TS
JP/EN

Takaya Suetake

APPLIED RESEARCH ENGINEER

Working at the boundary between
human sensing and complex systems.

I work in automotive system verification and pursue neurotechnology-related engineering through Cereportal alongside my full-time role.

Selected Work Contact GitHub ↗
Takaya Suetake presenting at Tokyo AI Festival
Tokyo AI Festival 2025Applied AI / Product R&D

ABOUT

Human × System

My long-term technical interest is consistent: turning information generated by people into systems that can actually be used.

At university, I worked on BCI and Human Sensing using EEG, eye tracking, and subjective evaluation. In industry, I entered the automotive field because of an interest in applying brain information to vehicle systems, and then gained broad experience across hardware, software, wireless communication, smartphone integration, and quality through mass-production system verification.

I am now working to recombine those experiences into applied Human Sensing and BCI R&D, with a focus on translating research prototypes into robust real-world systems.

4 yrsUndergraduate BCI / Human Sensing
6+ yrsAutomotive System Verification
2026SQiP Experience Paper

SELECTED WORK

Selected Work

Research, product development, and hands-on industry work — each with its own role and outcome.

01

Structuring Verification Perspectives and Sharing Quality Information with a Test Requirement Model (TRM)

SQiP Symposium 2026 — Experience Paper / Research Lead

I led the abstract, problem formulation, experiment design, analysis plan, and manuscript writing. The conference presentation itself was delivered by a collaborating community member.

The study compared direct test generation from source code with a TRM-mediated approach, reporting 67.4% perspective coverage for source-only generation versus 100% with TRM. Mutation analysis showed a smaller difference, 93.6% vs. 95.2%, leading to the interpretation that TRM's value is not simply more detected faults, but a more stable minimum detection level.

SQiP program ↗
02

RemoteClaudeOPS

Tokyo AI Festival 2025 / ADiXi Award

Developed and presented an AI-driven development platform combining natural-language implementation, Docker, WebSocket, a mobile UI, and port forwarding, and received the ADiXi Award.

GitHub repository ↗
03

Automotive Wireless Verification Program

SGL / Wi-Fi · Bluetooth · Smartphone — 2023–2025

Established and drove verification activities for Wi-Fi, Bluetooth, and smartphone integration from the initial phase of the next-generation model program for roughly two years.

See career details ↗

PROFESSIONAL CAREER

Automotive System Verification — 2020–Present

I have remained within the verification organization while expanding my role from mass-production system testing to wireless-domain leadership, LLM / evaluation infrastructure, automation, and requirements support.

Smartphone Integration

Joined as a software engineer, then moved into the verification organization as part of a team expansion. Worked on smartphone-integration verification for a mass-production automotive model.

SGL / Smartphone Integration

Led smartphone-integration verification for an upgraded model as a technical / project coordination role, working across external partner organizations rather than as a people manager.

SGL / Wi-Fi · Bluetooth · Smartphone

Established and drove verification activities for Wi-Fi, Bluetooth, and smartphone integration from the initial phase of the next-generation model program for roughly two years.

Advanced Evaluation Engineering / LLM

Selected within the verification organization for technically advanced work, including local LLM server setup, evaluation-support tooling, and evaluation-infrastructure related activities.

Requirements / Automation / Physical Verification

Working in evaluation automation on tooling that supports functional-requirement development and on various validation activities using physical systems.

Cereportal Inc. — Technical Lead / Engineer (Side Activity)

In parallel with my primary role, I work on applied R&D and business activities related to brain information science and Neurotechnology. As the sole technical member, I cover the engineering side with the goal of helping smaller organizations take steps into applied brain-information research. cereportal.com ↗

Public descriptions are generalized to avoid disclosing customer- or product-specific confidential information.

RESEARCH ORIGIN

Undergraduate BCI & Human Sensing

Across four undergraduate years, my research evolved from HCI to multimodal sensing, human-state estimation, and machine-learning-based EEG classification.

01

EEG × Unity / HCI

Undergraduate Year 1

Built and studied an interactive system that mapped information from a consumer EEG headset to the facial expression of a Unity character. This was my first attempt to use brain information as an input to a computer system.

EEGUnityHCI
02

Eye Tracking × EEG

Undergraduate Year 2

Combined a Tobii eye tracker with EEG to analyze relationships between gaze / eye movement and brain activity. The initial trend analysis did not produce sufficiently clear results, which later motivated reformulating the problem as a machine-learning classification task in my graduation research.

TobiiEEGMultimodal
Experimental environment for olfactory stimulation and frontal EEG; a diffuser is placed in the center
03

Olfactory Stimulus × Frontal EEG

Undergraduate Year 3 / Science Intercollegiate

Investigated whether subjective relaxation ratings for several scents showed trends corresponding to frontal EEG activity. Presented the research at the final stage of Science Intercollegiate.

Affective ComputingSubjective RatingFrontal EEG
04

EEG × FFT × CNN

Graduation Research

Extended the Year-2 research into a classification study. EEG was recorded from approximately 13 participants using an 8-channel 10–20 setup under roughly four visual conditions. Signals were transformed into the frequency domain at approximately 0–20 Hz and classified using a CNN.

Result: Performance was slightly above 50% for a four-class problem, compared with a 25% chance level. The model performed above random, but the result was not strong enough to claim practical discrimination accuracy.

8ch EEGFFTCNNDeep Learning

SKILLS

Cross-domain engineering

Human Sensing / BCI

EEG, ERP / P300 experience, Eye Tracking, subjective evaluation, visual stimulation

Signal / AI

FFT, EEG frequency analysis, CNN, Deep Learning, LLM, local LLM infrastructure

Systems

Automotive embedded systems, HW/SW integration, Wi-Fi, Bluetooth, smartphone integration

Quality / Research

Test design, requirements analysis, modeling, traceability, experimental design, manuscript writing

English

Technical reading: Experienced in reading academic literature in English through undergraduate BCI / EEG research.

Conversation: Practical speaking and technical presentation remain areas for continued improvement.

AWARDS / COMMUNITY

External Recognition

Science Intercollegiate

Finalist / Research Presentation

Human Sensing research using olfactory stimuli, subjective ratings, and frontal EEG.

CURRENT MISSION

Bringing Systems Engineering Back to BCI.

My goal is not simply to “return” to BCI / Human Sensing, but to bring back the systems perspective I gained from automotive engineering: embedded systems, communication, quality, verification, AI, and requirements engineering. I want to work as an Applied Research Engineer who can connect research results to systems that survive real-world constraints.

EEG Acquisition→Signal Processing→ML / BCI→System Integration→Verification

CONTACT / LINKS

Research, engineering, and collaboration.

Personal GitHub↗Cereportal GitHub↗Cereportal↗LinkedIn↗X (Twitter)↗Qiita↗SQiP Experience Paper↗