Shanshan Bi, Ph.D. Email & Phone Number

New York, New York, Us

Atlanta, Ga, Us

Atlanta, Ga, Us

Atlanta, Ga, Us

Cincinnati, Oh, Us

• Big data analysis, predictive modelling, fault prognosis and diagnosis, system control strategy optimization• Software development for industrial big data analysis, including machine learning algorithm design and implementation, data processing, and predictive modelling algorithms design

Cincinnati, Ohio, Us

Project 1: Reinforcement learning application to vehicle-to-vehicle communication based accident avoidance strategy for multiple self-driving cars (IMS/NSF)• Modeled the relation between the communication quality and vehicle speed, acceleration, path switching• Proposed one-class SVM to detect cyber network fault and predict the potential threats caused by cyber dynamics and uncertainties• Proposed a resilience solution to optimize the decision making of car speed and path selection in an online manner• Presented on 33rd Industry Advisory Board MeetingProject 2: One-class SVM Based Cyber Fault Prognosis for Industrial Networked Control Systems (IMS/NSF)• Applied LibSVM to classify faulty and healthy data• Analyzed the relation of the measured data and the faults in cyber-physical systems (CPSs)• Presented on 32nd Industry Advisory Board MeetingProject 3: Data Prediction Based Cyber Fault Prognosis for Industrial Networked Control Systems (IMS/NSF)• Evaluated PDF estimation method, including maximum likelihood estimation, histogram, and kernel density estimation (KDE)• Researched existing data and its PDF to predict possible faults in networked control systems (NCSs)• Presented on 31st Industry Advisory Board MeetingProject 4: Cyber Faults Diagnosis and Resilience Control for Industrial Networked Systems (IMS/NSF)• Analyzed the existing data to detect cyber faults and isolate it from hardware devices faults• Designed a PDF-based tuning optimal controller to mitigate the negative impact of cyber faults• Presented on 30th Industry Advisory Board Meeting

Rolla, Mo, Us

Project 5: Capacity Prediction Based on RBF Neural Network for Routing Path in Ad-hoc Networks (NSF)• Employed an RBF neural network to train a model for transmission power vs. capacity• Implemented data mining analysis to predict the maximum capacity of transmission path• Proposed peer2peer and end2end routing scheme based on capacity information provided by data analysis• First authored one paper in preparation to be submitted to IEEE Transactions on Instrumentation and MeasurementProject 6: Design of Hardware Platform for Multimodal Sensor Detection Networks (NSF)• Tested chemical sensors based on thin-film bulk acoustic resonator (FBAR)• Investigated the correlations between the measured frequency-vs-humidity and frequency-vs-temperature by data analysis• Drafted the technical report

Project: Integration, Dynamic Analysis and Operation for Waste Heat Recovery (973 National Project No. 2011CB710706) • Proposed a PDE mathematical model for organic Rankine cycle (ORC) system• Developed a data-driven model for ORC system by employing a recursive least square (RLS) estimator• Studied RLS based general minimum variance (GMV) adaptive controller to regulate ORC system• Drafted the technique reports and presentation materials for the cooperation with other institutions and universities• Co-authored four publications, and one registered software for ORC system identification and regulation• Patents and Invention Disclosures: Identification and Adaptive Control for Energy Recovery System Software

Doctor Of Philosophy (Ph.D.), Electrical, Electronics And Communications Engineering

Master'S Degree, Energy Recovery And Control