Silpaja Chandrasekar

Ph.D. in Computer Science

Computer Science, Medical Image Analysis, Cancer Cell Detection

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.

Dr. Silpaja commenced her career as a Programmer Analyst Trainee at CTS (Cognizant Technology Solutions). Later, she ventured into research and served as a Research Associate in the Department of Chemistry at IIT (Indian Institute of Technology) Madras. Her passion for research led her to become a full-time research scholar in the field. Additionally, Silpaja embraced the joy of teaching as an Assistant Professor at Sri Venkateswara College of Engineering, Chennai.

Silpaja’s research experience has honed her skills in writing manuscripts, news articles, and reviews. Moreover, she has actively participated as a peer reviewer for many journals. After taking a break to raise her two daughters, Silpaja is now eager to re-immerse herself in the realms of learning, research, and writing. She finds joy in cooking and spending quality time with her daughters. At present, she is a Tech writer in the field of Artificial Intelligence.

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Articles from Silpaja

Supercharging CLIP with LLMs: A New Era for Multimodal AI

Researchers from Microsoft and Tongji University introduced LLM2CLIP, a novel framework leveraging large language models (LLMs) to dramatically enhance CLIP’s multimodal capabilities. By fine-tuning LLMs in a new caption-contrastive space, the method unlocks richer text-image alignment and boosts cross-lingual performance.

14 Nov 2024

Agora Protocol Tackles AI Communication Challenges with Scalable Autonomous Networks

Agora, a new meta-protocol, solves the "Agent Communication Trilemma" by blending structured routines, natural language, and LLM-generated responses to enable scalable, autonomous networks of AI agents.

13 Nov 2024

PITOME Revolutionizes Transformers by Merging Tokens to Save Memory and Boost Speed

Researchers unveil PITOME, a novel token merging technique that accelerates transformer models while preserving critical information for enhanced AI efficiency.

12 Nov 2024

Can AI Think Like Kids? Study Reveals Major Gaps in LLMs’ Analogical Skills

Large language models (LLMs) struggle to generalize analogical reasoning across unfamiliar domains, unlike children and adults. This research highlights significant challenges for AI in achieving human-like abstraction and flexibility in analogy solving.

11 Nov 2024

ByteDance Unveils Revolutionary Image Generation Model That Sets New Benchmark

Researchers at ByteDance propose a novel randomized auto-regressive model (RAR) for image generation that outperforms existing methods, achieving state-of-the-art results on the ImageNet-256 benchmark. By maintaining compatibility with language modeling frameworks, RAR enhances bidirectional context learning during training.

10 Nov 2024

Researchers Develop LongPPL and LongCE to Enhance Long-Context Language Model Evaluation

This research paper highlights the limitations of perplexity (PPL) in evaluating long-context capabilities of large language models (LLMs) and proposes a new metric, LongPPL, which prioritizes key tokens. It also introduces LongCE, a re-weighting strategy for model fine-tuning, leading to more accurate long-context performance.

10 Nov 2024