Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness

Zihan Liang* , Ziwen Pan*, Ruoxuan Xiong | Accepted at EMNLP 2025 Main

• Proposed CRL-MMNAR, a causal multimodal framework that treats modality missing-not-at-random as signal—combining missingness-aware fusion (gated by observation patterns) with cross-modal reconstruction + contrastive learning, and a multitask predictor with a cross-fitted rectifier to correct observation-pattern bias

• Demonstrated consistent gains on MIMIC-IV and eICU, including AUC 0.8687→0.9824 for ICU admission (+13.1%) and 0.7989→0.8657 for readmission (+8.4%) on MIMIC-IV, and readmission AUC to 0.9294 (+13.8%) on eICU, with lower Brier scores indicating better calibration

CareLab at #SMM4H-HeaRD 2025: Insomnia Detection and Food Safety Event Extraction with Domain-Aware Transformers

Zihan Liang*, Ziwen Pan*, Sumon Kanti Dey, Azra Ismail | Accepted at AAAI ICWSM 2025

• Ranked 1st on SMM4H–HeaRD 2025 Task 5, developing a RoBERTa + GPT-4–augmented system with classweighted training and ensembling, achieving F1 = 0.958 and revealing the limits of rule-based span extraction in clinical and food-safety texts

Manuscripts

Adaptive Spatiotemporal Graph Neural Networks with Trend-Aware Prediction and Validation-Gated Calibration for Traffic Flow Forecasting

Zihan Liang* , Ziwen Pan*, Shuyang Yu | Drafting | May 2025 – Present

• Proposed an adaptive spatiotemporal GNN that fuses distance- and correlation-based graphs with dual temporal–spatial encoders and a trend-aware head, achieving state-of-the-art accuracy on PeMS benchmarks

• Designed a validation-gated calibration pipeline with hour-conditional quantile mapping and AR(1) residual correction, applied only when improving MAPE/MAE to ensure reliable and interpretable post-processing

MambaDATG: Domain-Adaptive Tri-Plane-Gated Pre-training for 3D Abdominal Segmentation

Y. Shen* , D. Jiang* , Zihan Liang* , Y. Wei, W. Yang, S. Wang, et al. | ICASSP 2026 Under Review | Mar. 2025 – Present

• Proposed MambaDATG, a tri-plane-gated selective SSM pre-training that fuses axial, coronal, and sagittal scans via voxel gating—capturing directional anisotropy with linear-time efficiency and zero inference overhead

• Introduced a domain-adaptive MIM stage adapting high-level layers on unlabeled CTs, improving small-organ segmentation (e.g., +1.8 Dice on BTCV) and revealing the tri-plane gate as the key performance driver

Learning Recommendations under Modality-Dependent Missingness via Category and Temporal Adaptation

Ziwen Pan*, Zihan Liang* , Ruoxuan Xiong | KDD 2026 Under Review | Mar. 2025 – Present

• Proposed TMC-MNR, a multimodal recommendation framework handling MNAR data with cross-category transfer and temporal reweighting, achieving consistent top-K improvements on Amazon, MovieLens, and Yelp benchmarks

• Addressed modality bias by modeling MNAR selection with learned heads and inverse-probability weighting, enhancing robustness for under-represented users without inference overhead

Gated Dynamic Local-Global Attention for Sentiment Analysis: Enhancing Context Awareness in Short Texts

Ziwen Pan, Zihan Liang | IEEE Transactions on Knowledge and Data Engineering Under Review | Jun 2024 – Present

• Designed a Gated Dynamic Local-Global Attention (GDLGA) model integrating BERT global features with sliding-window local attention for short-text sentiment analysis

• Achieved state-of-the-art results on Sentiment140 (F1 87.49%, AUC 0.9484), validated through extensive ablation studies on attention and fusion mechanisms

Dynamic Policy Design for Autonomous Taxi Adoption: A Hierarchical Game-Theoretic Framework

Zihan Liang, Ziwen Pan | TRR Under Review | Jun 2024 – Present

• Formulated a three-tier dynamic Stackelberg game modeling interactions among government, platform, and workers in autonomous taxi adoption, deriving equilibrium policies that balance innovation incentives with social welfare

• First-Author; Accepted at TRB Annual Meeting 2026

Collaborative Research Projects

Research Assistant at Collective Action & Research for Equity (CARE) Lab, Emory University (Sept. 2024 - Present)

• Analyzed nearly 1,000 user data points and developed an interactive dashboard for MakerGhat, an India-based organization, utilizing JavaScript, Python, and HTML for seamless data visualization

• Maintaining a WhatsApp Education Bot using Python and Twilio for MakerGhat, enabling over 15,000 teachers across India to document lesson plans and teaching activities, impacting 600,000+ students and supporting 10,000+ educational projects

• Developing an Android application for MakerGhat (India-based non-profit) to help teachers record courses more effectively

Research Assistant at Polymath Jr REU Summer Program (Jun. 2025 – Aug. 2025)

• Developed and analyzed mathematical models using ordinary differential equations (ODEs) to study the community transmission of Clostridioides difficile (C. difficile)

• Collaborated with a team to identify optimal intervention strategies for controlling the spread of C. difficile in community settings

• Assisted in modifying and implementing code in MATLAB and R to simulate disease dynamics and evaluate mitigation strategies