(1) Scalable Multimodal Frameworks – Modern AI models must meet the growing demand for thousands of capabilities. My research has addressed this challenge by introducing: (a) Unified generative frameworks that flexibly accommodate diverse modalities and tasks, using a single architecture and a generative objective – VL-T5 (ICML 2021) / X-LXMERT (EMNLP 2020) / TVLT (NeurIPS 2022 Oral) and (b) Efficient finetuning frameworks that significantly reduce parameter and memory requirements for creating task-specific models – VL-Adapter (CVPR 2022) / LST (NeurIPS 2022) / Ctrl-Adapter (ICLR 2025 Oral)

(2) Faithful Multimodal Reasoning – Scaling alone is not enough. Large models that rely on black-box reasoning and encode all knowledge within their parameters often struggle with basic tasks and produce hallucinations. My research makes their reasoning process more accurate and interpretable by introducing: (a) Planning-based frameworks that decompose complex visual generation problems into faithful, human-interpretable step-by-step reasoning processes – VPGen (NeurIPS 2023) / VideoDirectorGPT (COLM 2024) / DiagrammerGPT (COLM 2024) / Video-MSG (2024) and (b) Retrieval-augmented generation (RAG) frameworks that enhance accuracy and factuality by retrieving relevant information before generating outputs – M3DocRAG (Findings of ICCV 2025) / HiREST (CVPR 2023)

(3) Evaluation and Refinement of Multimodal Generation – With recent advancements in multimodal generation models, conventional evaluation metrics have been often saturated and no longer provide meaningful insights into future research direction. To this end, my research introduces: (a) Fine-grained evaluation frameworks that comprehensively measure model skills in multiple dimensions to uncover detailed strengths and weaknesses – DALL-Eval (ICCV 2023) / VPEval (NeurIPS 2023) / DSG (ICLR 2024) / LayoutBench (CVPRW 2024 Oral) / FineCapEval (Findings of NAACL 2022) / M3DocVQA (Findings of ICCV 2025) / CAPTURe (ICCV 2025) and (b) Automatic model refinement frameworks that use these evaluations to detect models’ weaknesses and refine their reasoning process – EnvGen (COLM 2024) / DataEnvGym (ICLR 2025 Spotlight) / SELMA (NeurIPS 2024) / VideoRepair (Findings of ACL 2026)

I know these are far from perfect, but I hope this helps you in your applications!

• Academic Job Market (written in Dec 2024): Research Statement, Teaching Statement, DEI Statement
• Bloomberg Data Science Fellowship (written in Apr 2023): Research Statement
• PhD Admission (written in Dec 2019): Statement of Purpose.
  • This collection of SOPs for CS PhD programs looks useful as well!