Publications & Talks

Peer-reviewed publications, conference proceedings, and preprints by Dimitris Floros.

Journal Articles

[J1]
C. Chatzakis, D. Floros, A. Liberis, A. Gerede, K. Dinas, N. Pitsianis, and A. Sotiriadis, “STORK: Collaborative Online Monitoring of Pregnancies Complicated with Gestational Diabetes Mellitus,” Healthcare, vol. 10, no. 4, p. 653, Mar. 2022, doi: 10.3390/healthcare10040653.
DOILink
[J2]
N. Pitsianis, D. Floros, A.-S. Iliopoulos, and X. Sun, “SG-t-SNE-$\Pi$: Swift neighbor embedding of sparse stochastic graphs,” Journal of Open Source Software, vol. 4, no. 39, p. 1577, 2019, doi: 10.21105/joss.01577.
DOI
[J3]
C. Chatzakis, D. Floros, M. Papagianni, K. Tsiroukidou, K. Kosta, A. Vamvakis, N. Koletsos, E. Hatziagorou, I. Tsanakas, and G. Mastorakos, “The Beneficial Effect of the Mobile Application Euglyca in Children and Adolescents with Type 1 Diabetes Mellitus: A Randomized Controlled Trial,” Diabetes Technology & Therapeutics, vol. 21, no. 11, pp. 627–634, Nov. 2019, doi: 10.1089/dia.2019.0170.
DOI
[J4]
M.-T. Passia, D. Floros, and T. Yioultsis, “Eigenmode-free method-of-lines formulations for the fast synthesis of reconfigurable metasurfaces,” IEEE Open Journal Antennas Propagation, 2026.
Under review
[J5]
B. Higgins, D. Floros, M. Hernandez, Y. Alabaishi, L. Pratson, and D. Patino-Echeverri, “Estimating Headroom and Demand Flexibility requirements for fast large-load integration: A PJM case study,” Applied Energy, 2026.
Under review
[J6]
Z. Roberts, E. Kaufman, A. Gallaher, D. Floros, D. Patino-Echeverri, and E. Kalies, “Floating Solar Photovoltaics: Potential and Opportunities in the Southeastern U.S.” Renewable Energy, 2026.
Under review
[J7]
W. Wang, D. Floros, A. Bhattacharya, H. Sharma, V. Adetola, and D. Patino-Echeverri, “Generation of forecast ensembles for stochastic unit commitment when data on past forecasts of weather, load, and renewables is unavailable,” 2026.
In preparation
[J8]
E. Perez, D. Floros, T. H. Norris, and D. Patino-Echeverri, “OpenTwinGrid: Constructing Realistic Power Transmission Network Models from Public Sources,” International Journal of Electrical Power and Energy Systems, 2026.
Under review
[J9]
X. Zhang, M. Li, Y. Hai, V. Janita, D. Floros, and D. Patino, “Quantifying Benefits for Balancing Area Expansion in China Southern Power Grid,” Applied Energy, 2026, doi: 10.2139/ssrn.6403961.
Under reviewDOILink
[J10]
S. Hao, D. Floros, and D. Echeverri, “Storage and Flexibility Buffer the Impacts of Reduced Scenario Diversity: A Quantitative Analysis in Risk-Adjusted Stochastic Unit Commitment,” Journal of Energy Storage, 2026, doi: 10.2139/ssrn.5500799.
Under reviewDOILink
[J11]
D.-H. Pham, J. Kern, J. Qian, T. Wibowo, D. Floros, and D. Patino-Echeverri, “Synthetic weather ensembles reveal hidden risks of capacity shortfalls in bulk power systems,” Nature Energy, 2026.
Under review
[J12]
M. Hernandez, D. Floros, K. Bradbury, and D. Patino-Echeverri, “Learning to solve the unit commitment problem,” 2025.
Under review

Conference Proceedings

[C1]
M.-T. Passia, D. Floros, and T. Yioultsis, “Reconfigurable Graphene-Metasurface Analysis via an Eigenmode-Free Method-of-Lines Formulation,” in IEEE Conference on Electromagnetic Field Computation, 2026.
[C2]
D. Floros and D. Patino-Echeverri, “Flexible Storage Operation Policies Informed by Stochastic Unit Commitment Outcomes in Real-World Power Systems,” in International Conference on Energy Systems, Istanbul, Turkey, May 2026.
[C3]
D. Floros, T. Norris, E. P. Gonzalez, and D. Patino-Echeverri, “Synthesizing Realistic Electric Power Transmission Networks for Expediting Interconnection Studies,” in Proceedings of the 46th IAEE international conference, 2025.
[C4]
D. Floros, N. Pitsianis, and X. Sun, “Algebraic Vertex Ordering of a Sparse Graph for Adjacency Access Locality and Graph Compression,” in IEEE High Performance Extreme Computing Conference, 2024, pp. 1–7. doi: 10.1109/HPEC62836.2024.10938496.
DOILink
[C5]
D. Floros, X. Zhang, M. Hernandez, and D. Patino-Echeverri, “Flexible Storage Commitment in Energy Management Systems under Uncertainty,” in Proceedings of the 45th IAEE international conference, in IAEE conference proceedings., 2024.
[C6]
D. Floros, W. Wang, M. Hernandez, J. Kern, and D. Patino-Echeverri, “Generating Probabilistic Scenario Ensembles for Stochastic Unit Commitment,” in Proceedings of the 45th IAEE international conference, in IAEE conference proceedings., 2024.
[C7]
D. Patino-Echeverri, D. Floros, W. Wang, M. Hernandez, J. Kern, and X. Zhang, “Grace foreseer: A probabilistic forecast generator for stochastic unit commitment,” in INFORMS annual meeting 2024, United States, Oct. 2024.
[C8]
N. Pitsianis, D. Floros, T. Liu, and X. Sun, “Parallel Clustering with Resolution Variation,” in High Performance Extreme Computing Conference, 2023, pp. 1–8. doi: 10.1109/HPEC58863.2023.10363552.
DOILink
[C9]
D. Floros*, T. Liu*, N. Pitsianis, and X. Sun, “Fast graph algorithms for superpixel segmentation,” in IEEE High Performance Extreme Computing, 2022, pp. 1–8. doi: 10.1109/HPEC55821.2022.9926359.
DOILink
[C10]
T. Liu*, D. Floros*, N. Pitsianis, and X. Sun, “Digraph clustering by the BlueRed method,” in IEEE High Performance Extreme Computing, 2021, pp. 1–7. doi: 10.1109/HPEC49654.2021.9622834.
DOILink
[C11]
D. Floros, N. Pitsianis, and X. Sun, “Fast graphlet transform of sparse graphs,” in IEEE High Performance Extreme Computing Conference, 2020, pp. 1–8. doi: 10.1109/HPEC43674.2020.9286205.
DOI
[C12]
D. Floros, T. Liu, N. Pitsianis, and X. Sun, “Using graphlet spectrograms for temporal pattern analysis of virus-research collaboration networks,” in IEEE High Performance Extreme Computing Conference, 2020, pp. 1–7. doi: 10.1109/HPEC43674.2020.9286161.
DOI
[C13]
N. Pitsianis, A.-S. Iliopoulos, D. Floros, and X. Sun, “Spaceland embedding of sparse stochastic graphs,” in IEEE High Performance Extreme Computing Conference, 2019. doi: 10.1109/HPEC.2019.8916505.
DOI
[C14]
D. Floros*, T. Liu*, N. Pitsianis, and X. Sun, “Sparse dual of the density peaks algorithm for cluster analysis of high-dimensional data,” in IEEE High Performance Extreme Computing Conference, 2018. doi: 10.1109/HPEC.2018.8547519.
DOI
[C15]
S. Bontomitsidis, D. Floros, D. Manolas, K. Mylonakis, and N. Pitsianis, “LARK: Location-Aware Personalized Travel Guide with Rich Knowledge,” in EUNIS: Crossroads where the past meets the future, 2016.
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Preprints & Working Papers

[P1]
M. Hernandez, D. Floros, K. Bradbury, and D. Patino-Echeverri, “Solutions learning algorithm for the stochastic unit commitment problem.”
[P2]
D. Floros, X. Zhang, and D. Patino-Echeverri, “Cost, reliability, and environmental benefits of a risk-adjusted stochastic unit commitment model for systems with large long-duration energy storage assets,” 2025.
[P3]
D. Floros, N. Pitsianis, and X. Sun, “The Fiedler connection to the parametrized modularity optimization for community detection.” doi: 10.48550/arXiv.2310.14359.
Physics - Data Analysis Statistics and ProbabilityPhysics - Physics and SocietyDOIarXiv
[P4]
D. Floros, N. Pitsianis, and X. Sun, “A faster method for Boolean matrix multiplication and triangle locations on a network,” 2022.
[P5]
T. Liu, D. Floros, N. Pitsianis, and X. Sun, “Steerable Community Detection,” 2022.
[P6]
D. Floros, N. Pitsianis, and X. Sun, “A systematic association of subgraph counts over a network,” 2021. arXiv:2103.10838.
Computer Science - Discrete MathematicsarXiv
[P7]
N. Pitsianis, D. Floros, A.-S. Iliopoulos, K. Mylonakis, N. Sismanis, and X. Sun, “Rapid near-neighbor interaction of high-dimensional data via hierarchical clustering,” 2017. arXiv:1709.03671.
Computer Science - Machine LearningarXiv

Conference Presentations & Talks

[T1]
D. Floros, “Finding Solutions Within the Grid,” in Duke Today: Climate Pathfinders, Jan. 2026.
Link
[T2]
D. Floros, “Enhancing electric power-grid efficiency, flexibility, and reliability via a risk-adjusted stochastic unit commitment,” in Sustainable Energy Speaker Series, Carleton, Ottawa, Canada, 2025.
[T3]
B. Higgins, D. Floros, M. Hernandez, and D. Patino-Echeverri, “Webinar: Beyond traditional energy infrastructure: Data center flexibility.” Nicholas Institute for Energy, Environment and Sustainability, Duke University, Dec. 2025.
Link
[T4]
D. Floros, M. Hernandez, A. Aithal, J. von Windheim, and D. Patino-Echeverri, “GridSeer: Smart Dispatch for a Changing Climate.” Academic Innovation Showcase, KIETS Climate Leaders Program 2025 Fall Symposium, Raleigh, NC, USA, Oct. 2025.
[T5]
D. Floros, A. Aithal, J. von Windheim, and D. Patino-Echeverri, “GridSeer: Managing Uncertainty in Energy Operations.” Live Technical Demonstration and Pitch, RE+ Conference, Las Vegas, NV, USA, Sep. 2025.
[T6]
D. Floros, A. Aithal, J. von Windheim, and D. Patino-Echeverri, “GridSeer: AI-Power Energy Management Solutions for the Commercial & Industrial Sector.” Live Technical Demonstration and Pitch, DTECH International, Dallas, TX, USA, Mar. 2025.
[T7]
D. Floros, X. Liu, and D. Patino-Echeverri, “GRACE: A grid that is risk-aware for clean electricity,” in ARPA-E Energy Innovation Summit, Grapevine, TX, USA, 2024.
[T8]
D. Floros and D. Patino-Echeverri, “Probabilistic Forecast Generator to Enhance Uncertainty Characterization in Stochastic Unit Commitment,” in CMU Doctoral Student Participatory Workshop on Climate and Energy Decision Making, Pittsburgh, PA, USA, 2024.
[T9]
M. Hernandez, A. Aithal, D. Floros, J. von Windheim, and D. Patino-Echeverri, “GRACE: AI-driven grid for efficient clean energy.” Live Technical Demonstration and Pitch, Prototypes for Humanity, Dubai, UAE, Nov. 2024.
[T10]
D. Floros, M. Hernandez, K. Bradbury, and D. Patino-Echeverri, “Improving the performance of risk-adjusted stochastic unit commitment for clean electricity,” in INFORMS annual meeting 2024, United States, Oct. 2024.
[T11]
X. Zhang, D. Floros, M. Hernandez, and D. Patino-Echeverri, “A risk-adjusted stochastic unit commitment model to face increased uncertainty and variability from extreme weather and deeper renewables penetration,” in Proceedings of the USAEE/IAEE North American Conference, in USAEE/IAEE North American Conference Proceedings., 2023.
[T12]
D. Floros, M. Hernandez, X. Zhang, and D. Patino-Echeverri, “Electric power system costs savings from a risk-adjusted stochastic unit commitment model,” in ARPA-E Energy Innovation Summit, National Harbor, MD, USA, 2023.
[T13]
D. Floros and D. Patino-Echeverri, “GRACE: A grid that is risk-aware for clean electricity,” in ESIG Meteorology and Market Design for Grid Services Workshop, Denver, CO, USA, 2023.
[T14]
D. Floros, N. Pitsianis, and X. Sun, “LG-covid19-HOTP: Literature graph of scholarly articles relevant to COVID-19 study,” in Webinar “Machine Learning and COVID-19” at the Student Branch of IEEE-EMBs at AUTh, 2021.
[T15]
C. Chatzakis, D. Floros, N. Pitsianis, and A. Sotiriadis, “Remote Monitoring of Pregnancies Complicated by Gestational Diabetes Mellitus during the COVID-19 : Lockdown Using STORK,” in Metabolism - Clinical and Experimental, Elsevier, 2021. doi: 10.1016/j.metabol.2020.154592.
DOI
[T16]
D. Floros, N. Pitsianis, and X. Sun, “LG-covid19-HOTP: Literature graph of scholarly articles relevant to COVID-19 study,” in livemedia.gr by H. V. Bliatka, May 2020.
Link
[T17]
D. Floros, A.-S. Iliopoulos, N. Pitsianis, and X. Sun, “Multi-level data translocation for faster processing of scattered data on shared-memory computers,” in Workshop on Data Locality (COLOC), Euro-Par 2019, Gottingen, Germany, 2019.
PDF
[T18]
F. Blanning, D. Floros, and N. Pitsianis, “Parametric variation of a moodle quiz,” in MoodleMoot, Thessaloniki, Greece, 2019.
Code
[T19]
A.-S. Iliopoulos, D. Floros, Y. Zhang, N. Pitsianis, X. Sun, F.-F. Yin, and L. Ren, “Adaptive denoising over multiple anatomical regions with edge and texture preservation,” Medical Physics, vol. 44, no. 6, 2017, doi: 10.1118/1.4955838.
Image analysisMedical image noiseMultiscale methodsDOI
[T20]
T. Liu, D. Floros, N. Pitsianis, X. Sun, F.-F. Yin, and L. Ren, “Robust automatic co-segmentation of multiple medical images,” Medical physics, vol. 44, pp. 3024–3025, 2017,
PDF
[T21]
A.-S. Iliopoulos, D. Floros, N. Pitsianis, X. Sun, F.-F. Yin, and L. Ren, “Local statistical filtering via domain dissection for medical imaging,” in GPU Technology Conference, San Jose, CA, USA, 2016.
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[T22]
A.-S. Iliopoulos, D. Floros, Y. Zhang, N. Pitsianis, X. Sun, F.-F. Yin, and L. Ren, “Spatially Local Statistics for Adaptive Image Filtering,” Medical Physics, vol. 43, no. 6, 2016, doi: 10.1118/1.4955838.
Image analysisMedical image noiseMultiscale methodsDOI
[T23]
D. Floros, A.-S. Iliopoulos, N. Pitsianis, and X. Sun, “Windowed all-kNN search over multidimensional array data from medical imaging,” in GPU Technology Conference, San Jose, CA, USA, 2016.
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Thesis

[TH1]
D. Floros, “Efficient analysis of local and global structures in large networks,” Aristotle University of Thessaloniki, Greece, 2022. doi: 10.12681/eadd/52905.
DOI