Team

Megan Ansdell

Megan Ansdell is currently at NASA Headquarters in Washington, DC. Her expertise is in applying machine learning to astrophysical research problems as well as studying the formation and evolution of exoplanet systems and young stars with large observational datasets. Megan was previously a Flatiron Research Fellow in NYC, in a joint appointment between the Flatiron Institute's Center for Computational Astrophysics and the Center for Computational Mathematics. She obtained a PhD in Astronomy from the University of Hawaii on large-scale surveys of protoplanetary disks with ALMA. She also has a Masters in International Science and Technology Policy from the George Washington University, where she researched and advocated for international cooperation in human space exploration.

Daniel Angerhausen

Daniel Angerhausen is an astrophysicist and astrobiologist at the ETH Zurich, Department of Physics, Exoplanets and Habitability. A former NASA postdoctoral fellow, Dr. Angerhausen is also the founder and CEO of the science and tech communication start-up 'The Explainables'. On his search for planets around other stars, Daniel has flown five missions on the NASA airborne telescope SOFIA. Daniel is also mentor and science committee member of NASA Frontier Development Lab, an Artificial Intelligence/Machine Learning incubator tackling challenges in various fields of space sciences in collaboration with industry stakeholders such as Google Cloud, Nvidia or IBM. Daniel also plays Sepaktakraw, an artistic foot-volleyball game and competed several times at World Championships in South East Asia.

Umberto Michelucci

Umberto Michelucci is the co-founder and the chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for Artificial Intelligence (AI). Umberto studied physics and mathematics. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 20 years of practical experience in the fields of data warehouse, data science, and machine learning. His first book, Applied Deep Learning—A Case-Based Approach to Understanding Deep Neural Networks, was published by Springer in 2018. He has published his new book, Convolutional and Recurrent Neural Networks Theory and Applications, in 2019. He publishes his research results regularly in leading peer reviewed open journals and gives regular talks at international conferences for scientific dissemination. He is also the only Google Developer Expert in Machine Learning based in Switzerland.

Ben Moseley

Ben Moseley is a 4th Year PhD student at the University of Oxford’s Centre for Autonomous Intelligent Machines and Systems. He is a physicist by background and is interested in physics-based machine learning. He has worked with NASA’s Frontier Development Lab since 2019 to map lunar resources using machine learning, he has solved the wave equation using physics-informed neural networks and he has developed fast seismic simulation and inversion algorithms with deep learning. He was a geophysicist-turned ML researcher in the energy industry, where he co-founded a data science community at BP which connected and upskilled 400+ data scientists worldwide. The areas of AI he is most interested in are the blending of AI and physics, the intersection of learning and reasoning, the generalisation of neural networks and AI ethics.

Caroline Keufer-Platz

Inst. f. Teilchen-​ und Astrophysik
ETH Zürich

Prof. Dr. Sascha Quanz

Sascha Patrick Quanz (*1979), is Associate Professor at ETH Zurich, Department of Physics, Exoplanets and Habitability. Sascha Quanz is a scientist who has developed an excellent international network. He investigates the formation of new planetary systems and the physical and atmospheric properties of extrasolar planets, primarily through the direct imaging of optical and near-infrared wavelengths. He played a leading role in the development of new methods of analysis which make the detection of planets and circumstellar discs faster and more robust.

Anna Jungbluth

Anna Jungbluth is currently studying for a PhD in Physics at the University of Oxford researching solar cells. As part of her work, she designs and builds experimental set-ups  to study low energy optical transitions in her solar cells to better understand fundamental operating principles of the devices. She chose to research renewable energy sources to advance technologies that combat climate change.

Anna got interested in using machine learning to overcome scientific challenges after winning the UK finals of the European Space Agency sponsored Act in Space Hackathon in 2018. The following summer, she joined the NASA Frontier Development Lab as an AI researcher for the Super-Resolution Solar Magnetic Field team. Her team researched state-of-the-art super resolution algorithms and applied them to magnetic field images of the Sun to create high-resolution, long-term datasets for space weather studies. In 2020, she came back to the NASA Frontier Development Lab as a team lead for the Starspots team, researching stellar magnetism on Kepler stars.

Outside of her research, Anna is passionate about increasing gender diversity in STEM and loves to ski.

Alison Lowndes

After spending her first year with NVIDIA as a Deep Learning Solutions Architect, Alison is now responsible for NVIDIA’s Artificial Intelligence Developer Relations across the EMEA region. She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art & sharing this around the globe

Timothy Gebhard

Timothy Gebhard is a 3rd year Ph.D. student in the Max Planck ETH Center for Learning Systems. In his research, he is interested in developing machine learning algorithms for (astro-)physics and, more particularly, how such methods can be designed to incorporate existing scientific domain knowledge. Besides his core research, he is also passionate about reproducible research and software development for science, as well as science communication and public outreach.