Stephen P. Boyd
Professor
Develops convex optimization methods and software such as CVXPY, with applications in control and finance.
Stephen P. Boyd's lab at Stanford works on learning theory & optimization. 74 papers since 2023, 4 at top venues, mostly ACC and ICLR. 6 PhD students. 105 alumni, most often to OpenAI. Applications close .
At a glance
- papers, 2023–2026
- 19/ year
- at T1 venues
- 5%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 105
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 18 papers2026 · T1 0 · collecting
- 18 papers2025 · T1 2
- 22 papers2024 · T1 1
- 16 papers2023 · T1 1
T1 venuesOtherVenues: ACC, ICLR, ISCA, L4DC, MICRO
Research focuspaper titles that use each topic's words
- Learning theory & optimization26 papers
- convex20 papers
- convex optimization10 papers
- optimization methods1 paper
- finance1 paper
Paper list
From the lab homepage and DBLP.
202618 papers
- —A distributed method for cooperative transaction cost mitigation (opens in a new tab)
- Oper. Res. ForumA note on optimal product pricing (opens in a new tab)
- —Accelerating RF device modeling and circuit design via derivative-free optimization (opens in a new tab)
- ACCAdaptive Strategies for Pension Fund Management (opens in a new tab)
- —Aging aware battery control via convex optimization (opens in a new tab)
- —An operator splitting method for large-scale CVaR-constrained quadratic programs (opens in a new tab)
- —Automatic generation of explicit quadratic programming solvers (opens in a new tab)
- —Code generation for solving and differentiating through convex optimization problems (opens in a new tab)
- ACM Trans. Math. Softw.CuClarabel: GPU acceleration for a conic optimization solver (opens in a new tab)
- —Differentiating through a quadratic cone program (opens in a new tab)
- Oper. Res. ForumEstimating price elasticity matrices (opens in a new tab)
- —GPU-accelerated presolving for linear programming (opens in a new tab)
202518 papers
- —A Markowitz approach to managing a dynamic basket of moving-band statistical arbitrages (opens in a new tab)
- —A tax-efficient model predictive control policy for retirement funding (opens in a new tab)
- ICLR (T1)An Asynchronous Bundle Method for Distributed Learning Problems (opens in a new tab)
- —Convex-concave lecture slides (opens in a new tab)
- J. Optim. Theory Appl.Correction: Tax-Aware Portfolio Construction Via Convex Optimization (opens in a new tab)
- —Disciplined nonlinear programming (opens in a new tab)
- —Finding moving-band statistical arbitrages via convex-concave optimization (opens in a new tab)
- SIAM J. Matrix Anal. Appl.Fitting multilevel factor models (opens in a new tab)
- L4DCInformative Input Design for Dynamic Mode Decomposition (opens in a new tab)
- —Iteratively saturated Kalman filtering (opens in a new tab)
- —Learning parametric convex functions (opens in a new tab)
- —Multiple-response agents: Fast, feasible, approximate primal recovery for dual optimization methods (opens in a new tab)
202422 papers
- Robotics Auton. Syst.Approximate sequential optimization for informative path planning (opens in a new tab)
- IEEE AccessCompact model parameter extraction via derivative-free optimization (opens in a new tab)
- —Convex optimization in quantitative finance (opens in a new tab)
- —Convex optimization over risk-neutral probabilities (opens in a new tab)
- Trans. Mach. Learn. Res.Disciplined saddle programming (opens in a new tab)
- —Efficient Shapley performance attribution for least-squares regression (opens in a new tab)
- —Exponentially weighted moving models (opens in a new tab)
- —Factor fitting, rank allocation, and partitioning in multilevel low rank matrices (opens in a new tab)
- IEEE Trans. RoboticsFast path planning through large collections of safe boxes (opens in a new tab)
- —Implementation of an oracle-structured bundle method for distributed optimization (opens in a new tab)
- —Interpretable net load forecasting using smooth multiperiodic features (opens in a new tab)
- —Large-scale GNSS spreading code optimization (opens in a new tab)
202316 papers
- —A simple method for predicting covariance matrices of financial returns (opens in a new tab)
- —Bounds on efficiency metrics in photonics (opens in a new tab)
- Optim. Lett.Computing tighter bounds on the \(n\)-queens constant via Newton's method (opens in a new tab)
- —Covariance prediction via convex optimization (opens in a new tab)
- J. Glob. Optim.Fitting feature-dependent Markov chains (opens in a new tab)
- —InControl podcast: Interview with Stephen Boyd (opens in a new tab)
- —Minimizing oracle-structured composite functions (opens in a new tab)
- —Portfolio construction as linearly constrained separable optimization (opens in a new tab)
- —Portfolio construction using stratified models (opens in a new tab)
- —PV fleet modeling via smooth periodic Gaussian copula (opens in a new tab)
- ISCA (T1)RSQP: Problem-specific Architectural Customization for Accelerated Convex Quadratic Optimization (opens in a new tab)
- —RSQP: Problem-specific architectural customization for accelerated convex quadratic programming (opens in a new tab)
Where alumni went105 listed, 100% to industry
Industry 1Academia 0
Most often to
- OpenAI
Apply to StanfordFall 2027
Stanford Computer Science PhD
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