Bingjia Wang

EMAIL: adjbjz@r.postjobfree.com PHONE: +1-347-***-****

LinkedIn: https://www.linkedin.com/in/bingjia-wang-a6271b139/ GitHub: https://github.com/wbj218 Local Address: 808 Dryden Rd, Apt B, Ithaca, NY 14850 Permanent Address: No.5 Jianguomen North Street, Room 1511, Beijing, China 100005 EDUCATION

Cornell University – Ithaca, NY Dec 2020

Master of Engineering in Electrical and Computer Engineering Overall GPA: 3.91/4.00

• Relevant Coursework: Bayesian Estimation and Stochastic Optimization; Data Mining and Machine Learning; Natural Language Processing; Computer Vision; Datacenter Computing; Embedded Operating System; Operating System; Data Structure Rensselaer Polytechnic Institute – Troy, NY May 2019 Bachelor of Science in Electrical Engineering, Minor in Mathematics Overall GPA: 3.96/4.00 HONORS & CERTIFICATES

Dean’s Honor List on All Semesters, RPI

Summa Cum Laude, RPI (May 2019)

RPI Harold N. Trevett Award (May 2019): Awarded annually to a senior for outstanding scholarship in EE. Bose Lean Enterprise Yellow Belt Certificate (June 2018) SKILLS

Python (TensorFlow, Scikit-learn, PyTorch), Java, Node.js, C, R, SQL, AMPL, MATLAB & Simulink, Cadence WORK EXPERIENCE

Journeyman Fellowship Research Intern, U.S. Army CCDC Army Research Laboratory May-Aug 2020 Internalizing Succinct Gaussian Processes by Integrative Policy Search in Reinforcement Learning

• Used compressed online Gaussian Process to estimate the environment transition dynamics and the state occupancy measure.

• Used Gaussian Quadrature to compute the integrative policy gradient w.r.t. the estimated state-action occupancy measure.

• Update the policy by extending the Palyak-Ruppert Averaging.

• Experimented on a few OpenAI environment and observed an order of magnitude reduction on sample complexity during training. Electrical Engineer Co-Op, Bose Corporation - Framingham, MA Jan-Jun 2018

• Used C and Matlab to implement different embedded sensor systems to develop algorithm for headphone on-off head detection.

• Implemented cap-touch system on headphone earcup to detect gestures.

• Developed test procedure, collected and analyzed data for on-off head detection algorithm improvement. RESEARCH & PROJECTS

A Markov Decision Process Approach to Active Meta Learning, Cornell – Ithaca, NY arXiv Link Aug 2019-May 2020

• Built a stochastic scheduler on top of existing deep meta learning algorithm to actively select samples on which to train from different meta-training sets, in order to improve the sample efficiency.

• Developed scheduling schemes based on multi-armed UCB and Gittins indices algorithms to exploit covariates within subset.

• Further exploited the dependencies between different training subsets by modeling the problem as Markov Decision Processes.

• Experimented the schemes on 3 datasets and showed that they could improve training efficiency and maintain high accuracy. NLP Projects, Cornell – Ithaca, NY Aug-Dec 2020

• Question-answering: Developed 2 question-answering models using DrQA and pretrained Bert model (in PyTorch) to answer questions and predict questions’ answerability in SQuAD 2.0 dataset.

• Emotion Classification: Used FFNN, RNN and LSTM (in PyTorch) to classify different emotions using Tweeter posts dataset.

• Sequence-tagging: Built features and used HMM and MEMM to identify propagandistic spans of text for tokens.

• Real/Fake News Classification: Use n-gram language model with smoothing and Naïve Bayes to classify real and fake news. Machine Learning Projects, Cornell – Ithaca, NY Aug 2019-Dec 2020

• Drug MoA Prediction: Used LightGBM and neural network (multi-seeds FFNN and ResNet) to predict MoAs on different molecules.

• Modulation Prediction: Used Python with sklearn and TensorFlow to implement AdaBoost with Random Forest, CNN and ResNet to predict the signal modulation schemes.

• Divorce Prediction: Used R to implement various parametric and non-parametric machine learning methods (LR with regularization, SVM, trees and random forest, PCA with nearest neighbors) to predict divorce; Used LASSO, forward and backward selection, data shrinkage to identify important features and interpret the findings. SLAM Mapping Robot, Cornell – Ithaca, NY PROJECT WEBSITE Aug-Dec 2019 Used Raspberry Pi, piTFT, IMU, wheel encoder and ultrasonic ranger to design and build a SLAM mapping robot to achieve area dimension and altitude measurement functionalities and show real time result on piTFT screen.

• Used IMU with low-pass and complementary filters to compute robot heading direction and altitude measurement.

• Used wheel encoder and ultrasonic ranger to achieve distance measurement and edge detection.

• Designed algorithm to localize robot and show the robot position and altitude measurement on piTFT in real time.

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