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Publications

2017

A System for Privacy-Preserving Machine Learning on Personal Data

Bennett Cyphers M.E. thesis, MIT Dept of EECS, August 2017. Advisor: Kalyan Veeramachaneni.

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AnonML: Anonymous Machine Learning Over a Network of Data Holders

Bennett Cyphers and Kalyan Veeramachaneni. NIPS workshop on Private multiparty communication Barcelona, Spain. December, 2016

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ATM: A Distributed, Collaborative, Scalable System for Automated Machine Learning

Thomas Swearingen, Will Drevoy, Bennett Cyphersy, Alfredo Cuesta-Infantez, Arun Ross and Kalyan Veeramachanen, MIT Dept of CSE, 2017.

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Automatic data element linking

Katharine Xiao, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Kalyan Veeramachaneni.

Build your own deep learner, David Wong, EECS Meng

David Wong, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Kalyan Veeramachaneni.

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COAL: A Continuous Active Learning System

Jonathan Johannemann M.F. thesis, MIT Dept of EECS, June 2017. Advisor: Kalyan Veeramachaneni.

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Deep Mining: Scaling Bayesian Auto-tuning of Data Science Pipelines

Alec W. Anderson M.E. thesis, MIT Dept of EECS, September 2017. Advisor: Kalyan Veeramachaneni.

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FeatureHub: Towards Collaborative Data Science

Micah Smith and Kalyan Veeramachaneni. IEEE International Conference on Data Science and Advance Analytics, Tokyo, Japan. October, 2017.

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Learning Representations for Log Data in Cybersecurity

Igacio Arnaldo, Alfredo Cuesta-Infante, Akit Arun, Mei Lam, Costas Bassias, and Kalyan Veeramachaneni.  2017.

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Locally Private Machine Learning over a Network of Data Holders

Bennett Cyphers and Kalyan Veeramachaneni. IEEE International Conference on Data Science and Advance Analytics, Tokyo, Japan. October, 2017.

Sample, Estimate, Tune: Scaling Bayesian Auto-Tuning of Data Science Pipelines

Alec Anderson, Sebastien Dubois, Alfredo Cuesta-Infante and Kalyan Veeramachaneni. IEEE International Conference on Data Science and Advance Analytics, Tokyo, Japan. October, 2017.

SenseML: A Platform for Constructing IOT Data Pipelines

Donghyun Michael Choi, M.E. thesis, MIT Dept of EECS, September 2017. Advisor: Kalyan Veeramachaneni.

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Solving the “false positives” problem in fraud prediction

Roy Wedge, James Max Kanter, Santiago Moral Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni. Arxiv report.

Towards Automatically Linking Data Elements

Katharine Xiao, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Kalyan Veeramachaneni.

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2015

Autotuning Algorithmic Choice for Input Sensitivity.

Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O’Reilly, Saman Amarasinghe. Accepted 36th annual ACM SIGPLAN conference on Programming Language Design and Implementation, 2015.

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Bring Your Own Learner! A cloud-based, data-parallel commons for machine learning.

Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly. To appear in IEEE Computational Intelligence Magazine. Special Issue on Computational Intelligence for Cloud Computing (Feb. 2015).

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Building Predictive Models via Feature Synthesis

Ignacio Arnaldo, Una-May O’Reilly, Kalyan Veeramachaneni. Accepted, ACM conference on Genetic and Evolutionary Computation.

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Copula Graphical Models for Wind Resource Estimation.

Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O’Reilly. Accepted, International joint conference on Artificial Intelligence.

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Data Science Foundry for MOOCs

Sebastien Boyer, Ben U. Gelman, Benjamin Schreck, Kalyan Veeramachaneni. Accepted, IEEE/ACM Data Science and Advance Analytics Conference

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Deep Feature Synthesis: Torwards Automating Data Science Endeavors

James Max Kanter, Kalyan Veeramachaneni. Accepted, IEEE/ACM Data Science and Advance Analytics Conference (10% acceptance rate).

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Feature Factory: Crowd Sourcing Feature Discovery.

Kalyan Veeramachaneni, Una-May O’Reilly, Kiarash Adl. WIP session at ACM Learning @Scale, 2015.

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Gaussian Process-based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals.

Franck Dernoncourt*, Kalyan Veeramachaneni, Una-May O’Reilly. Accepted, 28th IEEE International Symposium on Computer-Based Medical Systems.

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Transfer Learning for Predictive Models in Massive Open Online Courses.

Sebastien Boyer, Kalyan Veeramachaneni. Accepted, 17th International Conference on Artificial Intelligence in Education.

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2014

A continuous developmental model for wind farm layout optimization

Dennis Wilson, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Hervé Luga, Una-May O’Reilly. ACM conference on Genetic and Evolutionary Computation.

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Flash: A GP-GPU Ensemble Learning System for handling Large Datasets

Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O’Reilly, 17th European Conference on Genetic Programming.

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FlexGP: Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems

Kalyan Veeramachaneni, Ignacio Arnaldo, Owen Derby, Una-May O’Reilly. Journal Of Grid Computing.

OpenTuner: an extensible framework for program autotuning

Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O’Reilly and Saman Amarasinghe, ACM 23rd International Conference on Parallel Architectures and Compilation.

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Likely to stop? Predicting Stopout in Massive Open Online Courses

Colin Taylor*, Kalyan Veeramachaneni, Una-May O’Reilly, arXiv report

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Technology for Mining the Big Data of MOOCs

Una-May O’Reilly, Kalyan Veeramachaneni. Winter 2014, Research and Practice in Assessment.

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Towards Feature Engineering at Scale for Data from Massive Open Online Courses

Kalyan Veeramachaneni, Una-May O’Reilly,Colin Taylor, arXiv report

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2013

Analyzing Millions of Submissions to Help MOOC instructors Understand Problem Solving.

Fang Han*, Kalyan Veeramachaneni and Una-May O’Reilly, NIPS workshop on Data Directed Education.

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beatDB : A Large Scale Waveform Feature Repository.

Franck Dernoncourt*, Kalyan Veeramachaneni and Una-May O’Reilly. NIPS Workshop on Machine Learning for Clinical Data Analysis and Healthcare.

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Building MultiClass Nonlinear Classifiers with GPUs.

Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O’Reilly. NIPS Workshop on Big Learning

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Cloud Driven Design of a Distributed Genetic Programming Platform.

Owen Derby*, Kalyan Veeramachaneni, and Una-May O’Reilly. Applications of Evolutionary Computation, Lecture Notes in Computer Science.

Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems.

Dennis Wilson*, Kalyan Veeramachaneni, and Una-May O’Reilly. Applications of Evolutionary Computation, Lecture Notes in Computer Science.

Copula-Based Wind Resource Assessment.

Kalyan Veeramachaneni, Teasha Feldman-Fitzthum, Una-May O’Reilly, Alfredo Cuesta-Infante. NIPS Workshop on Machine Learning for Sustainability.

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Copula-Based Wind Resource Assessment.

Kalyan Veeramachaneni, Teasha Feldman-Fitzthum, Una-May O’Reilly, Alfredo Cuesta-Infante. NIPS Workshop on Machine Learning for Sustainability.

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Introducing Graphical Models to Analyze Genetic Programming Dynamics.

Erik Hemberg, Constantin Berzan*, Kalyan Veeramachaneni, Una-May O’Reilly. Proceedings of the twelfth workshop on Foundations of genetic algorithms.

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Learning regression ensembles with genetic programming at scale.

Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, Una-May O’Reilly. Proceeding of the fifteenth ACM annual conference on Genetic and evolutionary computation conference.

Modeling Service Execution on Data Centers for Energy Efficiency and Quality of Service Monitoring.

Monica Vitali, Una-May O’Reilly, and Kalyan Veeramachaneni, IEEE International Conference on Systems, Man and Cybernetics.

MOOCdb: Developing Data Standards for MOOC Datascience.

Kalyan Veeramachaneni, Zachary A. Pardos, Una-May O’Reilly, MOOCShop at Artificial Intelligence in Education, 2013.

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MoocViz: A Large Scale, Open Access, Collaborative Data Analytics Framework for MOOCs.

Franck Dernoncourt*, Choung Do, Sherif Halawa, Una-May O’Reilly, Colin Taylor, Kalyan Veeramachaneni and Sherwin Wu, NIPS workshop on Data Directed Education.

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On Learning to Generate Wind Farm Layouts.

Dennis Wilson*, Emmanuel Awa, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O’Reilly. Proceeding of the fifteenth ACM annual conference on Genetic and evolutionary computation conference.

Learning Blood Pressure Behavior from Large Physiological Waveform Repositories.

Alexander Waldin*, Kalyan Veeramachaneni, Una-May O’Reilly, ICML Workshop on Healthcare 2013.

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