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Ashutosh is a PhD student in Machine Learning at the Institute of AI, TU Braunschweig, advised by Prof. Dr. Michel Besserve. His research develops machine learning methods for understanding complex systems, with a focus on causality, in particular causal and probabilistic representation learning.
He holds a Master of Science in Quantitative Data Science Methods from the Eberhard Karls University of Tübingen. His master's thesis, conducted at the Max Planck Institute for Intelligent Systems, applied optimal transport theory to linear Independent Component Analysis and resulted in a paper at the TPM workshop at UAI 2026. Prior to his graduate studies, he worked three years as a Software Engineer at HSBC (Global Cloud Economics). He has also completed an internship in the financial stability department of the Deutsche Bundesbank and an Erasmus Exchange at KU Leuven, where he focused on econometrics and machine learning.
Skills & Methods
Research Methods
Optimal Transport
Causal Inference & Discovery
Bayesian Methods
Independent Component Analysis
Econometrics
Time Series Analysis
Probabilistic ML
Programming & Frameworks
Python
PyTorch
R
MATLAB
SQL
Git
Tools & Infrastructure
Docker
FastAPI
LangChain
GCP / AWS
Background
Education
PhD in Machine Learning
Institute of AI, TU Braunschweig
Sept 2026 – Present
Machine learning for complex systems and causality, in particular causal and probabilistic representation learning, with applications in sustainable energy systems, decision sciences, econometrics, and generative AI.
Advisor: Prof. Dr. Michel Besserve
Education
Master Thesis: Optimal Transport in Linear ICA
Max Planck Institute for Intelligent Systems and University of Tuebingen
Completed 2026
Project
Heilbronn Hackathon 2026: TuebiFit
Cursor Hackathon
March 2026
Architected a full-stack, LLM-powered agentic application utilizing LangChain, FastAPI, and retrieval-augmented generation (RAG) pipelines.
View GitHub.
Experience
Intern Quantitative Analyst (Financial Stability)
Deutsche Bundesbank, Frankfurt
July 2025 – Sept 2025
- Applied machine learning and time series forecasting to analyze investment fund portfolios.
- Focused on financial stability insights for non-bank financial intermediaries.
Education
Erasmus Exchange (Econometrics & ML)
KU Leuven, Belgium
Feb 2025 – June 2025
Focused on Econometrics and Machine Learning.
Experience
Graduate Research Assistant (Optics & Sensing)
Max Planck Institute for Intelligent Systems
July 2024 – June 2025
Award
Winner - Best Pitch & Co-creation Prize
Neckarthon (Startup Center Tübingen)
November 2024
Won dual prizes for "Tue-bi-smart," an ML-enabled transport prototype developed for Stadtwerke Tübingen.
Education
M.Sc. Quantitative Data Science Methods
Eberhard Karls Universität Tübingen
Oct 2023 – Aug 2026
Specializing in Econometrics and Machine Learning.
Experience
Software Engineer (Cloud Economics)
HSBC Holdings PLC, Pune
Sept 2020 – Sept 2023
- Built real-time ETL pipelines (improving latency by 4x) and designed FinOps frameworks.
- Developed statistical models driving $500K in cloud cost savings.
Experience
Software Engineering Intern
Nvidia Corporation, Bengaluru
Aug 2019 – Dec 2019
Built a customer-facing Python tool with real-time Plotly visualizations to analyze SoC CPU usage.
Experience
Internship: Industrial Process Optimization
iCreate, Ahmedabad
May 2018 – July 2018
Analyzed manufacturing plants to identify automation opportunities and submitted a process optimization report.
Education
B.E. Electrical and Electronics Engineering
BITS Pilani, Goa Campus
2016 – 2020
A more personal account of how I got here →