Publications - Current Year

Data-driven generation of molecules with desired properties, also known as
inverse molecular design (IMD), has attracted significant attention in recent
years. Despite the significant progress in the accuracy and diversity of
solutions, existing IMD methods lag behind in terms of trustworthiness. The
root issue is that the design process of these methods is increasingly more
implicit and indirect, and this process is also isolated from the native
forward process (NFP), the ground-truth function that models the molecular
dynamics. Following this insight, we propose TrustMol, an IMD method built to
be trustworthy. For this purpose, TrustMol relies on a set of technical
novelties including a new variational autoencoder network. Moreover, we propose
a latent-property pairs acquisition method to effectively navigate the
complexities of molecular latent optimization, a process that seems intuitive
yet challenging due to the high-frequency and discontinuous nature of molecule
space. TrustMol also integrates uncertainty-awareness into molecular latent
optimization. These lead to improvements in both explainability and reliability
of the IMD process. We validate the trustworthiness of TrustMol through a wide
range of experiments.

Haptic feedback contributes to immersive virtual reality (VR) experiences.
Designing such feedback at scale, for all objects within a VR scene and their
respective arrangements, remains a time-consuming task. We present Scene2Hap,
an LLM-centered system that automatically designs object-level vibrotactile
feedback for entire VR scenes based on the objects' semantic attributes and
physical context. Scene2Hap employs a multimodal large language model to
estimate the semantics and physical context of each object, including its
material properties and vibration behavior, from the multimodal information
present in the VR scene. This semantic and physical context is then used to
create plausible vibrotactile signals by generating or retrieving audio signals
and converting them to vibrotactile signals. For the more realistic spatial
rendering of haptics in VR, Scene2Hap estimates the propagation and attenuation
of vibration signals from their source across objects in the scene, considering
the estimated material properties and physical context, such as the distance
and contact between virtual objects. Results from two user studies confirm that
Scene2Hap successfully estimates the semantics and physical context of VR
scenes, and the physical modeling of vibration propagation improves usability,
perceived materiality, and spatial awareness.