Jeff M Phillips
Jeff M Phillips
Professor,
Kahlert School of Computing, University of Utah
BS Computer Science, Rice University (2003)
BA Mathematics, Rice University (2003)
Ph.D. Computer Science, Duke University (2009)
CI Postdoctoral Fellow
School of Computing, University of Utah
(2009-2011)
Director,
Utah Center for Data Science
Director,
Data Science Program
in the Kahlert School of Computing, including the
Graduate Program in Data Science
, and the
BioTech Data Science Program
part of
Utah DB Group
Utah and an AI-lead for
NSF-Simons CosmicAI
Institute
Students
Teaching
Books
Funding
News
External Service
Publications
Bio
U of Utah Address:
50 S Central Campus Dr. 3190
Salt Lake City, UT 84112
(801) 585-8224 (main office)
(801) 581-5843 (fax)
Email:
jeffp
|at|
cs.utah.edu
Office:
3404 Merril Engineering Building
CV:
CV
(probably out of date)
Official UU page:
Elements Bio
Research Interests:
Algorithms for Big Data Analytics:
Geometric Data Analysis
, Computational Geometry, Coresets and Sketches, Machine Learning, Spatial Statistics, Data Management, AI, Handling Uncertainty, Data Mining.
Students and Postdoc:
Meysam Alishahi
(PhD) expected graduation in Spring 26
Foad Namjoo
(PhD) started 2023
Hamid Shafieasl
(PhD) started 2024
Gazi Abdur Rakib
(PhD) started 2024
Remy Ogasawara
(PhD) started 2025
Cullen Anderson
(BS at UMass, REU at UU) expected graduation in 2026
Ashley Lujan
(MS) started 2025
Jens Kristian (JK) Schou
(RAI postdoctoral Fellow) started 2025
Former Students
Teaching:
Foundations of Data Analysis
| CS/DS 3190 | Spring 2026 | MW 3-4:20pm | WEB L101
old:
Foundations of Data Analysis (Math for Data)
Fall 2022
Fall 2021
Fall 2020
Fall 2019
Fall 2017
Fall 2016
Data Mining
Fall 2025
Spring 2025
Spring 2020
Spring 2019
Spring 2018
Spring 2017
Spring 2016
Spring 2015
Spring 2014
Spring 2013
Spring 2012
Ethics in Data Science (CS/DS 3390)
Spring 2021
High-Dimensional Data Analysis
Fall 2024
Programming for BioMedical Data Science
(run & led by
Rebecca Barter
Fall 2024
Fall 2025
Probability and Statistics for Engineers
Spring 2023
Fall 2014
Data Mining Seminar
Spring 2015 (Matrix Sketching)
Fall 2013 (MCMD)
Fall 2012 (sampling)
Fall 2010 (uncertainty)
Data Science Seminar
Spring 2023
Fall 2021
Fall 2020
Spring 2020
Spring 2019
Spring 2018
Spring 2017
Spring 2016
Fall 2014
Spring 2014
Spring 2012
Models of Computation for Massive Data |
Fall 2013
Fall 2011
Books:
Mathematical Foundations for Data Analysis
Springer-Nature 2021.
Funding:
NSF Spatial Data Multi-Agent Simulations
NSF-Simons AI Institute for Cosmic Origins
NSF Geometry of Learning on Structured Data Objects
Extracting Models from Reactive Flow Data
previous:
Big Data Summaries
STORM : Spatio-Temporal Data
SEAL : Secure Cloud Analytics
Detecting Spectrum Offenders
Interactive and Online Sampling and Analytics
Persistent Data Summaries
Noisy Geometric Data Analysis
News and notes:
A number of Utah students (mostly Michael Matheny, Foad Namjoo, and Simon Gonzalez) have contributed to the pyScan library for finding spatial anomalies. This
repo by Simon Gonzalez
is the best starting point.
Peter Jacobs
(joining U Wisconsin Statistcs as postdoc) defended his PhDs in Summer 2025.
Hasan Pourmahmood
(joining Walmart) and
Mingxuan Han
(joining Meta) defended their PhDs in Summer 2024.
I organized (with
Alex Munteanu
) the
CG-Week
(which includes SOCG)
Workshop on Geometry and Machine Learning
. It took place June 13, 2024 in Athens, Greece. I also co-organized previous iterations in
2023
2022
2021
2019
2018
2017
, and
2016
Tao Yang
(joining Amazon) and
Benwei Shi
(joining Meta) defended their PhDs in late Fall 2023.
I was co-PC Chair for
SoCG 2024
I am spending Fall 23 - Spring 24 on sabbatical in Leipzig, Germany at
SCaDS.AI
Uni Leipzig
, and
MPI for Math in Sciences
I was promoted to (Full) Professor in Summer 2023.
Prince Osei Aboagye
(joining Visa Research) defended his PhD in Summer 2023.
Zhuoyue Zhao
(joining U Bufallo as Assistant Prof) and
Zhao Chang
(joining Xidan University as faculty) both defneded their PhDs in Spring/Summer 2020. Both were coadvised by Feifei Li.
My "Math for Data" book
Mathematical Foundation for Data Analysis
is published by
Springer
and also available on
Amazon
I helped form a new university center, the
Utah Center for Data Science
. It launched in October 2019, and I am the founding director.
I helped create a new undergraduate major, a
Bachelors of Science in Data Science
. It
launched
in Fall 2019, and I am the director.
I post and
live-stream
many videos of my lectures on Youtube. They now appear on the
Utah Data Youtube Channel
. Older versions are
here
and
here
Selected Program Committees:
SoCG 2024 (co-PC Chair)
2021
2016
SODA 2024
2015
ICDT 2026, ICDT 2024,
2017
ICDE 2022 (Demo Co-Chair),
2014
FWCG 2021
2014
2012
ESA (track A) 2021
2013
(track B) 2017
ICALP 2020
NeurIPS
2018, 2019, 2020, 2021, 2022, 2024, 2025
AIStats
2018, 2019, 2020, 2021,2022, 2025
SIGSPATIAL 2017
2018, 2019, and 2020,
PODS 2017
2015
CG:YRF 2016
ICDE 2016
MASSIVE 2014
CIKM 2013
KDD 2012
Selected Journal Service:
Editorial Board
Computing in Geometry and Topology
(2021 - ).
Action Editor
Transactions on Machine Learning Research
(2023 - ).
Associate Editor
IEEE Transactions on Knowledge and Data Engineering
(2016 - 2020),
Associate Editor
SIAM Journal of Scientific Computing, Special Issue for Software and Big Data
(2014 - 2016).
Publications:
DBLP
Google Scholar
Semantic Scholar
ArXiv
The General Expiration Streaming Model: Diameter, k-Center, Counting, Sampling, and Friends
Lotte Blank, Sergio Cabello, MohammadTaghi Hajiaghayi, Robert Krauthgamer, Sepideh Mahabadi, Andre Nusser, Jeff M. Phillips, Jonas Sauer
International Colloquium on Automata, Languages, and Programming (ICALP)
. July 2026.
arXiv:2509.07587
September 2025.
Hardness of High-Dimensional Linear Classification
Alexander Munteanu, Simon Omlor, Jeff M. Phillips.
Symposium on Computational Geometry (SoCG)
. June 2026.
arXiv:2603.19061
March 2026.
A Topology-Preserving Coreset for Kernel Regression in Scientific Visualization
(to appear)
(Best TVCG Paper)
Weiran Lyu, Nathaniel Gorski, Jeff M. Phillips, Bei Wang.
IEEE PacificVis 2026
. April 2026. (TVCG Track)
Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System
Foad Namjoo, Neng Wan, Devan Mallory, Yuyi Chang, Nithin Sugavanam, Long Yin Lee, Ning Xiong, Emre Ertin, Jeff M. Phillips.
EAI SmartSP 2025 Conference
. December 2025.
arXiv:2510.19938
October 2025.
Enhancing Urban Paratransit Reliability: A Spatial-Temporal and Causal Analysis of Service Inefficiencies
Arman Malekloo; Xiaoyue Cathy Liu; Nikola Markovic; Chenxi Liu; Jeff Phillips.
ASCE Journal of Urban Planning and Development
. 2025.
Dimension-Independent Kernel eps-Covers
Jeff M. Phillips and Hasan Pourmahmood-aghababa.
Computing in Geometry and Topology
. June 2025.
preliminary version at as
For Kernel Range Spaces a Constant Number of Queries Are Sufficient
at
Fall Workshop on Computational Geometry
October 2022.
arXiv:2306.16516
June 2023.
Zombie Hashing: Reanimating Tombstones in Graveyard
Benwei Shi, Yuvaraj Chesetti, Jeff M. Phillips, and Prashant Pandey.
International Conference on Management of Data (SIGMOD)
. June 2025.
Estimation of Large Zipfian Distributions with Sort and Snap
Peter Matthew Jacobs, Jeff M. Phillips, Anirban Bhattacharya, Debdeep Pati, and Lekha Patel.
Artificial Intelligence and Statistics (AIStats)
. May 2025.
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian Optimization
(Oral)
Zhitong Xu, Haitao Wang, Jeff M. Phillips, Shandian Zhe.
International Conference on Learning Representations (ICLR)
. April 2025.
Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study
Cullen Anderson and Jeff M. Phillips.
Transactions on Machine Learning Research (TMLR)
. February 2025.
Fast Comparative Analysis of Merge Trees Using Locality-Sensitive Hashing
Raghavendra Sridharamurthy, Nancy Lyu, Jeff M. Phillips, and Bei Wang.
IEEE VIS: Visualization & Visual Analytics (VIS)
. October 2024.
arXiv:2409.08519
September 2024.
No Dimensional Sampling Coresets for Classification
Meysam Alishahi and Jeff M. Phillips.
International Conference on Machine Learning (ICML)
(Spotlight)
. July 2024.
arXiv:2402.05280
February 2024.
Linear Distance Metric Learning with Noisy Labels
Meysam Alishahi, Anna Little, and Jeff M. Phillips.
Journal of Machine Learning Research (JMLR)
. April 2024.
arXiv:2306.03173
June 2023.
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
Tao Yang, Cuize Han, Chen Luo, Parth Gupta, Jeff M. Phillips, and Qingyao Ai.
The Web Conference
. May 2024.
arXiv:2305.16606
May 2023.
Sketching Multidimensional Time Series for Fast Discord Mining
Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, and Eamonn Keogh.
IEEE International Conference on Big Data
. December 2023.
Locally Adaptive and Differentiable Regression
Mingxuan Han, Varun Shankar, Jeff M. Phillips, Chenglong Ye.
Journal of Machine Learning for Modeling and Computing
4(4):103-122. 2023.
arXiv:2308.07418
August 2023.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, and Bei Wang.
ACM Transactions on Interactive Intelligent Systems
. 2023.
arXiv:2104.02797
April 2021.
KDD21 Tutorial
Ferret: Reviewing Tabular Datasets for Manipulation
Devin Lange, Shaurya Sahai, Jeff M. Phillips, and Alexander Lex.
25th EG Conference on Visualization (EuroVis).
June 2023.
OSF.IO/anj8v
December 2022.
Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization
Prince Osei Aboagye, Yan Zheng, Jack Shunn, Chin-Chia Michael Yeh, Junpeng Wang, Zhongfang Zhuang, Huiyuan Chen, Liang Wang, Wei Zhang, and Jeff Phillips.
International Conference on Learning Representations (ICLR).
April 2023.
An Experimental Study On Classifying Spatial Trajectories
Hasan Pourmahmood-aghababa and Jeff M. Phillips.
Knowledge and Information Systems (KAIS).
December 2022.
arXiv:2209.01322
September 2022.
Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li, Jeff Phillips, Xin Yu, Robert Kirby, Shandian Zhe.
Neural Information Processing Systems (NeurIPS).
December 2022.
arXiv:2210.12704
October 2022.
Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression
Mingxuan Han, Chenglong Ye, Jeff M. Phillips.
Transactions on Machine Learning Research (TMLR).
October 2022.
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Prince Osei Aboagye, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Zhongfang Zhuang, Huiyuan Chen, Liang Wang, Wei Zhang, Jeff M. Phillips.
Association for Machine Translation in the Americas (AMTA).
September 2022.
Using Existential Theory of the Reals to Bound VC Dimension
Austin Watkins and and Jeff M. Phillips.
Canadian Conference on Computational Geometry (CCCG).
August 2022.
Normalization of Language Embeddings for Cross-Lingual Alignment
Prince Osei Aboagye, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Liang Wang, Hao Yang, and Jeff M. Phillips.
International Conference on Learning Representations (ICLR).
April 2022.
Self-Adaptable Point Processes with Nonparametric Time Decays
Zhimeng Pan, Zheng Wang, Jeff M. Phillips, and Shandian Zhe.
Neural Information Processing Systems (NeurIPS).
December 2021.
Approximate Maximum Halfspace Discrepancy
Michael Matheny and Jeff M. Phillips.
International Symposium on Algorithms and Computation (ISAAC).
December 2021.
arXiv:2106.13851
June 2021.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Sunipa Dev, Masoud Manajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff M. Phillips, and Kai-Wei Chang.
Conference on Emperical Methods in Natural Language Processing (EMNLP)
November 2021.
arXiv:2108.12084
August 2021.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar.
Conference on Emperical Methods in Natural Language Processing (EMNLP)
November 2021.
arXiv:2007.00049
July 2020.
Constrained Non-Affine Alignment of Embeddings
Yuwei Wang, Yan Zheng, Yanqing Peng, Michael Yeh, Zhongfang Zhuang, Das Mahashweta, Bendre Mangesh, Feifei Li, Wei Zhang, and Jeff M. Phillips.
International Conference on Data Mining (ICDM)
December 2021.
arXiv:1910.05862
September 2021.
Capturing Intent behind Selection In Scatterplot Visualizations
Kiran Gadhave, Jochen Gortler, Zach Cutler, Carolina Nobre, Oliver Deussen, Miriah Meyer, Jeff M. Phillips, and Alexander Lex.
Information Visualization
. August 2021.
OSF Preprint
January 2020.
Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning
Jiahui Chen, Joe Breen, Jeff M. Phillips, Jacobus Van der Merwe.
Cluster Computing
, DOI 10.1007/s10586-021-03393-2;
accepted August 2021.
arXiv:2107.06080
July 2021.
Finding an Approximate Mode of a Kernel Density Estimate
Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, and Wai Ming Tai.
European Symposium on Algorithms (ESA)
September 2021.
arXiv:1912.07673
December 2019.
talk on YouTube
Orientation-Preserving Vectorized Distance Between Curves
Jeff M Phillips and Hasan Pourmahmood-Aghababa.
Mathematical and Scientific Machine Learning (MSML)
August 2021.
arXiv:2007.15924
July 2020.
Spatial Independent Range Sampling
Dong Xie, Jeff M. Phillips, Michael Matheny, and Feifei Li.
ACM Symposium on Management of Data (SIGMOD)
. June 2021.
At-the-time and Back-in-time Persistent Sketches
Benwei Shi, Zhuoyue Zhao, Yanqing Peng, Feifei Li, and Jeff M. Phillips.
ACM Symposium on Management of Data (SIGMOD)
. June 2021.
Efficient Oblivious Query Processing for Range and kNN Queries
Zhao Chang, Dong Xie, Feifei Li, Jeff M. Phillips, and Rajeev Balasubramanian.
Transactions on Knowledge and Data Engineering (TKDE)
accepted February 2021.
A Deterministic Streaming Sketch for Ridge Regression
Benwei Shi and Jeff M. Phillips.
International Conference on Artificial Intelligence and Statistics (AIStats)
April 2021.
arXiv:2002.02013
February 2020.
Semantic Embedding for Regions of Interest
Debjyoti Paul, Jeff M. Phillips, and Feifei Li.
Very Large Data Bases Journal (VLDBJ)
. February 2021. (https://doi.org/10.1007/s00778-020-00647-0)
Inferencing Hourly Traffic Volume using Data-Driven Machine Learning and Graph Theory
Zhiyan Yi, Xiaoyue Cathy Liu, Nikola Markovic, and Jeff M. Phillips.
Computers, Environment and Urban Systems
, Vol 85. January 2021.
The GaussianSketch for Almost Relative Error Kernel Distance
Jeff M. Phillips and Wai Ming Tai.
International Conference on Randomization and Computation (RANDOM)
August 2020.
arXiv:1811.04136
December 2019.
Scalable Spatial Scan Statistics for Trajectories
Michael Matheny, Dong Xie, and Jeff M. Phillips.
ACM Transactions on Knowledge Discovery from Data (TKDD)
14(6) no. 73. September 2020.
arXiv:1906.01693
June 2019.
Sketched MinDist
Jeff M. Phillips and Pingfan Tang.
International Symposium on Computational Geometry (SOCG)
June 2020.
arXiv:1907.02171
July 2019.
talk on YouTube
On Measuring and Mitigating Biased Inferences of Word Embeddings
Sunipa Dev, Tao Li, Jeff M. Phillips and Vivek Srikumar.
AAAI Conference on Artificial Intelligence (AAAI)
February 2020.
arXiv:1908.09369
August 2019.
Simple Distances for Trajectories via Landmarks
Jeff M. Phillips and Pingfan Tang.
ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)
November 2019.
arXiv:1804.11284
June 2019.
The Kernel Spatial Scan Statistic
Mingxuan Han, Michael Matheny, and Jeff M. Phillips.
ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)
November 2019.
arXiv:1906.09381
June 2019.
Closed Form Word Embedding Alignment
(KAIS Special Issue for ICDM 2019)
Sunipa Dev, Saffia Hassan, and Jeff M. Phillips.
Knowledge and Information Systems
. January 2021.
International Conference on Data Mining (ICDM)
November 2019.
arXiv:1806.01330
June 2018.
On the VC Dimension of Metric Balls under Frechet and Hausdorff Distances
Anne Driemel, Andre Nusser(
), Jeff M. Phillips, Ioannis Psarros.
Discrete & Computational Geometry (DCG)
accepted 2021.
International Symposium on Computational Geometry (SoCG)
June 2019.
arXiv:1903.03211
November 2019 (
: adds upper bound with Hausdorff in high dimensions, Andre Nusser added as co-author).
Independent Range Sampling, Revisited Again
Peyman Afshani and Jeff M. Phillips.
International Symposium on Computational Geometry (SoCG)
June 2019.
arXiv:1903.08014
March 2019.
Attenuating Bias in Word Vectors
Sunipa Dev and Jeff M. Phillips.
International Conference on Artificial Intelligence and Statistics (AIStats)
April 2019.
arXiv:1901.07656
January 2019.
Computing Approximate Statistical Discrepancy
Michael Matheny and Jeff M. Phillips.
International Symposium on Algorithm and Computation (ISAAC)
December 2018.
arXiv:1804.11287
April 2018.
Toward Classifying Unknown Application Traffic
Ryan Baker, Ren Quinn, and Jeff M. Phillips, Jacobus (Kobus) Van der Merwe.
DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop
December 2018.
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
Aria Rezaei, Jie Gao, Jeff M. Phillips, and Csaba D. Toth.
ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)
November 2018.
arXiv:1809.07392
September 2018.
Practical Low-Dimensional Halfspace Range Space Sampling
Michael Matheny and Jeff M. Phillips.
European Symposium on Algorithms (ESA)
September 2018.
arXiv:1804.11307
April 2018.
Approximating the Distribution of the Median and other Robust Estimators on Uncertain Data
Kevin Buchin, Jeff M. Phillips and Pingfan Tang.
International Symposium on Computational Geometry (SOCG)
June 2018.
arxiv:1601.00630
January 2016.
Near-Optimal Coresets of Kernel Density Estimates
(Invited to DCG Special Issue)
Jeff M. Phillips and Wai Ming Tai.
International Symposium on Computational Geometry (SOCG)
June 2018.
Discrete & Computational Geometry (DCG)
63, 867--887, 2020.
arxiv:1802.01751
February 2018.
Fully Convolutional Structured LSTM Networks for Joint 4D Medical Image Segmentation
Yang Gao, Jeff M. Phillips, Yan Zheng, Renqiang Min, P. Thomas Fletcher, and Guido Gerig.
IEEE International Symposium on Biomedical Imaging (ISBI)
April 2018.
Improved Coresets for Kernel Density Estimates
WaiMing Tai and Jeff M. Phillips.
29th Annual ACM-SIAM Symposium on Discrete Algorithms (SoDA)
January 2018.
arxiv:1710.04325
October 2017.
Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
Yan Zheng, Yi Ou, Alexander Lex, and Jeff M. Phillips.
IEEE Transactions on Big Data
, (accepted 2019).
earlier version:
Visual Data Science (VDS)
October 2017.
arxiv:1709.04453
September 2017.
Project Page (and code)
Visualizing Sensor Network Coverage with Location Uncertainty
Tim Sodergren, Jessica Hair, Jeff M. Phillips, and Bei Wang.
Visual Data Science (VDS)
October 2017.
arxiv:1710.06925
September 2017.
Relative Error Embeddings for the Gaussian Kernel Distance
Di Chen and Jeff M. Phillips.
Algorithmic Learning Theory (ALT)
October 2017.
arxiv:1602.05350
March 2026. (this fixes a version in the ALT version;
h/t
Distributed Trajectory Similarity Search
Dong Xie, Feifei Li, and Jeff M. Phillips.
International Conference on Very Large Databases (VLDB)
August 2017.
Coresets for Kernel Regression
Yan Zheng and Jeff M. Phillips.
ACM Conference on Knowledge Discovery and Data Mining (KDD)
August 2017.
arxiv:1702.03644
February 2017.
Project Page
Scalable Spatial Scan Statistics through Sampling
Michael Matheny, Raghvendra Singh, Kaiqiang Wang, Liang Zhang and Jeff M. Phillips.
ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)
November 2016.
The Robustness of Estimator Composition
Pingfan Tang and Jeff M. Phillips.
Conference on Neural Information Processing (NeurIPS)
December 2016.
matlab code
epsilon-Kernel Coresets for Stochastic Points
Lingxiao Huang, Jian Li, Jeff M. Phillips, and Haitao Wang.
European Symposium on Algorithms (ESA)
August 2016.
arxiv.org:1411.0194
November 2014.
Efficient Frequent Directions Algorithm for Sparse Matrices
Mina Ghashami, Edo Liberty, and Jeff M. Phillips.
ACM Conference on Knowledge Discovery and Data Mining (KDD)
August 2016.
arxiv.org:1602.00412
February 2016.
C/python code
Coresets and Sketches
Jeff M. Phillips.
Handbook of Discrete and Computational Geometry
. 3rd edition, CRC Press, Chapter 48.
2016.
arxiv.org:1601.00617
January 2016.
Streaming Kernel Principal Component Analysis
Mina Ghashami, Daniel Perry, and Jeff M. Phillips.
International Conference on Artificial Intelligence and Statistics (AISTATS)
May 2016.
arxiv.org:1512.05059
December 2015.
Julia Code
An Integrated Classification Scheme for Mapping Estimates and Errors of Estimation from the American Community Survey
Ran Wei, Daoqin Tong, and Jeff M. Phillips.
Computers, Environment and Urban Systems (CEUS)
April 2016.
Subsampling in Smooth Range Spaces
Jeff M. Phillips and Yan Zheng.
Algorithmic Learning Theory
(ALT)
October 2015.
short version
appeared in
Computational Geometry : Young Researchers Forum
June 2015.
L_infity Error and Bandwidth Selection for Kernel Density Estimates of Large Data
Yan Zheng and Jeff M. Phillips.
ACM Conference on Knowledge Discovery and Data Mining (KDD)
August 2015.
Project Page
Geometric Inference on Kernel Density Estimates
Jeff M. Phillips, Bei Wang, and Yan Zheng.
International Symposium on Computational Geometry (SOCG)
June 2015.
full version:
arXiv:1307.7760
March 2015.
early version appeared as
Kernel Distance for Geometric Inference
Jeff M. Phillips and Bei Wang.
22nd Fall Workshop on Computational Geometry
October 2012.
Improved Practical Matrix Sketching with Guarantees
Mina Ghashami, Amey Desai, and Jeff M. Phillips.
Transactions on Knowledge and Data Engineering (TKDE)
28:07, pp 1678--1690, 2016.
2016.
earlier shorter version
appeared in
22nd Annual European Symposium on Algorithms (ESA)
September 2014.
Reproduce our results
on
APTlab
(You may need to log in, and then click link again)
arXiv:1501.06561
January 2015.
Continuous Matrix Approximation on Distributed Data
Mina Ghashami, Jeff M. Phillips, and Feifei Li.
40th International Conference on Very Large Data Bases (VLDB)
September 2014.
full version:
arXiv:1404.7571
April 2014.
Python Code
Frequent Directions: Simple and Deterministic Matrix Sketching
Mina Ghashami, Edo Liberty, Jeff M. Phillips and David P. Woodruff.
SIAM Journal of Computing (SICOMP)
45:5, 2016.
arXiv:1501.01711
January 2015.
Python Code
, with some backend in C.
this mainly extends and replaces:
Relative Errors for Deterministic Low-Rank Matrix Approximations
Mina Ghashami and Jeff M. Phillips.
25th Annual ACM-SIAM Symposium on Discrete Algorithms (SoDA)
January 2014.
arXiv:1307.7454
June 2013.
Quality and Efficiency for Kernel Density Estimates in Large Data
Yan Zheng, Jeffrey Jestes, Jeff M. Phillips, Feifei Li.
ACM Conference on the Management of Data (SIGMOD)
June 2013.
Project Page
Nearest Neighbor Searching Under Uncertainty II
Pankaj K. Agarwal, Boris Aronov, Sariel Har-Peled, Jeff M. Phillips, Ke Yi, and Wuzhou Zhang.
32nd ACM Symposium on Principles of Database Systems (PoDS)
June 2013.
ACM Transactions on Algorithms
13:1, 2016.
arXiv:1606.00112
June 2016.
Range Counting Coresets for Uncertain Data
Amirali Abdullah, Samira Daruki, and Jeff M. Phillips.
29th Annual ACM Symposium on Computational Geometry (SoCG)
June 2013.
arXiv:1304.4243
April 2013.
Radio Tomographic Imaging and Tracking of Stationary and Moving People via Kernel Distance
Yang Zhao, Neal Patwari, Jeff M. Phillips, and Suresh Venkatasubramanian.
12th ACM-IEEE Conference on Information Processing in Sensor Networks (IPSN)
April 2013.
eps-Samples for Kernels
Jeff M. Phillips.
24th Annual ACM-SIAM Symposium on Discrete Algorithms (SoDA)
January 2013.
arXiv:1112.4105
April 2012.
Sensor Network Localization for Moving Sensors
Arvind Agarwal, Hal Daume III, Jeff M. Phillips, and Suresh Venkatasubramanian.
2nd IEEE ICDM International Workshop on Data Mining in Networks (DaMNet)
December 2012.
Efficient Protocols for Distributed Classification and Optimization
Hal Daume III, Jeff M. Phillips, Avishek Saha, and Suresh Venkatasubramanian.
23rd International Conference on Algorithmic Learning Theory (ALT)
October 2012.
arXiv:1204.3523
April 2012.
See also a similar independent work:
arXiv:1204.3514
(on arXiv same day)
Ranking Large Temporal Data
Jeffrey Jestes, Jeff M. Phillips, Feifei Li, and Mingwang Tang.
38th International Conference on Very Large Databases (VLDB)
August 2012.
PVLDB
5:1412-1423, 2012.
arXiv:1208.0222
August 2012.
Project Page
Mergeable Summaries
Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang, Jeff M. Phillips, Zhewei Wei, and Ke Yi.
31st ACM Symposium on Principals of Database Systems (PODS)
May 2012.
ACM Transactions on Database Systems (TODS)
38:26, 2013.
appeared as "
Mergeable Coresets
" in
Third Workshop on Massive Data Algorithmics
June 2011.
Protocols for Learning Classifiers on Distributed Data
Hal Daume III, Jeff M. Phillips, Avishek Saha, and Suresh Venkatasubramanian.
15th Interntational Conference on Artificial Intelligence and Statistics (AISTATS)
April 2012.
full version
as
arXiv:1202.6078
February 2012.
Efficient Threshold Monitoring for Distributed Probabilistic Data
Mingwang Tang, Feifei Li, Jeff M. Phillips, Jeffrey Jestes.
28th IEEE International Conference on Data Engineering (ICDE)
April 2012.
Code and Data
Uncertainty Visualization in HARDI based on Ensembles of ODFs
Fangxiang Jiao, Jeff M. Phillips, Yaniv Gur, and Chris R. Johnson.
5th IEEE Pacific Visualization Symposium (PacificVis)
February 2012.
Lower Bounds for Number-in-Hand Multiparty Communication Complexity, Made Easy
Jeff M. Phillips, Elad Verbin, Qin Zhang.
23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SoDA)
January 2012.
arXiv:1107.2559
July 2011.
SIAM Journal of Computing (SICOMP)
(to appear, 2015).
Generating A Diverse Set Of High-Quality Clusterings
(Best-Paper-Award)
Jeff M. Phillips, Parasaran Raman, Suresh Venkatasubramanian.
2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings (MultiClust)
September 2011.
arXiv:1108.0017
. August 2011.
Geometric Computation on Indecisive Points
Allan G. Jorgensen, Maarten Loffler, Jeff M. Phillips.
12th Algorithms and Data Structure Symposium (WADS)
August 2011.
long version as
Geometric Computatations on Indecisive and Uncertain Points
as
arXiv:1205.0273
. May 2012. (merged with
this
Horoball Hulls and Extents in Positive Definite Space
P. Thomas Fletcher, John Moeller, Jeff M. Phillips, Suresh Venkatasubramanian.
12th Algorithms and Data Structure Symposium (WADS)
August 2011.
older
long version
as
arXiv:0912.1580
December 2009.
Comparing Distributions and Shapes Using the Kernel Distance
Sarang Joshi, Raj Varma Kommaraju, Jeff M. Phillips, Suresh Venkatasubramanian.
27th Annual Symposium on Computational Geometry (SoCG)
June 2011.
long version
as
arXiv:1001.0591
March 2011.
Spatially-Aware Comparison and Consensus for Clusterings
Jeff M. Phillips, Parasaran Raman, and Suresh Venkatasubramanian.
10th SIAM Intenational Conference on Data Mining (SDM)
April 2011.
arXiv:1102.0026
February 2011.
(Approximate) Uncertain Skylines
Peyman Afshani, Pankaj K. Agarwal, Lars Arge, Kasper Green Larsen, and Jeff M. Phillips.
14th International Conference on Database Theory (ICDT)
March 2011.
Theory of Computing Systems
52, 342--366 (Special Issue : ICDT 2011).
Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images
Fangxiang Jiao, Jeff M. Phillips, Jeroen Stinstra, Jens Krueger, Raj Varma Kummaraju, Edward Hsu, Julie Korenberg, Chris R. Johnson.
5th International Workshop on Medical Imaging and Augmented Reality (MIAR)
September 2010.
Stability of epsilon-Kernels
Pankaj K. Agarwal, Jeff M. Phillips, Hai Yu.
18th Annual European Symposium on Algorithms (ESA)
September 2010.
long version
as
arXiv:1003.5874
March 2010.
Universal Multi-Dimensional Scaling
Arvind Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian.
16th Annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
August 2010.
long version
as
arXiv:1003.0529
March 2010.
related code
media:
Data Mining Made Faster
Incremental Multi-Dimensional Scaling
Arvind Agarwal, Jeff M. Phillips, Hal Daume III, Suresh Venkatasubramanian.
The Learning Workshop at Snowbird
April 2010.
Lipschitz Unimodal and Isotonic Regression on Paths and Trees
Pankaj K. Agarwal, Jeff M. Phillips, Bardia Sadri.
9th Latin American Theoretical Informatics Symposium (LATIN)
April 2010.
long version
as
arXiv:0912.5182
December 2009.
Shape Fitting on Point Sets with Probability Distributions
Maarten Loffler, Jeff M. Phillips.
17th Annual European Symposium on Algorithms (ESA)
September 2009.
long version as
Geometric Computatations on Indecisive and Uncertain Points
as
arXiv:1205.0273
. May 2012. (merged with
this
An Efficient Algorithm for Euclidean 2-Center with Outliers
Pankaj K. Agarwal, Jeff M. Phillips.
16th Annual European Symposium on Algorithms (ESA)
September 2008.
long version
as
arXiv:0806.4326
September 2008.
Algorithms for epsilon-Approximations of Terrains
(Best Student Paper)
Jeff M. Phillips.
35th International Colloquium on Automata, Languages, and Programming (ICALP)
July 2008.
long version
as
arXiv:0801.2793
May 2008.
Spatial Scan Statistics for Graph Clustering
Bei Wang, Jeff M. Phillips, Robert Schrieber, Dennis Wilkinson,
Nina Mishra, Robert Tarjan.
8th SIAM Intenational Conference on Data Mining (SDM)
April 2008.
Value-Based Notification Conditions in Large-Scale Publish/Subscribe Systems
Badrish Chandramouli, Jeff M. Phillips, Jun Yang.
33rd Intenational Conference on Very Large Data Bases (VLDB)
September 2007.
Outlier Robust ICP for Minimizing Fractional RMSD
Jeff M. Phillips, Ran Liu, Carlo Tomasi.
6th International Conference on 3-D Digital Imaging and Modeling (3DIM)
August 2007.
long version
as
Duke University Technical Report CS-2006-05
and
arXiv: cs.GR/0606098
May 2006.
poster
abstract
for
4th Eurographics Symposium on Geometry Processing (SGP)
. June 2006.
Segmenting Motifs in Protein-Protein Interface Surfaces
Jeff M. Phillips, Johannes Rudolph, Pankaj K. Agarwal.
Proceedings of the 6th Workshop on Algorithms in Bioinformatics (WABI)
September 2006.
Spatial Scan Statistics: Approximations and Performance Study
Deepak Agarwal, Andrew McGregor, Jeff M. Phillips, Suresh Venkatasubramanian, Zhengyuan Zhu.
12th Annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
August 2006.
On Bipartite Matching under the RMS Distance
Pankaj K. Agarwal, Jeff M. Phillips.
18th Canadian Conference on Computational Geometry (CCCG)
August 2006.
The Hunting of the Bump: On Maximizing Statistical Discrepancy
Deepak Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian.
17th Annual ACM-SIAM Symposium on Discrete Algorithms (SoDA)
January 2006.
abstract
for
Fall Workshop
on Computational Geometry
. November 2005.
Guided Expansive Spaces Trees: A Search Strategy for Motion- and
Cost-Constrained State Spaces
Jeff M. Phillips, Nazareth Bedrossian, and Lydia E. Kavraki.
IEEE International Conference on Robotics and Automation (ICRA)
April 2004.
Spacecraft Rendezvous and Docking with Real-Time, Randomized
Optimization
Jeff M. Phillips, Lydia E. Kavraki, and Nazareth Bedrossian.
AIAA Guidance, Navigation, and Control
. August 2003.
Probabilistic Optimization Applied to Spacecraft Rendezvous and Docking
Jeff M. Phillips, Lydia E. Kavraki, and Nazareth Bedrossian.
AAS/AIAA Space Flight Mechanics Meeting
. February 2003.
Simulated Knot Tying
Jeff M. Phillips, Andrew M. Ladd, Lydia E. Kavraki.
IEEE International Conference on Robotics and Automation (ICRA)
. May 2002.
Manuscripts:
Johnson-Lindenstrauss Dimensionality Reduction on the Simplex
Rasmus J. Kyng, Jeff M. Phillips, and Suresh Venkatasubramanian.
20th Fall Workshop on Computational Geometry
October 2010.
A Gentle Introduction to the Kernel Distance
Jeff M. Phillips, Suresh Venkatasubramanian.
arXiv:1103.1625
March 2011.
Chernoff-Hoeffding Inequality and Applications
Jeff M. Phillips.
arXiv:1209.6396
February 2013.
Rethinking Abstractions for Big Data: Why, Where, How, and What
Mary Hall, Robert M. Kirby, Feifei Li, Miriah Meyer, Valerio Pascucci, Jeff M. Phillips, Rob Ricci, Jacobus Van der Merwe, Suresh Venkatasubramanian.
University of Utah, School of Computing, Tech Report: UUCS-13-002
April 2013.
arXiv:1306.3295
June 2013.
Learning In Practice: Reasoning About Quantization
Annie Cherkaev, Waiming Tai, Jeff M. Phillips, and Vivek Srikumar.
arXiv:1905.11478
May 2019.
Hiding Signal Strength Interference from Outside Adversaries
Mingxuan Han, Jeff M. Phillips, and Sneha Kumar Kasera.
arXiv:2112.10931
December 2021.
On Mergable Coresets for Polytope Distance
Benwei Shi, Aditya Bhaskara, Wai Ming Tai, and Jeff M. Phillips.
arXiv:2306.03173
November 2023.
Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance
Peter Matthew Jacobs, Foad Namjoo, and Jeff M. Phillips.
arXiv:2504.11299
April 2025.
Trajectory Minimum Touching Ball
Jens Kristian Refsgaard Schou, and Jeff M. Phillips.
arXiv:2505.02472
May 2025.
Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models
Philip Quirke, Narmeen Oozeer, Chaithanya Bandi, Amir Abdullah, Jason Hoelscher-Obermaier, Jeff M. Phillips, Joshua Greaves, Clement Neo, Fazl Barez, Shriyash Upadhyay
arXiv:2506.00051
June 2025.
Understanding and Mitigating Dataset Corruption in LLM Steering
Cullen Anderson, Narmeen Oozeer, Foad Namjoo, Remy Ogasawara, Amirali Abdullah, Jeff M. Phillips
arXiv:2603.03206
March 2026.
Curveball Steering: The Right Direction To Steer Isn't Always Linear
Shivam Raval, Hae Jin Song, Linlin Wu, Abir Harrasse, Jeff M. Phillips, Amirali Abdullah
arXiv:2603.09313
March 2026.
Convolutional Maximum Mean Discrepancy for Inference in Noisy Data
Ritwik Vashistha, Jeff M. Phillips, Abhra Sarkar, Arya Farahi
arXiv:2604.12022
April 2026.
Small and Stable Descriptors of Distributions for Geometric
Statistical Problems
Jeff M. Phillips.
Ph.D. Thesis: Department of Computer Science, Duke University
January 2009.
Breif History of Jeff:
Born and raised in the suburbs of Milwaukee, Wisconsin by parents
John
and Geri Phillips. One sister,
Michelle
, now in Washington DC.
Married
Bei Wang
in summer 2009. Two sons Stanley, born in 2013, and Max, born 2015.
Received undergraduate education at
Rice University
Graduated with a BS in Computer Science and a BA in Mathematics in
2003. Former member of
Jones Residential College
Former member of the
Kavraki Lab
with
Lydia
Kavraki
Interned at
Draper
Labs
near NASA JSC with
Nazareth Bedrossian
in 2002.
Interned at
AT&T Research -- Shannon Labs
with
Suresh
Venkatasubramanian
in 2005.
Interned at
Yahoo!
Research
with
Michael
Mahoney
in 2007.
Attended graduate school in the
Duke
Computer Science Department
with advisor
Pankaj K. Agarwal
Successfully defended my
PhD
thesis
January 19, 2009.
Served as Postdoctoral Associate in the
Duke
Computer Science Department
with supervisor
Pankaj K. Agarwal
Served as a
CI Postdoctoral Fellow
at the
University of Utah
with mentor
Suresh Venkatasubramanian
Appointed as Assistant Professor in the
School of Computing
at the
University of Utah
in Fall 2011.
In Summer 2017, received tenure, and now serve as Associate Professor in the
School of Computing
at the
University of Utah
Some of this material is based upon work supported by the
NSF
under Grants
#0937060 and #1019343 to the Computing Research Association for the CIFellows Project,
CCF-1115677,
IIS-1251019,
CCF 1350888,
ACI-1443046,
CNS-1514520, and
CNS-1564287.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Computing Research Association. The funding makes much of this work possible -- thank you!
US