Integrated Calendar

The kinetics of protein–DNA recognition, along with its thermodynamic properties, including affinity and specificity, play a central role in shaping biological function. Protein–DNA recognition kinetics are characterized by two key elements: the time taken to locate the target site amid various nonspecific alternatives; and the kinetics involved in the recognition process, which may necessitate overcoming an energetic barrier. In my presentation, I will describe the complexity of protein-DNA kinetics obtained from molecular coarse-grained simulations of various protein systems. The kinetics of protein-DNA recognition are influenced by various molecular characteristics, frequently necessitating a balance between kinetics and stability. Furthermore, protein-DNA recognition may undergo evolutionary optimization to accomplish optimal kinetics for ensuring proper cellular function.

The skeletal muscle tissue that allows our bodies to move, is comprised of enormous muscle fibers, termed myofibers. Myofibers must grow with our body and adapt to its needs throughout life. This is accomplished by adding nuclei via cell-to-cell fusion. However, the fusion mechanism is poorly understood. To gain a better understanding of the fusion and repair mechanisms I recapitulated myoblast-to-myofiber fusion in culture, which allowed me for the first time to visualize the fusion and regeneration processes at high resolution, generating the seminal observations that form the central hypothesis for my PhD.

: I’ll describe two projects:
In the first, we examined the circuitry that underlies olfaction in a mouse model with severe developmental degeneration of the OB. The olfactory bulb (OB) is a critical component of mammalian olfactory neuroanatomy. Beyond being the first and sole relay station for olfactory information to the rest of the brain, it also contains elaborate stereotypical circuitry that is considered essential for olfaction. In our mouse model, a developmental collapse of local blood vessels leads to degeneration of the OB. Mice with degenerated OBs could perform odor-guided tasks and even responded normally to innate olfactory cues. I will describe the aberrant circuitry that supports functional olfaction in these mice.
The second project focusses on the nucleus of the lateral olfactory tract. This amygdaloid nucleus is typically considered part of the olfactory cortex, yet almost nothing is known about its function, connectivity, and physiology. I will describe our approach to studying this intriguing structure and will present some of its cellular and synaptic properties that may guide hypotheses about its function.

Innovative battery electrode materials are essential for unlocking the full potential of Li-ion batteries in various aspects of modern life. A primary focus is identifying novel materials with greater elemental diversity that offer improved stability, rapid charge capabilities, and high performance. Promising candidates, like early transition metal oxides, are earth-abundant and present opportunities for next-generation anode materials due to their redox voltage and more than a single stable oxidation state.
Exploring fundamental design principles for improved de/lithiation mechanisms will influence battery functionality and advance energy storage capabilities. The first part will delve into the impact of the insulator-metal transition during lithiation, focusing on two distinctive Wadsley-Roth (WR) structures. Our findings underscore the critical role of disorder within these structures in determining kinetics and retained capacities for these anodes. The second part proposes a novel strategy leveraging the induction effect to reduce the operation voltage of Mo-oxide-based anodes. This reduction opens the door for Mo-based oxide anodes as an alternative to graphene. Understanding these key aspects can guide the search for alternatives to existing anodes for advancing the development of Li-ion batteries with enhanced performance in the energy storage field.

We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f(f(z))=f(z). The proposed model f is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namely f(x)=x. We define the target manifold as the set of all instances that f maps to themselves. (2) Instances that form the source distribution should map onto the defined target manifold. This is achieved by optimizing the idempotence term, f(f(z))=f(z) which encourages the range of f(z) to be on the target manifold. Under ideal assumptions such a process provably converges to the target distribution. This strategy results in a model capable of generating an output in one step, maintaining a consistent latent space, while also allowing sequential applications for refinement. Additionally, we find that by processing inputs from both target and source distributions, the model adeptly projects corrupted or modified data back to the target manifold. This work is a first step towards a ``global projector'' that enables projecting any input into a target data distribution. Work done at UC Berkeley

The Origin Recognition Complex (ORC) seeds the replication-fork by binding DNA replication origins, which in budding yeast contain a 17bp DNA motif. High resolution structure of the ORC-DNA complex revealed two base-interacting elements: a disordered basic patch (Orc1-BP4) and an insertion helix (Orc4-IH).
To define ORC elements guiding its DNA binding in-vivo, we mapped genomic locations of 38 designed ORC mutants. We revealed that different ORC elements guide binding at different motifs sites, and these correspond only partially to the structure- described interactions. In particular, we show that disordered basic patches are key for ORC-motif binding in-vivo, including one lacking from the structure. Finally i will discuss how those disordered elements, which insert into the minor-groove can still guide specific ORC-DNA recognition.

Recent advancements in audio and language processing have yielded significant progress in audio analysis and synthesis. In the realm of audio analysis, researchers are addressing the crucial challenges of Automatic Speech Recognition (ASR), Sound Localization, Event Detection, Emotion Recognition, Speaker Diarization, and Speaker Identification. Meanwhile, in the synthesis domain efforts are focused on Speech Synthesis, Speech Separation, and Audio Vocoders. Despite the progress made, there remains a significant void in the advancement of neural audio generative models that possess the capability to understand audio landscapes and skillfully create or improve new auditory surroundings. In this talk, I will address two pivotal research directions aimed at closing this gap: 

(i) The development of an oracle-powered speechbot involves achieving a profound understanding of the acoustic environment and integrating comprehensive world knowledge. I'll present Spectron, a speechbot that leverages a Large Language Model (LLM) to perform question answering (QA) and speech continuation.

(ii) The second challenge revolves around audio separation for a multitude of sources. While current audio separation literature predominantly focuses on isolating single-source domains like speech or sound events, the real-world scenario demands the separation of diverse sources such as speech, noise, and acoustic events. I will present a solution capable of separating numerous speakers based on a single microphone recording as well as a theoretical upper bound for the single channel speech separation.

Concluding the discussion, I will outline future research directions, focusing on the evolution of multi-agent speechbots, the advancement of generative audio models within the 3D domain, and the fusion of synthetic sounds into real-world environments.

Short Bio:

Eliya Nachmani currently serves as a research scientist at Google Research, specializing in machine learning for audio processing. Prior to his role at Google, he conducted research at Facebook AI Research (FAIR) and pursued his Ph.D. at Tel-Aviv University. Eliya holds a Master of Science in Electrical Engineering from Tel-Aviv University and a Bachelor of Science in Electrical Engineering from the Technion. Website: https://sites.google.com/view/eliya-nachmani/home