World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
24390
World Ranking
5464
National Ranking
2494

Overview

Daniel Kifer is affiliated with Pennsylvania State University in the United States. Their research primarily focuses on the intersection of computer science and geophysics, with a particular emphasis on artificial intelligence and privacy-preserving technologies in data.

Kifer's scholarly output includes over 100 publications in computer science, with substantial contributions to artificial intelligence (79 publications), computer vision and pattern recognition (14 publications), geophysics (8 publications), sociology and political science (6 publications), and mechanical engineering (5 publications). Their work also covers important topics such as privacy-preserving technologies in data (54 publications), cryptography and data security (26 publications), stochastic gradient optimization techniques (14 publications), privacy, security, and data protection (10 publications), domain adaptation and few-shot learning (10 publications), model reduction and neural networks (8 publications), and seismology and earthquake studies (8 publications).

Regarding co-authorship, Daniel Kifer frequently collaborates with a group of researchers, including C. Lee Giles, Ankur Mali, Philip Leclerc, John M. Abowd, and Robert Ashmead, all contributing to a significant number of joint publications.

Their research articles have appeared in multiple reputable venues. Among the frequent publication venues are arXiv (Cornell University), where 36 of their works are published, Harvard Data Science Review with 4 publications, SSRN Electronic Journal and Journal of Privacy and Confidentiality each with 3 publications, and Nature Communications with 2 publications.

Selected recent papers by Daniel Kifer include:

  • Differentiable modelling to unify machine learning and physical models for geosciences, 2023, Nature Reviews Earth & Environment
  • The Data Synergy Effects of Time-Series Deep Learning Models in Hydrology, 2022, Water Resources Research
  • Physics-informed deep learning for prediction of CO2 storage site response, 2021, Journal of Contaminant Hydrology
  • Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes, 2023, Nature Communications
  • The 2020 Census Disclosure Avoidance System TopDown Algorithm, 2022, Harvard Data Science Review

Best Publications

  • L-diversity: Privacy beyond k-anonymity

    Ashwin Machanavajjhala;Daniel Kifer;Johannes Gehrke;Muthuramakrishnan Venkitasubramaniam

  • L-diversity: privacy beyond k-anonymity

    A. Machanavajjhala;J. Gehrke;D. Kifer;M. Venkitasubramaniam

  • Detecting change in data streams

    Daniel Kifer;Shai Ben-David;Johannes Gehrke

  • No free lunch in data privacy

    Daniel Kifer;Ashwin Machanavajjhala

  • Privacy: Theory meets Practice on the Map

    Unknown

  • Context-aware citation recommendation

    Qi He;Jian Pei;Daniel Kifer;Prasenjit Mitra

  • Injecting utility into anonymized datasets

    Daniel Kifer;Johannes Gehrke

  • Worst-Case Background Knowledge for Privacy-Preserving Data Publishing

    D. J. Martin;D. Kifer;A. Machanavajjhala;J. Gehrke

  • Pufferfish: A framework for mathematical privacy definitions

    Daniel Kifer;Ashwin Machanavajjhala

  • Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

    Kuai Fang;Chaopeng Shen;Daniel Kifer;Xiao Yang

  • Private Convex Empirical Risk Minimization and High-dimensional Regression

    Daniel Kifer;Adam Smith;Abhradeep Thakurta

  • Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

    Kuai Fang;Chaopeng Shen;Daniel Kifer;Xiao Yang

  • HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

    Chaopeng Shen;Eric Laloy;Amin Elshorbagy;Adrian Albert

  • Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks

    Xiao Yang;Ersin Yumer;Paul Asente;Mike Kraley

  • Attacks on privacy and deFinetti's theorem

    Daniel Kifer

  • DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

    Cristian Bucilă;Johannes Gehrke;Daniel Kifer;Walker White

  • Crime Rate Inference with Big Data

    Hongjian Wang;Daniel Kifer;Corina Graif;Zhenhui Li

  • Privacy-Preserving Data Publishing

    Bee-Chung Chen;Daniel Kifer;Kristen LeFevre;Ashwin Machanavajjhala

  • A Simple Baseline for Travel Time Estimation using Large-scale Trip Data

    Hongjian Wang;Xianfeng Tang;Yu-Hsuan Kuo;Daniel Kifer

  • Learning to read irregular text with attention mechanisms

    Xiao Yang;Dafang He;Zihan Zhou;Daniel Kifer

  • A rigorous and customizable framework for privacy

    Daniel Kifer;Ashwin Machanavajjhala

  • Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network

    Xiao Yang;Ersin Yumer;Paul Asente;Mike Kraley

Frequent Co-Authors

C. Lee Giles
C. Lee Giles Pennsylvania State University
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
Ashwin Machanavajjhala
Ashwin Machanavajjhala Duke University
Zhenhui Li
Zhenhui Li Pennsylvania State University
Adam Smith
Adam Smith Boston University
Abhradeep Thakurta
Abhradeep Thakurta Google (United States)
Joseph Y. Halpern
Joseph Y. Halpern Cornell University
Amin Elshorbagy
Amin Elshorbagy University of Saskatchewan
Kuolin Hsu
Kuolin Hsu University of California, Irvine

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