BLISS AI Speaker Series 2025

Join our free BLISS Speaker Series Summer 2025!

We are excited to feature Daniel Duckworth, Senior Research Software Engineer at Google DeepMind, who will discuss "Radiance Fields are Dead (and why that’s OK)", lasting approximately 45 minutes. After the talk, seize the opportunity to connect with fellow AI enthusiasts to share ideas and questions while enjoying free drinks and pizza. Door close by 7.15pm, so please come early! Also, "attend"ing (RSVP) here on Meetup is strictly necessary to be guaranteed entry.
Please note that Meetup has recently been quite keen on promoting its Plus program. However, you are not obligated to purchase it, as both our events and the platform remain free.

Who is this event for?
This event is open to everyone interested in state-of-the-art AI research. We especially design it for students, PhD candidates, academic researchers, and industry professionals with a research focus in machine learning.

Abstract: Our innate ability to reconstruct the 3D world around us from our eyes alone is a fundamental part of human perception. For computers, however, this task remained a significant challenge — until the advent of Neural Radiance Fields (NeRFs). Upon their introduction, NeRFs marked a paradigm shift in the field of novel view synthesis, demonstrating huge improvements in visual realism and geometric accuracy over prior works. The subsequent proliferation of NeRF variants has only expanded their capabilities, unlocking larger scenes, achieving even higher visual fidelity, and accelerating both training and inference. Nevertheless, NeRF is no longer the tool of choice for 3D reconstruction. Why? Join a researcher from the front lines as we explore NeRF’s foundations, dissect its strengths and weaknesses, see how the field has evolved, and explore the future of novel view synthesis.

Join our free BLISS Speaker Series Summer 2025!

We are excited to feature Dr. Adel Bibi, Senior Researcher at University of Oxford, who will discuss "Progress in AI Safety and Security", lasting approximately 45 minutes. After the talk, seize the opportunity to connect with fellow AI enthusiasts to share ideas and questions while enjoying free drinks and pizza. Door close by 7.15pm, so please come early! Also, "attend"ing (RSVP) here on Meetup is strictly necessary to be guaranteed entry.
Please note that Meetup has recently been quite keen on promoting its Plus program. However, you are not obligated to purchase it, as both our events and the platform remain free.

Who is this event for?
This event is open to everyone interested in state-of-the-art AI research. We especially design it for students, PhD candidates, academic researchers, and industry professionals with a research focus in machine learning.

Abstract: TBD

Bio: Adel Bibi is a senior researcher in machine learning and computer vision at the Department of Engineering Science since 2023, University of Oxford, a Research Member of the Common Room at Kellogg College, and a member of the ELLIS Society. Bibi is an R&D Distinguished Advisor with Softserve. Previously, he was a senior research associate and postdoctoral researcher with Philip H.S. Torr. He received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) in 2016 and 2020, respectively, advised by Bernard Ghanem. Bibi was awarded an Amazon Research Award in 2022 in the Machine Learning Algorithms and Theory track, in addition to receiving the Google Gemma 2 Academic Award in 2024. He was also awarded the Systematic AI Safety Award by the UK AI Safety Institute and EPSRC in 2025. Bibi received four best paper awards: a NeurIPS23 workshop, an ICML23 workshop, a 2022 CVPR workshop, and one at the Optimization and Big Data Conference in 2018. His contributions include over 30 papers published in top machine learning and computer vision conferences. He has also received four outstanding reviewer awards (CVPR18, CVPR19, ICCV19, ICLR22) and a Notable Area Chair Award at NeurIPS23. Currently, Bibi is leading a group in Oxford focusing on the intersection between AI safety of large foundational models in both vision and language (covering topics such as robustness, certification, alignment, adversarial elicitation, etc.) and the efficient continual update of these models.

Join our free BLISS Speaker Series Summer 2025!

We are excited to feature Jannik Kossen, Research Scientist at Meta AI and PhD Student at University of Oxford, who will discuss "Detecting Hallucinations in Large Language Models Using Semantic Entropy", lasting approximately 45 minutes. After the talk, seize the opportunity to connect with fellow AI enthusiasts to share ideas and questions while enjoying free drinks and pizza. Door close by 7.15pm, so please come early! Also, "attend"ing (RSVP) here on Meetup is strictly necessary to be guaranteed entry.
Please note that Meetup has recently been quite keen on promoting its Plus program. However, you are not obligated to purchase it, as both our events and the platform remain free.

Who is this event for?
This event is open to everyone interested in state-of-the-art AI research. We especially design it for students, PhD candidates, academic researchers, and industry professionals with a research focus in machine learning.

Abstract: Large language models can 'hallucinate' factually incorrect outputs, presenting significant risks for their adoption to high-stakes applications. Jannik will present joint work recently published in Nature (https://www.nature.com/articles/s41586-024-07421-0) on detecting hallucinations in large language models using semantic entropy, which mitigates hallucinations by quantifying the model's own uncertainty over the meaning of generations. He will also discuss a recent pre-print (https://arxiv.org/abs/2406.15927) that proposes a method to drastically reduce the cost of uncertainty quantification in LLMs by predicting semantic entropy from latent space, and he may ramble about uncertainties in LLMs more generally.

Bio: Jannik is an AI research scientist at Meta FAIR, building LLMs for code generation. At some point, the University of Oxford, where he worked on uncertainty and data-efficiency in vision and language models, will probably grant him a PhD. He previously studied Physics in Bremen and Heidelberg, has interned at Google and DeepMind, but now lives in Berlin.