Ph.D. Candidate, Operations Management and Data Analytics
UNC Kenan-Flagler Business School, University of North Carolina at Chapel Hill
On the 2026–2027 academic job market. Kenan-Flagler job-market candidates.
Governments do not decarbonize transportation. Households buy cars, one at a time, through channels and over infrastructure that someone else designed. I study that intervening layer, using archival data, econometrics, and machine learning to measure how the structure of a distribution channel and the design of a charging network determine whether a clean technology actually reaches the households a policy was written to serve.
How a sales-channel statute shapes the diffusion of a clean technology: Washington State’s restriction on direct-to-consumer vehicle sales, and the aggregate and distributional consequences of that channel policy for electric-vehicle adoption.
Where and for whom charging infrastructure moves electric-vehicle adoption: a ZIP-level study of charging networks, household income, and adoption, and of which siting and technology choices produce the most uptake.
How the sectoral and geographic diversity of a firm’s supply base relates to its carbon-emission-reduction strategy. Working paper; details of the empirical design available on request.
Before the doctoral program, my research contributions were computational. As co-author of the genetics study below I performed the formal analysis, software, and visualization for genome-wide scans and interaction tests — early evidence, I note in my research statement, of taking ownership of the analytical layer of an empirical project in an unfamiliar domain. It is not part of the current agenda.
Makhanova, N., Morgan, A. P., Kayashima, Y., Makhanov, A., Hiller, S., Zhilicheva, S., Xu, L., Pardo-Manuel de Villena, F., & Maeda, N. (2017). Genetic architecture of atherosclerosis dissected by QTL analyses in three F2 intercrosses of apolipoprotein E-null mice on C57BL6/J, DBA/2J and 129S6/SvEvTac backgrounds. PLOS ONE, 12(8), e0182882. doi:10.1371/journal.pone.0182882
INFORMS Annual Meeting — presenter.
Production and Operations Management Society (POMS) Annual Conference — presenter.
Manufacturing and Service Operations Management (MSOM) Conference — presenter.
Decision Sciences Institute Annual Conference — presenter.
Dataverse Community Meeting, Harvard University — presented CORE-RE.
Joint Physics Conference, SPS Zone 5 / NC Section AAPT — presenter and award recipient.
UNC Kenan-Flagler Business School. Undergraduate; quantitative analysis, data-driven decision making, business modeling, and managerial applications of analytics.
Analytics, operations, and supply chain management: repeated offerings of Data: Tools & Analytics; Business Modeling: Prescriptive Analytics; Undergraduate Business Analytics; Introduction to Operations Management; and Supply Chain Management. Undergraduate, M.B.A., and professional audiences.
My goal is to prepare students to make sound, evidence-based decisions in organizations where data, algorithms, and digital risk sit close to strategy. Three commitments organize my classroom. First, anchor every technical concept to a decision someone actually has to make — introduce regression or optimization only after a managerial question has become uncomfortable to answer by intuition alone. Second, build hands-on capability with the tools students will use the week after graduation, working directly in Python, R, SQL, and Excel on realistic, messy data. Third, hold high expectations for students who arrive with very different quantitative preparation, pairing rigor with scaffolding so that separating support from standards never makes rigor exclusionary. I integrate generative AI explicitly: students learn to prompt, critique, and verify AI-generated code and analysis — and to find the flaw in output that looks polished.
At undergraduate, M.B.A., specialized master’s, and executive levels. I am interested in developing electives at the intersection of AI governance, analytics, and technology strategy.
Ph.D., Business Administration — Operations Management and Data Analytics
UNC Chapel Hill, Kenan-Flagler Business School
M.S., Cybersecurity — Information Security
Georgia Institute of Technology, GPA 4.0/4.0
M.S., Analytics — Business Analytics
Georgia Institute of Technology, GPA 3.9/4.0
M.B.A. (STEM-designated)
UNC Chapel Hill, Kenan-Flagler Business School — Beta Gamma Sigma, top 20%
M.S., Computer Science — Machine Learning
Georgia Institute of Technology, GPA 4.0/4.0
B.S. Physics, B.S. Mathematics, B.S. Computer Science — Minor in Statistics
High Point University, University Honors Program
Selected doctoral and graduate coursework: stochastic modeling; game theory and empirical analysis; applied econometrics; optimization; optimization for machine learning and neural networks; machine learning in econometrics; Bayesian statistics; generalized linear models; structural models; microeconomics; operations management.
I study the operational layer between a policy and an adopter: sales-channel statutes and charging networks that decide whether a clean technology reaches the households a policy was written to serve. My dissertation — two essays set in Washington State — uses archival data from state registries, geocoded infrastructure records, and census data with econometric and machine-learning methods, triangulating across estimators when a setting does not deliver clean identification.
Before and alongside my doctoral training I worked as a software engineer, machine-learning practitioner, and security analyst, building systems that had to survive real users rather than idealized ones. That experience left me skeptical of models whose conclusions depend on implementing parties behaving as assumed — and it is what makes the large administrative and transactional data assembly in my research feasible.
Fellowship, UNC Sustainability Center.
UNC Kenan-Flagler Merit Fellowship.
Kenan Institute Leadership Award.
Beta Gamma Sigma.
Kenan Scholar, Kenan Institute of Private Enterprise.
Dean’s Fellows Program.
First Place, UNC Security and More Cybersecurity Challenge.
Richard Tapia Celebration of Diversity in Computing Scholarship.
Best Undergraduate Student Paper, Poster, and Presentation, Joint Physics Conference SPS Zone 5 / NC AAPT.
UNC Kenan Institute / Institute for Private Capital, Business Intern — automated acquisition and transcription of research video content to improve search and review workflows.
UNC Odum Institute, Systems Programmer — designed CORE-RE, a Kubernetes/BinderHub/JupyterLab/GitLab reproducible research environment; developed DataverseML.
JADE Learning / TPC Training, Software Engineer, Security Analyst, Full-Stack Developer — web applications, analytics tools, identity-verification features, security modules, and automated data workflows for online professional education.
UNC Pharmacology Lab, IT Programmer / Application Analyst — machine-learning methods, scientific web applications, data-aggregation and research-workflow software.
I am the creator of Juka, an open-source, cross-platform programming language — “code once, run everywhere” — at jukalang.com and github.com/jukalang. Hands-on systems engineering is what makes assembling the large administrative and transactional datasets behind my dissertation feasible.
Full documents open in a new tab. Committee members: the CV and statements below are current; reference contact information is available on request.
Andrey_Makhanov@kenan-flagler.unc.edu
UNC Kenan-Flagler Business School, McColl Building, Chapel Hill, NC 27599
Full contact information available on request.