Former Students of Jeff Phillips

Former Students of Jeff Phillips

Mingxuan Han (PhD 2024) Localized Kernel Smoothing Estimators: Detection, Interpolation, Generalization & Smoothness | first job @ Meta

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.
Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression.
Mingxuan Han, Chenglong Ye, Jeff M. Phillips. Transactions on Machine Learning Research (TMLR). October 2022.
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.
Hiding Signal Strength Interference from Outside Adversaries.
Mingxuan Han, Jeff M. Phillips, and Sneha Kumar Kasera. arXiv:2112.10931. December 2021.

Hasan Pourmahmood-Aghababa (PhD 2024) Understanding and Facilitating How High-Dimensional Data Works | first job as Senior Data Scientist @ Walmart

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.
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.
For Kernel Range Spaces a Constant Number of Queries Are Sufficient.
     Hasan Pourmahmood-aghababa and Jeff M. Phillips. preliminary version at Fall Workshop on Computational Geometry. October 2022.
     arXiv:2306.16516. June 2023.
(and others under review)

Benwei Shi (PhD 2023, BS Thesis 2018) Small Space Data Structures for Data Analysis | first job @ Meta

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.
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.
On Mergable Coresets for Polytope Distance.
     Benwei Shi, Aditya Bhaskara, Wai Ming Tai, and Jeff M. Phillips. arXiv:2306.03173. November 2023.
(and more under review)

Tao Yang (PhD 2023, co-advised by Qingyao Ai) Optimizing Ranking Effectiveness and Fairness | first job @ Amazon

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. arXiv:2305.16606. May 2023.
(and many more without me as co-author)

Anna Bell (BS Thesis 2023) The Data-First Revolution: The Historical-Scientific Identity of Data Science and Ethical Considerations for its Future in Academic Research

Prince Osei Aboagye (PhD 2023) Understanding The Geometry of Structured Vectorized Representations | first job @ Visa Research

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.
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.
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.
(and another under review)

Yanqing Peng (PhD 2022, co-advised by Feifei Li) Toward Designing Efficient and Secure Systems for Big Data | first job @ Meta

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.
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.
(and several other without me, not listed)

Yuwei Wang (MS 2021, co-advised by Feifei Li) | first job @ Facebook

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.

Zhuoyue Zhao (PhD 2021, co-adivsed by Feifei Li) Approximate Query Processing via Random Sampling | first job @ Assistant Prof @ U. Buffalo

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.
(and several other without me, not listed)

Zhao Chang (PhD 2021, co-adivsed by Feifei Li) Scalable and Secure Data Analysis in Cloud | first job @ faculty at Xidan University

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.
(and several other without me, not listed)

Austin Watkins (BS Thesis 2020) Using Existential Theory of the Reals to Bound VC-Dimension | first job @ PhD student and Johns Hopkins CS

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.

Sunipa Dev (PhD 2020) The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability | first job NSF CI Posdoctoral Fellow @ UCLA --> Google Research.

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 (accepted).
     arXiv:2104.02797. April 2021.
     KDD21 Tutorial
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. arXiv:2007.00049. July 2020.
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.
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.
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.

Wai Ming Tai (PhD 2020) Geometry of Kernel Density Estimate | first job postdoc @ UChicago.

Finding the Mode of a Kernel Density Estimate.
     Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, and Wai Ming Tai. arXiv:1912.07673. December 2019.
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.
Approximate Guarantees for Dictionary Learning.
     Aditya Bhaskara and Wai Ming Tai. Conference on Learning Theory (COLT). June 2019. arXiv:1905.12091. May 2019.
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) August 2019.
    arxiv:1802.01751. February 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.

Jiahui (Karen) Chen (BS Thesis 2020) Practical and Configurable Network Traffic Classification using Probabilistic Machine Learning | first job @ Meta --> PhD student at UT Austin

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.

Michael Matheny (PhD 2019) Approximate Statistical Discrepancy | first job @ Amazon.

Approximate Maximum Halfspace Discrepancy.
     Michael Matheny and Jeff M. Phillips. International Symposium on Algorithms and Computation (ISAAC). December 2021.
     arXiv:2106.13851. June 2021.
Spatial Independent Range Sampling.
     Dong Xie, Jeff M. Phillips, Michael Matheny, and Feifei Li. ACM Symposium on Management of Data (SIGMOD). June 2021.
Scalable Spatial Scan Statistics for Trajectories.
     Michael Matheny, Dong Xie, and Jeff M. Phillips. ACM Transactions on Knowledge Discovery from Data (TKDD) (accepted 2020)
    arXiv:1906.01693. 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.
Computing Approximate Statistical Discrepancy.
     Michael Matheny and Jeff M. Phillips. International Symposium on Algorithm and Computation (ISAAC). December 2018.
    arXiv:1804.11287. April 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.
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.

Pingfan Tang (PhD 2019) Robust Estimation and Sketching of Points, Lines, Trajectories and other Shapes | first job @ Google.

Sketched MinDist.
     Jeff M. Phillips and Pingfan Tang. International Symposium on Computational Geometry (SOCG). June 2020.
    arXiv:1907.02171. July 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.
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.
The Robustness of Estimator Composition.
     Pingfan Tang and Jeff M. Phillips. Conference on Neural Information Processing (NeurIPS). December 2016.
     matlab code

Stone Mele (BS) | first job @ MS student in Computing @ UofU

Jian Ying (MS Thesis 2019) Corrected Moran's I Statistic | first job @ Biostatisticial at the Huntsman Cancer Institute

Zahra Fahimfar (MS Thesis 2018) Detecting Potential Lensed Galaxies Behind Foreground Galaxy Targets Using Machine Learning Techniques

Giorgi Kvernadze (BS Thesis 2018) Data-Driven Secret Santa | first job @ MS student in Computing @ UofU.

Roy Y. Wong (MS Thesis 2017) Moran's I Spatial Auto-Correlation and Anomaly Detection Utilizing PCA and High Dimensional Feature Vectors

Safia Hassan (BS Thesis 2017) Evaluating the Relationship between Vector Spaces of Word Embeddings | first job @ MS student in Computing at UofU.

Closed Form Word Embedding Alignment. (Invited to KAIS Special Issue)
Sunipa Dev, Saffia Hassan, and Jeff M. Phillips. International Conference on Data Mining (ICDM). November 2019.
arXiv:1806.01330. June 2018.

Mina Ghashami (PhD 2017) On FrequentDirections | first job @ Rutgers CS as postdoc --> Visa Research --> Amazon.

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.
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.
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.

Yan Zheng (PhD 2017) Algorithms and Coresets for Large Scale Kernel Smoothing | first job @ Visa Research

Visualization of Big Spatial Data using Coresets for Kernel Density Estimates.
     Yan Zheng, Yi Ou, Alexander Lex, and Jeff M. Phillips. Visual Data Science (VDS). October 2017.
    arxiv:1709.04453. September 2017.
    Project Page (and code).
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
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.
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

Drew McClelland (BS Thesis 2017) Analysis of Mapping Techniques on a Spatial Scan Statistic | first job @ Qualtrics

Yi Oi (MS 2017) Visualization of Large Scale Spatial Data Using Kernel Density Estimates | first job @ Expedia

Visualization of Big Spatial Data using Coresets for Kernel Density Estimates.
     Yan Zheng, Yi Ou, Alexander Lex, and Jeff M. Phillips. Visual Data Science (VDS). October 2017.
    arxiv:1709.04453. September 2017.
    Project Page (and code).

Sierra Allred (BS Thesis 2016) College Cost Analyzer: Pick Want You Can Pay For | first job @ MS program in Utah SoC

KaiQiang Wang (MS 2016) Stability of the Null Distribution for Spatial Scan Statistics | first job @ Google
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.

Liang Zhang (MS 2015) Stability of Spatial Scan Statistics | first job @ Microsoft
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.

Raghvendra Singh (MS Thesis 2015) Scalable Spatial Scan Statistics | first job @ InsideSales
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.

Jamie Iong (BS Thesis 2015) Solving K-depth Coverage problem using Sweep Line Algorithm and Red Black Tree | first job @ EMC

Tami Porter-Jones (BS Thesis 2015) Detecting Large DNA Rearrangements Using NGS Data | first job @ Myriad Genetics

Amey Desai (MS Thesis 2014) Streaming Algorithms for Matrix Approximation | first job @ UrbanEngines (Bay Area startup) -> Google
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.

Shashanka Krishnaswamy (MS 2013) Quality Control in Weather Updates Via Quantiles | first job @ Amazon

Supraja Jayakumar (MS 2013) Uncertain Centerpoints | first job @ Cerner Systems

Alex Clemmer (BS Thesis 2013) Streaming LDA | first jobs @ Hacker's School + Microsoft
In 2017 Alex's paper was awarded best paper at ACM SIGIR!