Ongoing research

Toward a Data-Centric Trust Pipeline for Trustworthy AI. Investigates how integrity, bias, synthetic data generation and provenance form an interdependent ecosystem for building trustworthy AI.

Objective

Propose the Data-Centric Trust Pipeline as a unified model to raise transparency, interpretability and accountability across the data lifecycle.

Problem lines

• Problem. How to ensure consistency, contextualization and completeness when datasets lack metadata and standardization?
  Rationale. Integrity failures compromise reproducibility and auditability, amplifying downstream errors.
• Problem. Which approaches mitigate disparities across demographic groups when fairness metrics are incomplete?
  Rationale. Studies show 20–35% error variation across groups: fairness demands continuous treatment.
• Problem. How to validate the authenticity and usage limits of synthetic data, which may reproduce structural bias?
  Rationale. Without traceability, synthetic data distorts interpretation in sensitive analyses.
• Problem. How do provenance systems evolve from passive repositories into interpretive records of decisions?
  Rationale. Provenance is essential for explainability and auditing in AI pipelines.

Refs.: Schwabe et al. (2024) · Buolamwini & Gebru (2018) · Hameed et al. (2024) · Ahmed et al. (2023) · Longpre et al. (2024).

An Architecture-Aware Conceptual Framework for Cloud Capacity Planning. Integrates architectural characteristics, workload semantics and operational objectives (SLO, PLO, RLO) into a unified conceptual model.

Objective

Propose C2PF as an architecture-aware framework capable of producing proactive, explainable and repeatable capacity decisions.

Problem lines

• Problem. How to move past reactive models based only on historical metrics, which miss variation and concurrency?
  Rationale. The absence of architectural context leads to inaccurate forecasts and overprovisioning.
• Problem. How to combine architectural components, workload classification and performance objectives?
  Rationale. There are semantic and cognitive gaps between architects, operators and planners.
• Problem. How to quantify the effects of patterns (microservices, event-driven, serverless) on capacity consumption?
  Rationale. Architectural factors can add up to 30% overhead, altering scalability and latency.

Refs.: Pereira (2023, 2025) · Richards & Ford (2020) · Lichtenthäler et al. (2023) · Gunther (2010).

Capital Intensity, Market Dynamics, and Competitive Differentiation Across the Value Chain. Analyzes how the layers — semiconductors, cloud, models, inference, platforms and applications — organize competition, investment and value capture.

Objective

Propose a conceptual model of the generative-AI value chain and evaluate hypotheses on capital intensity, infrastructure concentration, capability diffusion and productivity impacts.

Problem lines

• Economic structure and capital intensity — how the distribution of capital across layers shapes competition.
• Infrastructure concentration — the extent to which supply shows structural concentration.
• Capability diffusion — how quickly frontier capabilities diffuse.
• Application entry barriers — how APIs and platforms influence development.
• Productivity impacts — evidence of gains in knowledge work.

Refs.: LeCun, Bengio & Hinton (2015) · Kaplan et al. (2020) · Hoffmann et al. (2022) · Brynjolfsson, Li & Raymond (2023).

A systematic literature review. Critically analyzes optimized scheduling algorithms for Kubernetes, focusing on applicability, benefits and challenges in general computing, AI and edge.

Problem lines

• Problem. How do optimized algorithms behave in production compared with the experimental tests in the literature?
  Rationale. Most studies occur in controlled environments; there is a gap on practical applicability.
• Problem. How do multi-criteria algorithms improve allocation in heterogeneous clusters (CPU/GPU, edge/cloud)?
  Rationale. The combined effectiveness of latency, GPU and energy is still uncertain.
• Problem. Which ML models are most effective at forecasting demand and adapting the scheduler?
  Rationale. Systematic comparisons of QoS and cost-benefit are lacking.

Refs.: Ahmed et al. (2021) · Harichane et al. (2022) · Carvalho & Macedo (2023) · Menouer (2021).