Cheng Qiu

Vanderbilt University

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516 58TH ST

Brooklyn, New York

My interests lie in the fields of computer science and artificial intelligence, with a particular focus on computer vision, machine learning, and their interdisciplinary applications. I am captivated by the transformative potential of AI to interpret and interact with the physical world, from developing intelligent systems that perceive environments visually to crafting algorithms capable of learning from complex, unstructured data. My work in computer vision, such as enhancing emotion classification models through the use of facial perturbations, highlights my passion for creating deep learning systems that combine theoretical innovation with practical impact.

Looking ahead, I am eager to contribute to cutting-edge advancements in AI research, especially in bridging the gap between perception and decision-making systems. I am deeply interested in addressing challenges such as improving the robustness and fairness of AI models, designing intelligent systems tailored for human-centric applications, and leveraging simulation to tackle data scarcity. My ultimate goal is to develop AI technologies that are not only precise and reliable but also interpretable and adaptable, with applications across diverse fields like healthcare, robotics, and education.

Beyond research, I have a range of interests that keep me grounded and energized. I enjoy playing video games, particularly gacha games, and am a passionate badminton player. Whenever I have free time, you can often find me on the badminton court, enjoying the competition and camaraderie. I’m always excited to meet new people who share my enthusiasm for games or sports. Whether you’re interested in discussing my research, joining me for a game, or hitting the court for a badminton match, feel free to reach out — I’d love to connect!

news

Oct 14, 2024 Submitted my first independent manuscript titled “Leaving Some Facial Features Behind”! :smile:
Sep 20, 2024 Submitted the manscript titled “Paraphrase identification with deep learning: A review of datasets and methods”! :sparkles: