Darinka Dentcheva

Professor

School: School of Engineering and Science

Department: Mathematical Sciences

Building: Peirce

Room: 302

Phone: (201) 216-8640

Fax: (201) 216-8321

Email: ddentche@stevens.edu

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

General Information



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
  • SES Doctoral Committee Member
  • Department of Mathematical sciences Chair
  • SES Research Committee Member
  • Reappointment committee for associate dean of graduate education Member
Professional Service
  • Journal of Nonsmooth Analysis and Optimization Associate Editor
  • SIAM Review Section Editor
  • ESAIM: Control, Optimisation and Calculus of Variations since 2013. Associate Editor
  • 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 and Department of Energy

Appointments

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

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

Books and chapters

10. A. Shapiro, D. Dentcheva, A. Ruszczyński. Lecture Notes on Stochastic Programming Modeling and Theory, SIAM and MPS, 2009; second edition 2014, third edition 2021.

9. G. Consigli, D. Dentcheva, F. Maggioni (Eds.): Stochastic Optimization: Theory and Applications SI in memory of Marida Bertocchi, Annals of Operations Research, 2020

8. J. De Loera, D. Dentcheva, G. Pflug, R. Schultz, New Directions in Stochastic Optimisation Oberwolfach Reports 15 (3), 2019, 2303–2384.

7. D. Dentcheva, A. Ruszczyński. Chapter 9: Portfolio Optimization with Risk Control by Stochastic Dominance Constraints, in Stochastic Programming, The state of the Art (Editor Gerd Infanger), Springer, International Series in Operations Research and Management Sciences, 2011.

6. D. Dentcheva, A. Ruszczyński , T. Szántai (Eds.) Stochastic Modeling and Optimization (in Honor of András Prékopa’s 80th Birthday), Annals of OR, Vol. 200, issue 1, 2012.

5. D. Dentcheva, A Ruszczyński. Risk-Averse Portfolio Optimization via Stochastic Dominance Constraints, in (C. F. Lee, Editor) Handbook of quantitative finance, Springer Verlag, 2010.

4. D. Dentcheva and J. Revalski ( Eds) Variational Analysis and Optimization, SIAMJournal on Optimization, Volume 18, Issue 3, pp. ix 1127, 2007.

3. D. Dentcheva: Optimization problems with probabilistic constraints, in: Probabilistic and Randomized Methods for Design under Uncertainty (Calafiore, G. and F. Dabbene, Eds.) Springer Verlag, London, 47–95, 2005.

2. D. Dentcheva. Regular selections of multifunctions and random sets, Habilitationsschrift Humboldt - University Berlin, Germany, 2005, LAP LAMBERT Academic Publishing, 2018

1. Dentcheva, W. Römisch. Optimal power generation under uncertainty via stochastic programming, in: Stochastic Programming Methods and Technical Applications, Lecture Notes in Economics and Mathematical Systems Vol. 458, Springer Verlag, Berlin 1998, 22–56.


Papers:

D. Dentcheva, Y. Lin, Bias Reduction in Sample-Based Optimization, SIAM Journal on Optimization, to appear

D. Dentcheva, A. Ruszczynski, Subregular recourse in nonlinear multistage stochastic optimization, Mathematical Programming, 189 (2021), 249–270.

D. Dentcheva, A. Ruszczynski, Risk forms: representation, disintegration, and application to partially observable two-stage systems, Mathematical Programming, 181 (2020),2, 297-317.

C.A. Vitt, D. Dentcheva, X. Xiong, Risk-averse classification, Annals of Operations Research, 2019, 1-35

D. Dentcheva, A. Ruszczynski, Time-Coherent Risk Measures for Continuous-Time Markov Chains, SIAM Journal on Financial Mathematics, 9(2018) 2, 690–715.

D. Dentcheva, G.J. Stock, On the price of risk in a mean-risk optimization model, Quantitative Finance, 18 (2018) 10,1699-1713. DOI:10.1080/14697688.2018.1436765

W.J. Ma, C. Oh, Y. Liu, D. Dentcheva, M.M. Zavlanos, Risk-Averse Access Point Selection in Wireless Communication Networks, IEEE Transactions on Control of Network Systems, DOI:10.1109/TCNS.2018.2792309.

D. Dentcheva, A. Ruszczynski, Risk-Averse Control of Continuous-Time Markov Chains, 2017 Proceedings of the Conference on Control and its Applications, 78-85, DOI: 10.1137/1.9781611975024.11.

W.-J. Ma, M. M. Zavlanos, D. Dentcheva, Risk-Averse Sensor Planning using Distributed Policy Gradient, 2017 American Control Conference of the of the IEEE Control Systems Society

D. Dentcheva, S. Penev, A. Ruszczynski: Statistical Estimation of Composite Risk Functionals and Risk Optimization Problems, Annals of the Institute of Statistical Mathematics, 69 (4),(2017) 737-760.

D. Dentcheva, G. Martinez, Eli Wolfhagen, Augmented Lagrangian Methods for Solving Optimization Problems with Stochastic-Order Constraints, Operations Research, 64 (6) (2016) 1451-1465.

D. Dentcheva, E. Wolfhagen. Two­-stage optimization problems with multivariate stochastic­order constraints, Mathematics of Operations Research, a41 (2016) 1, 1-22.

D. Dentcheva, E. Wolfhagen. Optimization with multivariate stochastic dominance constraints, SIAM Journal on Optimization, 25 (2015) No. 1, 564-588.

N. Chatzipanagiotis, D. Dentcheva, M. M. Zavlanos, An augmented Lagrangian method for distributed optimization, Mathematical programming, Ser. A, 152 (2015) No. 1, 405-434

D. Dentcheva, A Ruszczynski: Risk preferences on the space of quantile functions, Mathematical programming, Ser. B, 148 (2014), No. 1-2, 181-200.

D. Dentcheva, Two-stage risk averse optimization with dominance constraints, in Safety, Reliability, Risk, and Life-Cycle Performance of Structures and Infrastructures; Deodatis, Elingwood, Frangopol (eds.), Taylor & Francis Group, London, Proceedings of ICOSSAR 2013, 8p.

D. Dentcheva, E. Wolfhagen: Optimization with multivariate stochastic dominance constraints, in Safety, Reliability, Risk, and Life-Cycle Performance of Structures and Infrastructures; Deodatis, Elingwood, Frangopol (eds.), Taylor & Francis Group, London, Proceedings of ICOSSAR 2013, 8p.

D. Dentcheva, W. Römisch: Stability and Sensitivity of Stochastic Dominance Constrained Optimization Models, SIAM Journal on Optimization, 23 (3), No. 3, 1672-1688

D. Dentcheva, A. Ruszczynski: Common Mathematical Foundations of Expected Utility and Dual Utility Theories, SIAM Journal on Optimization, 23 (2013), No. 1, 381-405.

N. Chatzipanagiotis, D. Dentcheva, M. M. Zavlanos, Approximate Augmented Lagrangians for Distributed Network Optimization, Proceedings of the 51st IEEE Conference on Decision and Control, Dec. 2012, 5840-5845.

D. Dentcheva, A. Ruszczynski: Convex Analysis Approach to Utility Theories: Dual Utility, Comptes Rendus de l'Academie Bulgare des Sciences, 65 (2012) No. 12 1641-1648.

D. Dentcheva, A. Ruszczynski: Convex Analysis Approach to Utility Theories: Expected Utility, Comptes Rendus de l'Academie Bulgare des Sciences, 65 (2012) No. 11 1483-1488.

D. Dentcheva, G. Martinez, Regularization methods for optimization problems with probabilistic constraints, Mathemathical Programming, Ser. A, 138 (2013) No. 1-2, 223-251,

D. Dentcheva, G. Martinez, Two-stage stochastic optimization problems with stochastic ordering constraints on the recourse, European Journal of Operational Research (2011), DOI:10.1016/j.ejor.2011.11.044

D. Dentcheva, G. J. Stock, L. Rekeda, Mean-risk tests of stochastic dominance, Statistics & Decisions 28 (2011) 97-118.

D. Dentcheva, G. Martinez, Augmented Lagrangian method for probabilistic optimization, Annals of operations research 200 (2012) No. 1, 109-130, DOI:10.1007/s10479-011-0884-5

D. Dentcheva, S. Penev, A.Ruszczynski: Kusuoka representation of higher order dual risk measures, Annals of Operations Research, 181 (2010) 325-335

D. Dentcheva, S. Penev: Shape-restricted inference for Lorenz curves using duality theory, Statistics and Probability Letters, 80 (2010) 403-412.

D. Dentcheva, A Ruszczynski: Robust stochastic dominance constraints, Mathematical Programming Series B, 123 (2010) 85-100.

D. Dentcheva, A Ruszczynski: Inverse Cutting Plane Methods for Optimization Problems with Second Order Stochastic Dominance Constraints, Optimization, 59 (2010) 323-338.

D. Dentcheva, A Ruszczynski: Stochastic dynamic optimization with discounted stochastic dominance constraints, SIAM J. Control and Optimization, 47 (2008) No.5, 2540-2556.

D. Dentcheva, A Ruszczynski: Duality between coherent risk measures and stochastic dominance constraints in risk-averse optimization, Pacific Journal of Optimization, 4 (2008), No. 3, 433-446.

D. Dentcheva, A Ruszczynski: Stochastic dominance for sequences and implied utility in dynamic optimization, Comptes Rendus de l'Academie Bulgare des Sciences, 57 (2008) 1, 15-22.

D. Dentcheva, A Ruszczynski: Composite semi-infinite optimization, Control and Cybernetics, 36 (2007) 3, 633-646.

D. Dentcheva, A Ruszczynski: Optimization with multivariate stochastic dominance constraints, Mathematical Programming, March 2009, Volume 117, Issue 1, 111–127.

D. Dentcheva, R. Henrion, A Ruszczynski: Stability and sensitivity of optimization problems with first order stochastic dominance constraints, SIAM Journal on Optimization 18 (2007), 322-337.

D. Dentcheva, A Ruszczynski: Inverse stochastic dominance constraints and rank dependent utility theory, Mathematical Programming 108 (2006), 297-311.

D. Dentcheva, A Ruszczynski: Portfolio Optimization with stochastic dominance constraints, Journal on Banking and Finance, 30/2 (2006) 433--451.

D. Dentcheva, A Ruszczynski: Inverse stochastic dominance constraints and quantile utility theory, Comptes Rendus de l'Academie Bulgare des Sciences, 58 (2005) No.2, 11-16.

D. Dentcheva, A Ruszczynski: Risk Shaping by Stochastic Dominance Constraints, Proceedings of NSF DMII Grantees Conference, Arizona, Scottsdale, 2005.

D. Dentcheva, A Ruszczynski: Semi-infinite probabilistic optimization: first-order stochastic dominance constraints, Optimization 53 (2004) 583--601.

D. Dentcheva, A Ruszczynski: Convexification of stochastic ordering constraints, Comptes Rendus de l'Academie Bulgare des Sciences 57 (2004) No.4, 11-16.

D. Dentcheva, B. Lai, A Ruszczynski: Dual approach to probabilistic optimization, Mathematical Methods of Operations Research, 60 (2004), No. 2, 331-346.

D. Dentcheva, A Ruszczynski: Stochastic optimization with nonlinear dominance constraints, Mathematical Programming, 99 (2004) 329-350.

D. Dentcheva, A Ruszczynski: Stochastic optimization with dominance constraints, SIAM Journal on Optimization, 14 (2003) 548-566.

D. Dentcheva, A Ruszczynski: Optimization under nonlinear stochastic dominance, Comptes Rendus de l'Academie Bulgare des Sciences 56 (2003) No.7, 26-31.

D. Dentcheva, A Ruszczynski: Optimization under linear stochastic dominance, Comptes Rendus de l'Academie Bulgare des Sciences 56 (2003), No.6, 5--10.

D. Dentcheva, W. Römisch: Lagrangian relaxation and duality gap estimation for non-convex stochastic optimization models, Mathematical Programming, 101 (2004) 515-535.

D. Dentcheva, Continuity of multifunctions characterized by Steiner selections, Nonlinear Analysis: Theory, Methods & Applications 47 (2001) 1985-1996.

D. Dentcheva, A. Prekopa, A. Ruszczynski, On convex probabilistic programs with discrete distributions, Nonlinear Analysis: Theory, Methods & Applications 47 (2001) 1997-2009.

D. Dentcheva, A. Prekopa, A. Ruszczynski, Bounds for integer stochastic programs with probabilistic constraints, Discrete Applied Mathematics, 124 (2002) 55-65.

D. Dentcheva, A. Prekopa, A. Ruszczynski, Concavity and efficient points for discrete distributions in stochastic programming, Mathematical Programming, vol.89 (2000) 55-79.

D. Dentcheva, On differentiability of metric projections onto moving convex sets, Annals of Operations Research 101 (2001) 283-298

D. Dentcheva, Approximations, extensions and univalued representations of multifunctions, Nonlinear Analysis: Theory, Methods & Applications 45 (2001) 85-108.

D. Dentcheva, Regular Castaing Representations with Application to Stochastic Programming, SIAM Journal on Optimization, vol.10 (2000) 732-749.

D. Dentcheva, W. Römisch, Differential stability of two-stage stochastic programs, SIAM Journal on Optimization, vol.11 (2000) 87-112.

D. Dentcheva, Differentiable selections and Castaing representations of multifunctions, Journal of Mathematical Analysis and Applications, 223 (1998) 371-396.

D. Dentcheva, W. Römisch, Optimal power generation under uncertainty via stochastic programming, in: Stochastic Programming Methods and Technical Applications, Lecture Notes in Economics and Mathematical Systems Vol. 458, Springer-Verlag, Berlin 1998, 22-56.

D. Dentcheva, Differentiable selections of set-valued mappings and asymptotic behavior of random sets in infinite dimensions, Preprint 97-7, Institute fuer Mathematik, Humboldt-Universitaet Berlin, Germany, 1997.

D. Dentcheva, S. Helbig, On variational principles, level sets, well-posedness and epsilon-solutions in vector optimization, Journal of Optimization Theory and Applications, 89 (1996) 325-349.

1D. Dentcheva, A. Möller, P. Reeh, W. Römisch, R. Schultz, G. Schwarzbach, J. Thomas, Optimale Blockauswahl bei der Kraftwerkeinsatzplanung, in: Mathematik - Schlüsseltechnologie für die Zukunft, (K.-H. Hoffmann, W. Jäger, T. Lohmann, H. Schunck Eds.), Springer-Verlag, Berlin 1996, 567-577, (in German).

D. Dentcheva, W. Römisch, R. Schultz, Strong convexity and directional derivatives of marginal values in two-stage stochastic programming, in: Stochastic Programming: Numerical Techniques and Engineering Applications, Lecture Notes in Economics and Mathematical Systems, Vol.423, Springer-Verlag, Berlin 1995, 8-21.

D. Dentcheva, S. Helbig, Level sets convergence and well-posedness in vector optimization, in: Proceeding of the International conference on Stability and Well-Posedness in Optimization, Luminy, France, 1995.

D. Dentcheva, R. Gollmer, J. Guddat, J.-J. Rückmann, Path-following methods in nonlinear optimization II: Exact penalty embedding, in: Approximation and Optimization in the Caribbean II, (M. Florenzano et al., Eds.), Verlag Peter Lang, Frankfurt am Main, 1995, 200-230

P. Braun, B. Brosowski, D. Dentcheva, Analysis of colored Petri nets using linear optimization, in: Algorithmen und Werkzeuge für Petri-Netze, (J. Desel, A. Oberweis, W. Reisig, Eds.), Workshop der GI-Fachgruppe 001, Berlin, 1994, Bericht 309 des AIFB Universität Karlsruhe (TH), 1--8.

D. Dentcheva, J. Guddat, J.-J. Rückmann, Path-following methods in nonlinear optimization III: Lagrange multiplier embedding, ZOR - Mathemtical Methods of OR 41 (1995) 127-152.

S. Helbig, D. Pateva, Several concepts for epsilon-efficiency, OR Spektrum 16 (1994) 179-186.

D. Pateva, Well-Posedness and regularity of one-parametric optimization problems, Mathematics and Education in Mathematics, Sofia, 1994, 375-381.

P. Milanov, D. Pateva, On Malfatti problem for equilateral triangles, Mathematics and Informatics 1 (1992) 3, 1-8.

D. Pateva, On singularities in one-parametric linear programs, Optimization, 22 (1991) 193-219.

D. Pateva, Most of the linear one-parametric optimization problems are regular, Mathematica Balcanica 34 (1990) 401-410.

D. Pateva, On a notion of weak singularity for one-parametric optimization problems, Mathematics and Education in Mathematics, Sofia, 1990, 382-387.

D. Pateva, Stationary solution for one-parametric linear optimization problems under regularity conditions, Mathematics and Education in Mathematics, Sofia, 1988, 334-339.

J. Alkalay, P. Goranov, C. Nedeva, D. Pateva, K. Julipov, Assessment of solutions for steel strip stretching process via simulation, in: Enhancement of steel strips useful properties, Ceskoslovenska Vedeckotechnicka Spolecnost, Dum techniky CSVTS, Ostrava, Gottwaldov, 1986

J. Alkalay, P. Goranov, R. Ivanov, C. Nedeva, D. Pateva, P. Milanov, Generation of production schemes for ferrous metallurgy by simulation and stochastic optimization. Proceedings of the IV international conference on Statistical Methods in the Experimental Research and Quality Control, 14-17 Oct. 1986, Varna, Zlatni Pjasaci, DKIT, Bulgaria (in Bulgarian).

Some of my earlier publications appeared under my former name Darinka Pateva.

Thesisi

2. D. Pateva, Structural analysis of parametric optimization problems, Ph.D. thesis, Humboldt University Berlin, Germany, 1989.

1. D. Dentcheva, Discrete time simulation based on Petri nets, Master Thesis, Humboldt University Berlin, Germany, 1982.

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