Divyanshu Daiya
Computer Science · Purdue University West Lafayette
Publications
10
Citations
62
Est. group size
—
Recurring co-author estimate
Active years
9
Publishing since 2018
Divyanshu Daiya's work spans two main directions: applying deep learning and diffusion-based generative models to human motion and interaction generation (e.g., animating multiple humans from sketches or generating collaborative human-agent interactions), and using machine learning methods for financial time-series problems such as stock movement prediction. Earlier work also touched on natural language processing tasks like text summarization. Prospective students would be exploring generative modeling techniques (diffusion models, latent diffusion, language models) applied to diverse domains including animation, human-agent interaction, and financial forecasting.
Publication output has been irregular over the past decade, with gaps in some years followed by a recent increase in activity around 2024-2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation
arXiv (Cornell University) · 2026
- Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation
arXiv (Cornell University) · 2026
- COLLAGE: Collaborative Human-Agent Interaction Generation Using Hierarchical Latent Diffusion and Language Models
2025
- Diffstock: Probabilistic Relational Stock Market Predictions Using Diffusion Models
2024
- DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models
arXiv (Cornell University) · 2024
- COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models
arXiv (Cornell University) · 2024
- Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer
2021
- Stock Movement Prediction That Integrates Heterogeneous Data Sources Using Dilated Causal Convolution Networks with Attention
2020
- Combining Temporal Event Relations and Pre-Trained Language Models for Text Summarization
2020
- Using Statistical and Semantic Models for Multi-Document Summarization
arXiv (Cornell University) · 2018
- arXiv (Cornell University)×5
- Haining Wang
Computer Science · Indiana University
- He Zhou
Computer Science · Indiana University
- Dan Goldwasser
Computer Science · Purdue University West Lafayette
- Daniel Dakota
Computer Science · Indiana University
- Huan Sun
Computer Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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