CV

Professional summary

Research engineer bridging large-scale infrastructure and collective-intelligence research. Ten years building and operating production systems at Microsoft Azure and Adobe; now Staff Software Engineer at Indeed Technologies Japan, alongside a University of Tokyo Ph.D. in Artificial Life with published work on swarm-based reservoir computing. Builds the data pipelines, analysis tooling, and interfaces that turn research and operational learning into dependable software—and cares about keeping those systems running under real load.

Professional experience

• Staff Software Engineer, Indeed Technologies Japan — Apr 2026–present
  • Design and build internal tooling infrastructure and operational practices for a large job platform, with a reliability-first approach drawn from years of incident command and resilience work in the same organization.
  • Lead applied-AI work for incident intelligence: agentic pipelines for recurring incident analysis and pattern identification; LLM-assisted synthesis over Jira, Slack, email, and internal documentation with privacy controls and human approval; Glean Agents workflows for incident-review preparation and data hygiene.
  • Own technical writing and communication of analysis findings to engineering leadership; continue organizational-learning practices established in the prior role.
• Staff Site Reliability Incident Analyst, Indeed Technologies Japan — Jul 2022–Mar 2026
  • Led resilience-engineering work for APAC: post-incident analysis, cognitive task analysis of operator decision-making, and cross-incident pattern analysis.
  • Mined incident data and built analysis tooling to connect findings to ongoing engineering investment rather than a siloed backlog.
  • Collaborated with Adaptive Capacity Labs on resilience methodology at scale. Public methods work includes Functional Resonance Analysis and learning across incidents at SREcon.
• Senior Software Engineer, Infrastructure, Adobe — Aug 2019–May 2022
  • Designed, built, and owned end-to-end a critical disaster-recovery data engineering pipeline, including monitoring, automation, and the full operational lifecycle; served as primary DRI.
  • Built automated backup-configuration systems and operated the file, object, and block storage underpinning the platform and business.
  • Drove continuous improvement across infrastructure teams and trained engineers on retrospective methodology.
• Service Engineer I & II (Azure SRE), Microsoft — May 2015–Aug 2019
  • Served as incident commander across hundreds of Azure services, including coordination roles in major security-vulnerability response.
  • Built data engineering pipelines and analytics tooling for outage-pattern analysis and reliability improvement at Azure scale (Learning at Scale).
  • Revised postmortem processes and led resiliency-engineering initiatives across the organization (A Tale of Two Postmortems; Ironies of Automation).

Earlier experience

• Adjunct Professor, Brigham Young University (2018–2019) — Designed and taught a project-based 500-level Data Engineering course.
• System Administrator & Research Assistant, Brigham Young University (2014–2015) — Built and studied an ELK log-analysis cluster architected for university IT and still in use; maintained CS department labs and core services.
• Software Developer, GunAuction.com (2012–2014) — Third-party integration API, distributor shopping cart, load balancing, Hadoop deployment.
• Transducer Lab Intern, NASA Kennedy Space Center / ASRC Aerospace (2009) — Sensor prototype evaluation for Constellation-era fuel lines and launch pads.

Research experience and affiliations

• External Researcher, Cross Labs — Apr 2023–present
  • Collective intelligence and Artificial Life research.
  • ALife Japan Future Talent Fellowship (2024–2025), with mentorship from SIG-Alife (JSAI).
• Doctoral Researcher, The University of Tokyo, Ikegami Laboratory — Apr 2022–Jun 2026
  • Dissertation research on heterogeneous swarm dynamics as computational reservoirs; see publications and the dissertation page.

Education

• Ph.D., Artificial Life — The University of Tokyo, 2026 (conferred June 2026)
  • Dissertation: Collective Nonlinear Dynamics in Heterogeneous Swarms: A Reservoir Computing Perspective
  • Supervisor: Takashi Ikegami
• M.S., Computer Science — Georgia Institute of Technology, 2019
• B.S., Information Technology — Brigham Young University, 2015

Selected publications

• Lund, T., Adams, A., Aubert-Kato, N., & Ikegami, T. (2026). State transitions unlock temporal memory in swarm-based reservoir computing. PeerJ Computer Science. (PeerJ)
• Lund, T. (2026). Collective Nonlinear Dynamics in Heterogeneous Swarms: A Reservoir Computing Perspective. Ph.D. dissertation, The University of Tokyo.
• Korecki, M., Carissimo, C., & Lund, T. (2023). aRtificiaL death: learning from stories of failure. ALIFE 2023, MIT Press. (co-first author)
• Sato, H., Lund, T., & Yoshida, T. (2023). Automata Quest: NCAs as a Video Game Life Mechanic. ALIFE 2023 workshop.

See all publications →

Selected talks

• Keynote and plenary speaker at SREcon Asia/Pacific (2019, 2023) and SREcon Americas (2018), on incident analysis, human factors, and the limits of automation — e.g. A Tale of Two Postmortems, Ironies of Automation, Learning at Scale.
• Functional Resonance Analysis: Diagramming Your System — tutorial, SREcon23 APAC.
• Patterns, Not Categories: Learning Across Incidents — SREcon23 APAC.
• State Transitions Unlock Temporal Memory in Swarm-Based Reservoir Computing — poster, ALIFE 2025.

See all talks → · Talk map →

Selected projects

• Swarm Studio: Reservoirs — Interactive browser sandbox for swarm reservoir computing (live).
• Genealogy Map — Client-side GEDCOM visualizer on an interactive map (live).
• LexiAtlas — Community index of language-learning resources (live).

See all projects →

Teaching and service

• Data Engineering — Adjunct Professor, Brigham Young University (Fall 2018); project-based 500-level course for seniors and graduate students. All teaching →
• Active member of church lay ministry.

Technical capabilities

• Resilience & incident intelligence — Incident command, cross-incident pattern analysis, organizational learning, technical writing for engineering leadership
• Applied AI & agentic workflows — LLM-assisted analysis pipelines, evaluation and human-in-the-loop approval, Glean Agents, AI-assisted development
• Distributed infrastructure — Large-scale data pipelines, disaster recovery, storage systems, monitoring and observability, Azure and enterprise platforms
• Research computing — Swarm and collective systems, reservoir computing, neural cellular automata; Python (primary), JavaScript, SQL, Bash
• Technical leadership — Process design, facilitation and training, cross-functional collaboration