I&Research Seminars
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Upcoming Invited Seminars 2024
Crescen Jicol
13th December 2024 10.00 AM Room: 55.309, Tanger Building |
Title: Decoding the User Experience: Towards a new generation of Personalized and Adaptive Virtual Reality Experiences Abstract: Recently, virtual reality (VR) head-mounted displays (HMDs) have received considerable interest from industry and academia alike, leading to substantial advancements and optimisation of their technical features. These developments have enabled unprecedented levels of user engagement across entertainment, training, education, and healthcare. However, user-centred factors such as emotions and individual differences may outweigh technical features in shaping the VR experience. Still, most VR content follows a "one-size-fits-all" approach, ignoring the profound influence of user factors on the quality of the experience. In this talk, I will share relevant research to support the importance of user factors and work contributing towards a vision for the future of VR: Personalized and Adaptive Experiences (PAEs). We envision such content to leverage pre-experience adaptation to user traits and real-time adjustments based on user states, leading to more compelling, and effective applications. Bio: Dr. Crescent Jicol is a Lecturer in Computer Science within the Human-Computer Interaction group, at the University of Bath. His work combines cognitive science with VR to investigate the role played by human factors in the formation of user presence and engagement. His research combines state of the art methodologies for physiological sensing and personality computation with the scope of enabling the next generaton of adaptive VR experiences. |
Past Invited Seminars 2023-2024
Peter Grünwald
11.00 AM Room: 55.309, Tanger Building |
Title: E is the new P Abstract: E-values are an alternative to p-values that effortlessly deal with optional continuation: with e-value based tests and the corresponding anytime valid (AV) confidence intervals, one can always gather additional data, while keeping statistically valid conclusions. Until June 2019, publications on e-values were few and far between: the concept did not even have a name. Then, in the course of a few months, four papers by different research groups appeared on arXiv that firmly established them as an important statistical concept. The first of these was Safe Testing (see below). By now, there have been 100s of papers and two international workshops on e-values. Allowing for optional continuation is just one way in which e-values provide more flexibility than p-values – they also allow to set a type of significance/confidence level alpha after seeing the data, which - even though practitioners unconsciously do it all the time - is a mortal sin in classical testing. In this talk I will introduce e-values, e-processes and AV confidence intervals, and discuss in detail the relation to Bayesian approaches, which are in some ways very similar (e-values also use priors) but in other ways quite different. Bio: Peter Grünwald is founder and former head of the machine learning group at CWI in Amsterdam, the Netherlands. Currently a member of CWI Management, he is also full professor of statistics at the mathematical institute of Leiden University. This spring he received an ERC Advanced Grant for designing a flexible theory of statistics, based on e-processes. From 2018-2022 Peter served as President of the Association for Computational Learning, the organization running COLT, the world’s prime annual conference on machine learning theory, which he chaired in 2015, having earlier chaired UAI, another major ML |
Zina Jarrahi
15th July 2024 9.30 h Room: Auditorium Poblenou Campus |
Title: Open Conversation with Zina Jarrahi Cinker Bio: Dr. Zina Jarrahi Cinker is a recognized Frontier Tech strategist, condensed matter physicist, MATTERVerse thought leader & Deep Science advocate. She serves as the Director General of MATTER, an international think tank of 30 country chapters, and Chief Creator of PUZZLE X-- the leading international event for Frontier Tech for the future--. PUZZLE X Barcelona supported by the Government of Spain, Generalitat de Catalunya, and Barcelona City Hall--, drives stakeholder dialogues among industry. governments, academia, and capital on how Frontier Technologies can shape the next chapter for cities, citizens, industries, and societies. Dr. Cinker received a Ph.D. in Condensed Matter Ultrafast Spectroscopy from Vanderbilt University and has spent the past decade helping the materialization of deep science into technologies with broader impact. She previously served as the Executive Director of the U.S. National Graphene Association, the main organization, and body in North America with over 5,000 international members and organizations. |
Daniel Rueckert
8th July 2024 14.30 h Room: 55.309 |
Title: AI and the future of radiology Abstract: Artificial Intelligence (AI) is changing many fields across science and our society. This talk will discuss how AI is changing medicine and healthcare, particularly in radiology. I will focus on how AI can support the acquisition of medical images and image analysis and interpretation. This can enable the early detection of diseases and support the improved personalised diagnosis. I will show several examples of this in the talk, including neuro and cardiovascular MR imaging. Furthermore, we will discuss how AI solutions can be privacy-preserving while also providing trustworthy and explainable solutions for clinicians. Biography: Daniel Rueckert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich where he directs the Institute for AI and Informatics in Medicine. He is also a Professor in the Department of Computing at Imperial College London. He joined Imperial College as a Lecturer in 1999, becoming Senior Lecturer in 2003 and full Professor in 2005. From 2016 to 2020 he served as Head of the Department at Imperial College. He has founded the Biomedical Image Analysis group consisting of four academics, 15 post-docs and 20 PhD students. He received a Diploma in Computer Science (equiv. to M.Sc.) from the Technical University Berlin and a Ph.D. in Computer Science from Imperial College London. Before moving to Imperial College, he was a post-doctoral research fellow at King’s College London where he has worked on the development of non-rigid registration algorithms for the compensation of tissue motion and deformation. The developed registration techniques have been successfully used for the non-rigid registration of various anatomical structures, including in the breast, liver, heart and brain and are currently commercialized by IXICO, an Imperial College spin-out company. During his doctoral and post-doctoral research he has published more than 500 journal and conference articles as well as graduated over 50 PhD students. Professor Rueckert is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing, MICCAI/Elsevier Book Series, and a referee for a number of international medical imaging journals and conferences. He has served as a member of organising and programme committees at numerous conferences, e.g. he has been General Co-chair of MMBIA 2006 and FIMH 2013 as well as Programme Co-Chair of MICCAI 2009, ISBI 2012 and WBIR 2012. In 2014, he has been elected as a Fellow of the MICCAI society and in 2015 he was elected as a Fellow of the Royal Academy of Engineering and as fellow of the IEEE. More recently has been elected as Fellow of the Academy of Medical Sciences (2019). |
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Shlomi Laufer 17th June 2024 11.00 h Room: 55.410 |
Title: Enhancing Clinical Skill Assessment Through Computer Vision Abstract: Medical education traditionally employs the apprenticeship model, where trainees learn directly under the supervision of experienced practitioners. This model necessitates close follow-up and typically provides extremely subjective and non-standardized feedback. Over the years, efforts to introduce more objective assessment tools have gained momentum, although these tools often remain time-consuming and can still be influenced by subjective evaluations. Recently, the integration of motion sensors with medical simulators has provided a more objective form of feedback. However, they are typically limited to different aspects of motion economy. In this presentation, I will explore how advancements in computer vision can be utilized to create more informative assessments and feedback on surgical skills. The discussion will include the roles of both surgeons and anesthesiologists. Additionally, I will demonstrate how introducing cameras into the operating room provides a new avenue for analyzing surgical workflows. Biography: Shlomi Laufer is an Asst. Prof. at the Faculty of Data and Decision Sciences (formerly Industrial Engineering and Management) in the Technion – Israel Institute of Technology. He graduated (summa cum laude) from the Technion with a bachelor degree in Electrical Engineering (2004), Worked at Mobileye for a couple of years and then completed his PhD (2012) in Bio-Engineering at the Hebrew University working with Prof. Boris Rubinsky. Following that, he held post-doc position at the Pre-Clinical MRI lab at Hadassah Ein Kerem. He later had a joint appointment in Electrical Engineering and the Department of Surgery at the University of Wisconsin-Madison, working with Prof. Barry Van-Veen (EE) and Carla Pugh (Surgery). His |
Jim Goodell
2nd April 2024 12:30h Room: 55.410 |
Title: A Foundational Guide to Learning Engineering Abstract: Learning engineering is a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision-making to support learners and their development (IEEE ICICLE). Ausubel (1978), and others have asserted the importance of background knowledge for effective new learning. This session is designed to expose foundational concepts and interconnections across various domains of learning engineering as background knowledge for future learning. Biography: Jim Goodell is editor of The Learning Engineering Toolkit: Evidence-Based Practices from the Learning Sciences, Instructional Design, and Beyond and co-author of the Science of Remote Learning and Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning. He is a nationally recognized expert on education data standards, leader of standards development for the U.S. Department of Education sponsored Common Education Data Standards (ceds.ed.gov), Chair of IEEE's Learning Technology Standards Committee, network facilitator for the U.S. Chamber of Commerce Foundation's T3 Innovation Network and has served as a judge for the XPRIZE Digital Learning Challenge. His work has led to greater coordination among key national and international data standards organizations. He works with leaders at state and local education agencies, education service providers, post-secondary institutions, military and corporate learning and development innovators, and federal program staff on data standards, next generation learning models, strategic planning, organizational development, policy development, process improvement, data systems architecture/policies, and technology implementation. Results of this work support student learning through education agencies/institutions and the millions of students they serve. |
Ágata Lapedriza
19th March 2024 12:30h Room: 52.S29 |
Title: Emotion perception: Challenges and use cases Abstract: One of the challenges Human-Centered AI systems face is understanding human behavior and emotions considering the context in which they take place. For example, current computer vision approaches for recognizing human emotions usually focus on facial movements and often ignore the context in which the facial movements take place. In this presentation, I will talk about our work on emotion perception and will discuss the challenges we face when we attempt to create automatic models to perceive emotions. In the talk, I will also discuss possible use cases of emotion perception technologies. |
Rubén Bonet
5th March 2024 12:30h Room: 52.219 |
Title: Fractus, the Deep Tech pioneer Abstract: In its academic origins at the Polytechnic University of Catalonia (UPC Barcelona Tech), Fractus emerged as a pioneer in antenna technologies, with revolutionary advancements in the field. Extensive research and development, coupled with a dedicated focus on R&D, have launched Fractus' technology to global recognition, accumulating adoption by a majority of mobile phone OEMs and network infrastructure operators. An exemplary manifestation of the advantages offered by Fractus technology is the absence of external antennas in today's mobile phones. Nevertheless, the journey has not been without its challenges, including pivotal moments that demanded strategic decisions. Notably, the company underwent a significant transformation, shifting its business model from product-centric to a licensing approach. This transition, though demanding, reflects the resilience and adaptability required in the dynamic landscape of cutting-edge technology. As we consider the implication of Fractus' trajectory, we gain insights into the intricate interplay between academic innovation and real-world industry evolution, offering valuable lessons for PhD students navigating their own paths of research and development. |
Emma Robinson
19th February 2024 15h Room: 55.309 |
Title: Computational methods for brain imaging analysis Abstract: Dr Robinson's research focuses on the development of computational methods for brain imaging analysis, and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex “ (Glasser et al, Nature 2016), and has featured as a central tenet in the HCP’s paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples. We are particularly interested in building sensitive models of cognitive development and developmental outcome for prematurely born babies from data collected for the Developing Human Connectome Project (dHCP) |
Silvia Budday
19th February 2024 15h Room: 55.309 |
Title: Exploring brain mechanics Abstract: Brain tissue is not only one of the most important but also the arguably most complex and |
Modelling in Biomechanics
24th November 2023 15:30h Room: 55.309 |
Title: Modelling in Biomechanics Abstract: We have the pleasure to announce a new edition of BCN-MedTech Seminars. Three colleagues will present each month with a common topic. Dissemination and possible collaborations within our environment will be the bases of the seminars to enrich our research! The first session will be about Modelling in Biomechanics and the two selected speakers are: - Zerihun G. Workineh (Postdoctoral researcher) - Morteza Rasouligandomani (Postdoctoral researcher) They are both from the Biomechanics and Mechanobiology team of the SIMBIOsys research group. At the beginning of the session Gemma Piella will give a short introduction to the BCN MedTech group and its research. This first seminar will take place at 55.309 on the 24th of November at 15:30 CET. We will share more information about the speakers the days before the seminar. |
Machine Learning Scientists from Amazon
28th November 2023 15:30h Room: 55.309 |
Title: Machine learning that powers Amazon Alexa Abstract: In this presentation you will learn how machine learning is applied in industrial settings at scale. We will explain how Alexa works and what ML algorithms enable you to get a reply in less than a second. We will also talk about opportunities for students in Amazon and the hiring process for internships 2024. Name of the speakers: Antonio Bonafonte (Senior ML scientist), Guillermo Cambara Ruiz (ML scientist), Elena Sokolova (ML science manager) |
Nicolás Escudero
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Title: Computer-based inventions: how to patent in the fields of software and artificial intelligence Abstract: What is the best strategy to protect our research results? Can we patent software? And algorithms? Our guest speaker Dr Nicolàs Escudero is an experienced European patent agent who will be sharing his experience with us and talking about the following aspects: · How software is interpreted in patents · How to patent software · Can artificial intelligence be patented? · Where to patent SW/IA · Recommendations and conclusions |
Researchers from the Institute for Ethnomusicology of Kunstuniversität Graz (KUG)
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Title: Computational Methods in Ethnomusicology: Case Studies from Kunstuniversität Graz (Austria) Abstract: Ethnomusicologists have always been prone to the use of technology for their research, from the recording equipment during fieldwork to the use of mechanical and more recently computational tools for music analysis. However, the wide spectrum of research objects and questions which ethnomusicological research engage with, the lack of reliable tools for culture-aware, high-level analyses, and the scarcity of large-enough, well curated datasets still present many challenges for the consolidation of computationally aided methodologies within the discipline of ethnomusicology at large. In the case of the Institute for Ethnomusicology at Kunstuniversität Graz (KUG), Austria, many of their members have found value in the use of computational methods for their research. In this seminar, we will present four ethnomusicological research projects carried out at our Institute which rely on these methods. The FWF funded project Tango-Danceability of Music in European Perspective, led by Kendra Stepputat, made use of motion capture technologies for dance movement analysis and the study of tango music danceability. In his PhD research on the Balinese improvisatory solo drumming tradition kendang tunggal, Kurt Schatz has developed a program to record, transcribe, analyze, generate and synthesize drum patterns in order to understand the inherent grammar of this tradition. Babak Nikzat and Rafael Caro Repetto are in the process of building the KUG Dastgāhi Corpus, containing solo recordings of vocal and instrumental performances of Iranian dastgāhi music, with the aim of contributing with quantitative information to the musicological study of the modal entities of this tradition. Finally, Sarah Weiss, who has been applying computational methods on symbolic data for her long-term research on Javanese pathet, will present her future project on the study of perceptions of similarity in different linear modal traditions, in which these methods will have a central role. In each of these case studies we will focus on the research questions and goals that motivated the use of computational tools, and we will reflect on the benefits and expectations of these methods, but also on the difficulties and limitations in their implementation. |
Gemma De les Coves
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Title: Universality in physics, computer science and beyond Abstract: Certain spin models are universal, meaning that they contain all other models when seen in the appropriate light [GDLC & T. Cubitt, Science 351, 1180 (2016)]. What is the relation between universal spin models, universal Turing machines and notions of universality in neural networks? And what is their relation to forms of unreachability such as uncomputability or undecidability? In this talk, I will explore these questions in light of our recent framework for universality (arxiv:2307.06851) and share some philosophical perspectives on these structures. Biography: Gemma De les Coves is an Associate Professor in Theoretical Physics at the University of Innsbruck, Austria. With her research group, she works on understanding the reach of notions of universality and their limitations across disciplines, as well as mathematical physics topics at the intersection of quantum information theory and noncommutative algebra. |
Roger Assaker
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Title: "FROM AEROSPACE & AUTOMOTIVE TO IN-SILICO MEDICINE" The talk covers the entrepreneurial journey using innovative Computer Modeling & Simulations (CM&S) technology and Artificial Intelligence (AI) to solve a market need and create value. This journey will be illustrated by 2 cases: · e-Xstream engineering - where we took advanced research on multi-scale material modeling from the university and developed it - Technology & Business - to become the leader in advanced material modeling technology used by the top Fortune 500 Industrial customers all around the world · MDsim - a cross-industry innovation using Computer Modeling & Simulation (CM&S) and Artificial Intelligence (AI) to improve the quality of life of millions of patients suffering from spine degeneration and deformity The talk will cover the technology stack (high-level) but also the business and other, less-tangible, items used during this journey. We will end-up by highlighting the similarity and differences between the use of CM&S in the industry (Aero, Auto, …) and in Medicine (i.e. In-Silico Medicine). |
Dr. Aleksei Tiulpin21st of September 2023 16h Room 55.309 |
Title: Osteoarthritis progression modeling and what we have been missing Abstract: He will discuss his past and recent research on modeling the progression of knee osteoarthritis using different modalities and methodological approaches, culminating with his lab’s recent work on active surveillance. Biography: Dr. Aleksei Tiulpin is an assistant professor at the University of Oulu, where he leads the Intelligent Medical Systems (IMEDS) research group. Additionally, he is a visiting professor at Aalto University in the Department of Electrical Engineering. Prior to this, Dr. Tiulpin conducted postdoctoral research at the Department of Computer Science at Aalto University and the Department of Electrical Engineering at KU Leuven. Dr. Tiulpin holds the title of docent (habilitation) from the University of Oulu in Machine Learning for Medical Imaging, and he is a member of ELLIS. He has published in MICCAI, IEEE TMI, PNAS, and other top venues. Currently, Dr. Tiulpin is broadly interested in building medical AI systems that can estimate their own uncertainty, process high-dimensional multimodal medical data, interact with users, and optimize clinical utility in general. |
John J. Costi5th of July 2023 10:30 h Room 52.119 (Roc Boronat building) |
Title: A multiaxial and multiscale assessment of lower back injury mechanisms Abstract: Chronic low back pain (LBP) is a crippling condition that affects quality of life and is a significant burden on the health care system and the workforce. The mechanisms of LBP are poorly understood; however, it is well known that the loss of intervertebral disc height due to degeneration is a common cause of low back and referred pain.
Biography: John is a Mechanical Engineer who completed his PhD on the biomechanics of the intervertebral disc in 2004 at Flinders University. In 2005-2006 he undertook a postdoctoral fellowship at the University of Vermont in Burlington, Vermont, USA. In 2009, he joined The College of Science and Engineering, Flinders University in his current position as an Academic staff member where he teaches Solid Mechanics and Biomechanics. From 2009-2011 he led a collaborative team to design and develop a novel, award-winning, six-degree-of-freedom hexapod robot for the three- dimensional loading of biological joints and tissues. His program of research aims to understand the fundamental
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Adrian GaldranThursday, 15th of June 2023 Room 51.100 15:00 h |
Title: A Tutorial on Model Calibration for Neural Nets Abstract: A calibrated machine learning model produces probabilistic predictions that are well-aligned with real probabilities: it tends to be more certain when it is correct. Unfortunately, the unique characteristics of modern neural networks, e.g. over-parametrization or iterative training dynamics, can often result in overfitting the training data and generating over-confident predictions. The goal of this talk is to introduce machine learning practitioners to the main ideas and methods of modern model calibration: its formal definition, how we can measure it, and how we can improve it. This talk is part of a tutorial (accepted to MICCAI 2023) on uncertainty quantification for medical image analysis, and it will also have a short hands-on code session: we will train a skin lesion classifier in pytorch, measure its calibration, and learn a post-processing transform to improve it. Biography: Adrian Galdran is a Marie Skłodowska-Curie Research Fellow at Universitat Pompeu Fabra in Barcelona jointly with the Australian Institute of Machine Learning in Adelaide, leading a project on Uncertainty Quantification for medical imaging. Prior to this, he worked as lecturer/post-doctoral researcher in biomedical image analysis at Bournemouth University in the UK, ETS Montréal in Canada and INESC-TEC Porto in Portugal. |
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Schahram DustdarFriday 24th of February 2023, 14:30 am 52.123 |
Title: Edge Intelligence - Research Opportunities in the Distributed Computing Continuum Abstract: As humans, Internet of Things, software services and AI continue to become the entangled fabric of distributed systems, systems engineers and researchers are facing novel challenges. In this talk, we analyze the role of AI in the context of IoT, Edge, and Cloud in the co-evolution of distributed systems for the new decade. We identify challenges and discuss a roadmap that these new distributed systems have to address in order to bring intelligence to the edge. We take a closer look at how a cyber physical fabric will be complemented by AI operationalization to enable seamless end to-end distributed systems. Biography: Schahram Dustdar is a Full Professor of Computer Science at the Vienna Technical University, heading the Research Division of Distributed Systems at the TU Wien, Austria. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, University Pompeu Fabra, Barcelona, Spain. From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA. From 1999 – 2007 he worked as the co founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by ProjectNetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. He is co-founder of edorer.com (an EdTech company based in the US) and co-founder and chief scientist of Sinoaus.net, a Nanjing, China based R&D organization focusing on IoT and Edge Intelligence. He is founding co-Editor-in-Chief of ACM Transactions on Internet of Things (ACM TIoT) as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Computing Surveys, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEEComputer. Dustdar is recipient of multiple awards: IEEE TCSVC Outstanding Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), IBM Faculty Award (2012). He is an elected member of the Academia Europaea: The Academy of Europe, as well as an IEEE Fellow (2016) and an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow (2021) and the AAIA president (2021).
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Seminars take place at UPF, Campus Poblenou, Roc Boronat, 138, Barcelona and will only be streamed/recorded if the speaker has granted permission. Rooms 55.309 streaming / 55.410 streaming / Auditorium streaming