Varun Chandola


I am an associate professor in the Computer Science and Engineering Department and the Institute for Computational and Data Sciences (ICDS) at the State University of New York (SUNY) at Buffalo. I completed my PhD from University of Minnesota, Department of Computer Science. My research is in the area of scalable anomaly detection and data mining for big graphs, temporal, and spatial data.

I lead the UB data science research group. I am also affiliated with the The Center for Hybrid Rocket Exascale Simulation Technology (CHREST) and the The Computer Science for Social Good (CS4G) group at UB. I am also the director of the ICDS/CDSE PhD program.


Explaining Supervised Learning Models: A Preliminary Study on Binary Classifiers

Learning Manifolds from Dynamic Process Data

longSil: an Evaluation Metric to Assess Quality of Clustering Longitudinal Clinical Data

Learning Deep Representations from Clinical Data for Chronic Kidney Disease

Identifying Patients Experiencing Opioid-Induced Respiratory Depression During Recovery From Anesthesia: The Application of Electronic Monitoring Devices.

Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data

Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data.

Tree-based Regularization for Interpretable Readmission Prediction

Query Log Compression for Workload Analytics

dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems

Similarity Metrics for SQL Query Clustering

Machine learning for energy-water nexus: challenges and opportunities

A survey of analytical methods for inclusion in a new energy-water nexus knowledge discovery framework

S-Isomap++: Multi Manifold Learning from Streaming Data

Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records.

A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences


Building a Fair Recommender System for Foster Care Services within the Constraints of a Sociotechnical System

The Center for Hybrid Rocket Exascale Simulation Technology

Beyond Compartment models: Using Big-Data to enhance models for controller development for the Artificial Pancreas

Manifolds for Extreme-scale Applied Data Science

Data is Social: Exploiting Data Relationships to Detect Insider Attacks