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Jonathan Wang

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math.AG math.RT Artificial Intelligence Machine Learning math.CA math.FA math.NT Robotics

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preprint2026arXiv

WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms

Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However, inferring health-related phenotypes from these signals presents significant challenges due to high sampling frequencies, multimodal dependencies, and extreme sequence lengths (e.g., weeks of recordings), compounded by a scarcity of ground-truth labels. To address these challenges, existing self-supervised learning (SSL) methodologies typically follow two paradigms: (1) learning rich morphological representations from short waveform segments while collapsing longitudinal dynamics through simple aggregation, or (2) modeling behavioral patterns from coarse, hand-crafted features (e.g. heart rate, step counts) spanning longer horizons but foregoing subtle, predictive signatures in raw waveforms. To bridge this gap, we propose WavesFM, a foundation model utilizing a two-stage SSL framework for longitudinal physiological data. Specifically, we decompose the learning problem into two stages: first, a segment-level encoder is pretrained to extract local embeddings from short waveforms; subsequently, a temporal encoder is trained to model the sequence of these embeddings across a multi-day horizon. This hierarchical approach overcomes the computational complexity of high-resolution, long-sequence data, allowing the overall model to capture both local signal semantics and the complex circadian and inter-day variations governing physiological dynamics. Pretrained on over 6.8M hours (N=324k individuals) of recordings for the first stage and 5.3M hours (N=10k) for the second stage, WavesFM demonstrates superior performance across 58 diverse tasks spanning demographics, lifestyle, health conditions, and medications.

preprint2022arXiv

Planar Robot Casting with Real2Sim2Real Self-Supervised Learning

This paper introduces the task of {\em Planar Robot Casting (PRC)}: where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane toward a desired target. PRC allows the cable to reach points beyond the robot workspace and has applications for cable management in homes, warehouses, and factories. To efficiently learn a PRC policy for a given cable, we propose Real2Sim2Real, a self-supervised framework that automatically collects physical trajectory examples to tune parameters of a dynamics simulator using Differential Evolution, generates many simulated examples, and then learns a policy using a weighted combination of simulated and physical data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented, Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes and Neural Networks (NNs), and three cables with differing stiffness, torsion, and friction. Results with 240 physical trials suggest that the PRC policies can attain median error distance (as % of cable length) ranging from 8% to 14%, outperforming baselines and policies trained on only real or only simulated examples. Code, data, and videos are available at https://tinyurl.com/robotcast.

preprint2016arXiv

On the reductive monoid associated to a parabolic subgroup

Let $G$ be a connected reductive group over a perfect field $k$ . We study a certain normal reductive monoid $\overline M$ associated to a parabolic $k$ -subgroup $P$ of $G$ . The group of units of $\overline M$ is the Levi factor $M$ of $P$ . We show that $\overline M$ is a retract of the affine closure of the quasi-affine variety $G/U(P)$ . Fixing a parabolic $P^-$ opposite to $P$ , we prove that the affine closure of $G/U(P)$ is a retract of the affine closure of the boundary degeneration $(G \times G)/(P \times_M P^-)$ . Using idempotents, we relate $\overline M$ to the Vinberg semigroup of $G$ . The monoid $\overline M$ is used implicitly in the study of stratifications of Drinfeld's compactifications of the moduli stacks $\mathrm{Bun}_P$ and $\mathrm{Bun}_G$ .

preprint2015arXiv

Radon inversion formulas over local fields

Let $F$ be a local field and $n\ge 2$ an integer. We study the Radon transform as an operator $M : \mathcal C_+ \to \mathcal C_-$ from the space of smooth $K$ -finite functions on $F^n \setminus \{0\}$ with bounded support to the space of smooth $K$ -finite functions on $F^n \setminus \{0\}$ supported away from a neighborhood of $0$ . These spaces naturally arise in the theory of automorphic forms. We prove that $M$ is an isomorphism and provide formulas for $M^{-1}$ . In the real case, we show that when $K$ -finiteness is dropped from the definitions, the analog of $M$ is not surjective.

preprint2014arXiv

A new Fourier transform

In order to define a geometric Fourier transform, one usually works with either $\ell$ -adic sheaves in characteristic $p>0$ or with $D$ -modules in characteristic 0. If one considers $\ell$ -adic sheaves on the stack quotient of a vector bundle $V$ by the homothety action of $\mathbb{G}_m$ , however, Laumon provides a uniform geometric construction of the Fourier transform in any characteristic. The category of sheaves on $[V/\mathbb{G}_m]$ is closely related to the category of (unipotently) monodromic sheaves on $V$ . In this article, we introduce a new functor, which is defined on all sheaves on $V$ in any characteristic, and we show that it restricts to an equivalence on monodromic sheaves. We also discuss the relation between this new functor and Laumon's homogeneous transform, the Fourier-Deligne transform, and the usual Fourier transform on $D$ -modules (when the latter are defined).

preprint2011arXiv

The moduli stack of $G$ -bundles

In this paper, we give an expository account of the geometric properties of the moduli stack of $G$ -bundles. For $G$ an algebraic group over a base field and $X \to S$ a flat, finitely presented, projective morphism of schemes, we give a complete proof that the moduli stack $Bun_G$ is an algebraic stack locally of finite presentation over $S$ with schematic, affine diagonal. In the process, we prove some properties of $BG$ and Hom stacks. We then define a level structure on $Bun_G$ to provide alternative presentations of quasi-compact open substacks. Finally, we prove that $Bun_G$ is smooth over $S$ if $G$ is smooth and $X \to S$ is a relative curve.

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Jonathan Wang

Research overview

Is this your work?

6 publications

WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms

Planar Robot Casting with Real2Sim2Real Self-Supervised Learning

On the reductive monoid associated to a parabolic subgroup

Radon inversion formulas over local fields

A new Fourier transform

The moduli stack of $G$ -bundles

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Building this map preview

Jonathan Wang

Research overview

Is this your work?

6 publications

WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms

Planar Robot Casting with Real2Sim2Real Self-Supervised Learning

On the reductive monoid associated to a parabolic subgroup

Radon inversion formulas over local fields

A new Fourier transform

The moduli stack of GGG-bundles

See the researcher in context

Building this map preview

The moduli stack of $G$ -bundles