Ravi Shekhar

Ravi Shekhar is a Lecturer (Teaching & Research) at the University of Essex’s School of Computer Science and Electronic Engineering (CSEE). Previously, he was a Post-Doctoral Researcher at Queen Mary University of London (2019–2023), working on projects like EMBEDDIA and SoDeStream. He holds a Ph.D. from the University of Trento (2015–2019), focusing on language and vision models, and an M.S. from IIIT Hyderabad. His research spans Conversational AI, Cross-Lingual NLP, and Social Media Analysis, with a focus on ethical AI and low-resource language challenges.

Education

• Ph.D., Language, Interaction and Computation Laboratory, University of Trento (2015–2019)
• M.S. (Research), Computer Science, IIIT Hyderabad (2010–2013)
• B.Sc. (Hons) Computer Science, Delhi University (2001–2005)

Research Interests: Dr. Shekhar’s work addresses challenges in NLP and multi-modal systems, including abusive language detection, cross-lingual models, and conversational AI. He emphasizes practical applications in social media moderation and ethical AI, with contributions to datasets like CoRAL and LEDA.

Key Contributions: His 2024 work on ‘Power and Vulnerability in Organisational Communication’ explores language dynamics in hierarchies, while ‘Denoising Labeled Data for Comment Moderation’ advances active learning techniques. He leads grants from Innovate UK and the EU, focusing on dialogue systems for safety-critical applications.

Awards

• Outstanding Reviewer (EMNLP 2019)
• Graduate Research Assistantship (University of Trento)
• Institute Fellowship (IIIT Hyderabad)

Advising & Grants: Supervised 8 graduate students (Master’s/PhD) and secured grants totaling £1.2M, including Innovate UK projects on commodities trading AI and harm mitigation in data systems. Active in mentoring underrepresented groups and promoting collaborative research networks.

Labs & Teams: Leads the NLP research group at Essex, collaborating with institutions like UvA Amsterdam and Queen Mary. Engages in open-source projects and dataset development, emphasizing reproducibility and code sharing.