Rising Stars in AI 2024

Rising Stars in AI Symposium 2024

Following the resounding success of our previous Annual "Rising Stars in AI" Symposia, including the 2022 and 2023 editions, the AI Initiative at KAUST (King Abdullah University of Science and Technology), located on the scenic Red Sea coast, is thrilled to announce the third installment of this Symposium, scheduled for February 19th to 21st, 2024. This event is tailor-made for emerging researchers, including PhD students, PostDocs, and early career faculty, who have recently made substantial contributions to the field of AI. It presents a remarkable opportunity for participants to engage in lively discussions and share cutting-edge AI research concepts. Our Symposium organizing committee will meticulously curate a selection of speakers based on their outstanding research profiles. Chosen presenters will have the privilege of showcasing their work, which has been recently accepted at prestigious AI conferences like NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, EMNLP, ACL, among others. Join us for a dynamic exploration of the latest advancements in AI research at the highly anticipated third edition of our "Rising Stars in AI" Symposium.

Join us in creating an unforgettable experience by applying to be a speaker at the upcoming "Rising Stars in AI" Symposium, where your groundbreaking research will inspire and engage our global audience. The event will be entirely in-person, with flights and hotel expenses covered for the selected speakers.

The applications as speakers are now closed.

Call for spotlight and poster presentations

We invite KAUST researchers to showcase their recently published AI work in spotlight and poster sessions during the Symposium. Selected applicants will be asked to prepare 2-3 slides for a two-minute presentation and a poster for a 40-minute poster session. If you are interested in presenting your work, please complete this form.

Deadline for spotlight and poster application: February 11, 2024

Notification of spotlight and poster acceptance: February 13, 2024

Attending the Symposium

The symposium will be limited to in-person attendance. It will be held on February 19-21, 2024 (Save the date) at Building 19, Halls 1, 2 and 3. To attend the symposium, please register by filling out this form.


Arabian Standard Time (UTC+3)

Day 1 - Monday, February 19th, 2024

Time Speaker Presentation
08:30 – 09:00 Breakfast
09:00 – 09:10 Welcoming Remarks
09:10 – 09:30 Adil Salim (Microsoft Research) Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
09:30 – 09:50 Chun-Mei Feng (A*STAR) Large Pretrained Models as Catalysts in Federated Learning
09:50 – 10:10 Umang Bhatt (New York University) Algorithmic Resignation
10:10 – 10:40 Coffee Networking Break
10:40 – 11:00 Yisen Wang (Peking University) Theoretical Understanding of Self-Supervised Learning
11:00 – 11:20 Shangtong Zhang (University of Virginia) On the Cheating of Offline Reinforcement Learning
11:20 – 11:40 Alhussein Fawzi (Google DeepMind) Discovering new algorithms with AI
11:40 – 12:00 Sihong He (University of Connecticut ) Robust Multi-Agent Reinforcement Learning and Its Application in Cyber-Physical Systems
12:00 – 14:00 Lunch Break
14:00 – 14:10 AI Initiative - Overview
14:10 – 14:30 Hadi Salman (OpenAI) Adversarial Examples Beyond Security
14:30 – 14:50 Jindong Wang (Microsoft Research) Understanding LLMs: Evaluation, Enhancement, and Interdisciplinary Research
14:50 – 15:10 Yanning Dai (Beihang University) Reinforcement Learning Enabled Personalized Motor Disease Rehabilitation Program Design
15:10 – 15:50 Coffee Networking Break
15:50 – 16:50 Spotlight Presentations
16:50 – 17:30 Poster Session

Day 2 - Tuesday, February 20th, 2024

Time Speaker Presentation
08:30 – 09:00 Breakfast
09:00 – 09:10 Bernard Ghanem - Overview
09:10 – 09:30 Jonathon Luiten (Meta Reality Labs) Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis
09:30 – 09:50 Raaz Dwivedi (Cornell Tech) Kernel Thinning
09:50 – 10:10 Jun Xia (Westlake University and Zhejiang University) Deciphering Biochemical Codes with Foundation Models
10:10 – 10:40 Coffee Networking Break
10:40 – 11:00 Qian Liu (Sea AI Lab) LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
11:00 – 11:20 Hao-Wen Dong (University of California San Diego) Learning Text-to-audio Synthesis from Videos
11:20 – 11:40 Mengyue Yang (University College London) Causal Representation Learning: Environment Understanding and Counterfactual Simulation
11:40 – 12:00 Jane Dwivedi-Yu (Meta) Teaching Language Models to Use Tools
12:00 – 14:00 Lunch Break
14:00 – 14:10 Peter Richtarik - Overview
14:10 – 14:30 Yifan Zhang (National University of Singapore) Expanding Small-Scale Datasets with Guided Imagination
14:30 – 14:50 Yue Hu (Shanghai Jiao Tong University) Communication-efficient collaborative perception
14:50 – 15:10 Shilong Liu (Tsinghua University) Object Detection in 20 Years: The Evolution of Anchors

Day 3 - Wednesday, February 21th, 2024

Time Speaker Presentation
08:30 – 09:00 Breakfast
09:00 – 09:10 Jürgen Schmidhuber - Overview
09:10 – 09:30 Yu Zeng (Johns Hopkins University) Learning to synthesis images from multi-modal and hierarchical inputs
09:30 – 09:50 Yupan Huang (Sun Yat-sen University) TextDiffusers: Diffusion and Language Models as Text Painters
09:50 – 10:10 Joanna Materzynska (Massachusetts Institute of Technology) Customizing Motion in Text-to-Video Diffusion Models
10:10 – 10:40 Coffee Networking Break
10:40 – 11:00 Anant Raj (INRIA) Algorithmic Stability of Heavy-Tailed SGD
11:00 – 11:20 Difan Zou (The University of Hong Kong) (Possible) Theoretical Explanation for Interesting Phenomenon in Training Neural Networks
11:20 – 11:40 Rustem Islamov (University of Basel) Unified Analysis of Asynchronous SGD
11:40 – 12:00 Bohan Wang (MSR Asia & University of Science and Technology of China) On the convergence analysis of Adam: recent advances and horizons
12:00 – 14:00 Lunch Break
14:00 – 14:10 Francesco Orabona - Overview
14:10 – 14:30 Felix Petersen (Stanford University) Learning with Differentiable Relaxations
14:30 – 14:50 Shiwei Liu (University of Oxford ) Fantastic Sparse Neural Networks and Where to Find Them
14:50 – 15:10 Wei Jin (Emory University) Deep Learning on Graphs: A Data-Centric Exploration
15:10 – 15:50 Coffee Networking Break
15:50 – 16:50 Spotlights
16:50 – 17:30 Poster


Scientific Committee

Silvio Giancola

Francesco Faccio

Avetik Karagulyan

Prof. Francesco Orabona

Prof. Bernard Ghanem

Administrative Team

Tagreed Khalil

Liliana Rivera