This panel brings together leading professionals to explore how Generative AI is reshaping technical practices and transforming the way we work in AI and ML. Panelists will dive into how they practically use GenAI tools in their day-to-day work to enhance innovation, productivity, and impact. The discussion will cover both the benefits and risks of these technologies, offering real-world examples, insights into ethical considerations, and practical strategies for when and how to responsibly integrate — or avoid — GenAI in different contexts. In addition, the panel will reflect on personal experiences, the importance of diverse perspectives, and advice for navigating this fast-changing field.
Katie Collins
PhD Student in Machine Learning, University of Cambridge
Katie Collins is a PhD candidate in the Machine Learning Group at the University of Cambridge's Computational and Biological Learning Lab. Her research explores trustworthy machine learning, data-efficient human-machine teaming, and Bayesian probabilistic modeling, with a focus on applied computational cognitive science and human-AI interaction. She is also a visiting student in Josh Tenenbaum's Computational Cognitive Science Group at MIT. Katie holds an MPhil in Machine Learning and Machine Intelligence from Cambridge and a BSc in Brain and Cognitive Sciences from MIT, where she also minored in Computer Science and Biomedical Engineering. She is affiliated with the Human-Oriented Automated Theorem Proving Effort led by Sir Tim Gowers and is a Student Fellow at the Leverhulme Centre for the Future of Intelligence. A Marshall Scholar and Cambridge Trust Scholar, Katie also founded the MITxHarvard Women in AI Group.
Savannah McNamara
Information Developer, Arm
Savannah is a Graduate Information Developer working at Arm. Before coming to Arm, her background was in theoretical physics. Seeing the place that computation has in the modern scientific landscape sparked her interest in industry, and led her to Arm. Now, she works on the A-profile architecture team, and is the lead author for the MPAM System Component Specification. Alongside her work on Arms CPU architecture specification, she is leading investigations in training AI models to augment her team’s productivity, and perfect their documentation processes. Outside of her team, she is currently representing Arm at the UN Global Compact Sustainability Initiative, helping to craft sustainability initiatives for Arm that meet Global Development goals.
Stephanie L. Hyland
Principal Researcher, Microsoft Research Health Futures
Dr. Stephanie L. Hyland is a Principal Researcher at Microsoft Research Health Futures in Cambridge, UK, specializing in machine learning applications for healthcare. Her recent work includes developing vision-language and multimodal models for radiology, such as the MAIRA-1 and MAIRA-2 models. Stephanie earned her PhD in Computational Biology and Medicine from the Tri-Institutional PhD Program of Cornell University, conducting research at Memorial Sloan Kettering Cancer Center and later at ETH Zürich. She also holds degrees in theoretical physics from Trinity College Dublin and mathematics from the University of Cambridge.
Tatiana Shavrina
Research Scientist Manager, Meta
Tatiana Shavrina, PhD, is a Research Scientist Manager at Meta's Llama team, focusing on large language models and artificial general intelligence. With over a decade of experience in AI and machine learning, she has held leadership roles at Snap Inc. and the Artificial Intelligence Research Institute. Tatiana earned her PhD in Computational Linguistics from the Higher School of Economics. She is passionate about open-source projects and multilingualism in language models, having led initiatives such as the mGPT project and contributed to the development of BLOOM.
Adeline Bailly
Artificial Intelligence Research Engineer at Helsing AI
Adeline holds a PhD in Machine Learning with Applications in Remote Sensing. She spent several years working for the French Ministry of Defence before joining Helsing as an AI Research Engineer. Her research is centred on machine learning for defence applications. She takes great pride in having one of her models operational on a satellite.
