machine learning

Prof. Dr. Mathias Risse, John F. Kennedy School of Government, Harvard University, Cambridge, MA, USA

Institutions

• UHH

 Prof. Dr. Andra Siibak, University of Tartu, Tartu, Estland

Present day children’s futures are decided by algorithms predicting their probability of success at school, their suitability for a job position, their likely recidivism or mental health problems. Advances in predictive analytics, artificial intelligence (AI) systems, behavioral-, and biometrics technologies, have started to be aggressively used for monitoring, aggregating, and analyzing children’s data. Such dataveillance happening both in homes, schools, and peer networks has a profound impact not only on children’s preferences, social relations, life chances, rights and privacy but also the "future of human agency - and ultimately, of society and culture" (Mascheroni & Siibak 2021: 169).

Building upon the findings of my different empirical case studies, I will showcase how the popular digital parenting practices and the growing datafication happening in the education sector, could create not only hypothetical data scares but also lead to real data scars in the lives of the young.

Institutions

• UHH

Vincent C. Müller is AvH Professor for Philosophy and Ethics of AI and Director of the Centre for Philosophy and AI Research (PAIR) at FAU Erlangen-Nuremberg

It is now frequently observed that there is no proper scope and no proper method in the discipline of AI-ethics. This has become an issue in the development towards maturity of the discipline, e.g. canonical problems, positions, arguments … secure steps forward. We propose a minimal, yet universal view of the field (again Müller 2020). Given this proposal, we will know the scope and the method, and we can appreciate the wide set of contributions.

Institutions

• UHH

Prof. Dr. Aimee van Wynsberghe, Rheinische Friedrich-Wilhelms-Universität Bonn, D

Institutions

• UHH

Institutions

• UHH

Gastvortrag von Lin Jia, Senior Data Scientists bei Booking.com. Sie wird an unserem Seminar über kausales maschinelles Lernen am 8. Juli 2024 teilnehmen und über vergangene und aktuelle Projekte zu kausaler Inferenz und kausalem maschinellem Lernen bei Booking.com referieren.

Booking.com ist eine der weltweit führenden digitalen Reise-Plattformen und verfügt über ein starkes Team von Datenwissenschaftlern, die Experten sind in der Anwendung und Entwicklung von Methoden für kausale Analysen und maschinelles Lernen in der Industrie.

Über die Referentin: Lin Jia ist eine leitende Datenwissenschaftlerin bei Booking.com. Sie ist spezialisiert auf die Verwendung von kausalen Beobachtungsansätzen zur Bewertung der Auswirkungen von Produktänderungen und leitet die Initiative zur Durchführung robuster und transparenter Kausalanalysen bei Booking.com. Sie wird ihre Erfahrungen aus verschiedenen Projekten zur kausalen Inferenz bei Booking.com teilen.

Der Vortrag ist offen für alle interessierten Forscher:innen und Studierenden, die etwas über die Aktivitäten der Industrie in der kausalen Datenwissenschaft erfahren möchten.

Institutions

• Fakultät für Betriebswirtschaft, Lehrstuhl für Statistik

Gerhard Wellein: Application Knowledge Required: Performance Modeling for Fund and Profit & Axel Klawonn: What can machine learning be used for in domain decomposition methods?

Gerhard Wellein is a Professor for High Performance Computing at the Department for Computer Science of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and holds a PhD in theoretical physics from the University of Bayreuth. He is a member of the board of directors of the German NHR-Alliance which coordinates the national HPC Tier-2 infrastructures at German universities. As a member of the scientific steering committees of the Leibniz Supercomputing Centre (LRZ) and the Gauss-Centre for Supercomputing (GCS) he is organizing and surveying the compute time application process for national HPC resources. Gerhard Wellein has more than twenty years of experience in teaching HPC techniques to students and scientists from computational science and engineering, is an external trainer in the Partnership for Advanced Computing in Europe (PRACE) and received the “2011 Informatics Europe Curriculum Best Practices Award” (together with Jan Treibig and Georg Hager) for outstanding teaching contributions. His research interests focus on performance modelling and performance engineering, architecture-specific code optimization, novel parallelization approaches and hardware-efficient building blocks for sparse linear algebra and stencil solvers.

Prof. Dr. Axel Klawonn heads the research group on numerical mathematics and scientific computing at the Universität zu Köln. The group works on the development of efficient numerical methods for the simulation of problems from computational science and engineering. This comprises the development of efficient algorithms, their theoretical analysis, and the implementation on large parallel computers with up to several hundreds of thousands of cores. A special focus in the applications is currently on problems from biomechanics/medicine, structural mechanics, and material science. The research is in the field of numerical methods for partial differential equations and high performance parallel scientific computing, including machine learning.

A multitude of ML tasks in particle physics, from unfolding detector effects to refining simulation and extrapolating background estimations, require mapping one arbitrary distribution to another. Several indirect methods have been developed to achieve this, such as classifier-based reweighting on a distribution level, or conditional generative models. However, training an ML model to perform a direct, deterministic mapping has long been a challenging prospect.

In this talk, I introduce the concept of Schrödinger Bridges, ML architecture closely related to Diffusion Models, which enables direct mapping of arbitrary distribution to arbitrary distribution. I demonstrate two implementation approaches with differing upsides and present state-of-the-art results applying Schrödinger Bridges to unfolding and refinement tasks.

Institutions

• UHH, Institut für Experimentalphysik

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

Angela Relógio, Medical School Hamburg MSH

For further details and hybrid links, please go to the webpage AI in Biology & Medicine

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

Christopher Gundler, Institute for Applied Medical Informatics, UKE

For further details and hybrid links, please go to the webpage AI in Biology & Medicine

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Niklas Dieckow - Data-driven methods for the Maxey-Riley equations

The Maxey-Riley equations (MRE) describe the motion of a small inertial particle suspended in a fluid flow. They are a system of implicit integro-differential equations with a singular kernel. Exact solution methods require the evaluation of an integral over the entire particle history in each time step, causing the computation time to grow quadratically in the number of steps. In this talk, data-driven methods such as SINDy (Sparse Identification of Nonlinear Dynamics) are discussed and employed to obtain approximations of the MRE that do not contain an integral term and are therefore easier to solve.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Philip Suskin - Development of a Conversational Interface Based on Institution-Specific Documentation Through LLM Finetuning

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Johanna Gleichauf - Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Emin Nakilcioglu - Parameter Efficient Fine Tuning for a Domain-Specific Automatic Speech Recognition

With the introduction of early pre-trained language models such as Google’s BERT and various early GPT models, we have seen an ever-increasing excitement and interest in foundation models. To leverage existing pre-trained foundation models and adapt them to specific tasks or domains, these models need to be fine-tuned using domain-specific data. However, fine-tuning can be quite resource-intensive and costly as millions of  parameters will be modified as part of training. PEFT is a technique designed to fine-tune models while minimizing the need for extensive resources and cost. It achieves this efficiency by freezing some of the layers of the pre-trained model and only fine-tuning the last few layers that are specific to the downstream task. With the help of PEFT, we can achieve a balance between retaining valuable knowledge from the pre-trained model and adapting it effectively to the downstream task with fewer parameters.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Alexander Itin - AI for engineering and science: selected use cases

Its going to be a little bit chaotic talk with many use cases.  My scientific background is dynamical systems and condensed matter physics.  I switched from academic to industrial research at some point, joining Bosch Research, and did many interesting projects there. I then gradually returned back to academy. I will briefly discuss applications of AI in engineering, and in a more detail in science: how they are different and what have in common.  Such use cases as virtual sensors, synthetic FIB/SEM data generation, anomaly detection in manufacturing, etc, will be briefly reviewed (engineering). I will consider in a little bit more detail inverse design of photonic structures using generative AI, predictive physics-informed models, acceleration of solvers using AI  (science).

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Niklas Dieckow

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Nathanael Winter

Following the successful application of machine learning methods in order to predict brake squeal as a classification task, this contribution addresses the transfer of those methods on to particle emission data, in order to correctly predict brake particle emissions as a regression task. First results proving the transferability of those methods will be presented.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Robert Kräuter - Development of a black-box soft sensor for a fluidization process

Solids water content is an important particle property in many applications of process engineering. Its importance on the quality of pharmaceutical formulations makes an in-line measurement of the water content especially desirable in fluidization processes. However, currently available measurement techniques are difficult to calibrate and scarcely applicable in real fluidized beds. A promising strategy for in-line monitoring of the water content is thus soft sensing, a method that expresses the targeted quantity as a correlation of other more reliable measurements. In this talk, we present the development of such a soft sensor using various black-box models. Our focus lies on strategies to reduce overfitting through feature engineering and hyperparameter tuning. These models are designed for processing real experimental data from a turbulent process, addressing challenges in data filtering, undersampling, outlier detection, and uncertainty propagation.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link