Fast Machine Learning for Science Conference 2025

Invited Speakers

Maciej Besta

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Maciej Besta leads research on large language models and graph computations at the Scalable Parallel Computing Lab at ETH Zurich and at the ETH Future Computing Lab; he also works on interconnects, general sparse computing, and others. Maciej published, as the main leading author, around 40 papers at top conferences and journals. He won, among others, the IEEE TCHPC Award for Excellence in High-Performance Computing Early Career (2024), the HiPEAC Tech Transfer Award (2024), and the OlympusMons Award for contributions to scalable storage systems (2024). His doctoral dissertation on irregular computations received awards from ETH (2021), IEEE (2021), SPEC (2022), and ACM (2022). Maciej also won Best Paper awards and nominations at ACM/IEEE Supercomputing 2013, 2014, 2019 (for 2 different papers), 2022, and 2023 (for 2 different papers); at ACM HPDC 2015 and 2016, and others. Finally, Maciej is supported by the Fellowship in The Explorers Club (2022).

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.

Mathieu Guillame-Bert

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Mathieu Guillame-Bert is a Research Engineer and Tech Lead at Google Zurich, where he leads the Decision Forests and Graph Neural Network teams. His research focuses on enabling machine learning models to effectively consume complex data representations, and improving the training and inference speed and resource consumption of ML models.

Michael Galkin

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Michael Galkin is a Research Scientist at Google Research in New York working on GNNs, generalization, and using structured representations for reasoning. His research includes works on graph transformers, geometric deep learning for life sciences and chip design, and efficient kernels for standard and equivariant GNNs.

Vladimir Gligorov

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Vava spent his student years being bothered by quantum nonlocality, but eventually discovered that not being able to do maths would prove less of a problem if he became an experimental physicist. Now he divides his time between thinking about the myriad contradictions in our theories of the microscopic and macroscopic universe, and building real-time analysis systems to help LHCb probe these contradictions to ever higher precisions. He is also involved in the International Masterclass programme, trying to make the next generation as excited about fundamental science as he is.

Luca Benini

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Luca Benini holds the chair of digital Circuits and systems at ETHZ and is Full Professor at the Università di Bologna. He received a PhD from Stanford University. His research interests are in energy-​efficient parallel computing systems, smart sensing micro-​systems and machine learning hardware. He is a Fellow of the IEEE, of the ACM, a member of the Academia Europaea and of the Italian Academy of Engineering and Technology. He received, among others, the 2024 IEEE Computer Society Open Source Hardware contribution Award.

Božidar Radunovic

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Dr Bozidar Radunovic is a Senior Principal Researcher at Microsoft Research in Cambridge, UK. His research interests are in design and building next generation compute infrastructure and access network for edge and cloud. He published over 50 papers and 50 industrial patents in several areas of systems, networking, wireless, algorithms and modelling. During his career, he spent 4 years in Azure for Operator CTO office where he contributed to the company strategy in telecom and radio access networks in particular.

Richard Stotz

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Richard Stotz is a Software Engineer at Google Zurich working on Decision Forests and Machine Learning for tabular data. His research focuses on algorithmic improvements of training and inference performance of machine learning models.

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.