Neuro-symbolic Intelligent Cyber-physical systEms (NICE)
Down here is a picture of me taken with the researh group when I was working in NICE Lab.
I am thrilled to share with you my latest research endeavor: the Temporal Logic Causal Fairness Analysis (TL-CFA) framework. As an AI enthusiast deeply committed to advancing fairness in decision-making systems, I have dedicated significant effort to developing TL-CFA. This framework represents a fusion of two powerful concepts: causal reasoning and temporal logic. By dissecting biases into direct, indirect, and spurious effects through causal inference techniques, TL-CFA offers a nuanced understanding of bias dynamics. Moreover, the incorporation of temporal logic allows us to explore how specific parameters influence bias trajectories over time, enabling us to predict potential escalations towards certain demographics. I am particularly excited about the innovative Signal Temporal Logic formulas we have designed within TL-CFA, which empower stakeholders to proactively identify and mitigate bias. Join me on this journey towards fostering greater fairness and transparency in AI systems!
In pursuing my Master's degree in Robotics and AI, I've gained a thorough understanding of key concepts such as kinematics, dynamics, and control theory in robotics. Additionally, I've delved deeply into artificial intelligence, particularly focusing on machine learning techniques like reinforcement learning. My expertise lies in utilizing PyTorch for deep learning applications, replacing TensorFlow with PyTorch due to its flexibility and efficiency. Proficiency in programming languages like Python and C/C++ has enabled me to develop and implement algorithms effectively. I'm well-versed in ROS for robot control and integration, MATLAB/Simulink for simulation and modeling, and Git for version control. These software tools have been essential in my practical applications within the field. My mathematical skills include a strong foundation in linear algebra, calculus, probability, optimization techniques, and graph theory. These mathematical competencies are crucial for solving complex problems encountered in robotics and AI research and development. Overall, my Master's program has equipped me with a diverse skill set and knowledge base, allowing me to contribute effectively to the advancement of robotics and AI technology.