Welcome 2017-2018 Cohort!!!
Bachelor’s Degree: Computer Science, Jawaharlal Nehru Technological University, India
Master’s Degree: Computer Science (Concentration in Data Analytics), Kennesaw State University
- Data Science and Analytics Graduate Intern, Equifax, 2017 – Present
- Graduate Research Assistant, Kennesaw State University, 2015-2017
- KSU Analytics Day 2018
- Computing Showcase 2016, 2017
- President - Graduate Student Association (GSA) 2016-2018, Kennesaw State University
- Treasurer - IEEE Computer Society (IEEE-CS) 2016-2017, Kennesaw State University
- Secretary - Robotics & Automation Society 2017, Kennesaw State University
- Peer Leader - International Orientation (2016-2017), Kennesaw State University
- Law Enforcement Training, Citizens Police Academy, KSU Police Department 2017
- Won 3rd Prize for Bert’s Big Adventure driving research project – KSU Analytics Day 2018
- Awarded Udacity Google Developer Scholarship 2018
- Student of the Year - Division of Student Affairs, Kennesaw State University 2017
- Graduate Student Involvement Award - International Student Association, Kennesaw State University 2017
- Golden Key International Honor Society Member
Professional Objectives: To be a data scientist with a demonstrated ability to deliver valuable insights via data analytics and advanced data-driven methods. Primary research interests are in Machine learning, algorithms for massive datasets, large scale optimization, and recommender systems. I want to work in Health Care and Government (not finite to these as I am still exploring my research interests) to build an appropriate data framework that can utilize advanced analytic means to aggregate data effectively to gain in depth insights.
Name: Andrew M. Henshaw
Bachelor’s Degree: Electrical Engineering, Georgia Tech
Master’s Degree: Electrical Engineering, Georgia Tech
Master’s Degree: Business Administration, Georgia State University
- Georgia Tech, School of Electrical Engineering, Research Engineer I, 1986-1990
- Georgia Tech Research Institute, Research Engineer II, 1990-1999
- APower Solutions, Vice President, 1999-2001
- Georgia Tech Research Institute, Sr. Research Engineer, 2001-
Courses taught: Software-Defined Radio Development with GNU Radio: Theory and Application, Georgia Tech Professional Education
Selected Publications/Presentations: Python Cookbook, Vol 1, 2002, “Sorting Objects Using SQL’s ORDER BY Syntax”
Service and Awards:
- International Test and Evaluation Association (ITEA) Atlanta Chapter, President, 1995
- Georgia State Soccer Association, Information Technology Committee Chair, 2009-2014
- Georgia State Soccer Association, Recreational Committee, 2007-
Professional Objectives: Enhance GTRI’s capabilities in data science and analytics
Name: Liyuan Liu
Bachelor’s Degree: Human Resource Management, Nanjing University of Information Science & Technology
Master’s Degree: Financial Economics, University of Detroit Mercy
Master’s Degree: Analytics, J. Mack Robinson College of Business, Georgia State University
- Intern, Equifax, 01/2018-present
- Graduate Research Assistant, Georgia State University Institute for Insight, 09/2015-09/2016
- Liyuan Liu, Meng Han, Yiyun Zhou, and Yan Wang; LSTM Recurrent Neural Networks for Influenza Trends Prediction; Proceedings in 14th International Symposium on Bioinformatics Research and Applications (ISBRA), 2018, Springer.
- Liyuan Liu, Meng Han, Yan Wang and Yiyun Zhou; Understanding Data Breach: A Visualization Aspect; Proceedings in 13th International Conference on Wireless Algorithms, Systems and Applications, 2018, Springer.
- Yiyun Zhou, Meng Han, Liyuan Liu, Jing (Selena) He, Yan Wang; Deep Learning Approach for Cyber-attack Detection; Proceedings in MiseNet of IEEE International Conference on Computer Communications, 2018.
- Bingchen Yu, Meng Han, Liyuan Liu, Yan Huang, Yi Liang, Liquan Bai; Data-driven Approach for Understanding the Mild Cognitive Impairment; Proceedings in 14th International Symposium on Bioinformatics Research and Applications (ISBRA), 2018, Springer.
Professional Objective: To work in data science in a corporate environment
Name: Mohammad Masum
Bachelor’s Degree: Mathematics, University of Dhaka, Bangladesh
Master’s Degree: Mathematical Sciences, East Tennessee State University
Work History: Instructor at East Tennessee State University
Courses taught: Probability and Statistics (non-calculus) at East Tennessee State University
MS Thesis: Vertex Weighted Spectral Clustering
Professional Objective: to work in data science in the Health sector, and to hold a faculty position at a university
Name: Dianna J. Spence
Bachelor’s Degree: Mathematics, College of William and Mary
Master’s Degree: Computer Science, Georgia State University
Doctorate Degree: Mathematics Education, Emory University
- Software Engineer, CCS, Inc., 1990-1998
- Software Engineer, Knowlagent, Inc., 2003-2005
- Faculty, Department of Mathematics and Department of Computer Science, University of North Georgia, 2005-present
- Discrete Mathematics
- Elementary Statistics
- Business Statistics
- Computer Science I (Java Programming)
- Special Topics in CS: Data Science
- Numerical Analysis
- Spence, D. J. (2018). Predicting Corporate Credit Risk: Comparison of Logistic Regression with a Hybrid Binary Classifier. Poster presented at KSU 11th Annual Analytics Day, April 20, 2018.
- Spence, D. J., Bailey, B., & Sharp, J. L. (2017). The impact of introducing student-directed projects in introductory statistics. Statistics Education Research Journal, 16(1), 240-261.
- Bailey, B., & Spence, D. J. (2014). Path elongation, constructions, and a progression of PE values in a single graph. Congressus Numerantium, 222, 203-213.
- Spence, D. J., Sharp, J. L., & Sinn, R. (2011). Investigation of factors mediating the effectiveness of authentic projects in the teaching of elementary statistics. Journal of Mathematical Behavior, 30, 319-332.
Professional Objective: To participate in data science in a corporate environment or in a consulting capacity while also making a contribution to the development of data science curriculum in the academic setting.
Name: Lauren Staples
Bachelor’s Degree: Biosystems Engineering, Clemson University
Master’s Degree: Statistics, University of New Hampshire
Work History: Children’s Oncology Group, Clinical Statistician, 2016-2017
Courses taught: STAT 8940 Applied Analysis Project with Nuesoft. Co-taught with Dr. Ni
Selected Publications/Presentations: An Optimized Route for Q100’s Bert and Kristin to Visit all Jersey Mike’s Subs in Atlanta for Charity
Service and Awards: 3rd Place at 4/20/2018 KSU Analytics Day
Professional Objective: Healthcare Analytics
Name: Shashank Hebbar
Bachelor’s Degree: Mechanical Engineering, Anna University
Master’s Degree: Industrial Engineering, North Carolina State University
- Analytics Intern, Cox Enterprises, Atlanta, GA
- Machine Learning Research Intern, SAS Institute, Cary, NC
- SAS Global Forum 2017- “Regularization techniques”
- Journal of Risk Management (Under Review)– “Borrower default trees in bipartite network” (co-author)
Professional Objective: To make the best use of my data science and machine learning skills in a corporate environment and contribute to the field of applied research.
Name: Jessica Rudd
Bachelor’s Degree: Anthropology, Emory University
Master’s Degree: Public Health, Emory University
- Leidos, Contracted to CDC Division of Viral Diseases, Analysis and Data Management Activity Team, 2010-2017
- Rwanda Zambia HIV Research Group, Research Project Coordinator, 2007-2010
- Predicting Systolic and Diastolic Blood Pressure from Time Variant Parametric Models with Longitudinal Data. Presented poster at R Day (Nov. 2016).
- Utilizing SAS(R) to Estimate Rates of Disease from Nationally Representative Databases. Scholarship winning poster for the SAS Global Forum (April 2017).
- A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit. First place poster at SAS Day (April 2017).
- Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. SAS Analytics 2017 Poster Winner (September 2017)
- Nonparametric Estimation of Time-Variant Quantiles and Statistical Models. Conference on Statistical Practice (February 2018)
- Evaluation of Nonparametric Smoothing Estimation Methods Using Time-Variant Longitudinal Data. Presented at Joint Statistical Meeting (August 2017).
- Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Presented at Conference on Statistical Practice (February 2018)
- Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Presented at SAS Global Forum 2018 (April 2018)
Peer Review Publications:
- Rudd J. (in press). Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Model Assisted Statistics and Applications.
- Rudd, M. P. H., GStat, J. M., & Priestley, J. L. (2017). A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit.
- O’Hagan, J. J., Carias, C., Rudd, J. M., Pham, H. T., Haber, Y., Pesik, N., ... & Swerdlow, D. L. (2016). Estimation of severe Middle East respiratory syndrome cases in the Middle East, 2012–2016. Emerging infectious diseases, 22(10), 1797.
- Assiri, A., Abedi, G. R., Saeed, A. A. B., Abdalla, M. A., al-Masry, M., Choudhry, A. J., ... & Rudd, J. (2016). Multifacility outbreak of Middle East respiratory syndrome in Taif, Saudi Arabia. Emerging infectious diseases, 22(1), 32.
- Schneider, E., Chommanard, C., Rudd, J., Whitaker, B., Lowe, L., & Gerber, S. I. (2015). Evaluation of patients under investigation for MERS-CoV infection, United States, January 2013–October 2014. Emerging infectious diseases, 21(7), 1220.
- Rha, B., Rudd, J., Feikin, D., Watson, J., Curns, A. T., Swerdlow, D. L., ... & Gerber, S. I. (2015). Update on the epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) infection, and guidance for the public, clinicians, and public health authorities-January 2015. MMWR. Morbidity and mortality weekly report, 64(3), 61-62.
- Al-Abdallat, M. M., Payne, D. C., Alqasrawi, S., Rha, B., Tohme, R. A., Abedi, G. R., ... & Haddadin, A. (2014). Hospital-associated outbreak of Middle East respiratory syndrome coronavirus: a serologic, epidemiologic, and clinical description. Clinical Infectious Diseases, 59(9), 1225-1233.
- Payne, D. C., Iblan, I., Alqasrawi, S., Al Nsour, M., Rha, B., Tohme, R. A., ... & Jarour, N. (2014). Stillbirth during infection with Middle East respiratory syndrome coronavirus. The Journal of infectious diseases, 209(12), 1870-1872.
- Cortese, M. M., Immergluck, L. C., Held, M., Jain, S., Chan, T., Grizas, A. P., ... & Gautam, R. (2013). Effectiveness of monovalent and pentavalent rotavirus vaccine. Pediatrics, peds-2012.
Professional Objective: To work in data science building analytics pipelines for extraction of meaning from large-scale data
Bachelor’s Degree: Pharmacy, China Pharmaceutical University
Master’s Degree: Statistics, University of Georgia
Work History: Data Scientist Intern at Hexaware Technologies and Ernst & Young, May-Aug, 2017
Courses taught: STAT 4210, Spring 2017 and Fall 2017, Kennesaw State University
- SAS Global Forum 2017: Binary Classification on Past Due of Service Accounts using Logistic Regression and Decision Tree
- SAS Global Forum 2016: Survival Analysis of Lung Cancer Patients using PROC PHREG and PROC LIFETEST.
Professional Objective: To work in data science in a corporate environment
Name: Lili Zhang
Bachelor’s Degree: Electronic Information Science and Technology, Central South University, China
Master’s Degree: Industrial Engineering, University of Tennessee-Knoxville
Work History: Research Associate at Hong Kong Polytechnic University, Hong Kong, China
Courses taught: STAT 8030 Programming in R, Fall 2017, Kennesaw State University
Conference and Journal Publications:
- Lili Zhang, Ying Xie and Guoliang Liu. “A Sentiment-change-driven event discovery system.” Proceedings of the International Conference on Web Intelligence, Leipzig, Germany, August 23-26, 2017, pp. 1035-1041. ACM. DOI: 10.1145/3106426.3109038
- Lili Zhang, Jennifer Priestley and Xuelei Ni. “Comparison of Bankruptcy Prediction Models with Public Records and Firmographics”. Computer Science & Information Technology: Proceedings of the International Conference on Data Mining & Knowledge Management Process, Melbourne, Australia, February 17-18, 2018, Vol 8(3), pp. 97-109. DOI: 10.5121/csit.2018.80309
- Lili Zhang, Jennifer Priestley and Xuelei Ni. “Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models”. International Journal of Database Management Systems 10.1 (2018): 1-14. DOI: 10.5121/ijdms.2018.10101
- Shashank Hebbar, Lili Zhang. “Linear Model Regularization.” SAS Global Forum 2017.
- Bogdan Gadidov, Lili Zhang, Yiyun Zhou. “Reducing Traveling Times for Cobb County Fire Department.” 10th Annual Analytics Day at Kennesaw State University 2017.
- Lili Zhang. “Implementing a sentiment-change-driven event discovery system on HPCC Systems.” 2017 HPCC Systems Summit Community Day.
Professional Objective: My professional goal is to be a data scientist to generate actionable insights for various real-world problems from the data. I also would like to help young generations with my knowledge and skills. I am interested in working in both industry and academia after graduation.
Name: Yiyun Zhou
Bachelor’s Degree: Computer Science, Chongqing University of Posts and Telecommunications
Master’s Degree: Analytics, J. Mack Robinson College of Business at Georgia State University
Master’s Degree: Financial Economics, University of Detroit Mercy
Work History: Equifax Data Science and Analytics Graduate Intern
- Yiyun Zhou, Meng Han, Liyuan Liu, Jing(Selena) He, Yan Wang, Deep Learning Approach for Cyber-attack Detection, proceedings in MiseNet of IEEE International Conference on Computer Communications, 2018.
- Yiyun Zhou, Meng Han, Liyuan Liu, Yan Wang, Yi Liang, Ling Tian, Improving IoT Services in Smart-home Using Blockchain Smart Contract, IEEE International Conference on Internet of Things,2018 (Pending)
- McGrath, R. J., Priestley, J. L., Zhou, Y., & Culligan, P. J. (2018). The Validity of Online Patient Ratings of Physicians: Analysis of Physician Peer Reviews and Patient Ratings. Interactive journal of medical research, 7(1), e8-e8.
- Liyuan Liu, Meng Han, Yiyun Zhou, and Yan Wang, LSTM Recurrent Neural Networks for Influenza Trends Prediction, proceedings in 14th International Symposium on Bioinformatics Research and Applications (ISBRA), 2018, Springer.
- Liyuan Liu, Meng Han, Yan Wang and Yiyun Zhou, Understanding Data Breach: A Visualization Aspect, proceedings in 13th International Conference on Wireless Algorithms, Systems and Applications, 2018, Springer.
Professional Objective: Working as a dynamic data scientist utilizing advanced analytics to help the private and public sectors achieve artificial intelligence goals.
Name: Edwin Baidoo
Bachelor’s Degree: Mathematics, Brooklyn College
Master’s Degree: Quantitative Finance, Rutgers University
- Data Science Intern, Equifax, Alpharetta, GA
- Market Analyst, InterContinental Exchange, Atlanta, GA
- Summer Analyst Intern, Assurant Specialty Property, Livingston, NJ
- Data Parsing Consultant, Citadel Investment Group, New York, NY
Courses taught: Business Calculus, Fall 2016
Professional Objective: Apply data science techniques in financial services sector
Bio: I am a Ph.D. student in the Department of Statistics and Analytical Sciences. Education: M.S. Information Technology, Southern Polytechnic State University in 2014 and B.S. Computer Science from Academy of Economic Studies of Moldova in 2008. Work experience: Data Scientist intern at Coca-Cola Enterprises. Integration Engineer co-op student at Cox Communication. Senior System Engineer for SunTrust Bank. Research Interests: My research interests are focused in the area of Big Data, Data Science and Analytics as follow: Data mining; Cognitive Analytics; Machine learning; Cluster Analysis; Interactive Analysis; Big Data Visualization; Large-scale systems and Cognitive computing systems. Outside of school I like to read, coding, play tennis, biking or running and eating healthy food.
Name: Bogdan Gadidov
Bachelor’s Degree: Industrial and Systems Engineering, Georgia Institute of Technology
Master’s Degree: Applied Statistics, Kennesaw State University
- Adjunct Faculty, University of New Hampshire, May 2017 - Present
- Graduate Teaching Assistant, Kennesaw State University, August 2013 - May 2015
- Model Validation Intern, SunTrust, May 2014 - August 2014
- Implementation Engineer, Noble Systems, July 2012 - July 2013
- Math 1111, Fall 2013, Kennesaw State University
- Math 1107, Spring 2014, Fall 2014, Spring 2015, Spring 2016, Kennesaw State University
- Data 801 Foundations of Data Analytics for the Master of Science in Analytics & Data Science program,
- Summer 2017, University of New Hampshire (UNH)
- 3 online courses in Graduate Certificate in Data Science program offered at UNH, Spring
- Data 800 (Introduction to Analytical Statistics)
- Data 820 (Programming for Data Science)
- Data 822 (Data Mining and Predictive Modeling)
- Gadidov B., & Priestley J. L. (2018). Does Yelp matter? Analyzing (and guide to using) ratings for a quick serve restaurant chain. In Srinivasan S. (Eds.), Studies in Big Data: Vol. 26. Guide to big data applications (pp. 503-522). Cham, Switzerland: Springer International Publishing. doi: https://doi.org/10.1007/978-3-319-53817-4_19
- Gadidov, B., Le, Linh. (2018, April). A case study of mining social media data for disaster relief: Hurricane Irma. Paper presented at SAS Global Forum 2018, Denver, CO. Retrieved from here.
- Gadidov, B., Zhang, L., & Zhou, Y. (2017, September). Reducing traveling times for the Cobb County Fire Department. Poster session presented at SAS Analytics Experience 2017, Washington, DC.
- Gadidov, B., & Ray, H. E. (2017, April). Analyzing residuals in a PROC SURVEYLOGISTIC model. Paper presented at SAS Global Forum 2017, Orlando, FL. Retrieved from here.
- Gadidov, B., & McBurnett, B. (2015, September). Population stability and model performance metrics replication for business model at SunTrust Bank. Paper presented at SouthEast SAS Users Group 2015, Savannah, GA. Retrieved from here.
Service and Awards: SAS Analytics Student Poster Competition, October 2014; Recognized as one of six SAS Analytics Student Poster winners.
Professional Objective: My short term goal after completing my studies would be to apply my knowledge in a data science team in an industry setting. My longer term goal is to have a faculty position at a university to continue teaching.
Name: Jie Hao
Bachelor’s Degree: Statistics, North China University of Technology
Master’s Degree: Mathematics, East Tennessee State University (ETSU)
- Math tutor at ETSU
- Research assistant at the department of Biostatistics and Epidemiology at ETSU.
Courses taught: STAT 8030 Programming in R (graduate level), Fall 2016, Kennesaw State University
- Shah, Lisa; Hao, Jie; Schneider, Jeremy; Linenberger, Kimberly; Ray, Herman; Rushton, Gregory. (2017) Repairing Leaks in the Chemistry Teacher Pipeline: A Longitudinal Analysis of Praxis® Chemistry Subject Assessment Examinees and Scores. The Journal of Chemical Education. Under review.
- Lisa Shah, Jie Hao, Christian A. Rodriguez, et al. (2017) Analysis of Praxis physics subject assessment examinees and performance: Who are our prospective physics teachers? Physical Review Physics Education Research. Under review.
- Le, L., Hao, Jie. , Xie, Y. and Priestley, J. (2016) Deep Kernel: Learning Kernel Function from Data using Deep Neural Network. BDCAT’16, December 06-09, 2016, Shanghai, China. doi: http://dx.doi.org/10.1145/3006299.3006312.
- Godbole, A. and Hao, Jie. (2016) Telescoping Sums, Permutations, and First Occurrence Distributions. The Mathematical Scientist, 41, 75-83.
- Hao, J. and Godbole, A. (2016) Distribution of the Maximum and Minimum of a Random Number of Bounded Random Variables. Open Journal of Statistics, 6, 274-285. doi: 10.4236/ojs.2016.62023.
Presentations: Presented a breakout session at SAS Global Forum 2017, Orlando, Florida
Service and Awards: SAS Base and Advanced Certification
Professional Objectives: Personal career goal is to find a data scientist position in a high-tech company. My research interests are machine learning, and deep learning in bioinformatics
Name: Linh Le
Bachelor’s Degree: Engineer Degree in Information Technology, Focus on Computer Network and Telecommunication, Hanoi University of Science Technology
Master’s Degree: Master of Science in Information Systems, Marshall University
Courses taught: STAT 3010 Computer Application of Statistics, Fall 2016, Kennesaw State University
- Ying Xie, Linh Le, Yiyun Zhou, Vijay Raghavan, Deep Learning for Natural Language Processing, Computational Analysis and Understanding of Natural Languages, 2018
- Bogdan Gadidov, Linh Le, A Case Study of Mining Social Media Data for Disaster Relief: Hurricane Irma, In Proceedings of SAS Global Forum 2018
- Ying Xie, Linh Le, Jie Hao, Unsupervised Deep Kernel for High Dimensional Data, In Proceeding of the 30th IEEE International Joint Conference on Neural Networks
- Linh Le, Jie Hao, Ying Xie, Jennifer Priestley, Deep Kernel: Learning the Kernel Function from Data Using Deep Neural Network, In Proceedings of the Third IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
- Ying Xie, Chenna Pooja, Linh Le, Visualization of High Dimensional Data in a Three Dimensional Space, In Proceedings of the Third IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
- Linh Le, Jennifer Priestley, Using the OPTGRAPH Procedure: Transformation of Transactional Data into Graph for Cluster Analysis, In Proceedings of SAS Global Forum 2017
Service and Awards:
- Best Paper in Research Track, Southern Data Science Conference 2018
- Ph.D. Research and Publication Award, Analytics and Data Science Institute, Kennesaw State University, 2017
- Winner in the student poster competition – SAS Analytics Experience 2016 Conference
- First Place in the HPCC Systems 2016 Poster Presentation Competition – HPCC Systems Engineering Summit in 2016
- Xie, Y., Le, L., and Zhou, Y. (2017) Dual Deep Learning Framework for Big and Unstructured Data. US Patent Application No. 62562898
Professional Objectives: I like doing research in an academic environment, so I plan to find a faculty position in a university.
Name: Bob Vanderheyden
Bachelor’s Degree: Applied Mathematics, San Diego State University
Master’s Degree: Mathematics, Virginia Tech
- 25+ years at various companies, providing Analytic Support to Marketing efforts.
- Current employer: IBM
Professional Objectives: Faculty position at a university