I am applying to PhD programs (Fall 2027 start) in machine learning for physiological time series — interpretable, frequency-domain representations and foundation models for clinical biosignals, from EEG to EMS audio.
You can contact me at:
m50816m50816 [at] gmail.com |
chisheng.m.chen [at] gmail.com
🚩 Jun 2026: Two short papers accepted at Digital Humanities 2026 (DH2026), Daejeon, South Korea — "Predicting Poets' Origins from Verse" and "Gendered Voices in Tang Poetry."
🎖️ May 2026: Recognized as Gold Reviewer at ICML 2026, placing among the top reviewers based on area chair ratings.
🚩 Jan 2026: Two papers accepted at IEEE ICASSP 2026 — "Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis" (oral presentation) and "Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance" (oral presentation).
🚩 Aug 2025: Two papers accepted at IEEE GLOBECOM Workshop 2025 — "Q-DPTS" and "Benchmarking Quantum and Classical Sequential Models."
🚩 Jun 2025: Paper accepted at IEEE QCE 2025 — "Quantum Reinforcement Learning Trading Agent for Sector Rotation."
🚩 May 2025: Paper accepted at IEEE CIBCB 2025 — "Enhancing Clinical Decision-Making."
🚩 Jan 2025: Paper accepted at IEEE ICASSP 2025 — "Quantum Multimodal Contrastive Learning Framework" (oral presentation).
My research builds machine learning for physiological time series along a single arc — from representation, to foundation models, to clinical deployment.
Representation: I develop interpretable, clinician-interrogable frequency-domain and geometric representations of biosignals
(FreqLens, FreqToken, SPD Token Transformer).
Foundation models: I scale these into cross-task, cross-dataset, and multimodal biosignal models
(Large Cognition Model, frequency-domain world models).
Deployment: my EEG-based depression-treatment model is in routine outpatient use at the
Precision Depression Intervention Center (PreDIC), Taipei Veterans General Hospital,
and I build multimodal EMS trauma-triage pipelines at Harvard/BIDMC.
As a secondary methodological line, I explore hybrid quantum-classical architectures for sequence modeling (QASA, QEEGNet).
Selected Publications
FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
CS Chen, X Zhang, EJ Kuo, GY Chen, Q Xie, F Zhang.
My core research develops interpretable, clinician-interrogable representations of physiological time series,
including FreqLens for frequency-domain attribution in forecasting
and SPD Token Transformers for EEG classification with Riemannian geometry. These representations scale into
cross-task, cross-dataset biosignal foundation models (Large Cognition Model). Applications extend to
urban telecommunication forecasting and Transformer-assisted learning in open quantum systems (Lindblad dynamics).
Developing AI systems for clinical neuroscience and emergency medicine.
My EEG-based depression treatment prediction models have been Clinically Deployed at the
Precision Depression Intervention Center (PreDIC)
at Taipei Veterans General Hospital, serving real outpatient patients.
At Harvard/BIDMC, I am building real-time EMS triage pipelines using multimodal AI for trauma prediction,
collaborating with surgeons on AI-assisted decision support systems.
I also develop multimodal contrastive learning methods for EEG-image alignment, such as MUSE.
Building speech and natural language processing systems for clinical settings,
including EMS audio transcription, automated clinical documentation,
and emergency page generation for trauma prediction workflows.
As a secondary methodological line, I explore hybrid quantum-classical architectures for time-series and sequential data,
including the Quantum Adaptive Self-Attention (QASA) Transformer, QuantumRWKV, and QEEGNet for quantum EEG classification.
Applications span EEG signal processing, financial time-series forecasting, and image generation.
Computer Vision
State Space ModelsFine-Grained RecognitionSurgical SafetyYOLO
Applying deep learning to visual recognition tasks, including surgical instrument detection for intraoperative safety,
and fine-grained food classification with foundation models such as Res-VMamba.
Research Experience
Department of Surgery, Harvard Medical School & Beth Israel Deaconess Medical CenterMA, USA
Searching new possible unconventional superconductors among Co-based quaternary chalcogenides with diamond-like structure CuInCo₂A₄ / AgInCo₂A₄ (A = Te, Se, S).
Contributed to Reinforcement Learning from Human Feedback (RLHF) pipelines through high-complexity AI data labeling, preference rankings, and model-behavior assessments for instruction following, multimodal reasoning, and safety alignment.
Microprocessor IP development for flagship 5G smartphones' display and AMBA SoC implementation.
Hardware virtualization architecture RTL design and IP verification with UVM and SystemVerilog.
Selected Work
Shippable, agentic-AI deliverables — LLM/agent systems that run in production, rank in open competition, or ship as open-source libraries — plus contract and production engineering. Open to select LLM/agent & quant engineering contracts.
A browser agent built as an explicit state machine with deterministic fault-injection recovery; a layered SEC 10-K item extractor (L1→L3 with confidence calibration); and a Strategy Lab of 22 research agents + 11 placebo controls, each with lookahead-free backtests, a per-provider cost ledger, and eval dashboards.
flyhypo — Grounded Functional-Hypothesis Generator for Fly Neurons
Independent · Proof of concept · 2026
Combines connectome structure (neuPrint) with literature evidence (PubMed) and an LLM (Gemini) to generate falsifiable functional hypotheses for Drosophila cell types — every claim traced to a specific paper id or connectivity number, with verification guards (verbatim re-grep, mis-attribution, number-existence) so it never fabricates.
paper-evidence — Grounding LLM Claims in Scientific Literature
Independent · Open-source library · 2026
A verification core where a claim about a source survives only if the source verifiably supports it: a verbatim-quote gate, numbers-must-sit-next-to-their-quote, and an independent cross-family LLM judge for paraphrase/citation faithfulness — plus a literature extractor (saturation search, citation snowball, recall scoring).
Berkeley RDI AgentX–AgentBeats · 3rd Place / 1,300+ teams · 2026
Co-built a multi-agent LLM evaluation system with role-specialized agents (examiner, patient, judge) and a verifiable scoring rubric (RAG + tool use + verifier).
Eleven traditional divination systems as deterministic Python engines (planet positions validated against Swiss Ephemeris to <0.006°) with streaming bilingual English/中文 LLM readings. Originated as placebo controls in a quant project, then rebuilt as a standalone full-stack app.
Co-Founder & Lead AI/Quant Engineer · Live in production · 2024–Present
Automated market-maker strategies driven by time-series ML with full backtesting and MLOps (Docker, CI/CD, on-chain event pipeline, live P&L observability). Production results: 50%–120% base fee APR on WBTC/USDC and ETH/USDC pairs.
RLHF / Alignment — Contract for OpenAI (via Mercor)
Contract · Remote (US) · Mar 2025 – Oct 2025
Preference rankings and model-behavior assessments feeding SFT, DPO, and reward modeling across hard reasoning, code, tool-use, and safety-alignment prompts; drove rubric design that improved labeling consistency on ambiguous edges.
Designed and deployed a comprehensive RAG-based AI tutoring system for the "General Physics" course at NYCU.
Expanded feature set covering full undergraduate physics curriculum with adaptive content delivery,
problem-solving guidance, and concept reinforcement.
Next.jsRAGGemini AISupabasepgvectorVercel AI SDKServing NYCU Students
Laser Physics AI Teaching Assistant
Designed and deployed a RAG-based AI tutoring system for the "Introduction to Lasers" course at NYCU Department of Electrophysics.
Features 8 learning modes including adaptive quiz generation, exam simulation,
interactive concept knowledge graph, and spaced-repetition study planning.
Guided undergraduate students through wet-lab experiments: Biological Safety Cabinet operation, E. coli transformation, PCR, gel electrophoresis, and plasmid purification.
Designed lab protocols and assessment rubrics; held weekly office hours and one-on-one troubleshooting sessions.
Two short-paper presentations, Jul 27–31, 2026 (upcoming)
1. "Predicting Poets' Origins from Verse: A Computational Analysis of Regional Linguistic Fingerprints in the Complete Tang Poems"
2. "Gendered Voices in Tang Poetry: A Corpus-Based Study of Female-Authored Poems and Male-Adopted Female Perspectives"
NLPDigital Humanities
IEEE ICASSP 2026 — Barcelona, Spain
Two oral presentations, May 2026
1. "Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures"
2. "Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance"
