Candan is invited to the Editorial Board of the CACM Research Highlights
January 2026
K. Selçuk Candan is invited to serve in the Editorial Board of the Communication of the ACM's Research Highlights.
ACM CACM
EMITLab Publications (from DBLP)
Data, models, and decisions for complex Dynamic systems
EMITLab's research innovates data-, machine learning-, and AI-systems to help enable robust and resilient human-centric environments
EMITLab in the Center for Assured and Scalable Data Engineering (CASCADE) at Arizona State University carries out research on data-, machine learning-, and AI-systems that couple real-world data, simulations, and causal models to support informed and effective decision making in critical complex dynamic systems, such as disasters, pandemics, water resources, and energy infrastructure. Grounded in NSF and USACE-funded work, including DataStorm, PanCommunity, PANAX, pCAR, CausalBench, Designing Nature to Enhance Resilience for Built Infrastructure in Western U.S. Landscape , PIRE: Building Decarbonization via AI-empowered District Heat Pump System, and the NSF Center for the Analysis of the Epidemic Expansion, the lab turns sparse, noisy, and complex data and models into actionable insights for robust and resilient human-centric environments.
News
Announcements
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Grants & research themes
How EMITLab’s research agenda responds to urgent needs
Research projects, especially those funded by NSF and USACE, anchor EMITLab’s work in urgent settings (disaster planning, epidemic response, sustainability, and smart buildings) while driving fundamental advances in data management and analysis for trustable decision making.
DataStorm & Data‑enabled Disaster Platforms
Infrastructures for exploring “alternative timelines” of disasters.
DataStorm develops data and computation fabrics that couple large‑scale simulations with real‑world data streams to enable disaster planning and response. EMITLab uses these platforms to help planners test hazard scenarios, compare strategies, and understand where small changes in behavior or infrastructure lead to large differences in outcomes.
PanCommunity , PREEMPT, APPEX , and PANAX
Multi‑model platforms for pandemic prediction and response.
These projects integrate specialized epidemic, mobility, and behavior models with data sources to explore how interventions impact communities. EMITLab develops the data and model driven simulaiton and machine learning techniques, enabling decision‑makers to weigh health, economic, and social trade‑offs under uncertainty.
Designing Nature to Enhance Resilience for Built Infrastructure
Data science for portfolios of natural & built water infrastructure.
In collaboration with the Network for Engineering with Nature (N-EWN), the "Designing Nature to Enhance Resilience for Built Infrastructure in Western U.S. Landscape" project develops spatio‑temporal models and causal tools to evaluate how wetlands, dams, recharge basins, and other interventions affect flows, ecosystems, and communities. The project team further develops reinforcement‑learning‑driven causal discovery and hydrologic forecasting.
E‑SDMS , CYDRES, PFI‑BDMC & PIRE
Data platforms and AI for efficient, low‑carbon building systems.
From energy‑simulation data management to AI‑empowered district heat pump systems, these efforts use data engineering and learning to detect HVAC faults, optimize operation, and support human‑centered, decarbonized districts. EMITLab’s models must work across testbeds, data regimes, and operating conditions.
pCAR & CausalBench
Plausible causal discovery and reproducible evaluation.
The pCAR project investigates how to uncover plausible causal structure in noisy, dynamic data, while CausalBench focuses on rigorous benchmarks and services for testing causal algorithms. Together, they ground EMITLab’s work on spatio-temporal forecasting, infrastructure planning, and decision platforms that leverage causal reasoning.
Multivariate Time‑series Models
Saliency‑aware representations for noisy, multiscale sequences.
Multivariate time‑series
Cross‑attention
Sensor & infrastructure data
EMITLab develops models and architectures that highlight salient subsequences in multivariate time‑series and fuse raw and salient views via cross‑attention. This research is supported by a constellation of NSF awards on epidemic simulations, building data management, big data, causal learning, and building services, including Data Management for Real‑Time Data Driven Epidemic Spread Simulations , E‑SDMS , DataStorm , BIGDATA: Discovering Context‑Sensitive Impact in Complex Systems , PFI‑BDMC , and pCAR .
Mission, vision & research
How EMITLab thinks about data, models, and decisions
Mission
EMITLab’s mission is to design data and AI systems that make complex physical and social systems understandable, actionable, and resilient. Rather than assuming perfect data and unlimited resources, the lab embraces sparcity, noise, heterogeneity, and uncertainty and builds platforms that support critical decisions in disasters, pandemics, water management, and energy systems.
- Couple heterogeneous data streams with multi‑model simulations.
- Embed causal reasoning into forecasting and scenario analysis.
- Balance data availability, accuracy, robustness, and resource constraints in real deployments.
Vision
EMITLab envisions computing infrastructures for data, models, and causal inference that are as dependable. In this vision, planners and decsion makers can explore “what‑if” futures through interactive simulations/digital twins, assess nature‑based interventions with causal evidence, and operate buildings and districts that are both comfortable and efficient.
- Interactive, data‑calibrated twins for hazards, epidemics, and infrastructure.
- Causal, spatio‑temporal awareness in water, energy, and building systems.
- Transparent, benchmarked algorithms that stakeholders can trust.
People
Faculty
President's Professor of Computer Science and Engineering, SCAI, ASU
Director, ASU Center for the Assured and Scalable Data Engineering (CASCADE)
Associate Director, NSF Center for the Analysis and Prediction of Pandemic Expansion (APPEX)
Adjunct Professor of Computer Science and Engineering, SCAI, ASU
Full Professor of Computer Science, University of Turin, Italy
Post-Doctoral Researchers
NSF Center for the Analysis and Prediction of the epidemic Expension (APPEX)
Current Students
Current students advised by EMITLab faculty, including MS, McS, and PhD theses.
Doctoral Students (PhD)
Tasnema's research focuses on machine learning and data science. Her work emphasizes time-series and spatio-temporal modeling, causal inference, and forecasting in complex dynamic systems. She applies these methods to real-world problems, such as epidemic modeling, situational awareness, and data-driven decision support.
Ertugrul's ongoing research includes projects in causal machine learning, such as the development of CausalBench, a benchmarking platform for evaluating causal discovery and causal inference methods within machine learning contexts. This research supports the broader goal of improving the accuracy and reproducibility of causal learning models across diverse data and application scenarios.
Abhinav's research focuses on machine learning and multi-objective optimization, with active work in causal machine learning and benchmarking frameworks. He contributes to developing CausalBench, a unified benchmarking platform for evaluating causal learning models and metrics, and works on optimized algorithms and causal analysis systems. His research spans both theoretical tools for causal ML evaluation and practical implementations of data-driven systems.
In addition to his contributions to CausalBench, Ahmet's work within the context of the PIRE project integrates causally informed methods into broader systems research, emphasizing causal discovery from complex, observational data. He is developing approaches that leverage causally-informed Petri nets to enable process discovery from time-series and system logs, blending formal process modeling with causal reasoning to better understand dynamic system behavior.
Pratanu's research advances causally informed data management and causal learning, particularly through developing benchmark frameworks like CausalBench that provide standardized datasets, algorithms, and metrics to evaluate causal inference methods. His research aims to overcome limitations of traditional data management techniques by leveraging causal knowledge. His work also includes application of spatio-temporal deep-learning and reinforcement learning approaches to complex prediction tasks, integrating causal insights into data-driven modeling.
Knowledge graphs are useful in many querying and knowledge management applications, and some difficult data environments have use-cases that would benefit from models that could autonomously create or modify knowledge graphs with context over multiple modalities and/or data-sets. The focus of Armin's research is to solve challenges in this space by developing novel methods to discover or manipulate knowledge graphs to improve querying and knowledge management in difficult data environments with complex multi-modal relationships.
Master Students (MS)
N/AUndergraduate Students (BS)
High School Students
Kanav Agarwal
Ongoing · K12
Iraj Shroff
Ongoing · K12
Alumni
Past students advised by EMITLab faculty, including MS, McS, and PhD theses. Public thesis links are provided where available via ASU KEEP, CORE, or other repositories.
Doctoral Students (PhD)
Christopher Mayer
Alumnus · 2005 · PhD
Quality-based Replication of Freshness-Differentiated Web Applications
Ping Lin
Alumnus · 2006 · PhD
PXStore: A Content- and Access- Privacy Preserving XML Data Store
Lina Peng
Alumnus · 2008 · PhD
Quality-Adaptive Execution and Optimization of Media Processing Workflows
Gisik Kwon
Alumnus · 2008 · PhD
Decentralized Resource Location and Multimedia Service Execution
Shibo Wu
Alumnus · 2008 · PhD
Power-Efficient Geographic Routing in Sensor Networks
Jong Wook Kim
Alumnus · 2009 · PhD
Exploiting Structured Knowledge for Segmentation, Enrichment, and Summarization of Data
Yan Qi
Alumnus · 2009 · PhD
System Support for Exploration and Expert Feedback in Resolving Conflicts
Mithila Nagendra
Alumnus · 2014 · PhD
Efficient Processing of Skyline Queries on Static Data Sources, Data Streams and Incomplete Datasets
TensorDB and Tensor-Relational Model (TRM)
Parth Nagarkar
Alumnus · 2017 · PhD
Query Workload-aware Index Structures for Range Searches
Jung Hyun Kim
Alumnus · 2017 · PhD
Efficient and Effective Node Proximity Computation in Graphs
Xinsheng Li
Alumnus · 2018 · PhD
On Density and Noise Challenges in Tensor-Based Data Analytics
Efficient Incremental Model Learning on Data Streams
Sicong Liu
Alumnus · 2020 · PhD
Feature Extraction from Multi-variate Time Series and Resource-Aware Indexing
On Feature Saliency and Deep Neural Networks
Shengyu Huang
Alumnus · 2021 · PhD
Optimization of Block-based Tensor Decompositions
Hans Behrens
Alumnus · 2023 · PhD
On Counter-Adversarial Resilience in Permeable Networked Systems
Mao-Lin Li
Alumnus · 2024 · PhD
Searching Latent Semantics in Complex Data
Metadata Supported Multi-Variate Multi-Scale Attention
Master’s Students (MS / McS)
Lakshmi Priya Mahalingam
Alumnus · MS
Query Optimization in Multimedia Databases
Amit Jindal
Alumnus · MS
Quorums-Replication of Data in Distributed Systems
Prakash Yamuna
Alumnus · MS
Similarity based Retrieval and Indexing of Temporal Structures
Namhee Cho
Alumnus · McS
Design and Implementation of a VRML Repository
Venkatesh Sangam
Alumnus · MS
Failure Handling in Quality-Quorums
Nikhil Iyer
Alumnus · MS
Server Replication and Object Placement in a Push-based System
J. Ramamoorthy
Alumnus · MS
RCDEX: An Index Structure for Sequence Matching in the Presence of Commutations
Prachi Tyagi
Alumnus · MS
Continuous Realtime Indexing
Ling Xu
Alumnus · McS
Text Recognition and Correction
Reshma Suvarna
Alumnus · MS
RDF indexing and retrieval
Maria Goveas
Alumnus · MS
Context-aware Text Segmentation
Atul Kolhatkar
Alumnus · MS
Text Summarization using Topic Development Patterns
Sangwoo Han
Alumnus · MS
Automated Reorganization of Text Segments Obtained from Multiple Documents
Vishal Shah
Alumnus · MS
MIST: On Most Informational Snippet Detection in a Document Corpus
Hardik Doshi
Alumnus · MS
OER-HMM: An Observations Equivalence Rules Based Hidden Markov Model
Venkata Cherukuri
Alumnus · MS
Mobility Aware Read/Write Protocol for RFID Rich Environments
Mike Balzer
Alumnus · McS
Sim Phor Na
Alumnus · McS
Mingyi Shu
Alumnus · McS
Robert Ivey
Alumnus · McS
Renwei Yu
Alumnus · 2011 · MS
Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space
CPR: Complex Pattern Ranking for Evaluating Top-k Pattern Queries over Event Streams
Tejas Bapat
Alumnus · 2009 · MS
Spsearch: Enabling Spatial Search in a RFID Rich Environment
Juan P. Cedeño
Alumnus · 2010 · MS
A Framework for Top-K Queries over Weighted RDF Graphs
On Summarization of Non-Linear Narratives
Mithila Nagendra
Alumnus · 2010 · MS
Efficient Processing of Join-based Skyline Queries
Mijung Kim
Alumnus · McS
Songling Liu
Alumnus · 2012 · McS
Sequence-based Web Page Template Detection
Leveraging Metadata for Extracting Robust Multi-Variate Temporal Features
Space Adaptation Techniques for Preference Oriented Skyline Processing
Aneesha Bhat
Alumnus · 2015 · MS
Locality Sensitive Indexing for Efficient High-Dimensional Query Answering
Multi-variate Time Series Measures and Their Robustness against Temporal Asynchrony
Load-balanced Range Query Workload Partitioning for cSHB Indexes
Manoj Tiwaskar
Alumnus · 2021 · MS
Selego: Robust Variate Selection for Accurate Time Series Forecasting
Vasishta Harekal
Alumnus · 2020 · MS (non-thesis)
Ajay Rana
Alumnus · 2021 · MS (non-thesis)
MD Shadab
Alumnus · 2020 · MS (non-thesis)
Fateh Singh
Alumnus · 2022 · MS (non-thesis)
Shubodeep Mitra
Alumnus · MS (thesis)
Abhinav Gorantla
Alumnus · MS (thesis)
Undergraduate Students (BS)
Sahir Jahan
Alumnus · Research Mentee (2023)
Utkarsh Bhagat
Alumnus · Research Mentee (2023)
Nickolas Martinez
Alumnus · NSF REU (2018)
Vivan So
Alumnus · NSF REU (2018)
Jiayong Mo
Alumnus · NSF REU (2018)
David Maitha
Alumnus · NSF REU (2018, 2019)
Jason Truong
Alumnus · NSF REU (2018)
Dalton Turner
Alumnus · NSF REU (2018, 2019, 2020)
Reece Bailey
Alumnus · NSF REU (2016–2017)
Divyanshu Khare
Alumnus · NSF REU (2016–2017)
Elliot Nester
Alumnus · NSF REU (2016–2017)
Sarah Fallah-Ad
Alumnus · NSF REU (2016–2017)
Sam Morton
Alumnus · NSF REU (2014–2016)
Anisha Gupta
Alumnus · NSF REU (2014–2016)
Steven Brown
Alumnus · Honors Contract (2013–2014)
Fatima Naveed
Alumnus · FSE294 (Fall 2014)
Adam Tse
Alumnus · MiNC (Spring 2014)
Arda Unal
Alumnus · FURI (Spring 2014)
Jessica Armstrong
Alumnus · FSE294 (Fall 2013)
High School Students
Raymond Zou
Alumnus · K12 (2023)
Pravneet Chadha
Alumnus · K12 (2023)
Ryan Jain
Alumnus · K12 (2021–2022)
Soma Tummala
Alumnus · K12 (2020–2021)
Daksh Gopalani
Alumnus · K12 (2019)
Andy Wong
Alumnus · K12 Human Trafficking (2018–2019)
Sanjana Sarkar
Alumnus · K12 Human Trafficking (2018–2019)
Andy Wong
Alumnus · K12 DataStorm (2018)
Douglas Yu
Alumnus · K12 ESDMS/EpiDMS (2016)
Jerry Zhu
Alumnus · K12 Energy Modeling (2015–2016)
Nancy Li
Alumnus · K12 ESDMS/EpiDMS (2015–2016)
Shivam Sadakar
Alumnus · K12 EpiDMS (Summer 2015)
Luke Zhang
Alumnus · K12 EpiDMS (Summer 2014)
Shivam Sadakar
Alumnus · K12 EpiDMS (Summer 2015)
Fiona Zhang
Alumnus · K12 EpiDMS (Spr./Summer 2014)
International Students
Fabrizo Antonelli
Alumnus · 2005
Co-Advisor. Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2005.
Eric Arnaduo
Alumnus · 2007
Co-Advisor. Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2007.
Luigi Di Caro
Alumnus · 2007
Co-Advisor. Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2007 (PhD 2011).
Mario Cataldi
Alumnus · 2008
Visiting Researcher. Universita di Torino (PhD 2010).
Marco Cagna
Alumnus · 2009
Co-Advisor. "Estrazione automatica di informazione dal Web: un approccio basato su pattern strutturali e visuali." Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2009.
Giacomo Cappellari
Alumnus · 2013
Supervisor. "Parallel Iteration Fabric: Efficient parallel skyline-window-join computation". Tesi di Laurea Magistrale. Facolta di Ingegneria. Politecnico di Torino. 2013.
Ilario Dal Grande
Alumnus · 2016
Co-Advisor. Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2016.
Leonardo Allisio
Alumnus · 2015
Co-Advisor. Tesi di Laurea Magistrale. Facolta di Scienze. Universita di Torino. 2015.
Silvestro Poccia
Alumnus · 2017–2018
PhD Student – Visitor (EU Marie Curie Project). Universita di Torino.
Francesco Di Mauro
Alumnus · 2018–2021
PhD Student. Universita di Torino.
Fabrizio Scarrone
Alumnus · 2020–2024
PhD Student. Universita di Torino.
Javier Redondo Anton
Alumnus · 2022–2025
PhD Student (EU EvoGames Plus project). Universita di Torino.
Postdoctoral Researchers and Research Engineers
Mehmet Donderler
Alumnus · Post-Doc (2002–2004)
Qing Li
Alumnus · Post-Doc (2006–2008)
Huiping Cao
Alumnus · Post-Doc (2007–2009)
Silvestro Pocia
Alumnus · Research Engineer (2015–2016)
Kaize Ding
Alumnus · Post-Doc (2023)
Bilgehan Arslan
Alumnus · Post-Doc (2021–2023)
Yoonhyuk Choi
Alumnus · Post-Doc (2023–2024)