Community on Multimodality: Participatory Action, Service, and Support (COMPASS)
Those in need of help often do not know how to locate or access service providers. Likewise, service-providing agencies often work in silos. The lack of communication also applies to volunteers; people do not know who to help and how they can be resourceful. Response becomes even more problematic when a problem demands the coordination of service providers, volunteers, and government structures, and after business hours, when the communication channels that can aid people in need become sparse. This project will (i) simplify the discovery and use of services, (ii) enable two-way communication between stakeholders (e.g., residents and service providers), (iii) deploy resources more efficiently, and (iv) assist stakeholders in assessing and promoting the wellbeing of their communities. The end result will be directly applicable to communities across the US, and has the potential to be influential beyond the human and physical services domain. It will advance computer science, electrical and computer engineering, and social and behavioral sciences to collectively address the challenges associated with this problem, and will create educational opportunities to encourage students to cross disciplinary boundaries. Women and underrepresented groups will be encouraged to participate in this project by collaborating with community-based organizations, and via programs at the University at Albany.
This multidisciplinary project takes a community-wide approach that will integrate people and data with analytics and engineering using social and behavioral sciences to maximize the efficiency of delivering human and physical services, and also improve the sense of connectedness of residents with service providers and government structures. The project will study the limitations of extant services, which will in turn inform the development of novel decision-making mechanisms with sufficient behavioral realism. The outcome will be an integrated "one-stop" service of services. This project will (i) develop new data mining methods for uncovering complex interdependencies within a dynamic sociotechnical system, (ii) devise novel information processing, machine learning, and control methods to dynamically optimize delivery of human and physical services under uncertainty with humans in the decision-making loop, and (iii) shed light on the ability of communities to integrate emerging technologies to become more connected in human interactions.
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Performance PeriodSeptember 2017 - August 2024
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SUNY at Albany
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Award Number1737443
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Lead PIDaphney-Stavroula Zois -
Co-PIWonhyung Lee -
Co-PICharalampos Chelmis
Project Material
- Social Workers as Information Navigators: Insights into the Use of the Web for Serving Clients
- Sequential Datum–wise Feature Acquisition and Classifier Selection
- Online Hierarchical Multi-label Classification
- Hybrid Loss for Hierarchical Multi–label Classification Network
- Modeling and predicting individual transitions within the homelessness system
- Why Do Some Homeless Succeed While Others Falter? A Network Science Perspective
- Bayesian Network Modeling and Prediction of Transitions Within the Homelessness System
- Sequential Datum–Wise Joint Feature Selection and Classification in the Presence of External Classifier
- Interpretability in the Context of Sequential Cost-Sensitive Feature Acquisition
- Datum–Wise Inference in Structured Environments
- Evaluating algorithmic homeless service allocation
- Noisy Label Detection and Counterfactual Correction
- Label Denoising and Counterfactual Explanation with A Plug and Play Framework
- Robust Learning with Noisy Label Detection and Counterfactual Correction
- Learning to Predict Transitions within the Homelessness System from Network Trajectories
- Sequential Bayesian Network Structure Learning
- Understanding service navigation pathways and service experiences among homeless populations
- Community-engaged technology development for bridging service users and service providers: lessons from the field
- Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data
- Hierarchical MultiClass AdaBoost
- Dynamic Instance-Wise Classification in Correlated Feature Spaces
- Peeking through the homelessness system with a network science lens
- Dynamic Feature Selection for Classification in Structured Environments
- Optimum Feature Ordering for Dynamic Instance–Wise Joint Feature Selection and Classification
- Dynamic Instance-Wise Joint Feature Selection and Classification
- Challenges and Opportunities in Using Data Science for Homelessness Service Provision
- Smart Homelessness Service Provision with Machine Learning
- Cyberinfrastructure for Social Good: Ensuring That No Homeless Individual Stays Behind
- Knowing When to Stop: Joint Heterogeneous Feature Selection and Classification
- Sequential Heterogeneous Feature Selection for Multi–Class Classification: Application in Government 2.0
- Coordination Networks among Local Human Service Organizations: Insights into Super-Connectors and Barriers
- Empirical insights on technology use for navigating human services
- On–The–Fly Feature Selection and Classification with Application to Civic Engagement Platforms
- Automated Optimal Online Civil Issue Classification using Multiple Feature Sets
- Understanding online civic engagement: a multi-neighborhood study of SeeClickFix
- Web and society: a first look into the network of human service providers
- Automating the Classification of Urban Issue Reports: an Optimal Stopping Approach
- Creating Public Value by Democratizing the Ecosystem of Human Service Providers
- WHAT MATTERS THE MOST? OPTIMAL QUICK CLASSIFICATION OF URBAN ISSUE REPORTS BY IMPORTANCE
- Improving Monitoring of Participatory Civil Issue Requests through Optimal Online Classification
I am an Associate Professor in the Electrical and Computer Engineering Department at the University at Albany, State University of New York (SUNY). During 2016-2022, I was an Assistant Professor in the same University. Before that, I was a postdoctoral research associate with the Coordinated Science Laboratory (CSL) at the University of Illinois, Urbana-Champaign (UIUC). I worked with Maxim Raginsky on problems related to decision making in uncertain environments. I received my Ph.D. in Electrical Engineering in 2014 from the University of Southern California (USC) under the supervision of Urbashi Mitra. I also received my MSc in Electrical Engineering in 2010 from USC and my undergraduate degree in Computer Engineering & Informatics in 2007 from the University of Patras in Greece.