Vivek Srivastava
Research ScientistTCS Research, Noida, Indiasrivastava.vivek2@tcs.comGoogle ScholarΒ LinkedIn
π Hello! I am Vivek Srivastava, a Research Scientist at TCS Research in the Media, Entertainment, and Advertising research area. My current research explores how AI systems and foundation models can learn from experience, adapt their behavior, and align with human goals. I am particularly interested in developing AI that augments human creativity and collaborates effectively with humans to solve complex, open-ended problems.
π¬My research aims to advance the capabilities of AI systems and foundation models through principled approaches to alignment, adaptation, and creative intelligence. Alongside developing new algorithms and evaluation methodologies, I investigate their applications to multimedia, human-AI collaboration, and computational creativity. This work has resulted in publications at leading AI venues and multiple filed patent applications in generative AI, computational creativity, and multimedia intelligence.
π Previously, I received my M.Tech in Computer Science and Engineering from IIT Gandhinagar, where my research focused on low-resource and code-mixed Indian languages. This work led to pioneering contributions to code-mixed machine translation and text generation, including open-source datasets and resources, tutorials, and shared tasks that continue to support multilingual NLP research.
π My research contributions were recognized with the Young Scientist Award at TCS Research in May 2026.
π July 2026 Papers presented at ICML and ACL
π June 2026 ArtMine accepted at ICML 2026 workshop on Human-AI Co-Creativity
π May 2026 Received TCS Young Scientist Award!
π Apr 2026 Attended ICLR 2026 in Brazil
π Apr 2026 Paper accepted at ICML 2026 Main Track!
π Apr 2026 Paper accepted at ACL 2026 Findings!
π Mar 2026 Papers accepted at ICLR 2026 Workshops on Recursive Self-Improvment, Representational Alignment, etc.Β
π Mar 2026 DoTA presented at WACV 2026
π Dec 2025 ArtPeer presented at NeurIPS 2025 Creative AI Track