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.
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"
