Researcher · KTH Royal Institute of Technology, Stockholm
Ph.D. in Mathematics. I use rigorous quantitative methods and data science to understand financial markets and digital assets, from volatility forecasting to tokenized real estate.
I'm a Researcher in Digital Finance at KTH Royal Institute of Technology. My work sits at the intersection of artificial intelligence, blockchain, DeFi, tokenized real estate and financial econometrics. With a Ph.D. in Mathematics and an interdisciplinary background, I study how emerging digital technologies are reshaping financial systems, investment behavior and real estate markets.
Beyond research, I care deeply about teaching, mentoring and interdisciplinary collaboration, and I enjoy projects that connect rigorous quantitative methods with real-world applications in finance and technology.
Looking for my papers? They are published under Fatemeh (Nazanin) Mottaghi, and KTH lists me as Seyedeh Fatemeh Mottaghi.
Outside academia: nature walks, learning languages, and quiet time with my plants in Stockholm.
Real-world assets on-chain and how they relate to traditional asset classes.
Neural networks and hybrid models for volatility forecasting and portfolio strategies.
Shock transmission, liquidity and total value locked during regulatory events and crashes.
Entropy-based efficiency analysis and dependence structures across digital and traditional assets.
Digital finance papers and earlier work in mathematics.
Full list on Google Scholar, ResearchGate and SSRN.
Each project shows the question, method, outputs and a short animation of the core idea. Animations are illustrative sketches of the concept, not plots of the papers' data.
Asks which legal, institutional and technical changes are needed to bring tokenized real-world assets into regulated exchanges. Integration turns out to depend on how ownership is legally recorded, how settlement, custody, finality and recovery duties are shared between institutions, and how trading and post-trade systems exchange consistent information. The analysis ends in a concrete change map for a Nordic hybrid model.
Maps the direction of information flow between real estate tokens and REITs, equities, crypto and gold. Tokens mainly receive information from equity markets, and in turbulent periods such as COVID-19 the link with Ethereum becomes two-way.
Tests whether real estate tokens share lasting statistical relationships with other real estate vehicles and with broader asset classes such as equities and bonds.
Weighs the gains (market access, security, ease of entry) against regulatory uncertainty, technology risks and possible price effects, using real-world cases of fractional ownership, property management and transactions.
GARCH captures volatility clustering, recurrent networks (GRU, LSTM and a combination) refine the forecast, and a simplified risk parity allocation uses it. The authors report that the resulting strategies outperform standard risk/return portfolios.
Studies how regulatory events, from MiCA to the FTX collapse, and market crashes affect liquidity and the volatility of total value locked, and what this means for the sustainability of DeFi and stablecoin markets.
Maps how volatility spills between ESG equity indices and an oil benchmark. Global and developed-market ESG indices and European carbon-efficient equities are net transmitters, while emerging-market ESG, clean technology and oil are net receivers, and so more exposed to outside shocks.
Combines physics-informed neural networks with a mixture-of-experts design for the Bernoulli free boundary problem, where the edge of the solution region is itself unknown.
Managed the program and mentored participants in the PropTech Talent Program, a joint initiative of KTH Royal Institute of Technology and Malmö University.
From fractional differential equations to asset tokenization. Teaching and mentoring are listed under Teaching, conference service under Talks.
Asset tokenization (blockchain in finance). Project: Real-World Asset Tokens and the Future of Market Infrastructure and Liquidity, funded by the Nasdaq Nordic Foundation (2026 – 2027).
Neural networks and free boundary problems.
Neural networks in financial time series analysis and portfolio optimization.
Fractional differential equations.
Blockchain, DeFi and financial systems.
University teaching in mathematics, statistics, programming and digital finance, plus mentoring and training in higher-education pedagogy.
Teaching Assistant, Jan – Oct 2024
Applied Statistics, Mathematics for Economists, Blockchain and DeFi
University Lecturer, Mar 2022 – Aug 2023
Calculus, Discrete Math, Python Programming, Differential Equations
Program Manager and Mentor, Mar – Jun 2025
KTH and Malmö University
Co-supervisor, KTH, spring 2026
AI-Assisted Portfolio Construction for Swedish Retail Investors: Performance and Explainability under Realistic Trading Constraints (F. de Mander and O. Ljung, with B. Steininger; TRITA-ABE-MBT-26427)
Teaching and Learning in Higher Education (KTH)
Teaching in the Glocal University (Karolinska Institutet)
Oral presentations
Poster
Organization
Open to new positions, research collaboration and speaking invitations.