Placeholders for later updates.

_pages/home.md

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 ### About the Lab
 
-The Vanderbilt Mathematical Programming and Intelligent Robotics Lab.
+The Vanderbilt Mathematical Programming and Intelligent Robotics (VAMPIR) Lab is dedicated to advancing the frontier of computational game theory, control systems, and trajectory optimization, with optimization serving as the core of our research. Our work centers on solving complex multilevel programming problems—including MPECs, EPECs, CVaR models, and MDPs—that underlie challenges in dynamic decision-making and planning. By leveraging rigorous tools from mathematical programming, we aim to tackle problems ranging from trajectory planning for autonomous systems to applications in medicine, where game-theoretic insights are being explored in collaboration with clinical research on anhedonic depression.
+
+Led by Professor Forrest Laine, our team includes PhD students Pravesh Koirala, Mel Krusniak, Andrew Cinar, and Yue Zhao, along with a number of talented undergraduates such as Aditya Shrey and Parker Palermo. Together, we have produced impactful contributions in areas like bilevel programming for racing, nonsmooth polyhedral collision detection, algorithmic collusion, and cybersecurity applications of discrete strategy games. What sets our lab apart is a commitment to rigorous mathematical programming approaches, distinguishing our work from more conventional machine learning pipelines. Our audience includes research partners and funding agencies seeking innovation grounded in theory, with real-world impact in engineering, medicine, and beyond.

_pages/team/_posts/2016-10-01-researcher-student-mel.md

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 education:
  - B.S. Computer Science, Yale University
 ---
-Miles (Mel) Krusniak has been a PhD student at the VAMPIR Lab since July 2022. Mel’s research interests include game theory for multiple robotics, software tools for equilibrium problems, and emergent behavior in many-agent decision-making problems.  
+
+I am a Mel Krusniak, a PhD student in the VAMPIR Lab at Vanderbilt University, where my research focuses on computational game theory, optimization, and control. My work centers on developing rigorous mathematical programming methods to solve challenging multilevel problems such as continuous games with imperfect information, multilevel optimization, and trajectory planning under uncertainty. I am particularly interested in bridging theory and application—whether in designing algorithms for collision avoidance in robotics or creating frameworks for strategic interactions in dynamic, real-world systems.
+
+Recently, I have worked on projects including Mixed Strategy Constraints in Continuous Games, which introduced a chance-constrained framework for handling coupled constraints in continuous mixed-strategy games, and Online Competitive Information Gathering for Partially Observable Trajectory Games, which explores online methods for rational planning in uncertain, adversarial environments. Across my research, I am motivated by the challenge of combining mathematical rigor with practical impact, and I see optimization and game theory as powerful tools for advancing both engineering and interdisciplinary applications.
 

_pages/team/_posts/2016-10-01-researcher-student-pravesh.md

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  - BE (Computer) & MSc (Computer Science), Tribhuvan University
 ---
 
-Pravesh works in Computational Game Theory and Multilevel Optimization. His work is focused towards modeling and finding solutions to complex strategic interactions in leader-follower games. He is also interested in Multi-Agent Deep Reinforcement learning.
+I am a PhD student and Graduate Research Assistant in the Laine Lab at Vanderbilt University, where my research lies at the intersection of computational game theory, optimization, and control. I am particularly interested in multilevel and hierarchical games such as Stackelberg games and integer programming games (IPGs), where decision-making becomes especially challenging due to nested objectives and combinatorial strategy spaces. My work focuses on developing algorithms that make these problems more tractable, with applications ranging from adversarial trajectory planning to cybersecurity.
+
+One of my key contributions has been in designing Monte Carlo–based methods for approximating local equilibria in multilevel games. Traditional concepts like Nash equilibrium often fail to scale in these contexts, so I have explored alternative solution approaches that preserve theoretical rigor while remaining computationally feasible. This line of work has been successfully applied to toll-setting problems, pursuit–evasion dynamics, and hierarchical optimization tasks, showing both scalability and robustness in real-world-inspired settings.
+
+More recently, I co-authored research on Locally Optimal Integer Solutions (LOIS), a new framework for analyzing and solving integer programming games. LOIS offers a practical alternative to pure Nash equilibria, especially in large-scale problems where exhaustive equilibrium computation is not possible. With this framework, we are able to characterize equilibrium-like behavior in integer strategy spaces, enabling analysis of competitive systems in areas such as cybersecurity, transportation, and multi-agent planning.
+
+Originally from Nepal, I bring with me a background that motivates my interest in solving problems with real-world societal impact. My long-term goal is to advance both the theory and application of game-theoretic optimization, contributing tools that not only deepen mathematical understanding but also directly support engineering and decision-making in complex, adversarial environments.

_pages/team/_posts/2023-11-14-researcher-director-forrest.md

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  - Ph.D. in Electrical Engineering and Computer Sciences. University of California, Berkeley, August 2021.
  - B.S. in Electrical Engineering and Computer Sciences. University of California, Berkeley, May 2015.
 ---
-Forrest is from Bellingham, Washington. He works on methods for solving various problems in game theory as applied to autonomous systems. 
+
+I am Forrest Laine, an Assistant Professor of Computer Science and Mechanical Engineering at Vanderbilt University, and the director of the Vanderbilt Mathematical Programming and Intelligent Robotics (VAMPIR) Lab. My research sits at the intersection of computational game theory, optimization, robotics, and healthcare applications, with a strong emphasis on mathematical rigor applied to real-world systems. I received both my PhD and BS in Electrical Engineering and Computer Sciences from UC Berkeley. A central theme of my work is the development of equilibrium-seeking algorithms in dynamic games and trajectory planning. For instance, I introduced the concept of a Generalized Feedback Nash Equilibrium (GFNE) in dynamic games with constraints, along with efficient numerical methods for approximation and trajectory-level computation—especially relevant to autonomous driving contexts. 
+
+In another line of research, I proposed methods for learning mixed strategies in trajectory games, where agents optimize multiple candidate trajectories in tandem during an offline training phase to reduce the computational burden during real-time deployment. Beyond theory, my interests extend into robotics and AI for healthcare, exploring how game-theoretic and control principles can inform interaction models and decision-making strategies in clinical contexts. Leading the VAMPIR Lab, I aim to bridge mathematical programming with intelligent robotic systems, enabling robust, novel approaches to planning and strategic interaction.

_pages/team/_posts/2023-11-14-researcher-student-andrew.md

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  - Mechanical Engineering B.S., Middle East Technical University, Ankara, Turkey
 ---
 
-Andrew Cinar is a 4th year PhD student in the VAMPIR Lab. He is interested in robotics and control. His current research focuses on multi-agent autonomy.
+My research focuses on the crossroads of robotics, control systems, and computational game theory. My interest lies in applying rigorous optimization and multilevel game-theoretic frameworks to real-world challenges, particularly in trajectory planning and dynamic interaction modeling in robotics.
+
+One of my recent contributions includes co-authoring “Does bilevel optimization result in more competitive racing behavior?” (2024), where I explored how incorporating bilevel optimization frameworks can yield more strategic and competitive behaviors in simulated racing scenarios. This project showcases the power of hierarchical decision-making models to better reflect complex competitive dynamics in autonomous systems.
+
+In addition, I worked on “Polyhedral Collision Detection via Vertex Enumeration” (2025), presenting a novel mathematical programming approach for collision detection in robotics. Our method leverages vertex enumeration within polyhedral sets to rigorously determine collision boundaries—balancing computational efficiency with safety-critical constraints in dynamic environments.
+
+Earlier, I contributed to QTOS: An Open-Source Quadruped Trajectory Optimization Stack (2023), a practical toolkit aimed at empowering quadruped roboticists with state-of-the-art trajectory generation and optimization tools. This open-source project reflects my commitment to translating theoretical insights into accessible and impactful software solutions for the robotics community.

_pages/team/_posts/2025-09-03-researcher-student-yue.md

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+---
+layout: member
+category: student
+title: Andrew Cinar
+image: researcher.png
+role: PhD Student
+permalink: 'team/andrew'
+social:
+    twitter: https://twitter.com/
+    linkedin: https://www.linkedin.com/
+    google-scholar: https://scholar.google.com/citations?user=KYvCJ3QAAAAJ
+    github: https://github.com/cinaral
+    website:
+    orcid: https://orcid.org/
+    research-gate: https://www.researchgate.net/
+education:
+ - Mechanical Engineering B.S., Middle East Technical University, Ankara, Turkey
+---
+
+I focus on the intersection of optimization, decision-making under uncertainty, and reinforcement learning frameworks. My work predominately centers around Conditional Value-at-Risk (CVaR)–based Markov Decision Processes (MDPs), where I develop and analyze risk-aware decision-making models aimed at improving robustness and reliability in sequential planning under stochastic environments.
+
+In one of my recent collaborative projects, I co-authored a paper with Andrew Cinar (and others) that advances the use of CVaR objectives in MDP settings—which allows decision-making policies to explicitly account for adverse, tail-risk outcomes while optimizing performance. This line of work helps ensure that systems not only perform well on average, but also remain resilient under challenging or extreme scenarios.
+
+I also had the opportunity to collaborate with Andrew Cinar on a project related to computational game theory and trajectory optimization. While my contributions in that space were more focused and supportive, working alongside him provided valuable experience in multilevel optimization and strategic modeling for dynamic systems.
+
+As I continue on this path, my goal is to further strengthen the integration between risk-aware optimization methods and game-theoretic frameworks, contributing novel mathematical tools and algorithms that enhance safety, robustness, and strategic planning in robotics, decision support systems, and other domains where uncertainty plays a critical role.