{"canonicalUrl":"https://humetric.net/p/profile-7ad39d0039e446b5a84e6afbf2c45fa1","profileVersion":"6088fc3a-523c-4316-8d27-40d7f029c89b","lastUpdated":"2026-10-19T15:35:16.238303+00:00","profile":{"display_name":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co","last_updated":"2026-10-19","slug":"profile-7e2dde02a3f14635","summary":"An AI/ML engineer and researcher with 10+ years of experience specializing in multi-agent self-improving LLM systems, alignment, interpretability, quantum-integrated machine learning, and deep reinforcement learning. A PhD candidate at the University of Toronto and Vector Institute affiliate with experience delivering end-to-end ML pipelines, cloud infrastructure, cluster orchestration, MLOps, and AI research.","years_experience":10,"skills":["Amazon EC2","Amazon S3","Apache Spark","AWS","Bash","C","C++","CI/CD","Cloudera Distribution of Hadoop","Cluster Provisioning","Django","Django REST","Docker","Docker Compose","DTI","Dynamical Systems Analysis","FastAPI","fMRI","GCP","Git","GitHub Actions","Hadoop","Hugging Face","Java","JavaScript","JAX","Job Scheduling","Kubernetes","LangChain","Linux","Linux System Administration","LLM Agents","LLM Fine-Tuning","MEG","Megatron","MRI","Multi-Agent Reinforcement Learning","Neuroimaging","Next.js","Node.js","NumPy","Pandas","PennyLane","PostgreSQL","Python","PyTorch","Qiskit","React","Scikit-learn","SciPy","SGLang","Shell","SQL","TensorFlow","TensorFlow Quantum","TensorFlowOnSpark","Topological Systems Analysis","TypeScript","verl","vLLM"],"experiences":[{"id":"e5305dec-b60d-44a9-95fd-dd13d7bc35e7","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Researcher","company":"Vector Institute for Artificial Intelligence","start_date":"2024","end_date":"Present","description":"Built a multi-agent LLM/RL pipeline for automated quantum machine-learning algorithm search; abstracted a novel advanced for-loop construct accepted at QTML 2025 (arXiv:2506.18260). Designed an agentic search framework with modular tool use, prompt chaining, and self-evaluation loops; architected the inference system; and authored a quantum backpropagation formulation, under review. Formulating artificial consciousness via Integrated Information Theory. Co-organiser of an AI-for-Reasoning reading group; driving LLM multi-token prediction on multi-hop reasoning and suicidality-detection agent projects; developing a DPO variant.","position":0},{"id":"671c11fb-067a-4494-91ae-3966b01820d6","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Ph.D. Researcher — CS & Medical Science","company":"University of Toronto","start_date":"2021","end_date":"2026","description":"PhD thesis: Agentic system for exploration of algorithmic ANNs. Contributed to the generative quantum eigensolver (GQE) (arXiv:2401.09253). Engineered virtual brain stimulation AI; integrated E-field modelling with connectome tractography for TMS target selection.","position":1},{"id":"d42b2c2b-a78f-4b74-833a-45a8b7965611","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Teaching Assistant — Deep Learning","company":"Neuromatch Academy","start_date":"2020","end_date":"2023","description":"Lead TA in 2021 for a global cohort of 1,000+ students. Delivered a deep-learning curriculum using PyTorch and led debugging sessions and model evaluation workshops across distributed online cohorts.","position":2},{"id":"6e1022f2-1a81-43e5-9644-[contact removed]","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Data Analyst","company":"Institute of Linguistics, Academia Sinica","start_date":"2019","end_date":"2021","description":"Designed and deployed multimodal neuroimaging pipelines covering MRI, fMRI, DTI, and MEG for a cross-age working-memory study. Reduced manual quality-control time by approximately 40% through automated topological analysis scripts.","position":3},{"id":"54ad8f97-406e-459f-97d2-b7f14e993ee3","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Research Assistant — Medical AI","company":"Center for AI in Medicine, Taipei Medical University","start_date":"2018","end_date":"2019","description":"Delivered a CNN-based diagnostic model for idiopathic normal-pressure hydrocephalus and brain-tumour segmentation. Published a CNN suicidality-prediction model in the Journal of Clinical Psychiatry, 82(2), 2021. Built and documented an end-to-end data pipeline from DICOM ingestion through preprocessing and model inference to clinical reporting.","position":4},{"id":"82d4b68c-e6b6-460a-b84c-7b705b88b464","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Software Engineer","company":"Cybersecurity Technology Institute, Institute for Information Industry","start_date":"2017","end_date":"2017","description":"Developed security tooling and data-processing scripts in Python and C++ within an agile team. Participated in code review and issue-tracking workflows.","position":5},{"id":"78db61a4-cbed-43c1-948d-425e0e17cf73","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Visiting Student — Big Data & ML Infrastructure","company":"Modeling & Informatics Lab, National Taiwan University","start_date":"2016","end_date":"2017","description":"Provisioned and administered a Hadoop/Spark CDH cluster for large-scale neural image analysis using the Thunder project and TensorFlowOnSpark. Aligned chatbot LU/NLG embeddings with brain activity signals.","position":6},{"id":"078df762-961b-4b86-9d8c-1b23998aecd9","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","title":"Clinical Psychologist","company":"Taipei Veterans General Hospital","start_date":"2011","end_date":"2013","description":"Worked in the Psychiatry Department, Neurological Institute, and Rehabilitation Center.","position":7}],"educations":[{"id":"6f9b6d4a-1e5f-4db4-b4c4-fa5a70163adc","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","school":"University of Toronto","degree":"Ph.D. Candidate","field":"Institute of Medical Science","start_year":2021,"end_year":2026},{"id":"3307c265-ac3a-436f-ad36-1b6fcda4c856","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","school":"National Chung Cheng University","degree":"M.S.","field":"Clinical Psychology","start_year":2009,"end_year":2012}],"projects":[{"id":"d[contact removed]-aa90-08d98247f66e","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"MetaScientist (Meta_Scientist)","description":"Self-improving agentic research framework pairing LLM multi-agent orchestration with Monte Carlo tree search (MCTS) to automatically search quantum machine-learning algorithm pipelines. Integrates web search, task planning, and a dual-loop research cycle. Implementation framework behind the QTML 2025 paper; extends MetaGPT/SELA.","url":"https://github.com/zuxfoucault/Meta_Scientist","technologies":["Python","MetaGPT","Monte Carlo Tree Search","Docker"],"position":0},{"id":"105c3ad1-8704-4e8e-ac13-0c59c7e50ece","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"DDB Knowledge Agent (ddb-knowledge-agent-py)","description":"Streaming RAG agent fusing pgvector similarity retrieval with PostgreSQL full-text search via reciprocal rank fusion (RRF). Includes ReAct tool calling, query-complexity-tiered model routing, semantic caching, and cost auditing. PostgreSQL runs under rootless Apptainer and is deployable on Docker-less HPC clusters.","url":"https://github.com/zuxfoucault/ddb-knowledge-agent-py","technologies":["Python","FastAPI","Server-Sent Events","Gemini","PostgreSQL","pgvector","Apptainer"],"position":1},{"id":"e28ae4b8-5d6b-4a51-942d-1ee3bf8c9955","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"GOAT Network Agent (goat-agent_demo)","description":"Autonomous Web3 agent registering an ERC-8004 on-chain identity and integrating with the x402 payment flow. Executes price tracking, conditional triggering, on-chain payment, and attested settlement end-to-end with no human intervention after launch.","url":"https://github.com/zuxfoucault/goat-agent_demo","technologies":["Node.js","Express","ERC-8004","x402"],"position":2},{"id":"75b316c3-d2cb-4633-8ae6-8abeedbfab87","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"optlab — Derivatives Pricing & Strategy Backtesting (optlab-py)","description":"Pricing library and vectorised backtester built to test whether the variance risk premium survives transaction costs. Includes Black-Scholes analytic Greeks, CRR trees with American exercise, variance-reduced Monte Carlo with a 12.7× improvement via antithetic and control variates, Heston characteristic-function pricing and Andersen QE simulation, implied-volatility inversion, and raw-SVI and Heston surface calibration. Across 24 simulated 10-year paths, delta hedging lifted short-straddle Sharpe from 0.24 to 0.49, while frictions consumed 70% of gross P&L institutionally and all of it at retail. Included 102 model-validation tests covering convergence orders, Greeks versus finite differences, no-arbitrage bounds, and P&L versus a hand-computed ledger.","url":"https://github.com/zuxfoucault/optlab-py","technologies":["Python","NumPy","SciPy","Pandas","pytest","GitHub Actions","Black-Scholes","CRR Trees","Monte Carlo Simulation","Heston Model","Andersen QE","Raw-SVI"],"position":3},{"id":"edb62fab-5c9a-4c96-a539-912bc79f8ca4","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"Quantum Backpropagation","description":"Publication by Wong, F.-T. (2026). Under review.","url":null,"technologies":["Quantum Machine Learning"],"position":4},{"id":"3c80c81c-518d-4fb2-90d9-eb8b9a26c867","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"Multi-Token Prediction and Alternative Tokenization Improve Multi-Hop Reasoning and Graph Search","description":"Publication by Frydenlund, A., Wong, F.-T., et al. (2026). Under review.","url":null,"technologies":["LLMs","Multi-Token Prediction","Multi-Hop Reasoning","Graph Search"],"position":5},{"id":"a0de[contact removed]d62-85e7-1ad0e610730f","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"Advanced For-Loop for QML Algorithm Search","description":"Publication by Wong, F.-T. (2025), accepted at QTML 2025.","url":"https://arxiv.org/abs/2506.18260","technologies":["Quantum Machine Learning","Algorithm Search"],"position":6},{"id":"0c1c3a57-2b78-4df2-bef6-a0b7f591031b","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"Generative Quantum Eigensolver (GQE)","description":"Publication by Nakaji, K., Wong, F.-T., et al. (2024).","url":"https://arxiv.org/abs/2401.09253","technologies":["Quantum Computing","Quantum Machine Learning"],"position":7},{"id":"7c8d6a4c-569e-4c48-8936-ba7ecd5b1698","person_id":"54d76a4a-8b03-449d-9713-cfe39d6d9f4f","name":"CNN Suicidality Prediction","description":"Publication by Chen, V. C.-H., Wong, F.-T., et al. (2021), Journal of Clinical Psychiatry 82(2).","url":null,"technologies":["Convolutional Neural Networks","Medical AI"],"position":8}]},"chunksVersion":"profile-chunks-v4","approvedDiscoveryQueries":[],"evidence":[{"id":"identity:0","type":"identity","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. 10 years experience","fieldKeys":["display_name","years_experience"]},{"id":"summary:0","type":"summary","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Summary / 簡介: An AI/ML engineer and researcher with 10+ years of experience specializing in multi-agent self-improving LLM systems, alignment, interpretability, quantum-integrated machine learning, and deep reinforcement learning. A PhD candidate at the University of Toronto and Vector Institute affiliate with experience delivering end-to-end ML pipelines, cloud infrastructure, cluster orchestration, MLOps, and AI research.","fieldKeys":["summary"]},{"id":"skills:0","type":"skills","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Skills / 技能: Amazon EC2, Amazon S3, Apache Spark, AWS, Bash, C, C++, CI/CD, Cloudera Distribution of Hadoop, Cluster Provisioning, Django, Django REST, Docker, Docker Compose, DTI, Dynamical Systems Analysis, FastAPI, fMRI, GCP, Git, GitHub Actions, Hadoop, Hugging Face, Java, JavaScript, JAX, Job Scheduling, Kubernetes, LangChain, Linux, Linux System Administration, LLM Agents, LLM Fine-Tuning, MEG, Megatron, MRI, Multi-Agent Reinforcement Learning, Neuroimaging, Next.js, Node.js, NumPy, Pandas, PennyLane, PostgreSQL, Python, PyTorch, Qiskit, React, Scikit-learn, SciPy, SGLang, Shell, SQL, TensorFlow, TensorFlow Quantum, TensorFlowOnSpark, Topological Systems Analysis, TypeScript, verl, vLLM","fieldKeys":["skills"]},{"id":"discovery:0","type":"discovery","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 年資: 10 years experience. Expertise / 專長: Amazon EC2, Amazon S3, Apache Spark, AWS, Bash, C, C++, CI/CD, Cloudera Distribution of Hadoop, Cluster Provisioning, Django, Django REST","fieldKeys":["display_name","years_experience","skills"]},{"id":"experience:0","type":"experience","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Researcher at Vector Institute for Artificial Intelligence. 2024 to Present. Built a multi-agent LLM/RL pipeline for automated quantum machine-learning algorithm search; abstracted a novel advanced for-loop construct accepted at QTML 2025 (arXiv:2506.18260). Designed an agentic search framework with modular tool use, prompt chaining, and self-evaluation loops; architected the inference system; and authored a quantum backpropagation formulation, under review. Formulating artificial consciousness via Integrated Information Theory. Co-organiser of an AI-for-Reasoning reading group; driving LLM multi-token prediction on multi-hop reasoning and suicidality-detection agent projects; developing a DPO variant.","fieldKeys":["experiences"]},{"id":"experience:1","type":"experience","ordinal":1,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Ph.D. Researcher — CS & Medical Science at University of Toronto. 2021 to 2026. PhD thesis: Agentic system for exploration of algorithmic ANNs. Contributed to the generative quantum eigensolver (GQE) (arXiv:2401.09253). Engineered virtual brain stimulation AI; integrated E-field modelling with connectome tractography for TMS target selection.","fieldKeys":["experiences"]},{"id":"experience:2","type":"experience","ordinal":2,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Teaching Assistant — Deep Learning at Neuromatch Academy. 2020 to 2023. Lead TA in 2021 for a global cohort of 1,000+ students. Delivered a deep-learning curriculum using PyTorch and led debugging sessions and model evaluation workshops across distributed online cohorts.","fieldKeys":["experiences"]},{"id":"experience:3","type":"experience","ordinal":3,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Data Analyst at Institute of Linguistics, Academia Sinica. 2019 to 2021. Designed and deployed multimodal neuroimaging pipelines covering MRI, fMRI, DTI, and MEG for a cross-age working-memory study. Reduced manual quality-control time by approximately 40% through automated topological analysis scripts.","fieldKeys":["experiences"]},{"id":"experience:4","type":"experience","ordinal":4,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Research Assistant — Medical AI at Center for AI in Medicine, Taipei Medical University. 2018 to 2019. Delivered a CNN-based diagnostic model for idiopathic normal-pressure hydrocephalus and brain-tumour segmentation. Published a CNN suicidality-prediction model in the Journal of Clinical Psychiatry, 82(2), 2021. Built and documented an end-to-end data pipeline from DICOM ingestion through preprocessing and model inference to clinical reporting.","fieldKeys":["experiences"]},{"id":"experience:5","type":"experience","ordinal":5,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Software Engineer at Cybersecurity Technology Institute, Institute for Information Industry. 2017 to 2017. Developed security tooling and data-processing scripts in Python and C++ within an agile team. Participated in code review and issue-tracking workflows.","fieldKeys":["experiences"]},{"id":"experience:6","type":"experience","ordinal":6,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Visiting Student — Big Data & ML Infrastructure at Modeling & Informatics Lab, National Taiwan University. 2016 to 2017. Provisioned and administered a Hadoop/Spark CDH cluster for large-scale neural image analysis using the Thunder project and TensorFlowOnSpark. Aligned chatbot LU/NLG embeddings with brain activity signals.","fieldKeys":["experiences"]},{"id":"experience:7","type":"experience","ordinal":7,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Experience / 經歷: Clinical Psychologist at Taipei Veterans General Hospital. 2011 to 2013. Worked in the Psychiatry Department, Neurological Institute, and Rehabilitation Center.","fieldKeys":["experiences"]},{"id":"education:0","type":"education","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Education / 學歷: Ph.D. Candidate. Institute of Medical Science. University of Toronto","fieldKeys":["educations"]},{"id":"education:1","type":"education","ordinal":1,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Education / 學歷: M.S.. Clinical Psychology. National Chung Cheng University","fieldKeys":["educations"]},{"id":"project:0","type":"project","ordinal":0,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: MetaScientist (Meta_Scientist). Built with / 使用技術: Python, MetaGPT, Monte Carlo Tree Search, Docker. Self-improving agentic research framework pairing LLM multi-agent orchestration with Monte Carlo tree search (MCTS) to automatically search quantum machine-learning algorithm pipelines. Integrates web search, task planning, and a dual-loop research cycle. Implementation framework behind the QTML 2025 paper; extends MetaGPT/SELA.","fieldKeys":["projects"]},{"id":"project:1","type":"project","ordinal":1,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: DDB Knowledge Agent (ddb-knowledge-agent-py). Built with / 使用技術: Python, FastAPI, Server-Sent Events, Gemini, PostgreSQL, pgvector, Apptainer. Streaming RAG agent fusing pgvector similarity retrieval with PostgreSQL full-text search via reciprocal rank fusion (RRF). Includes ReAct tool calling, query-complexity-tiered model routing, semantic caching, and cost auditing. PostgreSQL runs under rootless Apptainer and is deployable on Docker-less HPC clusters.","fieldKeys":["projects"]},{"id":"project:2","type":"project","ordinal":2,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: GOAT Network Agent (goat-agent_demo). Built with / 使用技術: Node.js, Express, ERC-8004, x402. Autonomous Web3 agent registering an ERC-8004 on-chain identity and integrating with the x402 payment flow. Executes price tracking, conditional triggering, on-chain payment, and attested settlement end-to-end with no human intervention after launch.","fieldKeys":["projects"]},{"id":"project:3","type":"project","ordinal":3,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: optlab — Derivatives Pricing & Strategy Backtesting (optlab-py). Built with / 使用技術: Python, NumPy, SciPy, Pandas, pytest, GitHub Actions, Black-Scholes, CRR Trees, Monte Carlo Simulation, Heston Model, Andersen QE, Raw-SVI. Pricing library and vectorised backtester built to test whether the variance risk premium survives transaction costs. Includes Black-Scholes analytic Greeks, CRR trees with American exercise, variance-reduced Monte Carlo with a 12.7× improvement via antithetic and control variates, Heston characteristic-function pricing and Andersen QE simulation, implied-volatility inversion, and raw-SVI and Heston surface calibration. Across 24 simulated 10-year paths, delta hedging lifted short-straddle Sharpe from 0.24 to 0.49, while frictions consumed 70% of gross P&L institutionally and all of it at retail. Included 102 model-validation tests covering convergence orders, Greeks versus finite differences, no-arbitrage bounds, and P&L versus a hand-computed ledger.","fieldKeys":["projects"]},{"id":"project:4","type":"project","ordinal":4,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: Quantum Backpropagation. Built with / 使用技術: Quantum Machine Learning. Publication by Wong, F.-T. (2026). Under review.","fieldKeys":["projects"]},{"id":"project:5","type":"project","ordinal":5,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: Multi-Token Prediction and Alternative Tokenization Improve Multi-Hop Reasoning and Graph Search. Built with / 使用技術: LLMs, Multi-Token Prediction, Multi-Hop Reasoning, Graph Search. Publication by Frydenlund, A., Wong, F.-T., et al. (2026). Under review.","fieldKeys":["projects"]},{"id":"project:6","type":"project","ordinal":6,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: Advanced For-Loop for QML Algorithm Search. Built with / 使用技術: Quantum Machine Learning, Algorithm Search. Publication by Wong, F.-T. (2025), accepted at QTML 2025.","fieldKeys":["projects"]},{"id":"project:7","type":"project","ordinal":7,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: Generative Quantum Eigensolver (GQE). Built with / 使用技術: Quantum Computing, Quantum Machine Learning. Publication by Nakaji, K., Wong, F.-T., et al. (2024).","fieldKeys":["projects"]},{"id":"project:8","type":"project","ordinal":8,"locale":"en","text":"Fu-Te Wong [contact removed] · [contact removed] · Toronto, Canada · github.co. Project / 專案: CNN Suicidality Prediction. Built with / 使用技術: Convolutional Neural Networks, Medical AI. Publication by Chen, V. C.-H., Wong, F.-T., et al. (2021), Journal of Clinical Psychiatry 82(2).","fieldKeys":["projects"]}]}