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Prof. Dr. Wojciech Samek

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Fraunhofer Heinrich Hertz Institute

Einsteinufer 37, D-10587 Berlin

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Prof. Dr. Wojciech Samek

Fellow

Fellow | BIFOLD

Professor | Technische Universität Berlin

Head of AI Department | Fraunhofer HHI

Wojciech Samek is a professor in the department of Electrical Engineering and Computer Science at the Technical University of Berlin and is jointly heading the department of Artificial Intelligence and the Explainable AI Group at Fraunhofer Heinrich Hertz Institute (HHI), Berlin, Germany. He studied computer science at Humboldt University of Berlin from 2004 to 2010, was visiting researcher at NASA Ames Research Center, CA, USA, and received the Ph.D. degree in machine learning from the Technische Universität Berlin in 2014. He is associated faculty at the ELLIS Unit Berlin and the DFG Graduate School BIOQIC, and member of the scientific advisory board of IDEAS NCBR. Furthermore, he is a senior editor of IEEE TNNLS, an editorial board member of Pattern Recognition, and an elected member of the IEEE MLSP Technical Committee. He is recipient of multiple best paper awards, including the 2020 Pattern Recognition Best Paper Award, and part of the expert group developing the ISO/IEC MPEG-17 NNC standard. He is the leading editor of the Springer book “Explainable AI: Interpreting, Explaining and Visualizing Deep Learning” and organizer of various special sessions, workshops and tutorials on topics such as explainable AI, neural network compression, and federated learning. He has co-authored more than 150 peer-reviewed journal and conference papers; some of them listed by Thomson Reuters as “Highly Cited Papers” (i.e., top 1%) in the field of Engineering.

2021 2020 Pattern Recognition Best Paper Award
2019 Best Paper Award at ICML Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations
2019 Honorable Mention Award at IEEE AIVR
2016 Best Paper Award at ICML Workshop on Visualization for Deep Learning
2010 Scholarship of the DFG Research Training Group GRK 1589/1
2006 Scholarship of the German National Merit Foundation (“Studienstiftung des deutschen Volkes”)

  • Deep Learning
  • Interpretable Machine Learning
  • Model Compression
  • Computer Vision
  • Distributed Learning

Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold SchmidtStefan Ropele, Wojciech Samek, Christian Langkammer

Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification

April 16, 2024
https://arxiv.org/pdf/2404.10433.pdf

Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression

April 15, 2024
https://doi.org/10.48550/arXiv.2404.09601

Andreas Rieckmann, Sebastian Nielsen, Piotr Dworzynski, Heresh Amini, Søren Wengel Mogensen, Isaquel Bartolomeu Silva, Angela Y Chang, Onyebuchi A Arah, Wojciech Samek, Naja Hulvej Rod, Claus Thorn Ekstrøm, Christine Stabell Benn, Peter Aaby, Ane Bærent Fisker

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study

April 09, 2024
https://doi.org/10.2196/48060

Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin

PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits

April 09, 2024
https://arxiv.org/abs/2404.06453

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space

March 24, 2024
https://doi.org/10.1609/aaai.v38i19.30096

News
Explainable AI| Oct 04, 2023

AI - finally explainable to humans

As of today it remains difficult to understand how an AI system reaches its decisions. Scientists at the Fraunhofer Heinrich-Hertz-Institut (HHI) and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin have collaborated for many years to make AI explainable. In their new paper the researchers present Concept Relevance Propagation (CRP), a new method for explainable AI that can explain individual AI decisions as concepts understandable to humans. 

News
Machine Learning| Nov 14, 2022

DSP Best Paper Prize

BIFOLD researchers Prof. Klaus-Robert Müller, Prof. Wojciech Samek and Prof. Grégoire Montavon were honored by the journal Digital Signal Processing (DSP) with the 2022 Best Paper Prize. The DSP mention of excellence highlights important research findings published within the last five years.

News
Machine Learning| Apr 28, 2022

“I want to move beyond purely ‘Explaining’ AI”

BIFOLD researcher Dr. Wojciech Samek has been appointed Professor of Machine Learning and Communications at TU Berlin with effect from 1 May 2022. Professor Samek heads the Department of Artificial Intelligence at the Fraunhofer Heinrich-Hertz-Institute. His goal is to further develop three areas: explainability and trustworthiness of artificial intelligence, the compression of neural networks, and so-called federated leaning. He aims to focus on the practical, methodological, and theoretical aspects of machine learning at the interface to other areas of application.

News
Machine Learning| Dec 15, 2021

Benchmarking Neural Network Explanations

Neural networks have found their way into many every day applications. During the past years they reached excellent performances on various largescale prediction tasks, ranging from computer vision, language processing or medical diagnosis. Even if in recent years AI research developed various techniques that uncover the decision-making process and detect so called “Clever Hans” predictors – there exists no ground truth-based evaluation framework for such explanation methods. BIFOLD researcher Dr. Wojciech Samek and his colleagues now established an Open Source ground truth framework, that provides a selective, controlled and realistic testbed for the evaluation of neural network explanations. The work will be published in Information Fusion.

Machine Learning| Mar 24, 2021

New workshop series “Trustworthy AI”

The AI for Good global summit is an all year digital event, featuring a weekly program of keynotes, workshops, interviews or Q&As. BIFOLD Fellow Dr. Wojciech Samek, head of department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute (HHI), is implementing a new online workshop series “Trustworthy AI” for this platform.

News
Machine Learning| Mar 10, 2021

Making the use of AI systems safe

BIFOLD Fellow Dr. Wojciech Samek and Luis Oala (Fraunhofer Heinrich Hertz Institute) together with Jan Macdonald and Maximilian März (TU Berlin) were honored with the award for “best scientific contribution” at this year’s medical imaging conference BVM. Their paper “Interval Neural Networks as Instability Detectors for Image Reconstructions” demonstrates how uncertainty quantification can be used to detect errors in deep learning models.

News
Machine Learning| Dec 09, 2020

BIFOLD PI Dr. Samek talks about explainable AI at NeurIPS 2020 social event

BIFOLD Principal Investigator Dr. Wojciech Samek (Fraunhofer HHI) talked about explainable and trustworthy AI at the “Decemberfest on Trustworthy AI Research” as part of the annual Conference on Neural Information Processing Systems (NeurIPS 2020). NeurIPS is a leading international conference on neural information processing systems, Machine Learning (ML) and their biological, technological, mathematical, and theoretical aspects.

BIFOLD Update| Aug 06, 2020

An overview of the current state of research in BIFOLD

Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.