Speaker Profile

Ergunova Olga Titovna, Doctor of Economics, Associate Professor of the Higher School of Production Management, Peter the Great St. Petersburg Polytechnic University. Principal Investigator of Russian Science Foundation Project No.25-28-01469. She acts as Executive Director of BRICS Women Scientists Association, RAS expert and Vice Chair of the Union of Young Russian Scientists. With over 22 years of experience in AI, digital transformation, smart cities and hospitality, she has published over 300 papers and edited multiple international academic works. She organizes global youth science contests and has received various national awards.

Lecture 1

Time: 09:30–11:30, April 18, 2026

Venue: Room 506, Guanli Building

Lecture Topic: Social Protection of Workers in the Platform Economy Digital Solutions and the Role of the State

Lecture Abstract

This lecture addresses the social protection deficit for workers in the platform economy. With the global gig economy reaching $674 billion in 2026 and 154–435 million workers, most lack access to health, pension, and injury insurance. The lecture compares regulatory models: the EU’s Directive 2024/2831 (presumption of employment), Spain’s Rider Law, China’s hukou reform and order-based injury insurance pilots, and Russia’s Federal Law No. 289-FZ (platform registry). It examines algorithmic management, gender and youth vulnerabilities, and the crisis of the classical employer-based protection model. A portable benefits system — where rights follow the worker across platforms — is proposed. Digital solutions such as LSTM forecasting, digital twins, and AI fairness auditing are introduced as tools for evidence-based policy. China and Russia can learn from each other in areas like per-order insurance, platform transparency, and dispute resolution.

Lecture 2

Time: 13:00-17:00, April 19, 2026

Venue: Room 506, Guanli Building

Lecture Topic: Cloud-Fog Architecture with Adaptive PID Feedback Control for LSTM-Based Labor Market Forecasting in Megacities

Lecture Abstract

This lecture presents a hybrid cloud-fog computing architecture integrated with LSTM deep learning and adaptive PID feedback control for labor market forecasting in megacities. Traditional models fail to capture the non-linear, volatile, and real-time nature of urban employment data. The proposed three-tier system includes: a cloud tier for global LSTM training and hyperparameter optimization; a fog tier for real-time inference, PID error correction, and low-latency decision support; and an edge tier for data ingestion from job portals, social media, and government statistics. The adaptive PID controller dynamically adjusts proportional, integral, and derivative gains based on residual errors, forming a closed feedback loop to correct prediction drift. Expected outcomes include a 15–25% reduction in RMSE, sub-500ms fog-tier latency, and threefold faster adaptation than open-loop models. This framework enables evidence-based, real-time labor policy for megacities.