CS & Physics Meet-Up by Lamarr & B3D

Europe/Berlin
Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space (Lamarr Institut, TU Dortmund University)

Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

Lamarr Institut, TU Dortmund University

Joseph von Fraunhofer Strasse 25 44227 Dortmund
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Ekaterina Moerova (MPI Bonn), Jens Buss (E5b), Jessica Koch (MPI Bonn), Kevin Schmidt
Description

The "CS & Physics Meet-Up by Lamarr & B3D" aims to bring together researchers from the fields of computer science, with a focus on machine learning and artificial intelligence, and physics, specifically particle physics, astroparticle physics, and radio astronomy.

The goal of the meet-up is to provide a mutual overview of each other's research topics and research questions, as well as learning tasks, methods, and data. Our goal is to identify commonalities and develop ideas for future collaborations.

The event will include presentations, poster sessions, and open discussions to develop project ideas for future collaborations between computer scientists and physicists of Lamarr and B3D.

Participants
  • Abhinav Tyagi
  • Abhinav Tyagi
  • Alexander Kier
  • Amal Saadallah
  • Ancla Müller
  • Andrei Kazantsev
  • Andrei Kazantsev
  • André Hinkenjann
  • Ankur Dev
  • Aron Kordt
  • Biljana Mitreska
  • Claudia Comito
  • Claudia Comito
  • Cyrus Walther
  • Dominik Baack
  • Dominik Bomans
  • Dominik Elsaesser
  • Dr. Vishnu Balakrishnan
  • Ekaterina Moerova
  • Felix Geyer
  • Frank Bertoldi
  • Gennadiy Andriyenko
  • Günther Heemann
  • Hermann Heßling
  • Holger Stiele
  • Jean-Marco Alameddine
  • Jens Buss
  • Jens Teubner
  • Jessica Koch
  • Jinglan Zheng
  • Johannes Albrecht
  • Jonah Blank
  • Jonah Blank
  • Julian Eßer
  • Kai Polsterer
  • Kamalpreet Kaur
  • Katharina Peters
  • Kaustuv Basu
  • Kaustuv Basu
  • Kevin Schmidt
  • Kostadin Cvejoski
  • Leonora Kardum
  • Lucas Kock
  • Lukas Pfahler
  • Maik Sowinski
  • Maik Sowinski
  • Maram Akila
  • Marcel Mielach
  • Maurice Günder
  • Michael Kramer
  • Mirco Hünnefeld
  • Mirko Bunse
  • Mirko Bunse
  • Mirko Bunse
  • Murad Elnagdi
  • Natalia Andrienko
  • Nils Wandel
  • Nils Wandel
  • Pascal Gutjahr
  • Pascal Gutjahr
  • Ralf Antonius Timmermann
  • Ramesh Karuppusamy
  • Ramses Sanchez
  • Sadia Mahjabin
  • Sascha Mücke
  • Sebastian Buschjäger
  • Sebastian Buschjäger
  • Sebastian Konietzny
  • Siba Mohsen
  • Stefan Michaelis
  • Stefanie Walch-Gassner
  • Subarna Chaki
  • Thanh Liem Ngo
  • Thomas Liebig
  • Thore Gerlach
  • Tim Ruhe
  • Tim-Eric Rathjen
  • Tim-Eric Rathjen
  • Tobias Uelwer
  • Toma Badescu
  • Vishnu Balakrishnan
  • Vukan Jevtic
  • Wolfgang Rhode
    • 09:30 10:30
      Arrival Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 09:30
        Arrival & Coffee 1h
    • 10:30 11:05
      Welcome & Introduction Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 10:30
        Opening 5m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        The hosts of the Meet-Up will give a warm welcome to participants and will give a short overview of the structure of the meeting.

        Speakers: Prof. Wolfgang Rhode (Physics Department, TU Dortmund University), Dr Jens Buss (Lamarr Institute, TU Dortmund University)
      • 10:35
        Introduction: Lamarr 15m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        The managing director of the Lamarr Institut, Stefan Michaelis, will give a short introduction to the institute and its research goals. The structure of the institute will be presented and an overview of the researchers will be outlined.

        Speaker: Dr Stefan Michaelis (Managing Director, Lamarr Institut, TU Dortmund University)
      • 10:50
        Introduction: B3D 15m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        Speaker: Prof. Frank Bertoldi (Argelander-Institute for Astronomy, University of Bonn)
    • 11:05 11:10
      coffee refil 5m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      Joseph von Fraunhofer Strasse 25 44227 Dortmund
      40
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    • 11:10 12:30
      Overview Talks: Physics Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 11:10
        Introduction: Radio Astronomy 25m

        20 mins Talks + 5 mins Q&A

        Speaker: Dr Ancla Müller (AIRUB)
      • 11:35
        Introduction: Exp. Particle Physics 25m

        20 mins Talks + 5 mins Q&A

        Speaker: Prof. Johannes Albrecht (Physics Department & Lamarr Institute, TU Dortmund University)
      • 12:00
        Introduction: Astroparticle Physics 25m

        Introductions to Astroparticle physics.

        In particular, Gamma-ray Astronomy and Neutrino Astronomy with a glace of Simulation topics.

        20 mins Talks + 5 mins Q&A

        Speakers: Dr Dominik Elsaesser, Dr Tim Ruhe (TU Dortmund University)
    • 12:30 13:30
      Lunch 1h Joseph-von-Fraunhofer Strasse 25, Floor U, Room U09 - Lamarr Cantina

      Joseph-von-Fraunhofer Strasse 25, Floor U, Room U09 - Lamarr Cantina

      Lamarr Institut, TU Dortmund University

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      The first lunch on Wednesday will be held at the Lamarr Institute facilities to save time on the first day. During lunch, there will be an opportunity for separate exchanges between PIs and coordinators.

    • 13:30 15:30
      Overview Talks: Machine Learning and Computer Science Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 13:30
        Hybrid Machine Learning for Scientific Discovery 20m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        Hybrid Machine Learning is all about inferring structured representations from empirical data, where by representations I mean transformed views of the data that make it more interpretable, or more usable for modelling and prediction. In this talk I will discuss how one can use neural networks to infer representations that satisfy partial differential equations, which one assumes model the physical processes underlying the empirical data; (ii) how simulation data from our theoretical models can be leveraged to encode mappings between infinite dimensional spaces; and (iii) how all these ideas open the door to new paradigms for scientific discovery.

        Speaker: Dr Ramses J. Sanchez (Lamarr Institute, University of Bonn)
      • 13:50
        Introduction to Trustworthy-AI 20m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        This presentation will give a brief overview on the various dimensions comprising Trustworthy AI in general, namely Fairness, Privacy, Autonomy, Transparency and Reliability. A specific focus is given to the last two as they are typically more concerned with the inner workings of the AI system as such. When considering AI as tool for academic, that is scientific, use understanding limitations of the systems as well as its underlying reasoning can aid the process of discovery.

        Speaker: Dr Maram Akila (Lamarr Institut, Fraunhofer IAIS)
      • 14:10
        Data Bases and Data Storage 20m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        Speaker: Prof. Jens Teubner (DBIS & Lamarr Institut, Fak. Informatik, TU Dortmund)
      • 14:30
        Coffee break 15m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space (Lamarr Institut, TU Dortmund University)

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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      • 14:45
        Introduction: Human-Centred Systems 20m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

        Lamarr Institut, TU Dortmund University

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        Human-centered Systems are designed to interact with humans and deliver explainable and comprehensible results.

        At the Lamarr Institute, we are developing human-centered approaches for bridging the gap between ML methods and human minds. On the one hand, human-centered systems adapt to human goals, concepts, values, and ways of thinking. On the other hand, these systems take advantage of the power of human perception and intelligence. Visual Analytics play a key role in combining human and machine intelligence. Thus, ML models are developed with involvement of human knowledge and then use this knowledge in generating explanations.

        Speaker: Prof. Natalia Andrienko (Lamarr Institute, Fraunhofer IAIS)
    • 15:30 16:00
      Coffee break 30m Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      Joseph von Fraunhofer Strasse 25 44227 Dortmund
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      Time for discussions

    • 16:00 17:30
      Initial Zündung: Prof. Dr. Reinhard Genzel Galaxien & Schwarze Löcher (eine vierzigjährige Reise) Auditorium Maximum; Vogelpothsweg 87, 44227 Dortmund (TU Dortmund)

      Auditorium Maximum; Vogelpothsweg 87, 44227 Dortmund

      TU Dortmund

      By chance, on the first day of the network meeting, there will be a lecture in the series "Initialzündung" of the TU Dortmund which has a thematic overlap with the network meeting. In the "Initialzündung" series, renowned scientists from all over the world who have been awarded a Nobel or Leibniz Prize, for example, are invited to TU Dortmund.

      • 16:00
        Vortrag: Galaxien & Schwarze Löcher (eine vierzigjährige Reise) 45m Auditorium Maximum

        Auditorium Maximum

        TU Dortmund

        Vogelpothsweg 87, 44227 Dortmund

        In the "Initialzündung" series, renowned scientists from all over the world who have been awarded a Nobel Prize or Leibniz Prize, for example, are invited to TU Dortmund.

        Prof. Dr. Reinhard Genzel was awarded the Nobel Prize in Physics in 2020 for his research on black holes. He is director at the Max Planck Institute for Extraterrestrial Physics (MPE) in Garching and professor at the Graduate School for Physics and Astronomy at the University of California at Berkeley.

        https://www.tu-dortmund.de/veranstaltungsdetail/initialzuendung-nobelpreistraeger-reinhard-genzel-an-der-tu-dortmund-35534/

        Speaker: Prof. Reinhard Genzel (Max-Planck-Institut für extraterrestrische Physik (MPE) )
      • 16:45
        Q&A: Galaxien & Schwarze Löcher (eine vierzigjährige Reise) 45m Auditorium Maximum

        Auditorium Maximum

        TU Dortmund

        ogelpothsweg 87, 44227 Dortmund

        Fragerunde nach dem Vortrag

    • 16:01 17:31
      Discussion groups (Open space): Networking Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      Open space for discussion groups

    • 19:00 21:30
      Social: Conference Dinner Restaurant "Il Golfo"

      Restaurant "Il Golfo"

      Rosental 10 44135 Dortmund
    • 09:30 09:50
      Overview Talks Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      Convener: Dr Jens Buss (Lamarr Institute, TU Dortmund University)
      • 09:30
        Introduction to Embodied AI 20m

        Embodied Artificial Intelligence (AI) refers to AI that is embedded in physical systems, such as robots, and can interact with the surroundings. Embodied agents thus learn from experience in order to improve their behavior, comparable to how human learning is based on exploration and interaction with the environment. This talk will give a brief overview of the interdisciplinary field of Embodied AI and recent research activities in the context of the Lamarr Institute.

        Speaker: Mr Julian Eßer (Lamarr Institute, Fraunhofer IML)
    • 09:50 10:40
      Pitch Talks: ML Methods and Application Examples (in Physics) Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      Convener: Dr Jens Buss (E5b, Lamarr)
      • 09:50
        ML Appl. Example Neutrino + Neutrino Map of the Universe 10m
        Speaker: Dr Mirco Huennefeld
      • 10:00
        Class-conditional label noise 10m
        Speaker: Dr Mirko Bunse (Lamarr Dortmund)
      • 10:10
        Deep Learning for real-time classification of astronomical radio signals 10m

        Continuous developments in systems for recording radio astronomical signals lead to a natural increase in the volume of data acquired. This increase, in turn, exacerbates the challenges associated with the storage and processing of this information. A potential solution to this challenge is the development of systems underpinned by deep machine learning. These systems, at the stage of signal acquisition, could classify the data and retain only those portions that contain significant scientific information for further analysis. However, the challenge resides in ensuring that these systems not only exhibit high accuracy across a vast array of signals but also maintain exceptional sensitivity to avoid overlooking weak signals that still have significant scientific value.
        This presentation will discuss efforts to implement such a model, utilizing radio pulsar data from the Effelsberg telescope as a case study.

        Speaker: Andrei Kazansrev (Max Planck Institute for Radio Astronomy)
      • 10:20
        Innovative approaches for galaxy cluster mass determination via deep learning 10m

        Determination of galaxy cluster masses from astronomical data is one of the primary prerequisites for their cosmological analysis and various deep learning methods have been tested with simulated data sets. Although the attention of the community is moving towards more advanced architectures like GANs and vision transformers, improvements can still be made with the traditional convolutional networks with simple physical inputs. These range from very simple exploitation of spherical symmetry of the objects, to training with polarization data in addition to the total intensity images. Feedback is welcome on whether there are some fundamental limitations for these convolutional networks and whether one should adopt newer models.

        Speaker: Kaustuv Basu (Uni Bonn)
      • 10:30
        Representing and rendering large noisy radio data cubes 10m

        To support visual analytics of large radio/volume data, we accelerated a source finding/filtering algorithm by employing GPUs. Also, we implemented a GPU based volume renderer that achieves real-time performance and can be used in interactive environments, like an Augmented Reality setup. When visually analysing unfiltered data the frame rates of the renderer drop significantly and data is often too big to fit onto GPUs. Very first tests with a neural representation of noisy volumes to reduce storage requirements show that there is a tradeoff between quality and efficiency. We would like to learn more about DL based representation of noisy volume data.

        Speaker: Prof. André Hinkenjann (Hochschule Bonn-Rhein-Sieg, Institut für Visual Computing)
    • 10:40 11:40
      Coffee break with poster session Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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    • 11:40 12:40
      Discussion groups (Open space): Open space for discussion groups

      Open space for discussion groups

      • 11:40
        Deep Learning for real-time classification of astronomical radio signals 1h Joseph-von-Fraunhofer Strasse 25, Floor 1, Room 101 - Lamarr Meeting Room South

        Joseph-von-Fraunhofer Strasse 25, Floor 1, Room 101 - Lamarr Meeting Room South

        Lamarr Institut, TU Dortmund University

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        Continuous developments in systems for recording radio astronomical signals lead to a natural increase in the volume of data acquired. This increase, in turn, exacerbates the challenges associated with the storage and processing of this information. A potential solution to this challenge is the development of systems underpinned by deep machine learning. These systems, at the stage of signal acquisition, could classify the data and retain only those portions that contain significant scientific information for further analysis. However, the challenge resides in ensuring that these systems not only exhibit high accuracy across a vast array of signals but also maintain exceptional sensitivity to avoid overlooking weak signals that still have significant scientific value. This presentation will discuss efforts to implement such a model, utilizing radio pulsar data from the Effelsberg telescope as a case study.

        Speaker: Andrei Kazantsev ( Max-Planck-Institut für Radioastronomie)
      • 11:40
        TBA 1h
    • 12:40 14:00
      Lunch 1h 20m Mensa (TUDortmund)

      Mensa

      TUDortmund

    • 14:00 14:50
      Pitch Talks: Inverse Problems Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      Convener: Dr Jens Buss (E5b, Lamarr)
      • 14:00
        Quantification 10m
        Speaker: Dr Mirko Bunse (Lamarr Institut)
      • 14:10
        Solving Inverse Problems with Deep Learning 10m

        Our work entails using generative models as priors for Fourier phase retrieval. We were successful in the Helsinki Tomography Challenge 2022 by employing a large synthetic dataset with end-to-end convolutional networks for limited-angle computer tomography. Additionally, our latest project focuses on extracting (ideally weak) earthquake signals from real, noisy Distributed Acoustic Sensing (DAS) data.

        Speakers: Sebastian Konietzny (Informatik LS8, TU Dortmund University), Dr Tobias Uelwer (Informatik LS8, TU Dortmund University)
      • 14:20
        Unfolding in Astroparticle Physics 10m
        Speakers: Leonora Kardum, Prof. Wolfgang Rhode
      • 14:30
        Atmospheric Noise Removal for FYST: Current Methods and ML Prospects 10m

        The Epoch of Reionization Spectrometer (EoR-Spec) instrument on the Fred Young Submillimeter Telescope (FYST) will undertake a Line Intensity Mapping (LIM) survey targeting the [CII] line across redshifts 3.5 − 8.0. The observed frequency range for EoR-Spec, 210 to 420 GHz, is substantially influenced by atmospheric emissions that affect LIM power spectrum measurements. One of the challenges is to efficiently separate the cosmological signal from the correlated atmospheric noise. Traditional data cleaning techniques, including various filtering methods and Principal Component Analysis (PCA), are currently employed to mitigate these effects.
        Machine Learning (ML) methods such as Convolutional Neural Networks (CNNs) and Gaussian Process Regression (GPR) can assist in tackling this inverse problem. In addition, incorporating outlier, glitch, and anomaly detection into the data reduction pipeline could strengthen the handling of systematics in detector timestreams.

        Speaker: Ankur Dev (Uni Bonn)
      • 14:40
        Leveraging Machine Learning for Next-Generation Radio Pulsar Searches 10m

        The discovery of new radio pulsars has significant implications for both Gravitational and Condensed Matter Physics. With the emergence of large datasets on the order of petabytes requiring quasi-real-time analysis, computational methods, particularly machine learning, have been increasingly important. This pitch will give a short ovreview of the progress made in applying machine learning techniques to radio pulsar searches over the last decade.

        Speaker: Dr Vishnu Balakrishnan (MPIfR)
    • 14:50 15:10
      Overview Talks Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 14:50
        Introduction: Resource-aware ML 20m

        The scientific area of Resource-Aware Machine Learning tries to "make the most out of a bad situation," i.e., match existing solutions' performance while only using a fraction of the resources or surpassing existing solutions' performance. To do so, we try to bridge the gap between the mathematical concepts of Machine Learning, their expression in software, and their execution in hardware. We study new algorithms for training small ML models, their post-processing (e.g., pruning) for model deployment, and their compilation to existing or new hardware.

        Speaker: Dr Sebastian Buschjäger (Lamarr Institute, TU Dortmund University)
    • 15:10 16:10
      Coffee break with poster session Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      40
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    • 16:10 17:10
      Discussion groups (Open space)

      Open space for discussion groups

      • 16:10
        Leveraging Machine Learning for Next-Generation Radio Pulsar Searches: Challenges and Opportunities 1h Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 304 - Lamarr Deep Dive Room (Lamarr Institut, TU Dortmund University)

        Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 304 - Lamarr Deep Dive Room

        Lamarr Institut, TU Dortmund University

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        The discovery of new radio pulsars has significant implications for both Gravitational and Condensed Matter Physics. With the emergence of large datasets on the order of petabytes requiring quasi-real-time analysis, computational methods, particularly machine learning, have been increasingly important. This session will begin with a review of the progress made in applying machine learning techniques to radio pulsar searches over the last decade. Following this, we will delve into the most recent advancements and the challenges that lie ahead. The aim is to foster a robust dialogue and establish collaborations between researchers in computer science and astronomy, working toward the next frontier in radio pulsar discovery.

        Speaker: Dr Vishnu Balakrishnan (Max Planck Institute for Radio Astronomy, Bonn)
      • 16:10
        TBA 1h
    • 17:10 18:10
      Wrap-Up Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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    • 19:00 21:00
      Social: Christmas Market
    • 09:30 10:30
      Pitch Talks: Problems and data sources Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

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      • 09:30
        Particle Shower Simulation 10m
        Speaker: Dominik Baack (TU Dortmund)
      • 09:40
        Radio Data Simulations 10m
        Speakers: Felix Geyer, Kevin Schmidt
      • 09:50
        Potential ML applications to accelarate ISM simulation 10m
        Speaker: Tim-Eric Rathjen
      • 10:00
        ML Solutions for Time Series Problems 10m
        Speaker: Dr Amal Sadallah (Lamarr Institute Dortmund)
      • 10:10
        Time Series Data - Machine Learning For Observatory Sites 10m

        The CCAT observatory on Cerro Chajnantor, Chile, will be operated entirely remote - with no crew at site during observations. Hence, we require reliable Predictive Maintenance (Outlier-/Anomaly-Detection) methods on its critical infrastructure to guarantee continuous operations. Moreover, Time Series data is collected from various radiometers at Atacama representing the atmospheric precipitable water vapor. This data - supplemented by data from meteorological services - will be utilized to make 5-day forecasts in order to schedule observations. We would like to learn if any RNN model forecast supersedes that of SARIMA.

        Speaker: Dr Ralf Timmermann (Uni Bonn)
      • 10:20
        P-adic numbers 10m
        Speaker: Prof. Thomas Liebig (Lamarrr Institut, TU Dortmund)
    • 10:30 11:30
      Coffee break with poster session Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      40
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    • 11:30 12:30
      Discussion groups (Open space)

      Open space for discussion groups

    • 12:30 13:15
      Wrap-Up Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      40
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    • 13:15 14:15
      Lunch 1h Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space

      Lamarr Institut, TU Dortmund University

      Joseph von Fraunhofer Strasse 25 44227 Dortmund
      40
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