Shahryar Minhas: s7minhas.com - Associate Professor - Department of Political Science - Social Science Data Analytics Initiative

### Broad strokes Generally, my research interests falls into two areas: - How can we do inference in the presence of interdependent observations - Estimating and utilizing an underlying "social space" from an observed network

### Inference #### standard dyadic design Much of international relations data consists of: - a set of units or nodes - a set of measurements, $y_{ij}$

### Inference #### the problem GLM: $y_{ij} \sim \beta^{T} X_{ij} + e_{ij}$ Networks typically show evidence against independence of {$e_{ij} : i \neq j$} Not accounting for dependence can lead to: - biased effects estimation - uncalibrated confidence intervals - poor predictive performance - inaccurate description of network phenomena

### Inference #### evaluation of solutions

### Inference #### Measuring effect of boko haram

### Measuring Social Space - Many political theories often refer to constructs that can not be observed directly - As a result, political scientists have come up with latent indicators of everything from: - ideological disposition of survey respondents - legislators - judges

### Social space #### asymmetric relations from a symmetric network

### Social space #### strategic determinants of foreign aid

### Social space #### ccp advancement

### Social space #### predicting subnational intrastate conflict

### multilayer nets - Networks that evolve over time or sets of networks that may evolve together over time provide interesting modeling opportunities

### Multilayer networks #### evolution of cooperation and conflict

### Multilayer networks #### Measuring state preference

### Multilayer networks #### Measuring Influence

### What's next - Mostly, continuing along the lines of what I've discussed - Also playing with agent based models a bit to derive some empirically testable insights