Bio

Bio


Koller has a broad range of interests spanning artificial intelligence, economics, and algorithms. Her main research interest focuses on probabilistic models for complex systems, covering representation, reasoning, decision making, and learning. Her current projects include: learning statistical patterns from structured data, with emphasis on biological and medical data, using probabilistic models for making decisions under uncertainty, learning and inference in Bayesian networks, and extraction of semantically meaningful information from images and related modalities.

Academic Appointments


Honors & Awards


  • Fellowship, Sloan Foundation (1996)
  • Young Investigator Award, ONR (1999)
  • Presidential Early Career Award for Scientists and Engineers, National Science Foundation (2001)
  • Computers and Thought Award, IJCAI (2003)
  • Cox Medal, Stanford University (2004)
  • Fellow, American Association for Artificial Intelligence (2004)
  • Elected Fellow, American Association for Artificial Intelligence MacArthur Foundation Fellowship (2004)
  • Infosys Award, ACM (2008)

Boards, Advisory Committees, Professional Organizations


  • Member, National Academy of Engineering (2011 - Present)

Professional Education


  • PhD, Stanford University (1994)

Research & Scholarship

Current Research and Scholarly Interests


Machine learning for computational systems biology

Teaching

2013-14 Courses


Postdoctoral Advisees


Graduate and Fellowship Programs


Publications

Journal Articles


  • Systematic analysis of genome-wide fitness data reveals novel gene function and drug action. Genome Biology Hillenmeyer, M., E., Ericson, E., Davis, R., W., Nislow, C., Koller, D., Giaever, G. 2010; 11 (R30)
  • Automated identification of pathways from quantitative genetic interaction data. Molecular Systems Biology Battle, A., J., Jonikas, M., Walter, P., Weissman, J., Koller, D. 2010; 6: 379
  • Learning Factor Graphs in Polynomial Time & Sample Complexity. Journal of Machine Learning Research Abbeel, P., Koller, D., Ng, A., Y. 2006; 7: 1743-1788
  • Learning Module Networks. Journal of Machine Learning Research Segal, E., Pe'er, D., Regev, A., Koller, D., Friedman, N. 2005; 6: 557-588
  • Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association. Journal of Machine Learning Research. Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J., Nebot, E. 2004
  • Discovering Molecular Pathways from Protein Interaction and Gene Expression data. Bioinformatics, 19(S1 (Proc ISMB)). Segal, E., Wang, H., Koller, D. 2003
  • Learning probabilistic models of Relational Structure. Journal of Machine Learning Research Getoor, L., Friedman, N., Koller, D., Taskar, B. 2002; 3: 679-707
  • Support vector machine active learning with applications to text classification. Journal of Machine Learning Research Tong, S., Koller, D. 2001; 2: 45-66
  • Asymptotic conditional probabilities: The unary case. SIAM Journal on Computing Grove, A., J., Halpern, J., Y., Koller, D. 1996; 1 (25): 1-51
  • Finding mixed strategies with small supports in extensive form games. International Journal of Game Theory Koller, D., Megiddo, N. 1996; 1 (25): 73-92
  • Structured representations and intractibility. Koller, D. 1996; 4 (28)
  • A response to: ``Believing on the basis of evidence Computational Intelligence Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1994; 1 (10): 21-25
  • Constructing Small Sample Spaces Satisfying Given Constraints Siam Journal on Discrete Mathematics Koller, D., Megiddo, N. 1994; 2 (7): 260-274
  • A Response to `Believing on the basis of evidence'. Computational Intelligence Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1994; 1 (10): 21-25
  • Random worlds and maximum entropy Journal of Artificial Intelligence Research Grove, A., J., Halpern, J., Y., Kol, D. 1994; 2: 33-88

Books and Book Chapters


  • Probabilistic Graphical Models: Principles and Techniques. D., N. MIT Press.. 2009
  • Introduction to Statistical Relational Learning. Probabilistic Relational Models. Getoor, L., Friedman, N., Koller, D., Pfeffer, A., Taskar, B. edited by Getoor, L., Taskar, B. 2007: 1
  • Introduction to Statistical Relational Learning. Relational Markov Networks. Taskar, B., Abbeel, P., Wong, M., F., Koller, D. edited by Getoor, L., Taskar, B. 2007: 1
  • Introduction to Statistical Relational Learning. Graphical Models in a Nutshell. Koller, D., Friedman, N., Getoor, L., Taskar, B. edited by Getoor, L., Taskar, B. 2007: 1
  • Introduction to Statistical Relational Learning. Probabilistic Entity-Relationship Models, PRMs, Plate Models. Heckerman, D., Meek, C., Koller, D. edited by Getoor, L., Taskar, B. 2007: 1
  • Sampling in Factored Dynamic Systems. Sequential Monte Carlo Methods in Practice Koller, D., Lerner, U. edited by Doucet, A., de Freitas, J., F.G., Gordon, N. 2001: 1
  • Learning Probabilistic Relational Models. Relational Data Mining Getoor, L., Friedman, N., Koller, D., Pfeffer, A. edited by Dzeroski, S., Lavrac, N. 2001: 307-335
  • Sampling in Factored Dynamic Systems. Sequential Monte Carlo Methods In Practice. Koller, D., Lerner, U. edited by Doucet, A., de Freitas, J., F.G., Gordon, N. 2000: 1
  • Game Theory MIT Encyclopedia for Cognitive Science Koller, D. edited by Wilson, R., A., Keil, F., C. 1999: 338-340

Conference Proceedings


  • Multiclass Boosting with Hinge Loss based on Output Coding. Gao, T., Koller, D. 2011
  • Convex envelopes of complexity controlling penalties: the case against premature envelopment. Jojic, V., Saria, S., Koller, D. 2011
  • Discovering deformable motifs in continuous time-series data. Saria, S., Duchi, A., Koller, D. 2011
  • Active Classification based on Value of Classifier. Gao, T., Koller, D. 2011
  • Fast and smooth: Accelerated dual decomposition for MAP inference. Jojic, V., Gould, S., Koller, D. 2010
  • Self-Paced Learning for Latent Variable Models. Kumar, P., Packer, B., Koller, D. 2010
  • Non-Local Contrastive Objectives. Vickrey, D., Lin, C., Koller, D. 2010
  • Discovering shared and individual latent structure in multiple time series. Saria, S., Koller, D., Penn, A. 2010
  • Combining Structured and Free-text Data for Automatic Coding of Patient Outcomes. Saria, S., McElvain, G., Rajani, A., Penn, A., Koller, D. 2010
  • Region-based Segmentation, Object Detection. Gould, S., Gao, T., Koller, D. 2009
  • MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts. Kumar, M., P., Koller, D. 2009
  • Learning a Small Mixture of Trees. Kumar, M., P., Koller, D. 2009
  • Cascaded Classification Models: Combining Models for Holistic Scene Understanding. Heitz, G., Gould, S., Saxena, A., Koller, D. 2008
  • Sentence Simplification for Semantic Role Labeling. Vickrey, D., Koller, D. 2008
  • Constrained Approximate Maximum Entropy Learning. Ganapathi, V., Vickrey, D., Duchi, J., Koller, D. 2008
  • Shape-Based Object Localization for Descriptive Classification. Heitz, G., Elidan, G., Packer, B., Koller, D. 2008
  • Online Word Games for Semantic Data Collection. Vickrey, D., Bronsan, A., Choi, W., Kumar, A., Turner-Maier, J., Wang, A., Koller, D. 2008
  • Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. Elidan, G., Packer, B., Heitz, G., Koller, D. 2008
  • Integrating Visual, Range Data for Robotic Object Detection. Gould, S., Baumstarck, P., Quigley, M., Ng, A., Y., Koller, D. 2008
  • Projected Subgradient Methods for Learning Sparse Gaussians. Duchi, J., Gould, S., Koller, D. 2008
  • Reasoning at the Right Time Granularity. Saria, S., Nodelman, U., Koller, D. 2007
  • Efficient Structure Learning of Markov Networks using L1-Regularization. Lee, S., I., Ganapathi, V., Koller, D. 2007
  • Max-margin classification of incomplete data. Chechik, G., Heitz, G., Elidan, G., Abbeel, P., Koller, D. 2007
  • Using Combinatorial Optimization within Max-Product Belief Propagation. Duchi, J., Tarlow, D., Elidan, G., Koller, D. 2007
  • Temporal, Cross-Subject Probabilistic Models for fMRI Prediction Task. Battle, A., Chechik, G., Koller, D. 2007
  • Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing. Elidan, G., McGraw, I., Koller, D. 2006
  • Continuous Time Markov Networks. El-Hay, T., Friedman, N., Koller, D., Kupferman, R. 2006
  • Object Pose Detection in Range Scan Data. Rodgers, J., Anguelov, D., Pang, H., C., Koller, D. 2006
  • Constructing Informative Priors using Transfer Learning. Raina, R., Ng, A., Y., Koller, D. 2006
  • Learning Object Shape: From Drawings to Images. Elidan, G., Heitz, G., Koller, D. 2006
  • The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces. Anguelov, D., Koller, D., Srinivasan, P., Thrun, S., Pang, H., C., Davis, J. 2005
  • Expectation Maximization, Complex Duration Distributions for Continuous Time Bayesian Networks. Nodelman, U., Shelton, C., R., Koller, D. 2005
  • Word-Sense Disambiguation for Machine Translation. Vickrey, D., Biewald, L., Teyssier, M., Koller, D. 2005
  • Learning Structured Prediction Models: A Large Margin Approach. Taskar, B., Chatalbashev, V., Koller, D., Guestrin, C. 2005
  • Expectation Propagation for Continuous Time Bayesian Networks. Nodelman, U., Koller, D., Shelton, C., R. 2005
  • Ordering-based Search: A Simple, Effective Algorithm for Learning Bayesian Networks. Teyssier, M., Koller, D. 2005
  • SCAPE: Shape Completion, Animation of People. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J. 2005
  • Identifying protein-protein interaction sites on a genome-wide scale. Wang, H., Segal, E., Ben-Hur, A., Koller, D., Brutlag, D. 2005
  • Max-Margin Parsing. Taskar, B., Klein, D., Collins, M., Koller, D., Manning, C. 2004
  • Probabilistic Discovery of Overlapping Cell Processes, Their Regulation. Battle, A., J., Segal, E., Koller, D. 2004
  • GeneXPress: A Visualization, Statistical Analysis Tool for Gene Expression, Sequence Data. Segal, E., Yelensky, R., Kaushal, A., Pham, T., Regev, A., Koller, D. 2004
  • Learning Associative Markov Networks. Taskar, B., Chatalbashev, V., Koller, D. 2004
  • Recovering Articulated Object Models from 3D Range Data. Anguelov, D., Pang, H., C., Koller, D., Srinivasan, P., Thrun, S. 2004
  • A Continuation Method for Nash Equilibria in Structured Games. Blum, B., Shelton, C., R., Koller, D. 2003
  • FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization, Mapping that provably converges. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B. 2003
  • Generalizing Plans to New Environments in Relational MDPs. Guestrin, C., Koller, D., Gearhart, C., Kanodia, N. 2003
  • Learning Continuous Time Bayesian Networks. Nodelman, U., Shelton, C., R., Koller, D. 2003
  • Learning on the Test Data: Leveraging `Unseen' Features. Taskar, B., Wong, M., F., Koller, D. 2003
  • Learning Module Networks. Segal, E., Pe'er, D., Regev, A., Koller, D., Friedman, N. 2003
  • Monitoring a complex physical system using a hybrid dynamic Bayes net. Lerner, U., Moses, B., Scott, M., McIlraith, S., Koller, D. 2002
  • Continuous Time Bayesian Networks. Nodelman, U., Shelton, C., R., Koller, D. 2002
  • Discriminative Probabilistic Models for Relational Data. Taskar, B., Abbeel, P., Koller, D. 2002
  • Probabilistic Hierarchical Clustering for Biological Data. Segal, E., Koller, D. 2002
  • Learning hierarchical object maps of non-stationary environments with mobile robots. Anguelov, D., Biswas, R., Koller, D., Limketkai, B., Sanner, S., Thrun, S. 2002
  • From Promoter Sequence to Expression: A Probabilistic Framework. Segal, E., Barash, Y., Simon, I., Friedman, N., Koller, D. 2002
  • Probabilistic Supervised Learning, Clustering in Relational Data. Taskar, B., Segal, E., Koller, D. 2001
  • Learning an Agent's Utility Function by Observing Behavior. Chajewska, U., Koller, D., Ormoneit, D. 2001
  • Active Learning for Structure in Bayesian Networks. Tong, S., Koller, D. 2001
  • Exact Inference in Networks with Discrete Children of Continuous Parents. Lerner, U., Segal, E., Koller, D. 2001
  • Using Probabilistic Models for Selectivity Estimation. Getoor, L., Taskar, B., Koller, D. 2001
  • Learning probabilistic models of Relational Structure. Getoor, L., Friedman, N., Koller, D., Taskar, B. 2001
  • Multi-Agent Influence Diagrams for Representing, Solving Games. Koller, D., Milch, B. 2001
  • Max-norm Projections for Factored MDPs. Guestrin, C., Koller, D., Parr, R. 2001
  • Utilities as Random Variables: Density Estimation, Structure Discovery. Chajewska, U., Koller, D. 2000
  • Reinforcement Learning Using Approximate Belief States. Rodríguez, A., Parr, R., Koller, D. 2000
  • Probabilistic Models for Agents' Beliefs, Decisions. Milch, B., Koller, D. 2000
  • Semantics, inference for recursive probability models. Pfeffer, A., Koller, D. 2000
  • Policy Search via Density Estimation. Ng, A., Y., Parr, R., Koller, D. 2000
  • Being Bayesian about Bayesian Network Structure:A Bayesian Approach to Structure Discovery in Bayesian Networks. Friedman, N., Koller, D. 2000
  • Discovering hidden variables: A structure-based approach. Elidan, G., Lotner, N., Friedman, N., Koller, D. 2000
  • Policy Iteration for Factored MDPs. Koller, D., Parr, R. 2000
  • Efficient Reinforcement Learning in Factored MDPs. Kearns, M., J., Koller, D. 1999
  • Learning probabilistic relational models. Friedman, N., Getoor, L., Koller, D., Pfeffer, A. 1999
  • Discovering the hidden structure of complex dynamic systems. Boyen, X., Friedman, N., Koller, D. 1999
  • Tractable Inference for Complex Stochastic Processes. Boyen, X., Koller, D. 1998
  • Structured representation of complex stochastic systems. Friedman, N., Koller, D., Pfeffer, A. 1998
  • Using Learning for Approximation in Stochastic Processes. Koller, D., Fratkina, R. 1998
  • Update rules for parameter estimation in Bayesian networks. Bauer, E., Koller, D., Singer, Y. 1997
  • Hierarchically classifying documents using very few words. Koller, D., Sahami, M. 1997
  • Using probabilistic information in data integration. Florescu, D., Koller, D., Levy, A. 1997
  • Nonuniform dynamic discretization in hybrid networks. Kozlov, A., V., Koller, D. 1997
  • Effective Bayesian Inference for Stochastic Programs. Koller, D., McAllester, D., Pfeffer, A. 1997
  • Object-Oriented Bayesian Networks. Koller, D., Pfeffer, A. 1997
  • P-Classic: A Tractable Probabilistic Description Logic. Koller, D., Levy, A., Pfeffer, A. 1997
  • Context-specific independence in Bayesian networks. Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D. 1996
  • Toward Optimal Feature Selection. Koller, D., Sahami, M. 1996
  • Local learning in probabilistic networks with hidden variables. Russell, S., J., Binder, J., Koller, D., Kanazawa, K. 1995
  • Constructing flexible dynamic belief networks from first-order probabilistic knowledge bases. Glesner, S., Koller, D. 1995
  • Generating, solving imperfect information games. Koller, D., Pfeffer, A. 1995
  • Representation dependence in probabilistic inference. Halpern, J., Y., Koller, D. 1995
  • Stochastic simulation algorithms for dynamic probabilistic networks. Kanazawa, K., Koller, D., Russell, S., J. 1995
  • A game-theoretic classification of interactive complexity classes. Feigenbaum, J., Koller, D., Shor, P. 1995
  • Generating new beliefs from old. Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1994
  • Forming Beliefs about a Changing World. Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1994
  • Fast Algorithms for Finding Randomized Strategies in Game Trees. Koller, D., Megiddo, N., von Stengel, B. 1994
  • (De)randomized construction of small sample spaces in NC. Karger, D., R., Koller, D. 1994
  • Statistical Foundations for Default Reasoning. Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1993
  • From Statistics to Belief. Bacchus, F., Grove, A., J., Halpern, J., Y., Koller, D. 1992
  • Random worlds, maximum entropy Grove, A., J., Halpern, J., Y., Koller, D. 1992
  • Asymptotic conditional probabilities for first-order logic. Grove, A., J., Halpern, J., Y., Koller, D. 1992
  • Achievable cases in an asynchronous environment. Attiya, H., Bar-Noy, A., Dolev, D., Koller, D., Peleg, D., Reischuk, R. 1987

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