2026 Peter J. Haas: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	51	5271	5123	2428	2344	183	12635

Research.com Recognitions

2016 - Fellow of the Institute for Operations Research and the Management Sciences (INFORMS)
2013 - ACM Fellow For leadership in probabilistic methods for the management and analysis of data and for system simulation.

Overview

Peter J. Haas is affiliated with the University of Massachusetts Amherst in the United States. Their primary field of study is Computer Science, with a focus on several subfields including Artificial Intelligence, Nuclear and High Energy Physics, Computer Networks and Communications, Signal Processing, and Computer Vision and Pattern Recognition.

Their research covers a range of topics, notably Data Management and Algorithms, Advanced Database Systems and Queries, High-Energy Particle Collisions Research, Quantum Chromodynamics and Particle Interactions, Palliative Care and End-of-Life Issues, Simulation Techniques and Applications, and Data Stream Mining Techniques.

Peter J. Haas has authored multiple papers in prominent academic venues. Recent publications include:

"Integrated, cross-sectoral psycho-oncology (isPO): a new form of care for newly diagnosed cancer patients in Germany" (2022) published in BMC Health Services Research
"Enhanced Simulation Metamodeling via Graph and Generative Neural Networks" (2022) presented at the 2022 Winter Simulation Conference (WSC)
"NIM: Generative Neural Networks for Automated Modeling and Generation of Simulation Inputs" (2023) featured in ACM Transactions on Modeling and Computer Simulation
"Evaluating Psychosocial Support Provided by an Augmented Reality Device for Children With Type 1 Diabetes" (2021) presented at the Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care
"Planning for Implementation Success of an Electronic Cross-Facility Health Record for Pediatric Palliative Care Using the Consolidated Framework for Implementation Research (CFIR)" (2022) published in International Journal of Environmental Research and Public Health

Frequent coauthors working with Peter J. Haas include Alexandra Meliou, Azza Abouzied, Matteo Brucato, J. Friedrich, and S. Gerassimov, reflecting collaboration across multiple research projects.

The scientist's frequent publication venues further illustrate their research breadth, with multiple papers appearing in arXiv (Cornell University), Proceedings of the VLDB Endowment, ACM Transactions on Modeling and Computer Simulation, BMC Health Services Research, and the 2022 Winter Simulation Conference (WSC).

Peter J. Haas has been recognized within the scientific community through awards such as the ACM Fellow title awarded in 2013 for leadership in probabilistic methods for the management and analysis of data and for system simulation. In 2016, they were also named a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS).

Best Publications

Online aggregation

Joseph M. Hellerstein;Peter J. Haas;Helen J. Wang

1325
Citations
Improved histograms for selectivity estimation of range predicates

Viswanath Poosala;Peter J. Haas;Yannis E. Ioannidis;Eugene J. Shekita

899
Citations
Large-scale matrix factorization with distributed stochastic gradient descent

Rainer Gemulla;Erik Nijkamp;Peter J. Haas;Yannis Sismanis

850
Citations
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches

Graham Cormode;Minos Garofalakis;Peter J. Haas;Chris Jermaine

572
Citations
Ripple joins for online aggregation

Peter J. Haas;Joseph M. Hellerstein

571
Citations
Stochastic Petri Nets: Modelling, Stability, Simulation

Peter J. Haas

489
Citations
CORDS: automatic discovery of correlations and soft functional dependencies

Ihab F. Ilyas;Volker Markl;Peter Haas;Paul Brown

425
Citations
Sampling-Based Estimation of the Number of Distinct Values of an Attribute

Peter J. Haas;Jeffrey F. Naughton;S. Seshadri;Lynne Stokes

410
Citations
The New Jersey Data Reduction Report.

Daniel Barbará;William DuMouchel;Christos Faloutsos;Peter J. Haas

381
Citations
MCDB: a monte carlo approach to managing uncertain data

Ravi Jampani;Fei Xu;Mingxi Wu;Luis Leopoldo Perez

355
Citations
Watermarking relational data: framework, algorithms and analysis

Rakesh Agrawal;Peter J. Haas;Jerry Kiernan

336
Citations
Ricardo: integrating R and Hadoop

Sudipto Das;Yannis Sismanis;Kevin S. Beyer;Rainer Gemulla

299
Citations
Interactive data analysis: the Control project

J.M. Hellerstein;R. Avnur;A. Chou;C. Hidber

277
Citations
Sequential sampling procedures for query size estimation

Peter J. Haas;Arun N. Swami

276
Citations
On synopses for distinct-value estimation under multiset operations

Kevin Beyer;Peter J. Haas;Berthold Reinwald;Yannis Sismanis

218
Citations
Automated hypothesis generation based on mining scientific literature

Scott Spangler;Angela D. Wilkins;Benjamin J. Bachman;Meena Nagarajan

200
Citations
Selectivity and Cost Estimation for Joins Based on Random Sampling

Peter J. Haas;Jeffrey F. Naughton;S. Seshadri;Arun N. Swami

180
Citations
A new two-phase sampling based algorithm for discovering association rules

Bin Chen;Peter Haas;Peter Scheuermann

177
Citations
Large-sample and deterministic confidence intervals for online aggregation

P.J. Haas

176
Citations
GORDIAN: efficient and scalable discovery of composite keys

Yannis Sismanis;Paul Brown;Peter J. Haas;Berthold Reinwald

147
Citations

Frequent Co-Authors

Volker Markl Technical University of Berlin

Berthold Reinwald IBM (United States)

Guy M. Lohman IBM (United States)

Joseph M. Hellerstein University of California, Berkeley

Paul P. Maglio University of California, Merced

Wang-Chiew Tan Facebook (United States)

Wolfgang Lehner TU Dresden

Tanveer Syeda-Mahmood IBM (United States)

Peter W. Glynn Stanford University

Nimrod Megiddo IBM (United States)

External Links

Wikipedia of Peter J. Haas Personal website of Peter J. Haas Google Scholar page

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