Throughout the year, HIDA, the six Data Science Schools, and other partners invite outstanding international Data Scientists to speak about their current research in the HIDA Lectures. In cooperation with HIDA, the HMC Fair Friday lecture series and the DKFZ Data Science Seminar also provide insights into the latest data science developments.
Energy, Earth and Environment, Health, Information, Matter, as well as Aeronautics, Space and Transport - The HIDA Lectures cover a wide range of topics and offer the opportunity to dive into the diversity of current approaches in Data Science.
We cordially invite the interested public to attend these lectures and especially doctoral researchers from the Helmholtz Association. Discuss with international researchers about different application fields of Data Science and gain insights into the diverse activities of the Schools, our partners, and HIDA.
All events of the series are open to the public.
Our Lecture Series
DKFZ Data Science Seminar
Together with the DKFZ and other partners, we host a lecture series that brings together leading researchers in the field of data science to discuss methodological advances and medical applications.
HMC FAIR Friday
Dive into the world of FAIR data with our event series, organized in cooperation with the Helmholtz Metadata Collaboration. It is aimed at experienced stakeholders in research data management as well as scientists from all research fields of the Helmholtz Association and beyond.
HIDA Lecture Series
In collaboration with the six Helmholtz Information & Data Science Schools, we invite renowned speakers to talk about new developments in data science in the Schools' respective research areas. All lectures are open to anyone interested.
Our previous HIDA Lectures:
- 23.06. 2022: HIDA Lecture @DASHH with Pushmeet Kohli (Deepmind): "Leveraging AI for Science"
- 27.04.2022: HIDA Lecture @HEIBRiDS with Dirk Brockmann (Humboldt Universität zu Berlin und Robert-Koch-Institut): "The Data Donation Project - How wearable sensors can help in dealing with the COVID-19 crisis"
- 24.03.2022: HIDA Lecture @MarDATA with Markus Reichstein (MPI für Biogeochemie, Jena): "Machine-Learning-Model-Data-Integration for a Better Understanding of the Earth System"
- 23.02.2022: HIDA Lectures @HIDSS4Health with Julia A. Schnabel (TU München, Helmholtz Munich): "AI-enabled Imaging"
- 26.01.2022: HIDA Lectures @HIDSS4Health with Ulf Leser (Humboldt-Universität zu Berlin): "Data Science for Supporting Molecular Tumor Boards"
- 07.12.2021: HIDA Lectures @HDS-LEE with Steven Brunton (University of Washington): "Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics"
- 20.10.2021: HIDA Lectures @HEIBRiDS with Dennis Shasha (Courant Institute of New York University): "SafePredict and Friends"
- 29.09.2021: HIDA Lectures @MUDS with Niki Kilbertus (Helmholtz AI): "(How) can we find true cause effect relationships in data?"
- 16.06.2021: HIDA Lectures @HEIBRIDS with Alan Akbik (Humboldt-Universität zu Berlin): "TARS: Few-Shot Learning for Natural Language Processing"
- 15.04.2021: HIDA Lectures @DASHH with Anatole von Lilienfeld (Universität Wien): "Quantum Machine Learning in Chemical Compound Space"
We organize our HIDA Lecture Series in cooperation with the six Helmholtz Information & Data Science Schools. Together with our partners, the Helmholtz Metadata Collaboration HMC and the German Cancer Research Center DKFZ, we are involved in two additional Lecture Series.
Watch again: Past HIDA Lectures
Murat Eren is a computer scientist and microbial ecologist who studies microbial lifestyles through integrated multi-omics. Established at the intersection between computer science and microbiology, his group combines state-of-the-art molecular tools and computational strategies to shed light on the ecology and evolution of naturally occurring microbial populations, and to understand strategies by microbial life responds to environmental change and thrive within their ever-changing environments.
HIDA Lecture @ MarDATA: "The Era of High-Resolution, Integrated Multi-Omics"
Abstract: The vast majority of life on our planet is microbial. An astonishing number of microbial organisms living in terrestrial and marine habitats represent a biomass that exceeds every living organism that can be seen by naked eye. They complement their numbers with their inconceivable diversity that allow microbes to synthesize or break down a wide array of chemical substrates, and govern biogeochemical cycles that make Earth a habitable planet for much less talented organisms (such as ourselves). While advances in molecular biology and sequencing technologies now enable unprecedented descriptions of the diversity of our naturally occurring microbial overlords, we are far from being able to make sense of the extremely complex datasets our environmental surveys yield almost on a daily basis. The purpose of this talk is to highlight some of the recent progress and challenges that are particularly suitable for data scientists to ponder.
Itzik Klein (Senior Member, IEEE) received the B.Sc. and M.Sc. degrees in aerospace engineering and the Ph.D. degree in geo-information engineering from the Technion-Israel Institute of Technology, Haifa, Israel, in 2004, 2007, and 2011, respectively. He is currently an Assistant Professor and the Head of the Autonomous Navigation and Sensor Fusion Laboratory, Hatter Department of Marine Technologies, University of Haifa. His research interests include data driven based navigation, novel inertial navigation architectures, autonomous underwater vehicles, sensor fusion, and estimation theory.
HIDA Lecture @ SummerAcademy: "Data-Driven Inertial Sensing"
Abstract: The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this talk, we address data-driven based navigation algorithms, recently derived at the autonomous navigation and sensor fusion lab. The purpose of those algorithms is to enhance common navigation and estimation tasks and open new possibilities for accurate and robust navigation. Data driven inertial navigation topics included in this talk will highlight hybrid learning and end to end learning approaches for different platforms and applications such as: pedestrian dead reckoning with inertial sensors, quadrotor dead recooking, learning vehicle trajectory uncertainty by hybrid models, and autonomous underwater vehicle navigation.
Dirk Brockmann is Professor at the Institute of Biology at Humboldt-University of Berlin. Between 2007-2013 he was Professor for Applied Mathematics at Northwestern University. At Northwestern University he was on the faculty of Northwestern’s Institute on Complex Systems where he still holds an external faculty position.
Dirk Brockmann is member of the Institute of Theoretical Biology as well as the Integrated Research Institute for the Life-Sciences at Humboldt University of Berlin. He is also head of the project group Computational Epidemiology at the Robert Koch-Institute, Germany’s federal public health institute.
In 2017 he launched the project Complexity Explorables, a collection of interactive illustration on complex systems that continously expands in examples and is designed for teachers, instructors and people that want to gain an intuitive unterstanding of the beauty of complex dynamical processes in nature.
HIDA Lecture @ HEIBRiDS: "The Data Donation Project - How wearable sensors can help in dealing with the COVID-19 crisis"
Abstract: A theoretical physicist by training, Dirk Brockmann research focuses on complex systems at the interface of physics, life sciences and social sciences. He is particularly interested the application of dynamicals systems theory, stochastic processes and network science to infectious disease dynamics and related contagion processes. In this context he is currently investigating dynamical processes on time-dependent networks, complex contagion processes and the emergence of cooperation in evolutionary processes. He is known for his work on human mobility and its role on the global spread of infectious diseases. He talks here about the opportunities of infection disease research like the COVID-19 pandemic in using data from voluntary data donors.
Markus Reichstein is Director of the Biogeochemical Integration Department at the Max-Planck-Institute for Biogeochemistry. He is Professor for Global Geoecology at the FSU Jena since 2013 and founding Director at the Michael-Stifel-Center Jena for Data-driven and Simulation Science. His main research interests revolve around the response and feedback of ecosystems (vegetation and soils) to climatic variability with a Earth system perspective, considering coupled carbon, water and nutrient cycles. Of specific interest is the interplay of climate extremes with ecosystem and societal resilience. These topics are adressed via a model-data integration approach, combining data-driven machine learning with systems modelling of experimental, ground- and satellite-based observations. He has been serving as lead author of the IPCC special report on Climate Extremes (SREX), as member of the German Commitee Future Earth on Sustainability Research, and the Thuringian Panel on Climate. Recent awards include the Piers J. Sellers Mid-Career Award by the American Geophysical Union (2018), an ERC Synergy Grant (2019) and the Gottfried Wilhelm Leibniz Preis (2020).
HIDA Lecture @ MarDATA: "Machine-Learning-Model-Data-Integration for a Better Understanding of the Earth System"
Date: 24.03.2021, 15:00 Uhr
Abstract: The Earth is a complex dynamic networked system. Machine learning, i.e. derivation of computational models from data, has already made important contributions to predict and understand components of the Earth system, specifically in climate, remote sensing and environmental sciences. For instance, classifications of land cover types, prediction of land-atmosphere and ocean-atmosphere exchange, or detection of extreme events have greatly benefited from these approaches. Such data-driven information has already changed how Earth system models are evaluated and further developed. However, many studies have not yet sufficiently addressed and exploited dynamic aspects of systems, such as memory effects for prediction and effects of spatial context, e.g. for classification and change detection. In particular new developments in deep learning offer great potential to overcome these limitations.
Yet, a key challenge and opportunity is to integrate (physical-biological) system modeling approaches with machine learning into hybrid modeling approaches, which combines physical consistency and machine learning versatility. A couple of examples are given with focus on the terrestrial biosphere, where the combination of system-based and machine-learning-based modelling helps our understanding of aspects of the Earth system.
Julia A. Schnabel
Julia A. Schnabel is Professor of Computational Imaging and AI in Medicine at Technical University of Munich (TUM Liesel Beckmann Distinguished Professorship) and Director of a new Institute of Machine Learning in Biomedical Imaging at Helmholtz Center Munich (Helmholtz Distinguished Professorship), with secondary appointment as Chair in Computational Imaging at King’s College London. She graduated in Computer Science (equiv. MSc) from Technical University of Berlin, Berlin, Germany, and was awarded the PhD in Computer Science from University College London, UK. In 2007, she joined the University of Oxford, UK as Associate Professor in Engineering Science (Medical Imaging), where she became Full Professor of Engineering Science by Recognition of Distinction in 2014. She joined King’s College London as a new Chair in 2015, and in 2021 joined TUM and Helmholtz Munich for her current positions. Her research interests include machine/deep learning, nonlinear motion modeling, as well as multimodality and quantitative imaging, for cancer imaging, cardiac imaging, neuroimaging and perinatal imaging. Dr. Schnabel has been elected Fellow of IEEE (2021), Fellow of ELLIS (2019), and Fellow of the MICCAI Society (2018). She is an Associate Editor of the IEEE Transactions on Medical Imaging on whose steering board she serves since 2021, the IEEE Transactions of Biomedical Engineering, on the Editorial Board of Medical Image Analysis and Executive/Founding Editor of MELBA. She currently serves as elected Technical Representative on IEEE EMBS AdCom, as voting member of the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP), as Executive Secretary to the MICCAI board, and as member of ELLIS Health and ELLIS Munich.
HIDA Lectures @ HIDSS4Health: "AI-enabled Imaging"
Date: Feb 23, 2022
Abstract: Artificial intelligence, in particular from the class of machine / deep learning, has shown great promise for applications in medical imaging. However, the success of AI-based techniques is limited by the availability and quality of the training data. A common approach is to train methods on well annotated and curated databases of high-quality image acquisitions, which then may fail on real patient cases in a hospital setting. Another problematic is the lack of sufficient numbers of clinical label annotations in the training data, or example for early markers of disease. In this talk I will present some of our recent approaches in cardiac magnetic resonance imaging (CMR) that aim to address some of these challenges, by using AI as an enabling technique for improved CMR image reconstruction, realistic CMR augmentation and further downstream tasks.
Ulf Leser is a full professor at the Institute for Computer Science at Humboldt University Berlin. He obtained his PhD in Data Integration from Technische Universität Berlin. After app. three years in the software industry, he returned to academia as professor for Knowledge Management in Bioinformatics. His main research interests are semantic data integration, text mining, statistical Bioinformatics, and large-scale scientific data analysis. His group not only develops novel algorithms for these tasks but also applies them in a set of highly interdisciplinary collaborative projects for studying concrete biomedical questions, especially in cancer research. He is speaker of the Collaborative Research Center "Foundations of Workflows for Large-Scale Scientific Data Analysis (FONDA)" and of the collaborative research project "Comprehensive Data Integration for Precision Oncology (PREDICT)". From 2014-2019, he was speaker of the Research Training Group "Service-oriented Architectures for Health Care Systems (SOAMED)". Furthermore, he currently is principal investigator in the interdisciplinary graduate schools "Computational Methods for Precision Oncology (CompCancer)“ and “Helmholtz-Einstein International Berlin research school on Data Science“.
HIDA Lectures @ HIDSS4Health: "Data Science for Supporting Molecular Tumor Boards"
Abstract: Molecular Tumor Boards are interdisciplinary teams of medical experts which come together to obtain therapy recommendations for complex oncological cases. In recent years, these recommendations are more and more based on molecular profiling of tumors, in particular the sequencing of cancer genes followed by the detection of genetic variants. The PREDICT project set out to develop systems and algorithms for supporting such decision-making. We will discuss the main results of this project i.e. (1) VIST, a search engine specifically constructed to find clinically relevant documents given a patient’s variant profile, (2) I-VIS, a framework for building integrated precision oncology databases, and (3) BRONCO, the first open and annotated corpus of German medical texts. Building these systems required advanced NLP technologies, the application of modern machine learning methods, and robust data integration technologies.
Steven L. Brunton
Prof. Dr. Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Professor of Applied Mathematics and Computer science, and a Data Science Fellow at the eScience Institute. Steve received the B.S. in mathematics from Caltech in 2006 and the Ph.D. in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is a co-author of three textbooks, received the University of Washington College of Engineering junior faculty and teaching awards, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE).
HIDA Lectures @ HDS-LEE: "Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics"
Date: 07.12.2021, 17 Uhr
Abstract: This work describes how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. We also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems in fluid dynamics, and we will discuss how to incorporate these models into existing model-based control efforts. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.
Alan Akbik is a professor of machine learning at Humboldt University in Berlin. His research focuses on natural language processing (NLP), i.e., methods that enable machines to understand human language. This includes research topics such as transfer learning, few-shot learning, and semantic parsing, as well as application areas in large-scale text analysis. Akbik's research is operationalized in the form of the open-source NLP framework Flair, which allows anyone to use state-of-the-art NLP methods in their research or applications.
HIDA Lecture @ HEIBRiDS: "TARS: Few-Shot Learning for Natural Language Processing"
Date: June 16, 2021, 16:00
Abstract: Machine learning models for natural language processing (NLP) are typically trained with very large amounts of labeled training data. However, such data is often not readily available and very expensive to produce. In this talk I present TARS, a novel approach in the research area of "few-shot learning" which allows us to train text classification models with little training data - or even none at all! I show how the proposed approach can be applied to a continual learning setup in which a single model learns a number of different tasks in sequence, with the goal of learning all tasks. Finally, I give a brief overview of the Flair NLP framework (https://github.com/flairNLP/flair) we develop in my group together with the open source community, and show how you can use TARS (and other NLP components in Flair) in your own research or industry projects.
HIDA Lecture @ DASHH: "Quantum Machine Learning in Chemical Compound Space"
Date: April 15, 2021, 14:00
Abstract: Many of the most relevant observables of matter depend explicitly on atomistic and electronic details, rendering a first principles approach to computational materials design mandatory. Alas, even when using high-performance computers, brute force high-throughput screening of material candidates is beyond any capacity for all but the simplest systems and properties due to the combinatorial nature of compound space, i.e. all the possible combinations of compositional and structural degrees of freedom. Consequently, efficient exploration algorithms exploit implicit redundancies and correlations. I will discuss recently developed statistical learning based approaches for interpolating quantum mechanical observables throughout compound space. Numerical results indicate promising performance in terms of efficiency, accuracy, scalability and transferability.