Name: Beyond Deep Learning: Learning from Small and Heterogeneous Data
Uploaded: 2023-06-06T10:06:51Z
Description: Join us at our next symposium to discuss AI methods to overcome limitations in training data, computing resources, and interpretability.

Beyond Deep Learning: Learning from Small and Heterogeneous Data

Idiap Research Institute

Join us at our next symposium to discuss AI methods to overcome limitations in training data, computing resources, and interpretability.

Institut de recherche Idiap

<h1>Symposium Overview</h1><p>Traditional deep learning methods often yield large and complex models that lack interpretability, rely on extensive training data not readily available in relevant scenarios, require substantial computational resources, and struggle to handle heterogeneous and multi-modal data. </p><p>In this edition of Perspectives on AI, we are bringing together top minds in the field, AI enthusiasts, and innovators, to delve into recent methodological developments in AI to address these limitations. Guided by the ethos of &#x27;<strong>Doing more with less</strong>,&#x27; our invited distinguished speakers from academia and industry will share groundbreaking insights, discuss the latest advancements, and inspire collaborative solutions to achieve remarkable results with smaller training data sets, optimize computing resources, and enhance interpretability. </p><h1>Highlights of the <a href="https://perspectives-on-ai.idiap.ch/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">event</a></h1><ul><li><strong>Engaging Keynote Presentations</strong>: Gain valuable knowledge from academic and industry leaders as they share their expertise and shed light on the future of AI, focusing on strategies to overcome the limitations that have hindered its progress.</li></ul><ul><li><strong>Panel Discussions and Interactive Sessions</strong>: Participate in thought-provoking discussions with experts, exploring practical approaches, methodologies, and breakthroughs that are shaping the future of AI. Engage in interactive sessions where you can ask questions, exchange ideas, and collaborate with like-minded individuals.</li></ul><ul><li><strong>Networking Opportunities</strong>: Connect with a diverse community of AI enthusiasts, researchers, and professionals. Forge valuable connections, exchange ideas, and explore potential collaborations with individuals who share your passion for AI advancement.</li></ul><p>Don&#x27;t miss this unparalleled opportunity to <strong>be at the forefront of AI innovation</strong>. Whether you&#x27;re an AI practitioner, researcher, or simply curious about the future of technology, Perspectives on AI is the perfect platform to expand your knowledge, network with industry leaders, and be inspired by the limitless possibilities of AI.</p><p>Secure your spot today. Limited seats available!</p><h1>R﻿egistration fees</h1><p>Registration fees include admission, meals and coffee breaks. Participants are expected to book and pay their own accommodation and travel expenses. Registrants may be required to provide accreditation or equivalent proof of press membership after registration.</p><ul><li>On-site Academia CHF 50.-</li><li>On-site Students (BSc, MSc, PhD) CHF 25.-</li><li>On-site Industry CHF 100.-</li></ul><h1>Organisation and S﻿ponsors</h1><h2>S﻿ponsors</h2><p>Idiap research institute is organising this event in partnership with <a href="https://www.satw.ch/de/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">SATW</a>, <a href="https://alpict.ch/home" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">AlpICT</a>, <a href="https://www.sib.swiss/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">SIB Swiss Institute of Bioinformatics</a>,, Service des hautes écoles - Etat du Valais, Ville de Martigny, and TheArk Foundation.</p><h2>I﻿diap</h2><p>Founded in 1991, Idiap is a Swiss, independent, non-for-profit research institute. Our 150 scientists and engineers collaborate with leading universities and companies around the world. We are deeply rooted in the local entrepreneurship and innovation ecosystem. Our mission: developing Artificial Intelligence to the benefit of Society.</p><h2>O﻿rganisers</h2><p>D﻿r André Freitas, Group Leader, Idiap, Switzerland</p><p>D﻿r Raphaëlle Luisier, Group Leader, Idiap, SIB Swiss Institute of Bioinformatics, Switzerland</p><p>P﻿rof. Lonneke Van der Plas, Group Leader and Associate Professor, Idiap, Switzerland</p><p></p><p></p><p></p>

Agenda

Arrival & registration

Welcome

Prof. Elizabeth Tipton is an Associate Professor of Statistics, the Co-Director of the Statistics for Evidence-Based Policy and Practice (STEPP) Center, and a Faculty Fellow in the Institute for Policy Research at Northwestern University. Tipton's research focuses on the design and analysis of field experiments, with a particular focus on issues of external validity and generalizability in experiments; meta-analysis, particularly of dependent effect sizes; and the use of (cluster) robust variance estimation. Tipton earned her PhD in Statistics from Northwestern University in 2011. In 2005, she earned an M.A. in Sociology from the University of Chicago and in 2001 a B.A. in Mathematics from Transylvania University in Lexington, Kentucky. Prior to returning to Northwestern University, she was a member of the faculty at Teachers College, Columbia University for seven years. In 2020, she received the Frederick Mosteller Award.

Keynote session: Integrating diverse evidence: from science to policy-making

Prof. Alexandros Kalousis is a Professor at the University of Applied Sciences, Western Switzerland in the Geneva School of Business Administration, where he leads the Data Mining and Machine Learning group. He also holds a "Chargé de Cours" position in Department of Computer Science at the University of Geneva. He received his PhD from the University of Geneva in the area of machine learning. He has a BSc and MSc in Computer Science in the University of Athens, Greece. Over the years he has explored a variety of research problems, such as metric and kernel learning, feature selection and dimensionality reduction, regularisation, and meta-learning. He is currently exploring different research problems such as structured regularisation, learning of dynamical systems, life-long learning, imitation and reinforcement learning, many of them using deep learning methods.

There is more to learning than data

Cédric Travelletti completed his PhD in Statistics under the supervision of David Ginsbourger at the University of Bern in June 2023. Before his PhD, he obtained an MSc in Physics at ETH Zürich and worked as a data scientist at SwissRe and Deloitte. His research focuses on the computational and theoretical aspects of Bayesian inversion. He is particularly interested in developing sequential uncertainty reduction strategies for set estimation in large-scale inverse problems. Recently, his research has been devoted to creating more realistic uncertainty reduction models, either through the inclusion of expert knowledge or through constrained path planning.

Uncertainty reduction strategies over indirect, sparse data 

Coffee break

Dr Andreas Loukas is a Senior Principal Scientist and Machine Learning Lead at Prescient Design. His work focuses on the foundations and applications of machine learning to structured problems. He aims to find ways to exploit (graph, constraint, group) information, with the ultimate goal of designing algorithms that can learn from fewer data. He is also focusing on the theoretical analysis of neural networks and in using them to solve hard bioengineering problems (especially protein design). Andreas obtained his Ph.D. in computer science from TU Delft in 2015 and pursued postdoctoral studies at TU Berlin and EPFL. He became an SNSF Ambizione fellow at EPFL in 2018 and an Assistant Professor at the Computer Science department of the University of Luxembourg in 2021. He joined Genentech/Roche in 2022.

AbDiffuser: full-atom generation of in-vitro functioning antibodie

Dr Dorina Thanou is a senior research scientist and lecturer at EPFL, leading the development of the Intelligent Systems for Medicine and Health research pillar, under the Center for Intelligent Systems. Prior to that, she was a Senior Data Scientist at the Swiss Data Science Centre. She got her M.Sc. and Ph.D. in Communication Systems and Electrical Engineering respectively, both from EPFL, Switzerland, and her Diploma in Electrical and Computer Engineering from the University of Patras, Greece. She is the recipient of the Best Student Paper Award at ICASSP 2015, and the co-author of the Best Paper Award at PCS 2016. Her research interests lie in the broader area of signal processing and machine learning and she is particularly interested in applying her expertise to intelligent systems for biology and medicine. She is an ELLIS Scholar.

Panel:  The future of data science (Challenges & Opportunities)

Lunch, networking & poster session

Prof. Matteo Valleriani is Research Group Leader at the Max Planck Institute for the History of Science in Berlin, honorary professor at the Technische Universität of Berlin, professor by Special Appointment at the University of Tel Aviv and PI at The Berlin Institute for the Foundations of Learning and Data (BIFOLD). He investigates epistemic processes of transformation of scientific knowledge focusing on past eras and in particular on the Hellenistic period, the late Middle Ages, and the early modern period. Among his principal research endeavors, he leads the project “The Sphere: Knowledge System Evolution and the Shared Scientific Identity of Europe” (https://sphaera.mpiwg-berlin.mpg.de). In his research, Matteo Valleriani implements the development and the application of Machine Learning technology and the physics of complex systems.

Rich data: heterogeneity, lack of annotations, high dimensionality

Silvia holds an MSc in Computer Science from EPFL and a PhD in Computer Science from the University of York, UK. She has been a senior research fellow at the University of Trento and later at Politecnico di Milano, Italy. Here, she had the chance to work on Marie Curie and ERC projects relating to natural language processing. From 2012 to 2019, she was a Senior Manager and NLP expert at ELCA Informatique Switzerland, whose AI department she helped create and expand. Silvia joined the Swiss Data Science Center in 2019 and is currently its Chief Transformation Officer, in charge of the team leading organizations to digital transformation.

Matching ML solutions with organizations: a perspective from the Swiss Data Sc

Dr André Freitas leads the Neuro-symbolic AI Group at the Idiap Research Institute and is a Senior Lecturer (Associated Professor) at the Department of Computer Science at the University of Manchester. He is also the AI group leader at the digital Experimental Cancer Medicine Team (CancerResearchUK Manchester Institute). His main research interests are on enabling the development of AI methods to support abstract, explainable and flexible inference. In particular, he investigates how the combination of neural and symbolic data representation paradigms can deliver better inference. Some of his research topics include: explanation generation, natural language inference, explainable question answering, knowledge graphs and open information extraction. He is actively engaged in collaboration projects with industrial and clinical partners.

The might of abduction: integrating textual and numerical evidence.

Industrial Panel: Opportunities for economic & social impact

Closing remarks

16:30-17:00:  Networking & apero

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Beyond Deep Learning: Learning from Small and Heterogeneous Data

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Beyond Deep Learning: Learning from Small and Heterogeneous Data

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