Beibin Li

• Doctor of Philosophy
• Research Engineer at Microsoft

Failure attribution in LLM multi-agent systems-identifying the agent and step responsible for task failures-provides crucial clues for systems debugging but remains underexplored and labor-intensive. In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems. To support this initiative, we...

Problem definition: Cloud computing is a multibillion-dollar business that draws substantial capital investments from large companies such as Amazon, Microsoft, and Google. Large cloud providers need to accommodate the growing demand for computing resources while avoiding unnecessary overprovisioning of hardware and operational costs. The underlyin...

Dynamic eye-tracking paradigms are an engaging and increasingly used method to study social attention in autism. While prior research has focused primarily on younger populations, there is a need for developmentally appropriate tasks for older children. This study introduces a novel eye-tracking task designed to assess school-aged children’s attent...

Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for op...

Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for op...

We study the efficacy of Small Language Models (SLMs) in facilitating application usage through natural language interactions. Our focus here is on a particular internal application used in Microsoft for cloud supply chain fulfilment. Our experiments show that small models can outperform much larger ones in terms of both accuracy and running time,...

Introduction Much of our understanding of infant psychological development relies on an in-person, laboratory-based assessment. This limits research generalizability, scalability, and equity in access. One solution is the development of new, remotely deployed assessment tools that do not require real-time experimenter supervision. Methods The curr...

The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). This current work extends these findings to preschool...

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and too...

Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial effor...

Aims To compare different patterns of memory impairment in patients with two subtypes of mesial temporal lobe epilepsy (MTLE) and healthy controls. Methods Thirty‐five healthy controls and 41 patients with MTLE were recruited, of which 25 patients were diagnosed as hippocampal sclerosis (HS‐MTLE), and the rest 16 patients were lesion‐negative (MRI...

Detection of melanocytes serves as a critical prerequisite in assessing melanocytic growth patterns when diagnosing melanoma and its precursor lesions on skin biopsy specimens. However, this detection is challenging due to the visual similarity of melanocytes to other cells in routine Hematoxylin and Eosin (H&E) stained images, leading to the failu...

Much of our understanding of infant psychological development relies on in-person, lab-based assessment. This limits research generalizability, scalability, and equity in access. One solution is the development of new, remotely deployed assessment tools which don’t require real-time experimenter supervision. The current nationwide (Sweden) infant t...

BACKGROUND The molecular landscape of adult diffuse glioma has been extensively characterized by gene expression and DNA methylation profiling, but less attention has been paid to somatic copy number alteration (SCNA) data. This study aimed to give a rigorous, survival-focused analysis of glioma genome-wide SCNA data that builds on our previous wor...

Eye tracking (ET) experiments commonly record the continuous trajectory of a subject’s gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as l...

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologist...

Background: Looking pattern differences are shown to separate individuals with Autism Spectrum Disorder (ASD) and Typically Developing (TD) controls. Recent studies have shown that, in children with ASD, these patterns change with intellectual and social impairments, suggesting that patterns of social attention provide indices of clinically meaning...

Background Amorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and act...

Background Eye tracking (ET) is a powerful methodology for studying attentional processes through quantification of eye movements. The precision, usability, and cost-effectiveness of ET render it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder (ASD). Methods The autism biomarkers consortium fo...

Objective: To explore quantitative measurements of the visual attention and neuroelectrophysiological relevance of memory deficits in temporal lobe epilepsy (TLE) by eye tracking and electroencephalography (EEG). Methods: Thirty-four TLE patients and twenty-eight healthy controls were invited to complete neurobehavioral assessments, cognitive oculo...

Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing eye-tracking data, we develop a novel method that creates rich representations of oculomotor scanpaths to facilitate the learning of downstream tasks. The proposed stimul...

Random uniform sampling has been studied in various statistical tasks but few of them have covered the Q-error metric for cardinality estimation (CE). In this paper, we analyze the confidence intervals of random uniform sampling with and without replacement for single-table CE. Results indicate that the upper Q-error bound depends on the sample siz...

Background Combined whole-exome sequencing (WES) and somatic copy number alteration (SCNA) information can separate isocitrate dehydrogenase (IDH)1/2-wildtype glioblastoma into two prognostic molecular subtypes, which cannot be distinguished by epigenetic or clinical features. The potential for radiographic features to discriminate between these mo...

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identi...

Medical imaging is a fundamental part of clinical care that creates informative, noninvasive, and visual representations of the structure and function of the interior of the body. With advancements in technology and the availability of massive amounts of imaging data, data-driven methods, such as machine learning and data mining, have become popula...

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask R-CNN model, our duct-level segmenter tries to ident...

BACKGROUND Previously, we have shown that combined whole-exome sequencing (WES) and genome-wide somatic copy number alteration (SCNA) information can separate IDH1/2-wildtype glioblastoma into two prognostic molecular subtypes (Group 1 and Group 2) and that these subtypes cannot be distinguished by epigenetic or clinical features. However, the pote...

PURPOSE Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and...

In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category. Eliminating some groups of variables can expedite the process of data acquisition and avoid over-fitting. Researchers have used the group lasso...

In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence. Our system classifies ASD using representations of different facial attributes from convolutional neural networks, which are trained on images in the...

Most studies of executive function (EF) in Autism Spectrum Disorder (ASD) focus on cognitive information processing, emphasizing less the social interaction deficits core to ASD. We designed a mobile game that uses social and nonsocial stimuli to assess children’s EF skills. The game comprised three components involving different EF skills: cogniti...

Augmentative and Alternative Communication (AAC) apps are apps that enable non-speech communicative forms. One class of AAC apps are speech-generating devices (SGDs), where icons/pictures are tapped to produce spoken words. These apps are widely used to support communication and language learning for individuals with disabilities such as autism spe...

Conventional eye-tracking calibration use sparse points that require saccades to all locations or pursuit trajectories that require completeness. In this work, we constructed a hybrid calibration system combining smooth trajectory and sparse points and applied it with typically developing (TD) toddlers, toddlers with Autism Spectrum Disorder (ASD)...

Far infrared thermography, which can be used to detect thermal radiation emitted by humans, has been used to detect physical disease, physiological changes relating to emotion, and polygraph testing, but has not been used for eye tracking. However, because the surface temperature of the cornea is colder than the limbus, it is theoretically possible...

This paper modifies the DBSCAN algorithm to identify fixations and saccades. This method combines advantages from dispersion-based algorithms, such as resilience to noise and intuitive fixational structure, and from velocity-based algorithms, such as the ability to deal appropriately with smooth pursuit (SP) movements.

This paper modifies the DBSCAN algorithm to identify fixations and saccades. This method combines advantages from dispersion-based algorithms, such as resilience to noise and intuitive fixational structure, and from velocity-based algorithms, such as the ability to deal appropriately with smooth pursuit (SP) movements.

Researchers use fixation identification algorithms to parse eye movement trajectories into a series of fixations and saccades, simplifying analyses and providing measures which may relate to cognition. The Distance Dispersion (I-DD) a widely-used elementary fixation identification algorithm. Yet the "optimality" properties of its most popular greed...

Accelerometers have been widely used to record and classify human daily activities such as walking, sitting, and playing sports. However, these sensors have less often been used to classify Human-Robot Interaction (HRI), and rarely in the context of HRI with special populations. This paper uses triaxial accelerometers and gyroscopes embedded in a c...