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RESUME

Anchor 1

I am currently studying at Duke University for a Ph.D. in Electrical and Computer Engineering. My research focuses on Computer Vision, Machine Learning, and Artificial Intelligence. I am a passionate and committed researcher. Now seeking research positions.

Objective
Skills

Keras

Pytorch

OpenCV

Tensorflow

Amazon AWS

Research
experience​
Federated Unsupervised Representation Learning
Jan.2022-Jul.2022

Duke University, Research Advisor: Yiran Chen

Implemented an unsupervised distributed learning framework. Proposed personalized network branches at local clients.

Unsupervised Image Segmentation
May.2021-Aug.2021

Samsung Semiconductor, Research Advisor: Mostafa El-Khamy

Implemented a weakly supervised loss in addition to the cluster loss. Designed a box sampling process for better label continuity in neighboring pixels.

Causal Framework for Generalized Image Representation
Feb.2021-May.2021

Duke University, Research Advisor: Taoyang Chen

Developed causal graphs for image representation learning. Synthesized observational data for validating causal graphs. Implemented experiments to test assumptions. Adapted causal relation to real images to improve the generalization of the representation.

Unsupervised Visual Representation Learning
May.2020-August.2020

ABB Inc., Research Advisor: Remus Boca

Representation Learning using a contrastive learning framework. Improved representations by inducing adversarial noise. Verified quality representations on vision downstream tasks.

Positive Unlabeled Learning in Object Detection
Sep.2018-May.2019

 

Duke University, Research Advisor: Kevin Liang

Verified the performance drop using a popular dataset that has incomplete labels. Improved performance using Positive Unlabeled learning instead of Positive Negative Learning is commonly used. Implemented PU detectors with Faster Regional CNN and Single Shot Detector.

 
Interpretability and Transfer Learning of Deep Neural Network

May.2018-April.2019

 

Duke University, Research Mentor: Prof. Henry Pfister

Trained deep CNN models with both synthesized and real image datasets and tested them on a real dataset. Utilized Class Activation Mapping to localize multiple objects in images and interpreted the inner mechanism of the network.

 

Multiple Instance Learning for Training Weakly Supervised Learning Models

Sep.2017-May.2018

 

Duke University, Research Mentor: Dr. Jordan Malof

Built MI SVM and CNN models to study the underlying relationships between the complexity of a classifier, the positive ratio in a bag, and the accuracy of the classifier.

 

Prediction of Seizure in EEG and ECG signals

Feb.2017-May.2017

 

Duke University, Research Mentors: Professor Leslie M. Collins and Dr. Nicholas Czernek

Built feature extractors and reduced dimension with PCA and GA. Trained a model with seizure and non-seizure segments with the Random Forest algorithm.

 

 

Maximizing Bit Rate in Cognitive Radio System

Jul.2015-Jan.2016

 

Research Mentors: Prof. Mohamed-Slim Alouini and Dr. Osama Amin

Verified simulations of different modulation schemes against their analytical expressions. Derived an analytical expression for error probability of improper signaling of secondary users.

 
Languages

C++/C       > 5 years

Matlab     > 5 years

Python     > 3 years

Java          > 1 year

PHP          > 1 year

MySQL     > 1 year

Education
Education
Duke University

Aug.2019 - Present

 

Electrical and Computer Engineering, advised by Professor Lawrence Carin and Professor Yiran Chen.

 

Duke University

Aug.2016 - May.2018

 

Electrical and Computer Engineering, Master of Science, GPA 3.82.

Master’s Project: “Multiple Instance Learning in Deep Learning” advised by Dr. Jordan Malof and Professor Leslie Collins. Courses focus on learning machine learning algorithms and applications in NLP, Computer Vision, and Pattern Recognition.

 

University of Cambridge

Oct.2014 - Jun.2015

 

Electrical and Electronics Engineering, Master of Engineering, GPA 3.70.

Master’s Thesis: “Design and build of an audio amplifier” advised by Professor Richard McMahon. Focuses on designing and testing chip hardware like VLSI.

 

University of Cambridge

Oct.2011 - Jun.2014

 

Electrical and Electronics Engineering, Bachelor of Arts, GPA 3.64.

Three years of bachelor's degree program focusing on general engineering aspects including mathematics, information, electrical and electronics, aerodynamics, mechanics, and bioengineering. 

 

Professional experience
Research Scientist Intern,
Samsung Semiconductor., Remote, USA

May.2021-Aug.2021

 

Researched unsupervised Image Segmentation.

Perception and Computer Vision Research Intern, ABB Inc., Raleigh, USA

May.2020-Aug.2020

 

Researched unsupervised representation learning to classify hyperspectral images. Researched multiple object tracking.

Software Engineer, Aqueti Intl., Durham, USA

May.2017-Jul.2017

 

Researched systems to maximize the storage efficiency in a camera system. Built scripts to determine the maximum writing rates for the camera memory system.

Software Engineer, Wavebot, Beijing, China

Feb.2016-Jun.2016

 

Tested different laser hardware, modified laser-based SLAM, and programmed code to optimize the SLAM performance of an indoor service robot.

Software Engineer, Cambridge Silicon Radio, Cambridge, UK

Jun.2014-Sep.2014

Designed the A4WP wireless charging transmitter including a beacon procedure and a time module in C and Python and performed dynamic and static parameters exchange between the receiver and the transmitter.

Publications
Y. Yang*, K. Liang*, and L. Carin, "Object Detection as a Positive Unlabeled Problem", The British Machine Vision Conference (BMVC), Aug 2020
D Wang*, Y Yang*, C Tao, F Kong, R Henao, L Carin, "Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models", preprint
 
Y Chen, A Li, H Yang, T Zhang, Y Yang, H Li, S Banerjee, M Pajic, "AI-Powered IoT System at the Edge", International Conference on Cognitive Machine Intelligence (CogMi), Dec 2021
Guo Q, Chen J, Wang D, Yang Y, Deng X, Carin L, Li F, Tao C, "Tight mutual information estimation with contrastive Fenchel-Legendre optimization", Conference on Neural Information Processing Systems (NeurIPS), Nov 2022

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