Scott Alfeld

Research

Publications

• A. Sakar, M. Lanier, S. Alfeld, R. Garnett, N. Jacobs, Y. Vorobeychik
  "A Visual Active Search Fromwork for Geospatial Exploration"
  in Proceedings of the Winter Conference on Applications of Computer Vision, 2024
• Z. Kong, S. Alfeld
  "A Visual Active Search Fromwork for Geospatial Exploration"
  in Proceedings of European Conference on Artificial Intelligence (ECAI '23)
• A. Vartanian, W. Rosenbaum, S.Alfeld
  "Training-Time Attacks Against k-Nearest Neighbors."
  in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’23)
• N. G. Marchant, B. I. P. Rubinstein, S. Alfeld
  "Hard to Forget: Poisoning Attacks on Certified Machine Unlearning"
  in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’22)
• B. A. Miller, Z. Shafi, W. Ruml, Y. Vorobeychik, T. Eliassi-Rad, S. Alfeld
  “PATHATTACK: Attacking Shortest Paths in Complex Networks"
  in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD '21)
• D. Liu, Z. Shafi, W. Fleisher, T. Eliassi-Rad, S. Alfeld.
  “RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity."
  in Proceedings of the ACM Conference on Artificial Intelligence, Ethics and Society (AIES '21)
• S. Yu, L. Torres, S. Alfeld, T. Eliassi-Rad, Y. Vorobeychik
  “POTION: Optimizing Graph Structure for Targeted Diffusion"
  in Proceedings of Siam International Conference on Data Mining (SDM '21)
• S. Alfeld, A. Vartanian, L. Newman-Johnson, B. I. P. Rubinstein
  “Attacking Data Transforming Learners at Training Time"
  in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’19)
• A. Sen, S. Alfeld, X. Zhang, A. Vartanian, Y. Ma, X. Zhu
  "Training Set Camouflage"
  in Proceedings of the Conference on Decision and Game Theory for Security (GameSec '18)
• L. Tong, S. Yu, S. Alfeld, Y. Vorobeychik
  “Adversarial Regression with Multiple Learners"
  in Proceedings of the International Conference on Machine Learning (ICML '18)
• S. Yu, Y. Vorobeychik, S. Alfeld
  “Adversarial Classification on Social Networks"
  in Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS '18)
• S. Alfeld, X. Zhu, P. Barford
  “Explicit Defense Actions Against Test-Set Attacks"
  in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’17)
• A. Cahn, S. Alfeld, P. Barford, S. Muthukrishnan
  “What’s in the Community Cookie Jar?”
  in Proceedings of the IEEE/ACM Conference on Advances in Social Network Analysis and Mining (ASONAM ’16)
• S. Alfeld, X. Zhu, P. Barford
  “Machine Teaching as Search”
  in Proceedings of the Symposium on Combinatorial Search (SoCS '16)
• S. Alfeld, X. Zhu, P. Barford
  “Data Poisoning Attacks Against Autoregressive Models”
  in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’16)
• A. Cahn, S. Alfeld, P. Barford, S. Muthukrishnan
  “An Empirical Study of Web Cookies”
  in Proceedings of the World Wide Web Conference (WWW ’16)
• M. Malloy, S. Alfeld, P. Barford
  “Contamination Estimation via Convex Relaxations”
  in Proceedings of IEEE International Symposium on Information Theory (ISIT ’15)
• S. Alfeld, P. Barford
  “Targeted Residual Analysis for Improving Electric Load Forecasting”
  in Proceedings of IEEE Energy Conference (Energycon ’14)
• S. Alfeld, C. Barford, P. Barford
  “Toward an Analytic Framework for the Electrical Power Grid”
  in Proceedings of the Third International Conference on Future Energy Systems (e-Energy ’12)

• B. A. Miller, Z. Shafi, W. Ruml, Y. Vorobeychik, T. Eliassi-Rad, S.Alfeld
  "Attacking Shortest Paths by Cutting Edges"
  Transactions on Knowledtge Discovery from Data, 2023

• M. Stein, S. Alfeld
  "GARFD: Gradient-based Autoregressive Forecaster Defense"
  NeurIPS Workshop on Dataset Curation and Security, 2020
• X. Zhang, H. Ohannessian, A. Sen, S. Alfeld, X. Zhu
  "Optimal Teaching for Online Perceptrons"
  NIPS Constructive Machine Learning Workshop, 2016
• S. Alfeld, P. Barford, X. Zhu
  “Optimal Defense Actions Against Test Set Attacks”
  ICML Workshop on Reliable Machine Learning in the Wild, 2016.
• S. Alfeld, K. Berkele, S. DeSalvo, T. Pham, D. Russo, L.J. Yan, M.E. Taylor
  “Reducing the Team Uncertainty Penalty: Empirical and Theoretical Approaches”
  in Proceedings of the workshop on multiagent sequential decision making in uncertain domains, 2011.
• S. Alfeld, M.E. Taylor, P. Tandon, M. Tambe
  “Towards a Theoretic Understanding of DCEE”
  in Proceedings of the Distributed Constraint Reasoning workshop, May 2010.

Education

• PhD, May 2017
  Department of Computer Sciences
  University of Wisconsin--Madison
  Advisers: Paul Barford and Xiaojin (Jerry) Zhu
  Thesis Title: Learning in the Presence of Adversaries
• Masters, June 2015
  Department of Computer Sciences
  University of Wisconsin--Madison
• Bachelors, August 2008
  School of Computing
  University of Utah