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Molecular docking is a critical process in structure-based drug discovery to predict the binding conformations between a protein and a small molecule ligand. Recently, deep learning-based methods have achieved promising performance over traditional physics-based search-and-score methods. Despite their success on accurately predicting the binding poses of the small molecule ligands, modeling of protein flexibility

The main challenge of offline reinforcement learning, where data is limited, arises from a sequence of counterfactual reasoning dilemmas within the realm of potential actions: What if we were to choose a different course of action? These circumstances frequently give rise to extrapolation errors, which tend to accumulate exponentially with the problem horizon. Hence, it becomes crucial to acknowledge that

Numerous Natural Language Processing (NLP) tasks require precisely labeled data to ensure effective model training and achieve optimal performance. However, data annotation is marked by substantial costs and time requirements, especially when requiring specialized domain expertise or annotating a large number of samples. In this study, we investigate the feasibility of employing large language models (LLMs

Edge device participation in federating learning (FL) has been typically studied under the lens of device-server communication (e.g., device dropout) and assumes an undying desire from edge devices to participate in FL. As a result, current FL frameworks are flawed when implemented in real-world settings, with many encountering the free-rider problem. In a step to push FL towards realistic settings, we

Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest. However, they struggle to retain that performance when the data characteristics changes. In this paper, we focus on continual learning, where a pre-trained transformer is updated to perform well on new data, while retaining its performance on data it was previously

Developing a client-side segmentation algorithm for online sports streaming holds significant importance. For instance, in order to assess the video quality from an end-user perspective such as artifact detection, it is important to initially segment the content within the streaming playback. The challenge lies in localizing the content due to the intricate scene changes between content and non-content

Integrating structured knowledge into language model representations increases recall of domain-specific information useful for downstream tasks. Matching between knowledge graph entities and text entity mentions can be easily performed when entity names are unique or there exists entity linking data. When extending this setting to new domains, newly mined knowledge contains ambiguous and incorrect information

Randomized Control Trials (RCTs) are widely used across Amazon to causally estimate impacts of proposed feature changes, in order to make data-driven launch decisions. A key element of experimental design is the level of randomization, and the choice often relies on the cross-unit interaction structure. For instance, in the context of advertiser experiments, a treatment may affect the outcome of control

There are many experimental settings that may suffer from cross-unit (customers, seller, advertiser, etc.) spillovers, for instance through network effects. Such effects introduce bias and prevent the experimenter from drawing trustworthy insights on the data. One approach to dealing with such spillovers is to group units into clusters and randomize treatment status at the cluster level. Examples of clusters

Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives. This downstream performance can further be enhanced by full-scale fine-tuning which is often compute intensive, requires large labelled data, and can reduce out-of-distribution

Software development engineer

Sports highlights are an important form of media for fans worldwide, as they provide short videos that capture key moments from games, often accompanied by the original commentaries of the game’s announcers. However, traditional forms of presenting sports highlights have limitations in conveying the complexity and nuance of the game. In recent years, the use of Large Language Models (LLMs) for natural language

Style transfer for human face has been widely researched in recent years. Majority of the existing approaches work in 2D image domain and have 3D inconsistency issue when applied on different viewpoints of the same face. In this paper, we tackle the problem of 3D face style transfer which aims at generating stylized novel views of a 3D human face with multi-view consistency. We propose to use a neural radiance

We introduce Alexa Arena, a user-centric simulation platform to facilitate research in building assistive conversational embodied agents. Alexa Arena features multi-room layouts and an abundance of interactable objects. With user-friendly graphics and control mechanisms, the platform supports the development of gamified robotic tasks readily accessible to general human users, allowing high-efficiency data

The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri relies on accurate spoken-language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding the user’s intent from an imperfect spoken recognition result. However, due to the scarcity of non- English

Data Scientist

Applied Scientist