The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster perform...

Finding and developing new molecules is one of the great research endeavors of modern chemistry. From the development of new drugs to the creation of more sustainable materials, everything depends on finding new combinations of atoms with useful properties.

The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster performance with up to an order of magnitude fewer parameters.

The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster performance with up to an order of magnitude fewer parameters.

Abstract. Machine learning offers a promising path to annotating the large number of unidentified MS/MS spectra in metabolomics, addressing the limited cov

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult...

The highlights include but are not limited to the research areas of electronics, optoelectronics, computing technologies and theories, soft matter physics, ...

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

Internal emails from 2021 reveal tensions among researchers hunting for elusive Majorana particle

A new theoretical framework reveals universal principles governing network flows, predicting a threshold where flow becomes unsustainable and uncovering how dissipation can enhance performance in cert...

Assistant/Associate Professor in Statistical Learning

Networks are everywhere—from social connections among individuals, neural connections in brains, interactions among proteins in biological cells, to communication channels within large organizations.

Consider K network observations, with N nodes each and adjacency matrices {Ak; , k = 1, …, K }. We consider networks that are directed and with binary edges (that is, we just consider the presence or absence of connection...

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

Children’s cognitive abilities and social preferences separate in the later school years, with work-related and leisure-related peer choices diverging at older ages, indicating that older students lik...

Modeling human mobility is key for urban planning, sustainability, public health, and economic development. The authors show that simple machine-learned closed-form models are as predictive of mobilit...