Data Science, Machine Learning, Computer Science, Applied Mathematics

CHIQUN ZHANG

• **** Oriole Ave, Sunnyvale, CA, **087 • 412-***-**** • **********@*****.***

https://www.linkedin.com/in/chiqun0524

EDUCATION

CARNEGIE MELLON UNIVERSITY Pittsburgh, PA, USA

Ph.D. in Civil Engineering (focus on applied mathematics), GPA 4.0/4.0 September, 2017 Master of Machine Learning, GPA 3.95/4.0 May, 2017 Master of Civil Engineering, GPA 4.0/4.0 May, 2013 TIANJIN UNIVERSITY Tianjin, China

Bachelor of Science in Civil Engineering, GPA 3.82/4.0 May, 2012 WORK EXPERIENCE

VERDIGRIS TECHNOLOGIES California, USA

Data Scientist September, 2017 - Present

• Develop machine learning/deep learning algorithms to handle time series IoT data, such as energy disaggregation, classification, and clustering, energy forecasting, energy storage control, and equipment health monitoring.

• Perform model evaluation and selection on existing machine learning algorithms for smart building system.

• Implement continuous integration and deployment of machine learning product on cloud (AWS) Product Owner October, 2018 - Present

• Developed company Objectives and Key Results (OKRs) with founders

• Created Product Requirement Documents (PRDs) for product teams

• Defined company roadmap and set priorities

Scrum Owner of R&D Team January, 2018 - October, 2018

• Helped design deliverables and scope of work of new products

• Allocated workload to team members and conducted performance evaluation

• Diagnosed resource issues in organizational structure NORTH CHINA MUNICIPAL ENGINEERING DESIGN& RESEARCH INSTITUTE Tianjin, China Structure Engineer July - September, 2011

• Collaborated on the in-site-cast concrete water plant project in Inner Mongolia (Budget: $200,000)

• Optimized the reinforcement distribution of cesspools by finite element analysis (Reduced the budget by $12,000) SELECTED HONORS, AWARDS AND CERTIFICATIONS

• Deep Learning Specialization certified by deeplearning.ai

• AWS Certified Solution Architect Associate, AWS Certified Developer Associate

• Fenves Travel Grant, Carnegie Mellon University, 2014, 2016

• Dean’s Fellowship, Carnegie Mellon University, 2013

• Best Graduation Thesis of Tianjin University, 2012

• National Scholarship for excellent undergraduate, Ministry of Education of China, 2009 RESEARCH EXPERIENCE

Expert-guided Machine Learning for Synthesizing Complex Material TiO2

• Design human-machine interaction algorithms for active classification for narrowing search space.

• Non-convex stochastic optimization using direct and pairwise queries

• Learning inherent expert feature representation and understand scientific judgment and reasoning. Deep Learning Model on Graph-structured Data

• Introduced a novel flexible framework which can directly encode a graph into neural representations.

• Applied the derived model on a molecule chemical solubility prediction problem and compared the result with previous works.

High Dimensional Variable Selection via Lasso

• Answered if the true sparsity pattern could be recovered from the fitted model.

• Drew the statistical inferences form the fitted model, such as p-value and confidence interval. Enhancing Appliance Identification Based on Appliance-level Energy Signal Data

• Extracted an extensive set of features from waveform time-series data of current and voltage.

• Developed and compared three principled classifiers including naïve Bayes, KNN and SVM, with cross validation. Computational Modeling of Tactoid Dynamics in Chromonic Liquid Crystal

• Augmented Oseen-Frank energy and derived the governing equations for the phase transition dynamics and its corresponding numerical scheme.

• Implemented the derived model with parallel computing and studied and predicted tactoid nucleation, expansion and coalescence.

Disclination Dynamics in Nematic Liquid Crystal

• Devised a natural augmented mathematical theory account for the disclinations.

• Proposed a 2D numerical computational model based on thermodynamics, and kinematics to analyze liquid crystal disclination dynamics.

Numerical Method and Applications for the Generalized Disclination Theory

• Augmented the Weingarten Theorem for the g.disclination theory and proposed a method to obtain Burgers vector from disclination dipole.

• Built the connection between mathematical theories and physical description of the Volterra defects. JOURNAL PUBLICATION

• Davis Alexander, Chiqun Zhang, Aarti Singh, Reeja-Jayan Baby. Expert-guided machine learning and model mimicry: An application to advanced materials. Journal of Behavioral Decision Making, 2018

• Chiqun Zhang, Amit Acharya. On the relevance of (generalized) disclinations in defect mechanics. Journal of the Mechanics and Physics of Solids, 2018

• Chiqun Zhang, Amit Acharya, Saurabh Puri. Finite element approximation of the fields of bulk and interfacial line defects. Submitted. Journal of the Mechanics and Physics of Solids, 2018

• Chiqun Zhang, Amit Acharya, Noel Walkington, Oleg Lavrentovich. Computational modeling of tactoid dynamics in chromonic liquid crystal. Liquid Crystals, 2017

• Chiqun Zhang, Xiaohan Zhang, Amit Acharya, Dmitry Golovaty, and Noel Walkington. A non-traditional view on the modeling of nematic disclination dynamics. Quarterly of Applied Mathematics, 2016 CONFERENCES

Author and presenter for

• 7th International Conference on Multiscale Material Modeling, October 2014

• 87th Annual Meeting of the Society of Rheology, October 2015

• SIAM Conference on Mathematical Aspects of Material Science, May 2016

• PIRE-CNA 2016 Summer School, June 2016

• 26th International Liquid Crystal Conference, August 2016

• 88th Annual Meeting of the Society of Rheology, February 2017 Co-author (presenter Amit Acharya) for

• Microstructure evolution and Interfaces workshop, Oberwolfach, Germany March 2016

• SIAM Math Aspects of Materials Science, May 2016

• Advances in Mathematical Analysis of Material Defects in Elastic Solids, SISSA, Trieste, Italy, June 2016

• TIFR Tata Center for Interdisciplinary Sciences Colloquium, July 2016

• Recent Advances in Computational Methods for Nanoscale Phenomena, An Arbor Univ. of Michigan, Aug. 2016

• Engineering Science and Mechanics Dept. seminar, Penn State University, October 2016. MEMBERSHIP IN PROFESSIONAL ORGANIZATIONS

Society of Industrial and Applied Mathematics, Society of Rheology, American Society of Civil Engineers

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