ServiceNow AI Research

Spandana Gella

Spandana Gella

Research Lead

Agentic Harness & Defenses

Spandana Gella is a Research Lead and Sr. Manager for Agentic Defenses and Harnesses at ServiceNow AI Research and is also an Adjunct Faculty member at the School of Computer Science, McGill University. Her research focuses on building robust and safe AI agents spanning agentic harnesses, defenses against adversarial and unsafe behaviour, reliable agent evaluation, and multi-agent security and privacy. She received her Ph.D. in Computer Science from the University of Edinburgh, UK, where she was advised by Prof. Mirella Lapata and Prof. Frank Keller. Prior to joining ServiceNow, she worked at Amazon AI and AGI labs for six years. She has also worked as a visiting researcher at Meta AI Research and Microsoft Research Redmond and India. She is an active member of the academic community, serving as a core program committee member of EMNLP 2026. She has organised workshops, and served as Area Chair and Program Committee member at major NLP/ML conferences.

Interests
  • Multimodal Models
  • Safety
  • Large Language Models
  • Dialog

Publications

WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents. Workshop at the International Conference of Machine Learning (ICML),  2026.

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Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA. Language Resources and Evaluation Conference,  2026.

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CUA-Suite: Expert Trajectories and Pixel-Precise Grounding for Computer-use Agents . Workshop at the International Conference of Machine Learning (ICML),  2026.

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DRBench: A Realistic Benchmark for Enterprise Deep Research. International Conference on Learning Representations,  2026.

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Grounding Computer Use Agents on Human Demonstrations. International Conference on Learning Representations,  2026.

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StarFlow: Generating Structured Workflow Outputs From Sketch Images. European Chapter of the Association for Computational Linguistics (EACL),  2026.

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AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding. Neural Information Processing Systems (NeurIPS),  2025.

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Rendering-Aware Reinforcement Learning for Vector Graphics Generation. Neural Information Processing Systems (NeurIPS),  2025.

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ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval. Conference on Empirical Methods in Natural Language Processing (EMNLP),  2025.

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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering. Conference on Empirical Methods in Natural Language Processing (EMNLP),  2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Conference on Empirical Methods in Natural Language Processing (EMNLP),  2025.

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BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning. Conference on Language Modeling (COLM),  2025.

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AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery. Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL),  2025.

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SafeArena: Evaluating the Safety of Autonomous Web Agents. International Conference on Machine Learning (ICML),  2025.

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UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction. International Conference on Machine Learning (ICML),  2025.

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AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding. Workshop at the International Conference of Learning Representation (ICLR),  2025.

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WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation. Workshop at the International Conference of Learning Representation (ICLR),  2025.

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BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks. International Conference of Learning Representations (ICLR),  2025.

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BigDocs: A Permissively-Licensed Dataset for Training Vision-Language Models on Document and Code Tasks. Workshop at the Neural Information Processing Systems (NeurIPS),  2024.

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