About

Josiah Hester holds the Allchin Chair and is an associate professor of Interactive Computing and Computer Science at Georgia Tech. He previously served as an assistant professor at Northwestern University. His research focuses on intermittent computing and battery-free embedded computing systems, which he applies to health wearables, interactive devices, and large-scale sensing for sustainability and conservation. His work is supported by multiple grants from the NSF, NIH, and DARPA. Hester has been recognized as a Sloan Fellow in Computer Science, and in 2022, he received the NSF CAREER award. He has also been named one of Popular Science's Brilliant Ten, and has received awards from the American Indian Science and Engineering Society and 3M. His research has been featured in prominent outlets such as the Wall Street Journal, Scientific American, BBC, Popular Science, Communications of the ACM, and the Guinness Book of World Records.

Research topics

• Political Science
• Computer Science
• Computer Security
• Environmental ethics
• Environmental resource management
• Biology
• Psychology
• Environmental science
• Human–computer interaction
• Medicine
• Law
• Ecology
• Internet privacy
• World Wide Web

Selected publications

• Designing Loofah Wearables For Embodied Ecological Reflection

  2026-03-07

  articleOpen access

  Amid escalating ecological challenges, Human-Computer Interaction (HCI) researchers have begun adopting More-than-Human design (MtHD) approaches as a means of reimagining and strengthening the bonds between the human and non-human world. Taking a MtHD approach, this work investigates how loofah, a plant-based, biodegradable material, can be used to foster ecological awareness in everyday life. We present a material exploration of loofah, including initial encounters with loofah as a uniquely structural material, a design space focused on how loofah can be combined with various indicators that respond to different environmental factors like temperature, UV radiation, pH of water and soil, the Iron and moisture in soil and utilizing loofah as a substrate for plant growth. Based on this design space, we create two wearables: a hat and a glove. These artifacts incorporate environmental sensing capabilities and host live microgreens, highlighting loofah‘s potential as both an interface and habitat. Through a 15-day autoethnographic journaling process by the first and second authors, we reflect on the embodied experiences of “wearing” environmental change and cultivating on-body ecological practices. This pictorial contributes to the HCI community by introducing a biomaterial-based embodied interaction to provoke reflections on the relationships between materials, non-human forms, and the environment, while integrating functional considerations into MtHD.

  PublisherDOI

• Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai`i

  ArXiv.org · 2026-03-17

  articleOpen access

  Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai`i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.

  PublisherOA PDF

• Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i

  2026-04-13 · 1 citations

  articleOpen access

  Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai‘i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.

  PublisherDOI

• Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students

  JMIR Formative Research · 2026-02-26

  articleOpen access

  BACKGROUND: Digital behaviors such as sleep, social interaction, and productivity reflect how individuals' structure daily life. Among university students, online activity patterns mirror academic schedules, social rhythms, and lifestyle habits, with disruptions linked to sleep, stress, and well-being. Existing approaches-including wearables, apps, and surveys-yield useful insights but depend on self-report or active participation, limiting long-term adherence. Passive sensing of network traffic offers a scalable alternative for unobtrusive capture of smartphone usage patterns that preserves privacy. OBJECTIVE: This study evaluated whether encrypted smartphone network traffic, collected via a standard virtual private network (VPN), can capture patterns of digital behavior. We assessed feasibility (sustained data capture) and acceptability (usability, burden, and privacy perceptions) and examined whether traffic-derived features reveal aspects of digital behavior-including timing, intensity, and regularity-relevant to health and daily functioning. METHODS: We conducted a two-week prospective observational study at New York University. Participants installed the WireGuard VPN client on personal smartphones, enabling passive capture of encrypted network traffic. Feasibility was assessed using a mixed-methods approach combining quantitative measures of user retention and data coverage with qualitative analysis of semi-structured exit interviews. Acceptability was evaluated using the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and qualitative interview analysis. Exploratory analyses visualized traffic-derived features in relation to digital activity patterns. RESULTS: Thirty-eight students consented to participate, of whom 29 contributed valid network traffic data and formed the analytic cohort. Within this cohort, 93% of participants (27/29; Wilson 95% CI: 78-98%) contributed at least five days of monitoring, corresponding to 71% retention relative to all consented participants (27/38; Wilson 95% CI: 55-83%). Mean data coverage within the analytic cohort (N=27) was 74.1% (median 77.1%; bootstrap 95% CI: 66.3-81.4%). These participants contributed an average of 311.6 hours of monitored traffic (approximately 13 days, SD 3.5), ranging from 121 to 496 hours. Acceptability outcomes were evaluated among participants completing the exit survey and interview. Usability ratings were high (mean SUS score = 78) and perceived workload low (NASA-TLX scores minimal). Participants described the system as easy to install, unobtrusive, and generally trustworthy, though some reported temporarily disabling the VPN during activities they considered private. No inferential statistical tests were conducted; analyses were descriptive. Exploratory analyses indicated that traffic-derived features reflected daily digital activity rhythms and revealed distinctive lifestyle patterns, including gaming and irregular late-night food delivery use. CONCLUSIONS: VPN-based monitoring of encrypted smartphone traffic was feasible and acceptable, enabling sustained passive data collection with minimal burden. This approach shows promise as a scalable, device-agnostic method for digital phenotyping that captures fine-grained behavioral rhythms while preserving privacy. With broader validation, this technique could expand the toolkit for studying health and well-being in everyday life. CLINICALTRIAL: This study was not registered as a clinical trial because it did not involve randomization.

  PublisherDOI

• SpiderCam: Low-Power Snapshot Depth from Differential Defocus

  ArXiv.org · 2026-03-18

  articleOpen access

  We introduce SpiderCam, an FPGA-based snapshot depth-from-defocus camera which produces 480x400 sparse depth maps in real-time at 32.5 FPS over a working range of 52 cm while consuming 624 mW of power in total. SpiderCam comprises a custom camera that simultaneously captures two differently focused images of the same scene, processed with a SystemVerilog implementation of depth from differential defocus (DfDD) on a low-power FPGA. To achieve state-of-the-art power consumption, we present algorithmic improvements to DfDD that overcome challenges caused by low-power sensors, and design a memory-local implementation for streaming depth computation on a device that is too small to store even a single image pair. We report the first sub-Watt total power measurement for passive FPGA-based 3D cameras in the literature.

  PublisherOA PDF

• SpiderCam: Low-Power Snapshot Depth from Differential Defocus

  arXiv (Cornell University) · 2026-03-18

  preprintOpen access

  We introduce SpiderCam, an FPGA-based snapshot depth-from-defocus camera which produces 480x400 sparse depth maps in real-time at 32.5 FPS over a working range of 52 cm while consuming 624 mW of power in total. SpiderCam comprises a custom camera that simultaneously captures two differently focused images of the same scene, processed with a SystemVerilog implementation of depth from differential defocus (DfDD) on a low-power FPGA. To achieve state-of-the-art power consumption, we present algorithmic improvements to DfDD that overcome challenges caused by low-power sensors, and design a memory-local implementation for streaming depth computation on a device that is too small to store even a single image pair. We report the first sub-Watt total power measurement for passive FPGA-based 3D cameras in the literature.

  PublisherDOI

• A Greener Edge: A Framework on Carbon-aware Edge ML System Design (MobiSys 2026 Artifact Evaluation)

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-10

  otherOpen accessSenior author

  MicroGreen is a design-time framework that enables carbon-aware design for edge ML system. This artifact contains code and instructions on how to reproduce the results presented in the MicroGreen MobiSys 2026 paper.

  PublisherDOI

• Design of a wireless, fully implantable platform for in-situ oxygenation of encapsulated cell therapies

  Device · 2026-03-27

  article
  PublisherDOI

• Biohybrid Robots with Embedded Conductive Fibers for Actuation, Sensing, and Closed-loop Control

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-06

  articleOpen access

  Living organisms achieve adaptive actuation through the seamless integration of neural motor control circuitry and proprioceptive feedback. While biohybrid robotics aims to replicate these capabilities by merging engineered muscle with synthetic scaffolds, the field remains limited by interfaces that lack the efficiency and closed-loop regulation of natural neuromuscular systems. Here, we introduce a biohybrid muscle actuator system featuring a bioelectronic interface based on soft poly(3,4-ethylenedioxythiophene) (PEDOT) fibers for stimulation and sensing. These fibers conformally couple to muscle tissues, eliciting robust contractions at voltages as low as 1 V-requiring ultra-low power (0.376 ± 0.034 mW) and preserving long-term tissue viability. By leveraging the independent addressability of these fibers, we demonstrate selective actuation of individual muscle units to achieve precise spatiotemporal control of a two-muscle-powered walking biohybrid robot, reaching a locomotion speed of 5.43 ± 0.79 mm/min. When configured as strain sensors, the fibers exhibit a high gauge factor of 155.45 ± 6.59 and resolve contractile displacements within tens of micrometers. We demonstrate that this sensing modality can be integrated into a closed-loop controller to autonomously modulate stimulation based on real-time feedback, significantly mitigating muscle fatigue (p = 0.038) during continuous operation. This work establishes a versatile platform for efficient actuation and intrinsic feedback sensing, providing a blueprint for efficient, autonomous, and adaptive biohybrid machines.

  PublisherOA PDFDOI

• MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems

  arXiv (Cornell University) · 2026-01-20

  preprintOpen accessSenior author

  Multi-agent systems (MAS) have recently emerged as promising socio-collaborative companions for emotional and cognitive support. However, these systems frequently suffer from persona collapse--where agents revert to generic, homogenized assistant behaviors--and social sycophancy, which produces redundant, non-constructive dialogue. We propose MASCOT, a generalizable framework for multi-perspective socio-collaborative companions. MASCOT introduces a novel bi-level optimization strategy to harmonize individual and collective behaviors: 1) Persona-Aware Behavioral Alignment, an RLAIF-driven pipeline that finetunes individual agents for strict persona fidelity to prevent identity loss; and 2) Collaborative Dialogue Optimization, a meta-policy guided by group-level rewards to ensure diverse and productive discourse. Extensive evaluations across psychological support and workplace domains demonstrate that MASCOT significantly outperforms state-of-the-art baselines, achieving improvements of up to +14.1 in Persona Consistency and +10.6 in Social Contribution. Our framework provides a practical roadmap for engineering the next generation of socially intelligent multi-agent systems.

  PublisherDOI

Recent grants

• CAREER: Enabling Dynamic, Adaptive, and Reliable Battery-free Embedded Computing

  NSF · $251k · 2022–2024

• Collaborative Research: CNS Medium: Systems Foundations for Battery-free Body Area Intelligence and Sensing

  NSF · $308k · 2021–2025

• CRII: CSR: Systems and Tooling Enabling Adaptive Intermittent Computing

  NSF · $199k · 2019–2022

• Collaborative Research: HCC: Small: Toolkits for Creating Interaction-powered Energy-aware Computing Systems

  NSF · $250k · 2023–2025

• CPS: Medium: Batteryless Sensors Enabling Smart Green Infrastructure

  NSF · $1.2M · 2021–2025

Frequent coauthors

• Jacob Sorber

  Clemson University

  24 shared

• Nabil Alshurafa

  Northwestern University

  18 shared

• Kasım Sinan Yıldırım

  University of Trento

  17 shared

• Przemysław Pawełczak

  Delft University of Technology

  15 shared

• Sougata Sen

  Birla Institute of Technology and Science, Pilani

  13 shared

• Abu Bakar

  Universitas Muara Bungo

  10 shared

• Blaine Rothrock

  Northwestern University

  9 shared

• Rawan Alharbi

  Northwestern University

  8 shared

Education

• PhD Computer Science, School of Computing

  Clemson University

  2017

Awards & honors

• Sloan Fellow in Computer Science
• NSF CAREER (2022)
• Popular Science's Brilliant Ten
• American Indian Science and Engineering Society Most Promisi…
• 3M Non-tenured Faculty Award (2021)