Dr. Jennifer D’Souza | Computer Science and Artificial Intelligence | Best Researcher Award

Dr. Jennifer D’Souza | Computer Science and Artificial Intelligence | Best Researcher Award

Junior AI Research Group Lead, TIB Leibniz Information Centre for Science and Technology, Germany

๐Ÿ† Dr. Jennifer D’Souza, a distinguished researcher in Computer Science and Artificial Intelligence, was recently honored with the coveted Best Researcher Award. Serving as the Junior AI Research Group Lead at TIB Leibniz Information Centre for Science and Technology in Germany ๐Ÿ‡ฉ๐Ÿ‡ช, Dr. D’Souza has made remarkable contributions to the field. Her innovative work and dedication have significantly advanced the realms of AI and computer science. With a passion for exploration and a commitment to excellence, she continues to push the boundaries of knowledge, inspiring both colleagues and aspiring researchers alike. Dr. D’Souza’s accomplishments stand as a testament to her outstanding expertise and leadership in the field.

Profile

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Education

๐Ÿ‘ฉโ€๐ŸŽ“ Dr. Jennifer D’Souza, an accomplished scholar, holds a Ph.D. in Computer Science (2015) and an M.S. in Computer Science (2010) from The University of Texas at Dallas, USA ๐Ÿ‡บ๐Ÿ‡ธ. Her academic journey began with a Bachelor’s degree in Information Technology (2008) from St. Francis Institute of Technology, India ๐Ÿ‡ฎ๐Ÿ‡ณ. Dr. D’Souza’s academic pursuits reflect her dedication to the field of computer science, showcasing her commitment to advancing knowledge and expertise. With a strong educational background spanning prestigious institutions across two continents, she brings a wealth of experience and insight to her work in the ever-evolving domain of technology.

Research Experience

๐Ÿ‘ฉโ€๐Ÿ”ฌ Dr. Jennifer D’Souza has been a pivotal figure in the field of artificial intelligence. Since 2022, she has held the role of Junior AI Research Group Leader at TIB in Hannover, Germany ๐Ÿ‡ฉ๐Ÿ‡ช. Prior to this, she served as a Postdoctoral Researcher at TIB from 2019 to 2022. Dr. D’Souza’s journey in AI began in 2011 when she started as a Graduate Researcher at the University of Texas at Dallas, USA ๐Ÿ‡บ๐Ÿ‡ธ, and has since held significant positions such as Postdoctoral Researcher at the University of California, Davis, USA, and Graduate Research Intern at the University of Illinois, Chicago, USA. In 2018, she contributed her expertise as a Data Scientist at Chatterbox Labs Ltd. in Surrey, UK ๐Ÿ‡ฌ๐Ÿ‡ง.

Professional Activities and Memberships

๐ŸŒŸ Dr. Jennifer D’Souza has established herself as a prominent figure in the field of Natural Language Processing (NLP), boasting an impressive array of accomplishments. She served as the organizer of the 2024 LLMs4OL “Large Language Models for Ontology Learning” Shared Task at ISWC 2024. Additionally, Dr. D’Souza led the SimpleText Task4, setting the state-of-the-art in “Tracking the State-of-the-Art in Scholarly Publications” at CLEF 2024. Her editorial roles include being a Topic Editor for “Advances in Structured Information Extraction for Large Language Models” in the Frontiers in Artificial Intelligence journal and a Special Issue Editor for “Information Extraction and Language Discourse Processing” in the Information journal. Furthermore, she contributes as an Editorial Board Member for the “Special Issue on Knowledge Graph Generation from Text” in the Semantic Web journal. Dr. D’Souza’s organizational prowess is evident in her role as the Workshop and Tutorials Chair at the SEMANTiCS 2023 Conference and her involvement in the Workshop Organizing Committee for Text2KG in 2022, 2023, and 2024. In 2021, she orchestrated the NLP Contribution Graph “Structuring Scholarly NLP Contributions in the Open Research Knowledge Graph” Shared Task at Sem Eval 2021, showcasing her dedication and expertise in the field. ๐Ÿ“šโœจ

Funding

๐Ÿš€ Dr. Jennifer D’Souza, from 2024 to 2026, leads the Hydrant project, “Hybrid Intelligence through Interpretable AI in Machine Perception and Interaction,” funded by Niedersรคchsisches Ministerium for Wissenschaft (Zukunft ND’s), with a total budget of 3M Euro, of which her team contributes 460k Euro. Simultaneously, she is involved in the AWASES initiative, “AI-Aware Pathways to Sustainable Semiconductor Process and Manufacturing Technologies,” from 2024 to 2026, supported by Intel-Merck Cooperation’s USA, with a personal investment of 220k Euro. Dr. D’Souza’s prior project, SCINEXT, “Neural-Symbolic Scholarly Information Extraction,” from 2022 to 2025, funded by the Federal Ministry of Education and Research (BMBF), involved a total investment of 750k Euro. ๐ŸŒŸ

Honors and Recognitions

๐ŸŽ“ Dr. Jennifer D’Souza, a distinguished scholar, has been awarded the 2024 Visiting Scholarship at the Center for Interdisciplinary Research (ZiF) at the University of Bielefeld, Germany ๐Ÿ‡ฉ๐Ÿ‡ช. Her exceptional academic journey includes receiving the 2021 Best Paper Award for “Automated Mining of Leaderboards for Empirical AI Research” at ICADL 2021. Dr. D’Souza’s groundbreaking research exemplifies her dedication to advancing the field of artificial intelligence. With her innovative contributions, she continues to shape the future of interdisciplinary research, leaving a lasting impact on both academia and the AI community.

Publications Top Notes

Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge

Sieve-based entity linking for the biomedical domain

Improving access to scientific literature with knowledge graphs

Domain-independent extraction of scientific concepts from research articles

Classifying temporal relations with rich linguistic knowledge

SemEval-2021 Task 11: NLPContributionGraph–Structuring Scholarly NLP Contributions for a Research Knowledge Graph

The STEM-ECR dataset: grounding scientific entity references in STEM scholarly content to authoritative encyclopedic and lexicographic sources

Automated mining of leaderboards for empirical ai research

Toward representing research contributions in scholarly knowledge graphs using knowledge graph cells

Anaphora resolution in biomedical literature: a hybrid approach

FAIR scientific information with the open research knowledge graph