About
Highly innovative PhD Candidate in Electrical Engineering with a robust background in deep learning, signal processing, and machine learning, driving advancements in areas like medical imaging and anomaly detection. Proven ability to design and implement cutting-edge algorithms, publish in top-tier journals, and translate complex research into tangible results. Seeking Research Scientist or Machine Learning Engineer roles to leverage expertise in developing impactful AI solutions.
Work
Columbus, OH, US
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Summary
Led advanced research in deep learning and signal processing, developing novel algorithms for medical imaging and anomaly detection as part of PhD studies.
Highlights
Developed a novel deep learning framework for accurate image reconstruction and artifact reduction in Magnetic Resonance Imaging (MRI), achieving a 15% improvement in signal-to-noise ratio and faster acquisition times.
Designed and implemented a robust unsupervised anomaly detection system for industrial sensor data, reducing false positives by 20% and identifying critical equipment failures 2 weeks in advance of traditional methods.
Authored and co-authored 5 peer-reviewed publications in top-tier IEEE journals and conferences, contributing to the academic community and advancing the state-of-the-art in medical imaging and time-series analysis.
Collaborated with cross-functional teams of clinicians and engineers to translate research findings into practical solutions, successfully integrating models into experimental prototypes for real-world validation.
Mentored junior graduate students on research methodologies, experimental design, and technical writing, fostering a collaborative research environment and accelerating project timelines.
Wauwatosa, WI, US
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Summary
Contributed to the development of advanced deep learning solutions for medical imaging applications, enhancing diagnostic capabilities and patient care.
Highlights
Developed and optimized deep learning algorithms for medical image segmentation and reconstruction, improving model accuracy by 10% and reducing processing time by 15% for critical diagnostic tasks.
Implemented and evaluated various neural network architectures, including U-Net and GANs, to address challenges in low-dose CT reconstruction, resulting in superior image quality with reduced radiation exposure.
Conducted comprehensive data analysis and preprocessing on large-scale medical datasets, ensuring data integrity and optimizing input for machine learning models.
Collaborated with senior research scientists and clinical experts, presenting weekly progress reports and contributing to strategic discussions on product development and research directions.
Columbus, OH, US
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Summary
Instructed undergraduate courses in electrical engineering, facilitating student learning and improving academic performance.
Highlights
Taught and assisted in undergraduate courses including 'Signals and Systems' and 'Introduction to Electrical and Computer Engineering,' supporting over 150 students annually.
Developed supplementary course materials, led recitation sessions, and provided one-on-one tutoring, which contributed to a 10% improvement in average student exam scores.
Graded assignments and exams, providing constructive feedback to students that enhanced their understanding of complex engineering concepts.
Held regular office hours to provide academic support and guidance, fostering a positive learning environment and improving student engagement.
Education
Awards
Best Student Paper Award
Awarded By
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Recognized for innovative research in deep learning-based signal processing for medical applications.
Graduate Fellowship
Awarded By
The Ohio State University
Awarded for outstanding academic achievement and research potential in Electrical Engineering.
Publications
Published by
Journal of Machine Learning Research
Summary
Introduced a novel GAN-based approach for unsupervised anomaly detection in complex time-series data, demonstrating superior performance over traditional methods.
Languages
English
Mandarin Chinese
Skills
Programming Languages
Python, MATLAB, C++, Julia.
Machine Learning & Deep Learning
PyTorch, TensorFlow, Keras, Scikit-learn, OpenCV, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer Networks, Unsupervised Learning, Reinforcement Learning.
Signal & Image Processing
Digital Signal Processing (DSP), Image Reconstruction, Medical Imaging (MRI, CT), Compressed Sensing, Sparse Representation, Wavelet Transforms, Spectral Analysis.
Tools & Platforms
Git, Docker, Jupyter Notebook, VS Code, Linux, AWS (EC2, S3).
Data Analysis & Visualization
NumPy, Pandas, Matplotlib, Seaborn, Data Preprocessing, Statistical Modeling.