Selected work

Representative Papers

The Developer Experience of LGBTQIA+ People in Agile Teams: a Multivocal Literature Review

Apr 20, 2025

This study investigates the developer experience (DX) of LGBTQIA+ software engineers in agile teams, identifying core challenges—including self-isolation, diminished sense of belonging, and reduced collaborative efficacy—stemming from invisibility of identity, implicit bias, and institutional exclusion. Methodologically, it employs a multi-source literature review (MLR) synthesizing peer-reviewed research and industry grey literature, augmented by qualitative thematic analysis and cross-source triangulation. The study makes two key contributions: first, it identifies and conceptualizes “covert exclusion” and “contextual inclusion” as distinct socio-technical mechanisms shaping DX; second, it empirically demonstrates that targeted agile practices—such as gender-neutral ceremonies and psychological safety norms—significantly enhance belonging and team coordination performance among LGBTQIA+ engineers. These findings provide an evidence-based foundation and actionable pathways for cultivating inclusive engineering cultures within agile environments. (149 words)

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A Quantum Genetic Algorithm Framework for the MaxCut Problem

Jan 02, 2025

For the Maximum Cut (MaxCut) problem on large-scale graphs, this paper proposes a hybrid optimization framework integrating quantum and evolutionary computation. Methodologically, it introduces a Grover-enhanced quantum genetic algorithm, combined with a divide-and-conquer strategy comprising graph partitioning, parallel subgraph optimization, and contraction-based merging; the binary symmetry of MaxCut is explicitly leveraged in modeling to ensure both theoretical tractability and scalability. Theoretical analysis proves that the algorithm yields exact optimal cuts on complete graphs. Empirical evaluation on Erdős–Rényi random graphs shows median solution quality reaching 92–96% of the semidefinite programming (SDP) relaxation bound—substantially outperforming classical heuristics—and demonstrates robust scalability to large graphs.

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Recent publications

Latest Papers

The Developer Experience of LGBTQIA+ People in Agile Teams: a Multivocal Literature Review

Apr 20, 2025

This study investigates the developer experience (DX) of LGBTQIA+ software engineers in agile teams, identifying core challenges—including self-isolation, diminished sense of belonging, and reduced collaborative efficacy—stemming from invisibility of identity, implicit bias, and institutional exclusion. Methodologically, it employs a multi-source literature review (MLR) synthesizing peer-reviewed research and industry grey literature, augmented by qualitative thematic analysis and cross-source triangulation. The study makes two key contributions: first, it identifies and conceptualizes “covert exclusion” and “contextual inclusion” as distinct socio-technical mechanisms shaping DX; second, it empirically demonstrates that targeted agile practices—such as gender-neutral ceremonies and psychological safety norms—significantly enhance belonging and team coordination performance among LGBTQIA+ engineers. These findings provide an evidence-based foundation and actionable pathways for cultivating inclusive engineering cultures within agile environments. (149 words)

0 citationsRead paper

A Quantum Genetic Algorithm Framework for the MaxCut Problem

Jan 02, 2025

For the Maximum Cut (MaxCut) problem on large-scale graphs, this paper proposes a hybrid optimization framework integrating quantum and evolutionary computation. Methodologically, it introduces a Grover-enhanced quantum genetic algorithm, combined with a divide-and-conquer strategy comprising graph partitioning, parallel subgraph optimization, and contraction-based merging; the binary symmetry of MaxCut is explicitly leveraged in modeling to ensure both theoretical tractability and scalability. Theoretical analysis proves that the algorithm yields exact optimal cuts on complete graphs. Empirical evaluation on Erdős–Rényi random graphs shows median solution quality reaching 92–96% of the semidefinite programming (SDP) relaxation bound—substantially outperforming classical heuristics—and demonstrates robust scalability to large graphs.

0 citationsRead paper