van der Schaar Lab

WeCREATE: Inspiration Session 3 — Responsible AI

18 May 2023 saw WeCREATE Inspiration Session 3, focusing on the important and relevant topic of Responsible AI.

In this session we discussed the challenges and the growing importance of Responsible AI, especially given the recent rise of LLMs and beyond. The discussion with our three panelists was followed by a fantastic audience Q&A.

We had three fantastic speakers joining us for this session:

The session was hosted by: Prof Mihaela van der Schaar
Moderated by: Evgeny Saveliev

Watch the Recording

WeCREATE Inspiration Session 3 – Responsible AI

About the Panelists

Dr Jenn Wortman Vaughan

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. She currently focuses on Responsible AI—including transparency, interpretability, and fairness—as part of MSR’s FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn’s research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn’s 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a variety of best paper awards. Jenn co-founded the Annual Workshop for Women in Machine Learning (WiML), which has been held each year since 2006, and recently served as Program Co-chair of NeurIPS 2021.

Dr Lea Goetz

Lea is a Senior AI/ML Engineer working on responsible AI in drug discovery and clinical applications. She initially joined as an AI Fellow, where her research focused on unsupervised representation learning, causal discovery and uncertainty estimation on real-world datasets.
Lea received her PhD in computational neuroscience from University College London, where she focused on the dendrites of single neurons as biological substrate for sparse input representations and learning algorithms. Prior to her PhD she studied Natural Sciences at the University of Cambridge.

Dr Emily Denton

Emily Denton (they/them) is a Staff Research Scientist at Google, within the Technology, AI, Society, and Culture team, where they study the sociocultural impacts of AI technologies and conditions of AI development. Their recent research centers on emerging text- and image-based generative AI, with a focus on data considerations and representational harms. Prior to joining Google, Emily received their PhD in Computer Science from the Courant Institute of Mathematical Sciences at New York University, where they focused on unsupervised learning and generative modeling of images and video. Prior to that, they received their B.S. in Computer Science and Cognitive Science at the University of Toronto. Though trained formally as a computer scientist, Emily draws ideas and methods from multiple disciplines and is drawn towards highly interdisciplinary collaborations, in order to examine AI systems from a sociotechnical perspective.

Purpose of WeCREATE

  • Inspire female students and young professionals to embrace creative careers in Engineering, Comp Sci, Applied Maths and beyond.
  • Dispelling widely held myths and established views about these areas.
  • Highlight a diverse range of amazing female role models making their mark on the world.
  • Created for women, and open to all – no-one should be discouraged from pursuing AI and machine learning.

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Whether you’re a high school student wondering about your next big step into higher education, an undergraduate considering further study or career options outside academia, or a postgrad or professional looking for inspiration, we hope you’ll join us for future sessions!

Sign up here.

Evgeny Saveliev

Evgeny is one of the lab’s research engineers, and has been a part-time Ph.D. student since 2021. His educational background is Natural Sciences at the University of Cambridge, followed by postgraduate study in Computer Science at University of Southampton.

Evgeny was an AI Resident at Microsoft Research Cambridge before joining the lab, where he worked on projects covering meta-learning and reinforcement learning as applied to recommender systems. He also has experience in computational finance, having worked in a fintech start-up and commodities trading.

Evgeny facilitates turning the lab’s research code into robust production quality code, making it more scalable, applying software engineering best practices; he also collaborates with our PhD students on some research topics.

He is particularly interested in working on AutoML, ML for time-series, synthetic data, and federated learning.

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.