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A common theme in mathematics is that limits of finite objects are well behaved. This allows one to prove many theorems about finitely approximable objects, while leaving the general case open — examples for this are Gottschalk's conjecture, Kaplansky's direct finiteness conjecture, and many more. When the topology of the space is somewhat coarse, it becomes very hard to decide whether every object is approximable by finite ones, or whether there exist non-approximable objects. Some of the more famous problems in various fields of mathematics can be framed this way

In the previous talk we defined Subgroup Tests and the interactive proof system induced by them. In addition, we showed that if the Aldous--Lyons conjecture was true, then this interactive proof system contains only decidable languages. In this talk, we describe why the Halting Problem can be decided in our interactive proof system, which in turn refutes the Aldous--Lyons conjecture. This is done in two steps: The first relates Subgroup Test to a new subclass of non-local games which we term Tailored Games. The second shows that the techniques of MIP*=RE can be refined so that all the games in it are tailored, or in acronym fashion, that Tailored-MIP*=RE.

This talk is based on a joint work with Lewis Bowen, Alex Lubotzky and Thomas vidick.

Designing novel drugs can be tricky. The accumulated knowledge on the cause and progress is key in the development. Often there is a key protein or receptor that plays a key role. One strategy is to find a molecule that will strongly bind to its target and inhibit its activity.

In the Nir London Lab, we focus on a unique class of drugs called covalent drugs. These molecules act by forming a stable, covalent bond with their target protein. One important, recent example (not from our lab) is Paxlovid, an antiviral covalent drug against SARS-Cov-2 that inhibits an enzyme crucial for its replication. In this talk, I will describe the computational and experimental approaches we use to design and characterize novel covalent drugs and present two projects in which we combined covalent chemistry with two other emerging drug classes: targeted degraders and peptides.

Physiology or Medicine
Victor Ambros and Gary Ruvkun discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery in the small worm C. elegans revealed a completely new principle of gene regulation. This turned out to be essential for multicellular organisms, including humans. MicroRNAs are proving to be fundamentally important for how organisms develop and function.

Physics
John Hopfield introduced a spin model that can store and reconstruct information. Geoffrey Hinton built on Hopfield’s idea to invent the Boltzmann Machine, that is able to learn from examples to reconstruct a set of desired patterns. He also popularized and improved Backpropagation of Errors, a method actually used in today’s advanced AI technology (e.g. Deep Learning).

Chemistry
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.

Do we live in a simulation? Perhaps we should consider the possibility. Replicating real-world observations into a digital twin offers numerous potential benefits. For instance, in autonomous navigation, one could recreate safety-critical scenarios to test an agent's behavior more efficiently and without risking human lives. True-to-life simulations can enable counterfactual analysis, aiding in the interpretability of AI decision-making. Furthermore, they allow us to relive captured experiences for immersive entertainment.

In my research, I develop tools that enable these capabilities through the reconstruction of scene properties, such as geometry and color, and through perception methods like 3D scene segmentation and 3D object detection. This also includes the controllable generation and manipulation of content. To ensure scalability, my focus is particularly on representations that respect the symmetries in the data, such as rotation equivariance, and that can leverage large datasets at scale by minimizing the need for supervision.

Among these topics, in this talk, I will specifically highlight several of my recent papers. These include studies on 3D object detection from single images [1], neural fields for dynamic outdoor scene reconstruction [2], and piecewise equivariant representations [3].

[1] 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features. Chenfeng Xu, Huan Ling, Sanja Fidler, Or Litany. CVPR 2024

[2] Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering, Ido Sobol, Chenfeng Xu, Or Litany. NeurIPS 2024

[3] EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision. Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, Yue Wang. ICLR 2024

Bio: Or Litany is a Senior Research Scientist at the NVIDIA Toronto AI research team, and an Assistant Professor at the Technion where he leads the LIT-Lab specializing in 3D computer vision and generative AI. He is an Azrieli Faculty Fellow and a Taub Fellow. Previously, he conducted postdoctoral research at Stanford University under the guidance of Prof. Leonidas Guibas, and at Meta AI Research (FAIR), where he was hosted by Prof. Jitendra Malik.