Integrated Calendar

The remarkable properties of excitons in transition-metal-dichalcogenides (TMDs), together with the ability to readily control their charge carriers, have attracted a significant amount of interest in recent years. Despite the atomic dimensions of the hosting 2D semiconductors, TMD excitons exhibit strong interaction with light, both in absorption and photoemission processes, and practically dominate the optical response of these 2D materials. In this talk, I will introduce several approaches for achieving extremely strong light-exciton interactions. First, by optical and electrical manipulation of TMD excitons inside a van der Waals heterostructure cavity [1], second, via the formation of highly-confined, in-plane exciton polaritons [2], and third, through the realization of valley-polarized hyperbolic exciton polaritons [3].
These enhanced light–exciton interactions may provide a platform for studying excitonic phase-transitions, quantum nonlinearities and the enablement of new possibilities for 2D semiconductor-based optoelectronic devices.
[1] I. Epstein et al, "Near-unity Light Absorption in a Monolayer WS2 Van der Waals Heterostructure Cavity", Nano letters 20 (5), 3545-3552 (2020).
[2] I. Epstein et al, "Highly Confined In-plane Propagating Exciton-Polaritons on Monolayer Semiconductors", 2D Materials 7, 035031 (2020).
[3] T. Eini, T. Asherov, Y. Mazor, and I. Epstein, "Valley-polarized Hyperbolic Exciton Polaritons in Multilayer 2D Semiconductors at Visible Frequencies", Phys. Rev. B 106, L201405 (2022).

Natural products have provided inspiration for chemical biology and medicinal chemistry research. Their success raises the fundamental question whether the particular structural and biological properties of natural products can be translated to structurally less demanding compounds, readily accessible by chemical synthesis and yet still endowed with pronounced bioactivity.
The lecture will describe a logic for the simplification of natural product structure by means of “Biology Oriented Synthesis” (BIOS) and its evolution into the “Pseudo Natural Product” (PNP) concept. Pseudo-natural products can be regarded as the human-made equivalent of natural product evolution, i.e. the chemical evolution of natural product structure. Application of natural product inspired compound collections designed and synthesized following these principles in cell-based phenotypic assays and subsequent identification of the cellular target proteins demonstrate that the BIOS and PNPs may enable innovation in both chemical biology and medicinal chemistry research.

Click chemistry has revolutionized many facets of the molecular sciences, with the realization of reactions that are ‘‘modular, wide in scope, give very high yields, generate only inoffensive byproducts that can be removed by nonchromatographic methods and are stereospecific”. Yet surprisingly little attention has been given to the development of intrinsically chiral click reactions (potentially enantiospecific, rather than ‘only’ enantioselective due to chiral auxiliary groups), while the modularity of many click reactions is best compared to one-dimensional LEGO. Of course, much could be done within the constraints – hence forementioned revolution – but it drove attention towards an extension of available click chemistries. Kolb, H. C.; Finn, M.; Sharpless, K. B., Click chemistry: diverse chemical function from a few good reactions. Angew. Chem. Int. Ed. 2001, 40, 2004-2021.
The talk will focus on the resulting investigations in the field of S(VI) exchange chemistry, with specific emphasis on two fields: a) the development of the intrinsically enantiospecific click reactions and their use to e.g. make synthetic polymers with 100% backbone chirality that combine stability & degradabbility, and b) the development of multimodular click chemistry and single-polymer studies by a combination of AFM, TEM, scanning Auger microscopy

"Mechanistic studies of human disease-related biochemistry typically rely on animal models to devise hypotheses and conduct functional testing. The success of this approach is conditioned on conservation of biochemical pathways between humans and the animal, and the ability of the model to recapitulate key features of human disease which is rare . This is rarely true for complex human conditions such as neurological and cardiovascular diseases. In the absence of a suitable animal model, the study of human diseases has been limited to analysis of associations between clinical outcomes and physiological and/or molecular traits. Using the recent availability of multi-dimensional data from very large human cohorts we have devised principally novel approaches to identify associations of biochemicals with existing biochemical pathways in the context of human disease. This new ability allowed us to formulate a new paradigm akin to Koch postulates but applied to mechanistic component identification of complex disease. It relies on identification of putative disease drivers from human data, verification of these findings in animal models, deriving novel mechanism-related associations from the animal model, and back-testing the new associations in human data. This workflow is much more likely to correctly reflect shared biology between the animal model and humans as it pertains to disease, and thus serve as a true tool for mechanistic biochemical research."

Tensor networks describe high-dimensional tensors succinctly, in terms of a network or graph of local data. Many interesting tensors arise in this way -- from many-body quantum states in physics to the matrix multiplication tensors in algebraic complexity. While widely successful, the structure of tensor networks is still only partially understood. In this talk, I will give a gentle introduction to tensor networks and explain some recent advances in their theory. In particular, we will discuss the significance of the so-called “fundamental theorem”, which is at the heart of much of the success of tensor networks, and explain how to generalize it to higher dimensions. Before our work, "no go" results suggested that such a generalization might not exist!! Along the way, we will see how to turn an undecidable problem into one that admits an algorithmic solution. To achieve this we draw on recent progress in theoretical computer science and geometric invariant theory.