network

Die Digitalisierung und Automatisierung von Dienstleistungen hat in den letzten Jahren erheblich zugenommen, insbesondere durch den Einsatz von Künstlicher Intelligenz (KI), und wird durch Anwendungen wie ChatGPT, Bard oder Midjourney im Alltag immer präsenter. Unternehmen setzen KI-Technologien ein, um Kundenservice, Beratung, Personalisierung, Verfügbarkeit und andere Aspekte von Dienstleistungen zu verbessern und zu automatisieren. Gleichzeitig stellt sich die Frage nach neuen Anforderungen an die Gestaltung von Dienstleistungen und den Auswirkungen von KI auf die Zukunft des deutschen Dienstleistungssektors.

Auf der (DF)² Jahreskonferenz 2023 werden wir diskutieren, wie die Einbindung KI-basierter Systeme Dienstleistungen transformieren und optimieren kann, indem sie beispielsweise Qualität verbessern, Prozesse beschleunigen und personalisierte Erfahrungen für Nutzer ermöglichen. Dabei beleuchten wir ebenfalls die Herausforderungen, welche sich aus dem Einsatz von KI ergeben, wie etwa Fragen der Gestaltung KI-unterstützter Dienstleistungsarbeit sowie Fragestellungen zur Datensicherheit, Privatsphäre und Ethik bei der Verwendung von Algorithmen in Entscheidungsprozessen.

Hier geht es direkt zur Anmeldeseite für die begrenzten Teilnahmeslots

Institutions

• Das Deutsche Forum Dienstleistungsforschung (DF)²

unfortunately, today's talk has to be cancelled. We are very sorry

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.

Patrick Sontheimer - Long short-term memory for surrogate modeling

Long-Short Term Memory is a gated architecture designed to combat the vanishing/exploding gradient problem in recurrent neural networks (RNN). In the presentation, time series regression will be performed on multiple-input multiple-output data, to create a surrogate Model.

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

We invite you to join us in celebrating and promoting the new W1 Professorship appointments in the areas of data science and AI at UKE. The Data Science and AI Inaugural Lectures 2023 will take place on Thursday 13th July, 15:00-18:00 at Erika-Haus, UKE. This will be a great opportunity to get to know the professors and their research topics in the areas of data science and AI relevant to biomedicine and with broad application in the clinic.

bAIome, the Center for Biomedical AI at UKE, which brings together UKE faculty members with international background and expertise in diverse areas relevant to AI including high performance computation, data science, mathematics, and biomedical research, will be hosting this event and following the inaugural lectures will showcase current research projects and ventures with a poster session during the reception to provide a great environment for networking and cooperation.

RSVP a.reinicke-vogt@uke.de

Dr. Gerrit M. Beckhaus, LL.M. (Yale), EMBA and Christina Valdini, LL.M. (King’s College, London)

Freshfields Bruckhaus Deringer, Rechtsanwälte Steuerberater PartG mbB, Hamburg

The Network for Artificial Intelligence and Law (NAIL) invites you to its next event. We are delighted to welcome Dr. Gerrit M. Beckhaus and Christina Valdini, Freshfields Bruckhaus Deringer, Hamburg to talk on legal tech as a team member in a global law firm. The lecture will be followed by a discussion around the topic. The event will be held in English.

After the lecture and discussion, we would like to invite you to end the evening with us in a relaxed atmosphere, with pretzels and wine in the south lounge.

You can participate in presence or online. Please register for the event using the following link: Registration

Institutions

• Network for Artificial Intelligence and Law (NAIL)

Thorsten Strufe from the KIT in Karlsruhe will give a talk on privacy in the Metaverse.

Extended realities (XR), including Augmented reality (AR) and virtual reality (VR), are emerging technologies with a wide range of potential applications, including networking, gaming, healthcare, and education. However, as with any new technology, AR/VR also introduces new security and privacy challenges. AR and VR devices collect a wide range of personal data about users, including their physical movements, eye movements, and voice recordings. This data can be used to track users' activities, identify them, or even infer private attributes like health conditions or private preferences. Furthermore, XR applications integrate multiple modalities, such as audio, video, and haptic data streams, enlarging security and privacy exposure. Yet, there are always claims of how "anonymization" and "pseudonymization" were helping to achieve "GDPR compliance". In this talk I will present results from three of our recent studies, and we will discuss how claimed protection has proven and is ineffective under scrutiny.

CV:
Thorsten Strufe is professor of IT Security at Karlsruhe Institute of Technology (KIT/KASTEL), and adjunct professor for Privacy and Network Security at TU Dresden. He is a deputy speaker of the Excellence Centre for Tactile Internet with Human-in-the-Loop (CeTI), and a PI in the national IT security competence center KASTEL Security Research Labs.
His research interests lie in the areas of privacy and network security, especially in the context of social networking services and novel mixed reality applications. Recently, he has focused on studying privacy implications of user behavior and possibilities to provide privacy-preserving and secure networked services.
Previous posts include faculty positions at TU Dresden, TU Darmstadt, and Uni Mannheim, as well as postdoc/researcher positions at EURECOM (France) and TU Ilmenau.

Institution

• Computer Networks UHH

Visualization is an important and ubiquitous tool in the daily work of atmospheric researchers and weather forecasters to analyse data from simulations and observations. In this talk, I will present recent and ongoing work we are conducting at the Universität Hamburg’s Visual Data Analysis Group with respect to interactive 3-D visualization and feature detection using gridded atmospheric data. This includes methods for detection and interactive visual analysis of 3-D atmospheric fronts, as well as interactive 3-D analysis of simulated clouds and other volumetric data. Our methods are integrated into “Met.3D”, our open-source research software that makes novel interactive, 3-D, feature-based, and ensemble visualization techniques accessible to the meteorological community (https://met3d.wavestoweather.de). Since its first public release in 2015, Met.3D has been advanced within the German research projects “Waves to Weather (W2W)” and “Climate, Climatic Change, and Society (CLICCS)” and has evolved into a feature-rich visual analysis tool facilitating rapid exploration of gridded atmospheric data.

Speaker: Dr. Marc Rautenhaus
Bio:
Marc Rautenhaus leads the Visual Data Analysis Group at the Regional Computing Centre of Universität Hamburg. His research interests lie at the intersection of visual computing and weather and climate research, focussing on interactive 3-D visualization, feature detection, weather forecasting, and research software development of the meteorological interactive 3-D ensemble visualization framework Met.3D. He holds a master’s degree in Atmospheric Science from the University of British Columbia, Canada, and a PhD in Computer Science from the Technical University of Munich. Previous posts include researcher/postdoc positions at the German Aerospace Centre (DLR) and the Technical University of Munich.

Institution

• Computer Networks UHH

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.

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

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.

Yahya Saleh - Flow-induced bases and application to quantum molecular physics

In analogy to the use of normalizing flows to augment the expressivity of base probability distributions, I propose to augment the expressivity of bases of Hilbert spaces via composition with normalizing flows. I show that the resulting sequences are also bases of the Hilbert space under sufficient and necessary conditions on the flow. This lays a foundation for a theory of spectral learning, a nonlinear extension of spectral methods for solving differential equations. As an application I solve the vibrational molecular Schrödinger equation. The proposed numerical scheme results in several orders of magnitude increased accuracy over the use of standard spectral methods. 

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.

Moritz Braun - Generalizability and explainability of machine learning models for fatigue strength prediction of welded joints

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.

Frank Röder - Hindsight Instruction Grounding in Reinforcement Learning

This presentation addresses the challenge of sample inefficiency in robotic reinforcement learning with sparse rewards and natural language goal representations. We introduce a mechanism for hindsight instruction replay, leveraging expert feedback, and a seq2seq model for generating linguistic hindsight instructions. Remarkably, our findings demonstrate that self-supervised language generation, where the agent autonomously generates linguistic instructions, significantly enhances learning performance. These results underscore the promising potential of hindsight instruction grounding in reinforcement learning for robotics.

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.

Lars Stietz - Machine Learning in High Energy Physics

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.

Ana Almeida - Multivariate Time series: Data processing, Imputation and Forecasting

Data is a valuable tool for decision-makers, helping them make informed decisions. We can find multivariate time series in several contexts, such as finances, smart cities, and health. This type of data can bring additional challenges. This presentation will discuss the key concepts and techniques involved in working with multivariate time series data. Specifically, we will focus on the steps of data processing, imputation, and forecasting. 

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