arXiv subjects

Michelle Nguyen

Publications and source records attributed to Michelle Nguyen.

4 recordsLinked to original sources

arXiv2025

Commanding the Foul Shot: A New Ensemble of Free Throw Metrics

With the NBA's adoption of in-game limb tracking in 2023, Sony's Hawk-Eye system now captures high-resolution, 3D poses of players and the ball 60 times per second. Linking these data to key events opens a new era in NBA analytics. Here, we leverage a large dataset of 21,964 shot attempts from 72 NBA players to introduce a novel ensemble of metrics for evaluating free-throw shooting. Inspired by baseball analytics, we introduce command, which quantifies the quality of a free throw by measuring a shooter's accuracy and precision near the basket's bullseye. This metric recognizes that some makes (or misses) are better than others and captures a player's ability to execute quality attempts consistently. We demonstrate that command captures underlying skill more effectively than traditional make-or-miss statistics; early-season command predicts late-season success more reliably than traditional shooting percentage. To identify what drives command, we define launch-based metrics assessing consistency in release velocity, angle, and 3D position. Players with greater touch, i.e., more consistent launch dynamics, exhibit stronger command as they can reliably control their shot trajectory. Finally, we develop a physics model to identify the range of launch conditions that result in a make and to determine which launch conditions are most robust to small perturbations. This framework reveals ''safe'' launch regions and explains why certain players excel at free throws, providing actionable insights for player development.

stat.AP↗

arXiv2025

Efficient and Scalable Agentic AI with Heterogeneous Systems

AI agents are emerging as a dominant workload in a wide range of applications, promising to be the vehicle that delivers the promised benefits of AI to enterprises and consumers. Unlike conventional software or static inference, agentic workloads are dynamic and structurally complex. Often these agents are directed graphs of compute and IO operations that span multi-modal data input and conversion), data processing and context gathering (e.g vector DB lookups), multiple LLM inferences, tool calls, etc. To scale AI agent usage, we need efficient and scalable deployment and agent-serving infrastructure. To tackle this challenge, in this paper, we present a system design for dynamic orchestration of AI agent workloads on heterogeneous compute infrastructure spanning CPUs and accelerators, both from different vendors and across different performance tiers within a single vendor. The system delivers several building blocks: a framework for planning and optimizing agentic AI execution graphs using cost models that account for compute, memory, and bandwidth constraints of different HW; a MLIR based representation and compilation system that can decompose AI agent execution graphs into granular operators and generate code for different HW options; and a dynamic orchestration system that can place the granular components across a heterogeneous compute infrastructure and stitch them together while meeting an end-to-end SLA. Our design performs a systems level TCO optimization and preliminary results show that leveraging a heterogeneous infrastructure can deliver significant TCO benefits. A preliminary surprising finding is that for some workloads a heterogeneous combination of older generation GPUs with newer accelerators can deliver similar TCO as the latest generation homogenous GPU infrastructure design, potentially extending the life of deployed infrastructure.

cs.LG↗

arXiv2024

Massive interacting binaries as an enrichment source for multiple populations in star clusters

We present a suite of binary evolution models with massive primaries (10 $\leq$ M$_1$ $\leq$ 40 M$_\odot$) and periods and mass ratios chosen such that the systems undergo non-conservative mass transfer while the primaries have helium cores. We track the total mass and chemical composition of the ejecta from these systems. This material shows the abundance signatures of hot hydrogen burning which are needed to explain the abundance patterns seen in multiple populations in massive star clusters. We then calculate the total yield of a population of binary stars with masses, mass ratios, and periods consistent with their distribution in a field population. We show that the overall abundance of this material is enriched in helium, nitrogen, sodium, and aluminum, and depleted in carbon, oxygen, and magnesium, by amounts that are consistent with observations. We also show that such a population of binaries will return approximately 25% of its mass in this ejecta (compared to 4% if all the stars were single), over a characteristic timescale of about 12 Myr. We argue that massive binaries must be seriously considered as a contributor to the source of enriched material needed to explain the multiple populations in massive clusters, since essentially all massive stars are formed in binaries or higher order multiples, massive binaries are primarily formed in clusters, and massive binaries naturally produce material of the right composition.

astro-ph.SR↗

arXiv2016

All trees are six-cordial

For any integer $k>0$, a tree $T$ is $k$-cordial if there exists a labeling of the vertices of $T$ by $\mathbb{Z}_k$, inducing a labeling on the edges with edge-weights found by summing the labels on vertices incident to a given edge modulo $k$ so that each label appears on at most one more vertex than any other and each edge-weight appears on at most one more edge than any other. We prove that all trees are six-cordial by an adjustment of the test proposed by Hovey (1991) to show all trees are $k$-cordial.

math.CO↗