Tom Hope

Tom Hope is an Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem's School of Computer Science and Engineering, and a Research Scientist at The Allen Institute for AI (AI2). His work focuses on developing AI methods that augment and scale scientific knowledge discovery by harnessing vast repositories of scientific knowledge through literature, knowledge bases, and electronic medical records.

His educational background includes a PhD with Dafna Shahaf, followed by postdoctoral research at AI2 and the University of Washington working with Daniel Weld and Eric Horvitz. Prior to his PhD, he led an applied AI research team at Intel that published award-winning work.

Hope's research spans a full-stack spectrum from constructing new datasets and machine learning models to designing user-facing systems. His primary focus areas include text mining, knowledge graphs, information extraction, multimodal models, LLMs, and human-computer interaction for scientific discovery. He explores how computational approaches can transform the scientific process by helping researchers explore literature, generate hypotheses, and make informed decisions. His work has been featured in top venues including NAACL, EMNLP, ACL, CHI, AAAI, KDD, CACM, and PNAS, with coverage in Nature and Science.

His recent publications reveal a strong emphasis on scientific idea generation systems, literature-based hypothesis formation, and tools for navigating scientific literature. His work often bridges AI techniques with real-world scientific challenges, particularly in biomedicine.

• 2022 Azrieli Early Career Faculty Fellowship (awarded to eight scientists across all fields)
• KDD 2017 Best Research Paper Award
• AKBC 2021 Outstanding paper award
• Selected for 2021 Global Young Scientists Summit
• Selected for 2019 Heidelberg Laureate Forum
• ELLIS Society member
• Member of KDD 2020 Best Paper Selection Committee

Hope has advised numerous students from leading institutions including Carnegie Mellon, University of Washington, Georgia Tech, and others. His research is organized around five main thrusts: AI Inspiration systems for idea generation, Biomed Predictions for clinical applications, Knowledge Graph construction, Scientific Information Extraction, and Scientific Search engines for discovery. His work has significant implications for accelerating scientific discovery and improving clinical decision-making through evidence-based AI systems.