Professor & Head Graduate Advisor
Ph.D. University of Pennsylvania, Wharton School, 1979 Mathematical Programming

4181 Etcheverry Hall
(510) 642-4998
E-mail: hochbaum(at)

Personal Webpage:

"I like to take two approaches to finding the 'best method' for solving aproblem. One tries to devise an efficient algorithm for the problem, and the other tries to establish limits on the efficiency of the algorithm."


 Dorit S. Hochbaum is a Chancellor full professor at UC Berkeley, at Industrial Engineering and Operations Research (IEOR). Professor Hochbaum holds a Ph.D from the Wharton school of Business at the University of Pennsylvania. Prior to joining UC Berkeley in 1981, Professor Hochbaum held a faculty position at Carnegie Mellon university's GSIA. Her research interests are in areas of discrete optimization, network flow techniques,data mining, image segmentation, supply chain management and efficient utilization of resources. She did work on approximation algorithms, location problems; on movement of robots; on routing and distribution problems; on feasibility of VLSI designs; on distribution of data bases on computer networks; on clustering problems and on medical imaging, among others. She has contributed to the analysis of heuristics and approximation algorithms in the worst case, and on the average, and to the complexity analysis of algorithms in general, and nonlinear optimization algorithms in particular. Her recent applications work is on problems related to the homeland security with flow based pattern recognition algorithms, analyzing gene expression databases, scheduling and testing, production planning and supply chain streamlining for high tech industries and logistics and planning problems in various industries. Recent theoretical work focuses on particularly efficient techniques using network flow for data mining and image segmentation and for inverse problems, with applications varying from medical prognosis, error correction, medical imaging, nuclear threat detection, financial risk assessment and prediction, to group rankings and decision problems.

Professor Hochbaum served as the chair of the Manufacturing and Information Technology group at the Haas School of Business. She is the founder and director of the UC Berkeley Supply Chain Initiative. She is the founder and co-director of the RIOT project.

Professor Hochbaum is the author of over 150 papers that appeared in the Operations Research, Management Science and Theoretical Computer Science literature. She served as department editor for Management Science department of Optimization and Modeling, and on a number of editorial boards

Professor Hochbaum was named in 2004 as honorary doctorate of Sciences of the University of Copenhagen, for her work on approximation algorithms. In 2005 Professor Hochbaum was conferred the title of INFORMS fellow.  She was appointed the Pinhas Naor lecturer of the Technion for 2013, and a Research Excellence professor at the University of Vienna in 2007.She is the winner of the 2011 INFORMS Computing Society prize for her work on algorithms for image segmentation.  She was named SIAM (Society of Industrial and Applied Mathematics) fellow in 2014.


  • Devising Efficient Algorithms for Optimization Problems in Manufacturing and Management
  • The Complexity of Nonlinear Optimization Problems
  • Network Clustering and Partititioning
  • Network Flow Techniques
  • Supply Chain Management


Ph.D. Theses Supervised

  • “Solutions for the Multicovering Problem,” Nicholas Hall     

  • “A Lagrangean relaxation method for testing the infeasibility of certain VLSI routing problems,” Thomas A. Feo

  • “Efficient reduction of planar networks for solving certain combinatorial problems,” Thomas A. Feo

  • “On graph partitioning problems,” Mallek Khellaf 

  • “Approximation algorithms for problems in sequencing, scheduling, and communication network design,” David B. Shmoys     

  • “Deterministic and probabilistic aspects of the k-cut problem,” Olivier P. Goldschmidt     

  • “About strongly polynomial algorithms of some special classes of convex quadratic programming,” Sung-Pil Hong     

  • “Batch scheduling for manufacturing,” Dan Landy     

  • “Efficient algorithms for the ultimate pit limit problem,” Anna Chen

  • “Algorithms and complexity for cuts and selection problems on graphs,” Anu Pathria

  • “Algorithms for telecommunication networks,” Eli Olinick     

  • “Implementations of the pseudoflow algorithm for maximum flow, bipartite matching, flows in unit capacity networks, and parametric maximum flow,” Bala Chandran     

  • “Use and analysis of new optimization techniques for decision theory and data mining,” Erick Moreno-Centeno

  • “Geometric Models for Collaborative Search and Filtering,” Ephrat Bitton    

  • “Machine Learning Techniques in Nuclear Material Detection, Drug Ranking and Video Tracking,” Yan Yang

  • “Efficient Algorithms for Markov Random Fields, Isotonic Regression, Graph Fused Lasso, and Image Segmentation,” Cheng Lu