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Furkan Akgün is a Computer Science Master’s student at Technische Universität Berlin and an ELIZA Master’s Fellow. He currently works as a Student Research Assistant at the TU Berlin Machine Learning Group / BIFOLD, while also contributing to a multimodal AI project in medicine at Charité – Universitätsmedizin Berlin. Driven by a core mission to pioneer advanced AI applications in the medical field, his research primarily focuses on Computer Vision, Signal Processing, and Multimodal AI, with an additional interest in Explainable AI and Inverse Problems. He holds a double major in Computer Science and Electrical Engineering with a minor in Mathematics from Sabanci University, and completed an exchange semester at Sungkyunkwan University in South Korea under the GKS scholarship. Beyond academia, he served as a Board Member for both the IEEE Engineering Club and the Entrepreneurship Club, where he actively co-organized career and technology events.

Furkan Akgün is a Computer Science Master’s student at Technische Universität Berlin and an ELIZA Master’s Fellow. He currently works as a Student Research Assistant at the TU Berlin Machine Learning Group / BIFOLD, while also contributing to a multimodal AI project in medicine at Charité – Universitätsmedizin Berlin. Driven by a core mission to pioneer advanced AI applications in the medical field, his research primarily focuses on Computer Vision, Signal Processing, and Multimodal AI, with an additional interest in Explainable AI and Inverse Problems.

He holds a double major in Computer Science and Electrical Engineering with a minor in Mathematics from Sabanci University, and completed an exchange semester at Sungkyunkwan University in South Korea under the GKS scholarship. Beyond academia, he served as a Board Member for both the IEEE Engineering Club and the Entrepreneurship Club, where he actively co-organized career and technology events.

Barış Can Göktaş is a Master’s student in Information Systems at Technische Universität Berlin with a passion for AI applications in earth observation and space technology. Currently, he works as a Working Student, Space Operations Engineer at Planet Labs, where he debugs satellite-specific anomalies, triages fleet-wide issues, and drives data-driven improvements to mission productivity and operational efficiency. His career trajectory has been shaped by a lifelong fascination with satellite imagery and geographic data, from exploring maps as a child to competing in national geography competitions, to building his undergraduate project around 24 years of Landsat imagery analyzing Istanbul’s urban heat patterns. Deliberately choosing TU Berlin for its earth observation research focus, he now aims to combine AI with satellite operations to extract actionable insights for environmental monitoring and scientific progress. His research spans machine learning for geospatial analysis, clinical AI applications, and deep learning. He recently contributed to a peer-reviewed study on computational psychiatry at Charité – Universitätsmedizin Berlin. Looking ahead, he aims to pursue a PhD in Space Technology to deepen his impact on earth observation and autonomous space systems.

Barış Can Göktaş is a Master’s student in Information Systems at Technische Universität Berlin with a passion for AI applications in earth observation and space technology. Currently, he works as a Working Student, Space Operations Engineer at Planet Labs, where he debugs satellite-specific anomalies, triages fleet-wide issues, and drives data-driven improvements to mission productivity and operational efficiency. His career trajectory has been shaped by a lifelong fascination with satellite imagery and geographic data, from exploring maps as a child to competing in national geography competitions, to building his undergraduate project around 24 years of Landsat imagery analyzing Istanbul’s urban heat patterns. Deliberately choosing TU Berlin for its earth observation research focus, he now aims to combine AI with satellite operations to extract actionable insights for environmental monitoring and scientific progress. His research spans machine learning for geospatial analysis, clinical AI applications, and deep learning. He recently contributed to a peer-reviewed study on computational psychiatry at Charité – Universitätsmedizin Berlin. Looking ahead, he aims to pursue a PhD in Space Technology to deepen his impact on earth observation and autonomous space systems.

I am a Master’s student in Informatics at the Technical University of Berlin, specializing in computer vision and artificial intelligence. My research interests focus on AI technologies for medical applications, exploring how advanced vision systems can improve healthcare outcomes. My experience includes working as a student assistant at Fraunhofer IOSB, where I focused on object recognition using LiDAR data. Before starting my master’s, I worked as a full-time Software Engineer at Value Analytics Labs for one year. During my Bachelor’s degree, I worked on a research project developing Brain-Computer Interfaces for human detection on UAVs. For my thesis, I investigated the structural and behavioral similarities between the human brain and artificial neural networks in response to SSVEP-tagged stimuli.

I am a Master’s student in Informatics at the Technical University of Berlin, specializing in computer vision and artificial intelligence. My research interests focus on AI technologies for medical applications, exploring how advanced vision systems can improve healthcare outcomes.
My experience includes working as a student assistant at Fraunhofer IOSB, where I focused on object recognition using LiDAR data. Before starting my master’s, I worked as a full-time Software Engineer at Value Analytics Labs for one year. During my Bachelor’s degree, I worked on a research project developing Brain-Computer Interfaces for human detection on UAVs. For my thesis, I investigated the structural and behavioral similarities between the human brain and artificial neural networks in response to SSVEP-tagged stimuli.

I am a Master’s student in Computer Science at the Technical University of Berlin and a student researcher at the [Machine Learning Group](https://www.ml.tu-berlin.de/) of Prof. Dr. Klaus-Robert Müller, where I am supervised by Dr. Stefan Chmiela. My research lies at the intersection of machine learning, chemistry, and physics, with a focus on accelerating atomistic simulations using geometric deep learning and generative modeling.

I am a Master’s student in Computer Science at the Technical University of Berlin and a student researcher at the [Machine Learning Group](https://www.ml.tu-berlin.de/) of Prof. Dr. Klaus-Robert Müller, where I am supervised by Dr. Stefan Chmiela. My research lies at the intersection of machine learning, chemistry, and physics, with a focus on accelerating atomistic simulations using geometric deep learning and generative modeling.

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I am Rezwana Hussainzada, a Master’s student in Computational Neuroscience at the Bernstein Center for Computational Neuroscience (BCCN) Berlin, hosted by the Technical University of Berlin (TU Berlin). I hold a Bachelor’s degree in “Mathematics and its Applications” and a Master’s degree in Pure Mathematics (Geometry/Topology), during which I ranked 5th nationwide in the Scientific Olympiad for Iranian University Students. I have also served as a university teaching assistant and a voluntary mathematical instructor. My research interests lie at the trans-disciplinary intersection of mathematics, machine learning, and neuroscience, with a specific focus on topological neuroscience and utilizing Topological Data Analysis (TDA) to investigate the underlying space of data. Currently, my studies center on how high-dimensional medical data can be decoded through mathematically grounded AI, computational modeling of brain processes, and contributing to research at the intersection of neuroscience and artificial intelligence

I am Rezwana Hussainzada, a Master’s student in Computational Neuroscience at the Bernstein Center for Computational Neuroscience (BCCN) Berlin, hosted by the Technical University of Berlin (TU Berlin).

I hold a Bachelor’s degree in “Mathematics and its Applications” and a Master’s degree in Pure Mathematics (Geometry/Topology), during which I ranked 5th nationwide in the Scientific Olympiad for Iranian University Students. I have also served as a university teaching assistant and a voluntary mathematical instructor. My research interests lie at the trans-disciplinary intersection of mathematics, machine learning, and neuroscience, with a specific focus on topological neuroscience and utilizing Topological Data Analysis (TDA) to investigate the underlying space of data.

Currently, my studies center on how high-dimensional medical data can be decoded through mathematically grounded AI, computational modeling of brain processes, and contributing to research at the intersection of neuroscience and artificial intelligence

I am a Master’s student in Computer Science at TU Berlin, focusing on artificial intelligence, with a particular interest in the theoretical and ethical dimensions of AI systems. I completed my Bachelor’s degree in Information Technology Engineering with class distinction under a merit-based scholarship in India, where my thesis on career guidance prediction first introduced me to the real-world impact of machine learning. At TU Berlin, my studies span deep learning, computer vision, and formal programming methods, and I am currently engaged in seminars on Hot Topics in Computer Vision and Machine and Behavior. Outside of academia, I volunteer with Back on Track, working with school-age children in an educational setting. I was recently awarded the Niedersachsen Stipendium in recognition of my academic progress and integration efforts. Through this exchange program, I hope to grow as a researcher and contribute to building AI that is both responsible and meaningful for underrepresented communities.

I am a Master’s student in Computer Science at TU Berlin, focusing on artificial intelligence, with a particular interest in the theoretical and ethical dimensions of AI systems. I completed my Bachelor’s degree in Information Technology Engineering with class distinction under a merit-based scholarship in India, where my thesis on career guidance prediction first introduced me to the real-world impact of machine learning.
At TU Berlin, my studies span deep learning, computer vision, and formal programming methods, and I am currently engaged in seminars on Hot Topics in Computer Vision and Machine and Behavior. Outside of academia, I volunteer with Back on Track, working with school-age children in an educational setting. I was recently awarded the Niedersachsen Stipendium in recognition of my academic progress and integration efforts. Through this exchange program, I hope to grow as a researcher and contribute to building AI that is both responsible and meaningful for underrepresented communities.

I am a Mathematics Masters Student at TU Berlin, with a main focus on applied mathematics and in particular probability theory. During my bachelors i worked on the foundations and applications of generative models and sampling. Most notably the theory of optimal transport and stochastic dynamical systems excites me. Currently i am exploring the application of this rich theory to the natural sciences. Additionally, I work on xAI for Time Series Models at the Machine Learning Group at TU Berlin and i am involved with the great community of Eleuther AI working on xAI and AI Safety, in particular how newly discovered mechanism like subliminal learning or emergent misalignment can be understood and mitigated.

I am a Mathematics Masters Student at TU Berlin, with a main focus on applied mathematics and in particular probability theory. During my bachelors i worked on the foundations and applications of generative models and sampling. Most notably the theory of optimal transport and stochastic dynamical systems excites me. Currently i am exploring the application of this rich theory to the natural sciences. Additionally, I work on xAI for Time Series Models at the Machine Learning Group at TU Berlin and i am involved with the great community of Eleuther AI working on xAI and AI Safety, in particular how newly discovered mechanism like subliminal learning or emergent misalignment can be understood and mitigated.

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Amr is a final-year Media Engineering and Technology student at GIU Berlin, completing his B.Sc. on a DAAD merit scholarship after transferring from GUC Cairo, and will be pursuing an M.Sc. at Freie Universität Berlin. He works as a Research Working Student at Fraunhofer HHI, developing real-time C++ network simulations for V2X sidelink communication over 5G NR. His research interests span efficient small language models and agentic AI systems — encompassing model compression, structured pruning, quantization, tool-use, and the design of autonomous pipelines that reason and act reliably in constrained environments. He volunteers at Bliss e.V., a Berlin-based AI community, where he supports the media team in producing and broadcasting public AI lecture series — helping make cutting-edge research accessible to broader audiences. He holds a DAAD merit scholarship recognizing his academic excellence.

Amr is a final-year Media Engineering and Technology student at GIU Berlin, completing his B.Sc. on a DAAD merit scholarship after transferring from GUC Cairo, and will be pursuing an M.Sc. at Freie Universität Berlin. He works as a Research Working Student at Fraunhofer HHI, developing real-time C++ network simulations for V2X sidelink communication over 5G NR. His research interests span efficient small language models and agentic AI systems — encompassing model compression, structured pruning, quantization, tool-use, and the design of autonomous pipelines that reason and act reliably in constrained environments. He volunteers at Bliss e.V., a Berlin-based AI community, where he supports the media team in producing and broadcasting public AI lecture series — helping make cutting-edge research accessible to broader audiences. He holds a DAAD merit scholarship recognizing his academic excellence.

I am a PhD student in the Machine Learning Group at TU Berlin and at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). In addition, I am a Data Scientist at Aignostics, where I am working on data-driven drug target identification in collaboration with Bayer Pharmaceuticals. During my Master’s in Computer Science at TU Berlin, I worked on explainable graph neural networks for survival prediction in lung cancer and was funded by the Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA).

My research lies at the intersection of generative modeling, causal inference, and computational biology, with a focus on precision medicine applications and developing models that simulate single-cell response to perturbations. Feel free to reach out to me if you have similar research interests and would like to collaborate on a project!

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Grégoire Montavon is a Professor at Charité – Universitätsmedizin Berlin and a Research Group Lead in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received a Master’s degree in Communication Systems from the École Polytechnique Fédérale de Lausanne in 2009 and a Ph.D. in Machine Learning from the Technische Universität Berlin in 2013. He is a member of the ELLIS Unit Berlin, and an editorial board member of Pattern Recognition. He is recipient of the 2020 Pattern Recognition Best Paper Award. His research interests include explainable machine learning, deep neural networks, and unsupervised learning. His research focuses on methods of Explainable AI (XAI) and applications in medicine.

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