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.

Articles from Silpaja

Researchers Tackle 3D Scene Reconstruction Challenges by Categorizing and Mitigating Key Uncertainties

Researchers Tackle 3D Scene Reconstruction Challenges by Categorizing and Mitigating Key Uncertainties

Researchers introduced a comprehensive taxonomy for uncertainty in 3D scene reconstruction using NeRFs and GS, enhancing the robustness of these methods by integrating advanced uncertainty estimation techniques.

16 Sep 2024

Dual LLMs Streamline Robot Programming and Boost Code Safety

Dual LLMs Streamline Robot Programming and Boost Code Safety

Researchers developed a dual-LLM approach to automate robot programming, significantly reducing development time and enhancing code reliability.

15 Sep 2024

Researchers Predict Sea-Surface Debris Movement Around Malta Using AI and Physics Models to Aid Marine Conservation

Researchers Predict Sea-Surface Debris Movement Around Malta Using AI and Physics Models to Aid Marine Conservation

Researchers combined machine learning and physics-based models to predict and visualize sea-surface debris movement around Malta, enhancing marine conservation efforts.

13 Sep 2024

Boost Machine Learning Trust With HEX's Human-in-the-Loop Explainability

Boost Machine Learning Trust With HEX's Human-in-the-Loop Explainability

Researchers introduced HEX, a human-in-the-loop deep reinforcement learning method that improves trust and explanation quality in machine learning models used in high-stakes decision-making.

11 Sep 2024

Can Artificial Consciousness Be Achieved?

Can Artificial Consciousness Be Achieved?

Researchers explore the feasibility of developing artificial consciousness by using the human brain as a benchmark, highlighting the challenges and potential for AI to emulate or develop alternative forms of consciousness.

11 Sep 2024

Passive-Aggressive Algorithms and SVMs Lead in Fake News Detection, But Challenges Persist

Passive-Aggressive Algorithms and SVMs Lead in Fake News Detection, But Challenges Persist

Researchers evaluated various machine learning methods for false news detection, highlighting the strengths and limitations of passive-aggressive classifiers, SVMs, and random forests, while introducing a novel ChatGPT-generated dataset.

9 Sep 2024

Generative Chatbots Amplify False Memories in Witness Interviews, Posing New Ethical Risks

Generative Chatbots Amplify False Memories in Witness Interviews, Posing New Ethical Risks

Generative chatbots significantly increase the formation and persistence of false memories during simulated crime witness interviews, raising ethical concerns about their use in sensitive contexts.

8 Sep 2024

Researchers Revolutionize Character Animation With Deep Reinforcement Learning

Researchers Revolutionize Character Animation With Deep Reinforcement Learning

Researchers developed a multi-agent deep reinforcement learning method to simulate complex multi-character interactions in dynamic environments, including human-robot scenarios. This approach ensures physically accurate and adaptable motions.

5 Sep 2024

Machine Learning Predicts Recovery in Endurance Athletes But Requires Personalized Strategies

Machine Learning Predicts Recovery in Endurance Athletes But Requires Personalized Strategies

Researchers used machine learning to predict recovery status and heart rate variability in endurance athletes, revealing significant variability in individual responses and emphasizing the need for personalized training approaches.

4 Sep 2024

MIT's AI Risk Repository Launches Database of 777 AI Risks

MIT's AI Risk Repository Launches Database of 777 AI Risks

Researchers at MIT's AI Risk Repository created a comprehensive database categorizing 777 AI risks into two detailed taxonomies, providing a foundational tool for managing AI-related hazards.

4 Sep 2024

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