Darinka Dentcheva (ddentche)

Darinka Dentcheva

Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Department of Mathematical Sciences

North Building 301
(201) 216-8640
(201) 216-8321
Website

Education

  • PhD (2006) Humboldt Universitate (Habilitation in Mathematics)
  • PhD (1989) Humboldt Universitaet zu Berlin (Mathematics)
  • MS (1981) Humboldt Universitaet zu Berlin (Mathematics and Computer Science)

Research

optimization and optimal control of stochastic systems, mathematical models of risk and risk-averse optimization, statistics, numerical techniques

Experience

Optimization and Optimal control of Stochastic Systems Theoretical analysis, numerical methods, stability and sensitivity when the distributions are subjected to perturbation, numerical methods for solving these type of problems, statistical inference, and applications of these problems to machine learning, medicine, finance, robotics, energy production and distribution, and other areas of business and technology. I am particularly, interested in the Theory and Methods of Risk-averse Optimization and Control.

Statistical Analysis: generalized delta theorems of first and higher order for random sets and their measurable selections in infinite dimensional spaces, new statistical tests for first and higher- order stochastic dominance, novel framework for sample-based optimization, estimation of measures of risk and Lorenz functions. statistical inference for risk measures and general composite risk functionals, bias reduction in sample-based optimization.

Nonlinear Optimization: optimality conditions for composite infinite optimization problems. Multi-objective Optimization: new concepts of " - efficiency and level sets in abstract optimization; new variational principles and well-posedness analysis.
Singularity theory point of view in parametric optimization when the objective and the constraint functions are differentiable functions of a parameter.

Convex Analysis: generalized Steiner centers for convex sets, differentiability of the metric projection onto moving convex sets.

Set-Valued Analysis: relations between properties of multivalued mappings and families of their selections; applications to statistics and stochastic optimization.

Simulation and Petri Nets: a group-theoretical approach to Petri nets, new approach to network performance evaluation in stochastic Petri nets using stochastic optimization.

Modeling and numerical methods for large-scale big-data applied problems efficient approach to evaluating network performance under uncertain load, problems in ferrous metallurgy; optimal power generation under uncertainty, problems in robotics, medical applications.

Institutional Service

  • Faculty Senate Member
  • CRAFT Center co-director Member
  • Faculty Senate Chair
  • Budget advisor committee Member
  • Faculty Handbook committee Chair
  • Hiring committee for the Dean of the College of Online Education Member
  • CRAFT Center co-director Member
  • PhD Data Science Program coordination committee Member
  • SES Doctoral Committee Member
  • Department of Mathematical sciences Chair
  • SES Research Committee Member
  • Reappointment committee for associate dean of graduate education Member

Professional Service

  • Society of Industrial and Applied Mathematics Member of the SIAM Journals committee
  • SIAM Review Associate Editor
  • Society of Industrial and Applied Mathematics Chair of the SIAM review committee for SIOPT
  • ESAIM: Control, Optimisation and Calculus of Variations since 2013. Associate Editor
  • Journal of Nonsmooth Analysis and Optimization Associate Editor
  • SIAM Review Section Editor
  • University of Qatar External Evaluator
  • Auburn University’s Research Support Program (RSP). external proposal reviewer
  • University of Jordan External Evaluator
  • Bilkend University External Evaluator
  • Annals of Operational Research Guest-Editor of Special Issue
  • Frontiers of Applied Mathematics and Statistics: Section Optimization Associate Editor
  • Department of Energy Panelist
  • Departmental seminar Organizer
  • Workshop on Recent Advances in Stochastic Optimization Member of the Program Committee
  • 8th Rutgers-Stevens Workshop on Optimiation of stochastic systems Organizer

Consulting Service

Frequent reviewer and panelist for NSF, Department of Energy, ONR, AFOSR.

Appointments


2024--2025 Vice-Chair of the Faculty Senate,
Stevens Institute of Technology, Hoboken, New Jersey

2023--2024 Chair of the Faculty Senate,
Stevens Institute of Technology, Hoboken, New Jersey

2018--2021 Chair of the Department of Mathematical Sciences,
Stevens Institute of Technology, Hoboken, New Jersey

2007 - present Professor, Department Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey

2000 -2006 Associate Professor, Department Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey

1999 - 2000 Visiting Assistant Professor, Department of Industrial and Manufacturing Systems Engineering, Lehigh University, Pennsylvania

1997 - 1999 Visiting Scholar, RUTCOR, Rutgers Center for Operations Research, Rutgers University, New Brunswick, New Jersey

1994 - 1997 Research Scholar, Institute of Mathematics, Humboldt University, Berlin, Germany

1982 - 1994 Research Scholar, Department of Operations Research, Institute of Mathematics, Bulgarian Academy of Sciences, Sofia, Bulgaria (On leave)

Honors and Awards

Davis Memorial Research Award 2007 for excellence in research.

Award of the Board of Trustees of Stevens Institute of Technology for excellence in research

DAAD (Deutsche Akademische Austausch Dienst) visitor support.

Bulgarian Ministry of Education: outstanding student gold medal in 1981 and outstanding high school student gold medal in 1976.

Member of the Bulgarian Olympic team for the International Mathematical Olympiad 1976.

First prize in the national competition in problem solving in mathematics “Atanas Radev” for high-school students in 1976.

Professional Societies

  • SPS – Stochastic Programming Society Member
  • MOS – Mathematical Optimization Society Member
  • SIAM – Society of Industrial and Applied Mathematics Member

Grants, Contracts and Funds

AFOSR grant Optimization and Learning with Contextual Risk Models, September 15, 2024 - September 14, 2027 (co-PI)

ONR grant Risk-Averse Learning and Control for Distributed Dynamical Systems with Partial Information, $900,055, February 16, 2021 -February 15, 2024 (PI)

NSF Award IUCRC Phase I Stevens: Center for Research toward Advancing Financial Tech- nologies (CRAFT) $862,500, July 1, 2021 - June 30, 2026, (co-PI)

NSF Planning grant Industry-University Collaborative Research Centers for Stevens: Center for Cyber-SMART for the period of April 1, 2020 – March 30, 2021 (co-PI)

NSF Research award (DMS) for the period of September 2013–2016 on Time-Consistent Risk – Averse Control of Markov Systems (PI)

NSF CMMI award Successive Risk-Neutral Approximations of Dynamic Risk-Averse Optimization Problems July 2010–2013 (PI)

NSF DMS award Dynamic Stochastic Optimization with Stochastic Dominance Constraints and Risk Functionals, July 2006–2008 (PI)

NSF DMS award Semi-Infinite Probabilistic Optimization July 2003–2006 (PI)

NSF DMI award the period of August 2004–2007 on Risk-Averse Stochastic Optimization (PI)

DARPA Research award Error- Resilient Collective Decisions and Sensor Allocation, 2004 (Co-PI)

Humboldt University Berlin, Germany, award Stability and Asymptotic Behavior of Solutions to Stochastic Optimization Problems (1997–2000)

Deutsche Forschungsgemeinschaft multiple travel awards during 1995–1997.

Selected Publications

Book

  1. Dentcheva, D.; Ruszczynski, A. (2024). Risk-Averse Optimization and Control: Theory and Methods (pp. 1-451). Springer Nature.
    https://link.springer.com/book/10.1007/978-3-031-57988-2.
  2. Shapiro, A.; Dentcheva, D.; Ruszczynski, A. (2021). Lectures on Stochastic Programming: Modeling and Theory, Third Edition. Society for Industrial and Applied Mathematics.
    https://doi.org/10.1137/1.9781611976595.
  3. Shapiro, A.; Dentcheva, D.; Ruszczynski, A. (2021). Lectures on stochastic programming: modeling and theory (Third Edition ed.). Pennsylvania: Society for Industrial and Applied Mathematic.
    https://my.siam.org/Store/Product/viewproduct/?ProductId=38258337.
  4. Shapiro, A.; Dentcheva, D.; Ruszczyński, A. (2014). Lectures on Stochastic Programming: Modeling and Theory, Second Edition. Society for Industrial and Applied Mathematics.
    https://doi.org/10.1137/1.9781611973433.
  5. Shapiro, A.; Dentcheva, D.; Ruszczyński, A. (2009). Lectures on Stochastic Programming. Society for Industrial and Applied Mathematics.
    https://doi.org/10.1137/1.9780898718751.

Book Chapter

  1. Dentcheva, D.; Ruszczyński, A. (2024). Dynamic Risk Optimization. Springer Series in Operations Research and Financial Engineering (pp. 175-229). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_4.
  2. Dentcheva, D.; Ruszczyński, A. (2024). Elements of the Utility Theory. Springer Series in Operations Research and Financial Engineering (pp. 1-34). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_1.
  3. Dentcheva, D.; Ruszczyński, A. (2024). Measures of Risk. Springer Series in Operations Research and Financial Engineering (pp. 35-111). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_2.
  4. Dentcheva, D.; Ruszczyński, A. (2024). Multivariate and Sequential Stochastic Orders. Springer Series in Operations Research and Financial Engineering (pp. 293-325). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_6.
  5. Dentcheva, D.; Ruszczyński, A. (2024). Numerical Methods for Problems with Stochastic Dominance Constraints. Springer Series in Operations Research and Financial Engineering (pp. 327-368). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_7.
  6. Dentcheva, D.; Ruszczyński, A. (2024). Optimization of Measures of Risk. Springer Series in Operations Research and Financial Engineering (pp. 113-174). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_3.
  7. Dentcheva, D.; Ruszczyński, A. (2024). Optimization with Stochastic Dominance Constraints. Springer Series in Operations Research and Financial Engineering (pp. 231-292). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_5.
  8. Dentcheva, D.; Ruszczyński, A. (2024). Risk-Averse Control of Markov Systems. Springer Series in Operations Research and Financial Engineering (pp. 369-423). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-031-57988-2_8.
  9. Dentcheva, D. (2021). Chapter 4: Optimization Models with Probabilistic Constraints. Lectures on Stochastic Programming: Modeling and Theory, Third Edition (pp. 81-149). Society for Industrial and Applied Mathematics.
    https://doi.org/10.1137/1.9781611976595.ch4.
  10. Dentcheva, D.; Ruszczynski, A. (2015). Risk-Averse Portfolio Optimization via Stochastic Dominance Constraints. Handbook of Financial Econometrics and Statistics (pp. 2281-2302). Springer New York.
    https://doi.org/10.1007/978-1-4614-7750-1_83.
  11. Dentcheva, D.; Wolfhagen, E. (2014). Optimization with multivariate stochastic dominance constraints. Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures (pp. 2603-2610). CRC Press.
    https://doi.org/10.1201/b16387-376.
  12. Dentcheva, D. (2014). Two-stage optimization problems with stochastic-order constraints. Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures (pp. 2573-2578). CRC Press.
    https://doi.org/10.1201/b16387-372.
  13. Dentcheva, D.; Ruszczyński, A. (2010). Portfolio Optimization with Risk Control by Stochastic Dominance Constraints. International Series in Operations Research & Management Science (pp. 189-211). Springer New York.
    https://doi.org/10.1007/978-1-4419-1642-6_9.
  14. Dentcheva, D.; Ruszczyński, A. (2010). Risk-Averse Portfolio Optimization via Stochastic Dominance Constraints. Handbook of Quantitative Finance and Risk Management (pp. 247-258). Springer US.
    https://doi.org/10.1007/978-0-387-77117-5_15.
  15. Dentcheva, D. (2006). Optimization Models with Probabilistic Constraints. Probabilistic and Randomized Methods for Design under Uncertainty (pp. 49-97). Springer-Verlag.
    https://doi.org/10.1007/1-84628-095-8_2.
  16. Dentcheva, D.; Römisch, W. (1998). Optimal Power Generation under Uncertainty via Stochastic Programming. Lecture Notes in Economics and Mathematical Systems (pp. 22-56). Springer Berlin Heidelberg.
    https://doi.org/10.1007/978-3-642-45767-8_2.
  17. Dentcheva, D.; Römisch, W.; Schultz, R. (1995). Strong Convexity and Directional Derivatives of Marginal Values in Two-Stage Stochastic Programming. Lecture Notes in Economics and Mathematical Systems (pp. 8-21). Springer Berlin Heidelberg.
    https://doi.org/10.1007/978-3-642-88272-2_2.

Conference Proceeding

  1. Dentcheva, D.; Ye, M.; Yi, Y. (2022). Risk-averse sequential decision problems with time-consistent stochastic dominance constraints. no. 61st Conference on Decision and Control (CDC) (pp. 3605-3610). IEEE .
    https://doi.org/10.1109/cdc51059.2022.9993044.
  2. Ma, W.; Dentcheva, D.; Zavlanos, M. M. (2017). Risk-averse sensor planning using distributed policy gradient. 2017 American Control Conference (ACC) (pp. 4839-4844). IEEE.
    https://doi.org/10.23919/acc.2017.7963704.
  3. Chatzipanagiotis, N.; Dentcheva, D.; Zavlanos, M. M. (2012). Approximate augmented Lagrangians for distributed network optimizationL. 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (pp. 5840-5845). IEEE.
    https://doi.org/10.1109/cdc.2012.6426203.

Editorial, Journal

  1. Dentcheva, D. (2021). Introduction to Rotations in Three Dimensions and to the article Vandermonde with Arnoldi. yes. SIAM Review (2 ed., vol. 63, pp. 393-393). Philadelphia : SIAM .
  2. Dentcheva, D. (2021). Introduction to DeepXDE: A Deep Learning Library for Solving Differential Equations, to Fluid-Structure Interaction for the Classroom: Interpolation, Hearts, and Swimming, and to Improving the Accuracy of the Trapezoidal Rule. . yes. SIAM Review (1 ed., vol. 63, pp. 165-166). Philadelphia : SIAM .
  3. Dentcheva, D. (2020). Introduction to Time Correlation Functions of Equilibrium and Nonequilibrium Langevin Dynamics: Derivations and Numerics Using Random Numbers. and to the article An Introduction to Quantum Computing, without the Physics. yes. SIAM Review (4 ed., vol. 62, pp. 899-900). Philadelphia : SIAM .
  4. Dentcheva, D. (2020). Introduction to Hodge Laplacians on Graphs and to An Elementary Proof of a Matrix Tree Theorem for Directed Graphs. yes. SIAM Review (3 ed., vol. 62, pp. 683-683). Philadelphia : SIAM .
  5. Dentcheva, D. (2020). Introduction to Eight Perspectives on the Exponentially Ill-Conditioned Equation $\varepsilon y'' - x y' + y = 0$. yes. SIAM Review (2 ed., vol. 62, pp. 437–438). Philadelphia : SIAM .
  6. Dentcheva, D. (2020). Introduction to the Runge Example for Interpolation and Wilkinson's Examples for Rootfinding. yes. SIAM Review (1 ed., vol. 62, pp. 229-230). Philadelphia : SIAM .
  7. Dentcheva, D. (2019). Introduction to Characterizing Bad Semidefinite Programs: Normal Forms and Short Proofs. yes. SIAM Review (4 ed., vol. 61, pp. 837-838). Philadelphia : SIAM .
  8. Dentcheva, D. (2019). Introduction to Coin-Flipping, Ball-Dropping, and Grass-Hopping for Generating Random Graphs from Matrices of Edge Probabilities . SIAM Review (3 ed., vol. 61, pp. 547-548). Philadelphia : SIAM .
    https://epubs.siam.org/doi/abs/10.1137/19N974920.
  9. Dentcheva, D. (2019). Case for First Courses on Finite Markov Chain Modeling to Include Sojourn Time Cycle Chart. SIAM Review (2 ed., vol. 61, pp. 345–346.). SIAM PUBLICATIONS 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA.
  10. Dentcheva, D. (2019). Introduction to Dimensional and Scaling Analysis. SIAM Review (1 ed., vol. 60, pp. 157-158). PHILADELPHIA, PA 19104-2688 USA: SIAM PUBLICATIONS 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA.
  11. Dentcheva, D. (2018). A Primer on Noise-Induced Transitions in Applied Dynamical Systems,. SIAM Review (4 ed., vol. 60, pp. 967-967).
  12. Dentcheva, D. (2018). Research and Education in Computational Science and Engineering. SIAM Review (3 ed., vol. 60, pp. 705-706). SIAM PUBLICATIONS 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA.

Journal Article

  1. Chen, H.; Dentcheva, D.; Lin, Y.; Stock, G. J. (2025). Central limit theorems for vector-valued composite functionals with smoothing and applications. Annals of the Institute of Statistical Mathematics (5 ed., vol. 77, pp. 821-852). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10463-025-00934-z.
  2. Dentcheva, D.; Almen, A. (2024). On Risk Evaluation and Control of Distributed Multi-agent Systems. Journal of Optimization Theory and Applications. Springer Nature.
    https://doi.org/10.1007/s10957-024-02464-9.
  3. Dentcheva, D. (2023). Relations between risk-averse models in extended two-stage stochastic optimization. Serdica Mathematical Journal (1--3 ed., vol. 49, pp. 49-76). Sofia: Bulgarian Academy of Schiences.
    https://serdica.math.bas.bg/index.php/serdica/article/view/35.
  4. Dentcheva, D.; Vitt, C. A.; Sandberg, N.; Ruszczynski, A. (2023). The deepest event cuts in risk-averse optimization with application to radiation therapy design. Computational Optimization and Applications (3 ed., vol. 86, pp. 1347-1372). Springer Nature.
    https://link.springer.com/article/10.1007/s10589-023-00531-xhttps://link.springer.com/article/10.1007/s10589-023-00531-xhttps://link.springer.com/article/10.1007/s10589-023-00531-x.
  5. Dentcheva, D.; Ruszczynski, A. (2023). Mini-Batch Risk Forms. no. SIAM Journal on Optimization (2 ed., vol. 33, pp. 615-637). Philadelphia, PA: SIAM .
    https://doi.org/10.1137/22m1503774.
  6. Dentcheva, D.; Lin, Y.; Penev, S. (2022). Stability and Sample-Based Approximations of Composite Stochastic Optimization Problems. Operations Research (5 ed., vol. 71, pp. 1871-1888). Catonsville, MD: INFORMS.
    https://doi.org/10.1287/opre.2022.2308.
  7. Dentcheva, D.; Lin, Y. (2022). Bias reduction in sample-based optimization. SIAM Journal on Optimization (1 ed., vol. 32, pp. 130-151). Philadelphia, PA: SIAM .
    https://doi.org/10.1137/20m1326428.
  8. Dentcheva, D.; Ruszczyński, A. (2021). Subregular recourse in nonlinear multistage stochastic optimization. Mathematical Programming (1-2 ed., vol. 189, pp. 249-270). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-020-01612-z.
  9. Consigli, G.; Dentcheva, D.; Maggioni, F. (2020). Correction to: Preface: Stochastic optimization: theory and applications. Annals of Operations Research (2 ed., vol. 292, pp. 1001-1001). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10479-020-03727-0.
  10. Consigli, G.; Dentcheva, D.; Maggioni, F. (2020). Stochastic optimization: theory and applications. Annals of Operations Research (2 ed., vol. 292, pp. 575-580). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10479-020-03672-y.
  11. De Loera, J.; Dentcheva, D.; Pflug, G.; Schultz, R. (2019). New Directions in Stochastic Optimisation. Oberwolfach Reports (3 ed., vol. 15, pp. 2303-2384). European Mathematical Society - EMS - Publishing House GmbH.
    https://doi.org/10.4171/owr/2018/38.
  12. Vitt, C. A.; Dentcheva, D.; Xiong, H. (2019). Risk-averse classification. Annals of Operations Research.
    https://doi.org/10.1007/s10479-019-03344-6.
  13. Ma, W.; Oh, C.; Liu, Y.; Dentcheva, D.; Zavlanos, M. M. (2019). Risk-Averse Access Point Selection in Wireless Communication Networks. IEEE Transactions on Control of Network Systems (1 ed., vol. 6, pp. 24-36). Institute of Electrical and Electronics Engineers (IEEE).
    https://doi.org/10.1109/tcns.2018.2792309.
  14. Dentcheva, D.; Ruszczyński, A. (2019). Risk forms: representation, disintegration, and application to partially observable two-stage systems. Mathematical Programming (2 ed., vol. 181, pp. 297-317). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-019-01376-1.
  15. Dentcheva, D.; Stock, G. J. (2018). On the price of risk in a mean-risk optimization model. Quantitative Finance (10 ed., vol. 18, pp. 1699-1713). Informa UK Limited.
    https://doi.org/10.1080/14697688.2018.1436765.
  16. Dentcheva, D.; Ruszczyński, A. (2018). Time-Coherent Risk Measures for Continuous-Time Markov Chains. SIAM Journal on Financial Mathematics (2 ed., vol. 9, pp. 690-715). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/16m1063794.
  17. Dentcheva, D.; Penev, S.; Ruszczyński, A. (2017). Statistical estimation of composite risk functionals and risk optimization problems. Annals of the Institute of Statistical Mathematics (4 ed., vol. 69, pp. 737-760). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10463-016-0559-8.
  18. Dentcheva, D.; Martinez, G.; Wolfhagen, E. (2016). Augmented Lagrangian Methods for Solving Optimization Problems with Stochastic-Order Constraints. Operations Research (6 ed., vol. 64, pp. 1451-1465). Institute for Operations Research and the Management Sciences (INFORMS).
    https://doi.org/10.1287/opre.2016.1521.
  19. Dentcheva, D.; Wolfhagen, E. (2016). Two-Stage Optimization Problems with Multivariate Stochastic Order Constraints. Mathematics of Operations Research (1 ed., vol. 41, pp. 1-22). Institute for Operations Research and the Management Sciences (INFORMS).
    https://doi.org/10.1287/moor.2015.0713.
  20. Chatzipanagiotis, N.; Dentcheva, D.; Zavlanos, M. M. (2015). An augmented Lagrangian method for distributed optimization. Mathematical Programming (1-2 ed., vol. 152, pp. 405-434). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-014-0808-7.
  21. Dentcheva, D.; Wolfhagen, E. (2015). Optimization with Multivariate Stochastic Dominance Constraints. SIAM Journal on Optimization (1 ed., vol. 25, pp. 564-588). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/140955148.
  22. Dentcheva, D.; Martinez, G. (2013). Regularization methods for optimization problems with probabilistic constraints. Mathematical Programming (1-2 ed., vol. 138, pp. 223-251). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-012-0539-6.
  23. Dentcheva, D.; Römisch, W. (2013). Stability and Sensitivity of Stochastic Dominance Constrained Optimization Models. SIAM Journal on Optimization (3 ed., vol. 23, pp. 1672-1688). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/120886790.
  24. Dentcheva, D.; Martinez, G. (2012). Two-stage stochastic optimization problems with stochastic ordering constraints on the recourse. European Journal of Operational Research (1 ed., vol. 219, pp. 1-8). Elsevier BV.
    https://doi.org/10.1016/j.ejor.2011.11.044.
  25. Dentcheva, D.; Penev, S.; Ruszczyński, A. (2010). Kusuoka representation of higher order dual risk measures. Annals of Operations Research (1 ed., vol. 181, pp. 325-335). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10479-010-0747-5.
  26. Dentcheva, D.; Ruszczyński, A. (2010). Robust stochastic dominance and its application to risk-averse optimization. Mathematical Programming (1 ed., vol. 123, pp. 85-100). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-009-0321-6.
  27. Dentcheva, D.; Ruszczyński, A. (2010). Inverse cutting plane methods for optimization problems with second-order stochastic dominance constraints. Optimization (3 ed., vol. 59, pp. 323-338). Informa UK Limited.
    https://doi.org/10.1080/02331931003696350.
  28. Dentcheva, D.; Ruszczyński, A. (2009). Optimization with multivariate stochastic dominance constraints. Mathematical Programming (1-2 ed., vol. 117, pp. 111-127). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-007-0165-x.
  29. Dentcheva, D.; Ruszczyński, A. (2008). Stochastic Dynamic Optimization with Discounted Stochastic Dominance Constraints. SIAM Journal on Control and Optimization (5 ed., vol. 47, pp. 2540-2556). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/070679569.
  30. Dentcheva, D.; Henrion, R.; Ruszczyński, A. (2007). Stability and Sensitivity of Optimization Problems with First Order Stochastic Dominance Constraints. SIAM Journal on Optimization (1 ed., vol. 18, pp. 322-337). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/060650118.
  31. Dentcheva, D.; Ruszczyński, A. (2006). Inverse stochastic dominance constraints and rank dependent expected utility theory. Mathematical Programming (2-3 ed., vol. 108, pp. 297-311). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-006-0712-x.
  32. Dentcheva, D.; Ruszczyński, A. (2006). Portfolio optimization with stochastic dominance constraints. Journal of Banking & Finance (2 ed., vol. 30, pp. 433-451). Elsevier BV.
    https://doi.org/10.1016/j.jbankfin.2005.04.024.
  33. Dentcheva, D.; Lai, B.; Ruszczyński, A. (2004). Dual methods for probabilistic optimization problems*. Mathematical Methods of Operational Research (2 ed., vol. 60, pp. 331-346). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s001860400371.
  34. Dentcheva, D.; Ruszczynski, A. (2004). Optimality and duality theory for stochastic optimization problems with nonlinear dominance constraints. Mathematical Programming (2 ed., vol. 99, pp. 329-350). Springer Science and Business Media LLC.
    https://doi.org/10.1007/s10107-003-0453-z.
  35. Dentcheva, D.; Ruszczynski, A. (2003). Optimization with Stochastic Dominance Constraints. SIAM Journal on Optimization (2 ed., vol. 14, pp. 548-566). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/s1052623402420528.
  36. Dentcheva, D. (2001). On Differentiability of Metric Projections onto Moving Convex Sets. Annals of Operations Research (1-4 ed., vol. 101, pp. 283-298). Springer Science and Business Media LLC.
    https://doi.org/10.1023/a:1010945230381.
  37. Dentcheva, D.; Prékopa, A.; Ruszczynski, A. (2000). Concavity and efficient points of discrete distributions in probabilistic programming. Mathematical Programming (1 ed., vol. 89, pp. 55-77). Springer Science and Business Media LLC.
    https://doi.org/10.1007/pl00011393.
  38. Dentcheva, D.; Römisch, W. (2000). Differential Stability of Two-Stage Stochastic Programs. SIAM Journal on Optimization (1 ed., vol. 11, pp. 87-112). Society for Industrial & Applied Mathematics (SIAM).
    https://doi.org/10.1137/s1052623499316520.
  39. Dentcheva, D. (1998). Differentiable Selections and Castaing Representations of Multifunctions. Journal of Mathematical Analysis and Applications (2 ed., vol. 223, pp. 371-396). Elsevier BV.
    https://doi.org/10.1006/jmaa.1997.5735.

Courses

Nonlinear Optimization;
Advanced Methods of Optimization;
Dynamic programming and stochastic optimal control;
Dynamic programming and reinforcement learning;
Stochastic Optimization;
Optimization models and methods in finance;
Optimization models for data science;
Simulation
Probability
Intermediate Statistics
Real Analysis