Professor in Bioengineering and Pharmaceutics. He served on the National Institutes of Health Biomedical Computing and Health Informatics Study Section, published to date 130 peer-reviewed articles and is on the Advisory Editorial Board of the Journal of Pharmacokinetics and Pharmacodynamics and of Clinical Pharmacology and Therapeutics. He served as an Associate Editor of CPT: Pharmacometrics and Systems Pharmacology. He is a Fellow of the American Association of Pharmaceutical Scientists and a member of the Biomedical Engineering Society, the IEEE Engineering in Medicine and Biology Society, the International Society of Pharmacometrics, the International Society for the Study of Xenobiotics, the American Diabetes Association and Beta Gamma Sigma. He currently serves on the Board of Directors of the American Society of Clinical Pharmacology and Therapeutics. Paolo holds a Ph.D. in Bioengineering from the Polytechnic of Milan and a MBA from the University of Southern California.

Abstract:
The discovery and development of a new medicine is a long, complex process, characterized by varying degrees of uncertainty at all stages. In addition to incomplete knowledge about the underlying disease mechanisms, challenges can include uncertain translation from preclinical to clinical phases, high failure rate for molecules during clinical testing, and heterogeneity among patients. Quantitative approaches to pharmacology have recently benefited from the integration of diverse research areas (engineering, biology, chemistry and pharmacology, in addition to others) to solve outstanding problems in drug development. Molecule candidate selection, dosing regimens, clinical trials design and execution, and the identification of patient populations are examples of areas that benefit from quantitative pharmacology. These methodologies can be based on sophisticated computer statistical simulations and mechanistic modeling, and are often carried out within the developing disciplines of pharmacometrics and systems pharmacology. These borrow methods and approaches from many scientific fields in pursuit of a more complete understanding of both pharmacokinetics (what the body does to the drug) and pharmacodynamics (what the drug does to the body) characteristics of a drug molecule. This presentation will provide an overview of the history and application of these approaches, with practical examples in multiple therapeutic areas.


Future Wireless Services satisfying requirements in “Health” and “Smart City” applications
Thursday, May 31, 2018 - 14:00
Aula Magna A. Lepschy, Dept. of Information Engineering


Leo P. Ligthart
Professor at FIEEE, FIET

Short Bio:  Prof. Dr. Ir. Leo P. Ligthart FIEEE, FIETM; Em. Prof. Delft University of Technology Adjunct prof BIT, Beijing; ITS, Surabaya and UI, Jakarta, Indonesia Chairman Conasense; Member IEEE-AESS BoG
Abstract:
In the last decade wireless technology and systems created an explosive growth in a wide range of data services. Services started with voice, video and image transmissions. Bottleneck was often the limited data transmission capacity. However, recent breakthroughs in throughput allow for much higher data rates over the same wireless channel and thus for support of many more demanding services. The majority of on-going developments show that applications have COmmunications, NAvigation, SENnsing and SErvices in common. These applications play a key role in the Foundation CONASENSE operating as world-wide Brain Tank and as Scientific Platform.In the lecture, I chose for visionary wireless services which require stringent demands to be fulfilled by next generation wireless technologies and systems. Characteristic for the services is the importance for Society and/or the expectation of a substantial market potential.First application area is related to health. Most advanced applications and latest developments are directed into means for non-healthy persons while for healthy persons only less advanced technology is available. However, the large majority is healthy and wants to stay healthy as long as possible by using CONASENSE-type of services. The advanced services should be manifold but at least should allow for real-time individual health monitoring, on-line (video and speech) connection with data bases and advises for stimulating actions on what and how to do but also what not to do. Dedicated broadband wireless sensors with integrated new “aps” are needed in order to create a match between the best actions and the capabilities of the healthy person.Second application area is related to smart cities. We never had so many cities with a population of 5 million inhabitants or more and the number of these cities increases. Many societal problems have to be solved. Many big countries should work on breakthroughs to make the environment inside the cities more safe and healthy. A multi-disciplinary approach is urgently needed and there is a big role for future wireless CONASENSE related activities. 

 

 

 

Probabilistic and Deep Learning Techniques for Mobile Robots
Wednesday, September 26, 2018 - 14:00

Aula Magna A. Lepschy, Dept. of Information Engineering

Wolfram Burgard
Professor at University of Freiburg

Short Bio:
Prof. Burgard is a professor for computer science at the University of Freiburg and head of the research lab for Autonomous Intelligent Systems. His areas of interest lie in artificial intelligence and mobile robots.His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years Prof. Burgard and his group have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects.In his previous position from 1996 to 1999 at the University of Bonn Prof. Burgard was head of the research lab for Autonomous Mobile Systems. In 1997 they deployed Rhino as the first interactive mobile tour-guide robot in the Deutsches Museum Bonn in Germany (see corresponding overview article). In 1998 Prof. Burgard and his group went to Washington, DC, to install the mobile robot Minerva in the Smithsonian Museum of American History. Afterwards they produced several robots that autonomously operated in trade shows and Museums. In 2008, they developed an approach that allowed a car to autonomously navigate through a complex parking garage and park itself. In 2012, they developed the robot Obelix that autonomously navigated like a pedestrian from the campus of the Faculty of Engineering to the city center of Freiburg. Prof. Burgard have published over 250 papers and articles in robotic and artificial intelligence conferences and journals. In 2005, Prof. Burgard co-authored two books. Whereas the first one, entitled Principles of Robot Motion - Theory, Algorithms, and Implementations, is about sensor-based planning, stochastic planning, localization, mapping, and motion planning, the second one, entitled Probabilistic Robotics, covers robot perception and control in the face of uncertainty.

Abstract:

Autonomous robots are faced with a series of state estimation and learning problems to optimize their behavior. In this presentation I will start from probabilistic approaches and then describe recent methods developed in my group based on deep learning architectures for different perception problems including object recognition and segmentation and using RGB(-D) images. In addition, I will present a terrain classification approaches that utilize sound and vision. Finally, I will discuss the applicability of deep learning methods to robot navigation. For all approaches I will describe extensive experiments quantifying in which way the corresponding approaches extend the state of the art. This talk is designed to stimulate the discussion about potential directions in robotics and if they should be model- or data-driven.