Fast Machine Learning for Science Conference 2025

Invited Speakers

Torsten Hoefler

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Torsten Hoefler is a full professor at ETH Zurich where directs the Scalable Parallel Computing Laboratory (SPCL). Dr. Hoefler's research aims at understanding the performance of parallel computing systems ranging from parallel computer architecture through parallel programming to parallel algorithms.  He is also active in the application areas of Weather and Climate simulations as well as Machine Learning with a focus on Distributed Deep Learning.  In those areas, he has coordinated tens of funded projects and both an ERC Starting Grant and an ERC Consolidator Grant on Data-Centric Parallel Programming.

Maximilian Dax

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Maximilian Dax is a postdoctoral researcher at ETH Zurich and the ELLIS Institute Tübingen and a member of the LIGO Scientific Collaboration. He pursued his PhD at the Max Planck Institute for Intelligent Systems in Tübingen under the supervision of Bernhard Schölkopf (2020-2024) and interned at Google Research (2023).  His research focuses on probabilistic inference, generative modeling and density estimation, with an emphasis on scientific applications.  Together with his collaborators, he developed DINGO, a leading machine learning approach for gravitational-wave data analysis.

 

Yulia Sandamirskaya

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Yulia Sandamirskaya is heading a Research Centre "Cognitive Computing in Life Sciences" at Zurich University of Applied Sciences (ZHAW). Her Research Group develops neural-dynamics based cognitive architectures for real-time, embedded AI systems, spanning sensing, planning, decision making, learning, and control for the next generation of assistive robots. Specifically, they are developing neuronal network architectures, inspired by biological neuronal circuits and tailored for implementation in neuromorphic hardware. Currently, her group is working on controllers for flying robots and arms.

Giacomo Indiveri

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Giacomo Indiveri is a Professor at the Faculty of Science at the University of Zurich, Switzerland. He won an ERC Starting Grans on “Neuromorphic processors” in 2011 and an ERC Consolidator Grant on neuromophic cognitive agents in 2016.  His research interests lie in the study of neural computation, with particular interest in spike-based learning and selective attention mechanisms, and in the hardware implementation of real-time sensory-motor systems using analog/digital neuromorphic circuits and emerging VLSI technologies.

Luigi Cruz

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Luigi Cruz is a staff engineer at the SETI Institute currently working on the GPU-accelerated real-time digital signal processing pipeline deployed at the Allen Telescope Array. He also maintains multiple open-source projects like the PiSDR, an SDR-specialized Raspberry Pi image, CyberEther, a GPU-accelerated signal visualization library, and Radio Core, a Python library for demodulating SDR signals using the GPU with the help of CuPy.

Adam Thompson

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Adam Thompson is a Principal Technical Product Manager at NVIDIA where he focuses on building hardware and software platforms targeting real-time AI, autonomous sensors, and tying high speed sensor I/O to GPU-accelerated compute. Adam is also the creator of cuSignal – a GPU-accelerated signal processing library written in Python. With over 400,000 downloads, cuSignal is widely used in the sensor processing communities, and - as of CuPy v13, is fully integrated within CuPy library. He holds a Masters degree in Electrical and Computer Engineering from Georgia Tech and a Bachelors Degree in Electrical Engineering from Clemson University.

Patrick Kidger

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Patrick is a tech lead and ML researcher at Cradle.bio, and a visiting lecturer at Imperial. Previously, he has worked on bioML at Google X, and holds a PhD in scientific machine learning from Oxford on neural differential equations.

Yaman Umuroglu

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Yaman Umuroglu is a Principal Member of Technical Staff at AMD Research and Advanced Development. He holds a PhD degree from the Norwegian University of Science and Technology (NTNU) in domain-specific architectures for reconfigurable computing. At Xilinx Research and later at AMD, he initiated and led the FINN, BISMO and LogicNets projects for exploring ML co-design on FPGAs from different angles. His research takes a full-stack view of machine learning with neural networks with a focus on high-efficiency and high-performance implementations and spans hardware-network codesign, techniques for efficient arithmetic, sparsity and quantization.

Andrea Cossettini

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Andrea Cossettini is Project Leader and Lecturer at the Integrated Systems Laboratory (IIS) of ETH Zurich and Research Cooperation Manager of the ETH Future Computing Laboratory (EFCL). He pursued his PhD at the University of Udine (Italy), working on nanoelectrode array biosensors for high-frequency impedance spectroscopy and imaging of nano-particles in electrolyte. He joined ETH in 2019, and his research focus is on biomedical circuits and systems, with a particular emphasis on wearable ultrasound, wearable EEG, and edge-AI-enabled human-machine interfaces.