Mikhail Gorelkin
Framingham, MA, USA, Email: mikhail@gorelkin.com
SUMMARY
Principal AI Scientist | Mathematician | Engineer with over 20 years of experience transforming complex real-world problems into innovative engineering solutions. Delivers AI and Generative AI breakthroughs to global businesses and early startups where traditional approaches have failed. Proven track record of applying cutting-edge research, mathematical thinking, and engineering practices to improve production systems and create competitive advantages for businesses across industries.
KEY SKILLS
- Generative AI / LLMs and Agentic AI for AI Automation
- Artificial Intelligence, Natural Language Processing, Machine Learning, Deep Learning, Graph Neural Networks, Reinforcement Learning, Probabilistic Programming, Automated Decision Making
- Data Science, Forecasting, Optimization, Recommender Systems
- Complex Systems, Multi-Agent Modeling
- Mathematical Modeling, Algorithms
- Languages: Python, C++, C#, Scala, Julia, SQL | Core Gen-AI & Agents: Hugging Face, LLMs, Agent Development Kit (ADK), AutoGen, crewAI, LangChain, Agent2Agent (A2A) and MCP protocols, Faiss, Pinecone | Clouds: Google Cloud / Vertex AI, Amazon SageMaker, Amazon EC2 / Linux | Other Tools: PyTorch, TensorFlow / Keras, scikit-learn, spaCy, PySpark, Akka, Mesa, Pyro, PyMC.
MY CUSTOMER LIST INCLUDES
- Visa, Ford, ServiceNow, Deloitte, Boston Consulting Group, Fractal Analytics, IEEE, Scientific Systems Company, AES, Celsius, airSlate, ADΣXT, NOHOLD, Boston Biotech Clinical Research
EXPERIENCE
AI Consultancy, Boston, MA 04.2005 – current
Principal AI Scientist | Mathematician | Engineer
- Designed, architected, and engineered many AI and generative-AI solutions to tackle complex business challenges for Fortune 500 companies and start-ups.
Compuware Corp., Technology Department, Detroit, MI 03.2000 – 11.2004
Software Developer VII
- Researched and developed many advanced features for QALoad (automated performance and scalability testing software), which allowed the company to acquire several big clients like Bank of America from competitors. Prepared a paper for publication on discovering server scalability bottlenecks based on the Kruskal-Wallis test and modified Hodges-Lehmann estimators for statistical modeling.
EDUCATION
Voronezh State University,Voronezh, Russia
Master of Science, Mathematics
- Focused on Topological Methods in Nonlinear Functional Analysis.
CONTINUING EDUCATION WITH CERTIFICATION
- MCP: Build Rich-Context AI Apps with Anthropic, Anthropic, 2025
- Multi AI Agent Systems with crewAI, Parts 1 & 2, crewAI, 2025
- AI Agentic Design Patterns with AutoGen, Microsoft & Penn State University, 2024
- Train & Fine-Tune LLMs for Production, Intel, 2023
- Quantum Computation using Qiskit, IBM, 2022
- Quantum Computing, Coursera / Saint Petersburg State University, 2021
- Algorithmic Information Dynamics, Santa Fe Institute, 2018
- Parallel Programming, Coursera / École Polytechnique Fédérale de Lausanne, 2017
- Text Mining and Analytics, Coursera / University of Illinois at Urbana-Champaign, 2015
- Statistical Learning, Stanford University, 2014
- Mining Massive Datasets, Coursera / Stanford University, 2014
- Decision Making in a Complex & Uncertain World, Futur earn / University of Groningen, 2014
- Data Mining with Weka. Parts 1 & 2, University of Waik o, 2014
- Big Data and Social Physics, edX / MIT, 2014
- Introduction to Dynamical Systems and Chaos, Santa Fe Institute, 2014
- Introduction to Complexity, Santa Fe Institute, 2013
- Game Theory, Coursera / Stanford University, 2013
- Natural Language Processing, Coursera / Columbia Uni rsity, 2013
- Algorithms: Design and Analysis. Parts 1 & 2, Courser / Stanford University, 2013
- Machine Learning, Coursera / Stanford University, 2012
- Model Thinking, Coursera / University of Michigan - Ann rbor, 2012
CONTINUING EDUCATION WITHOUT CERTIFICATION
- Categories for AI, DeepMind, 2023
- Introduction to Quantum Computing & Quantum Machine Learning, IBM, 2022
- Introduction to Agent-Based Modeling, Santa Fe Institute, 2020
- Bayesian Methods for Machine Learning, Coursera / National Research University Higher School of Economics, 2019
- Superhuman OS, Integral Institute, 2018
- Deep Natural Language Processing, University of Oxford & Google DeepMind, 2017
- Knowledge-Based AI: Cognitive Systems, Udacity / Georgia Tech, 2016
- Probabilistic Graphical Models, Coursera / Stanford University, 2016
- Deep Learning (TensorFlow), Udacity / Google, 2016
- Approximation Algorithms. Parts 1 & 2, Coursera / École Normale Supérieure, 2016
- Functional Programming Principles in Scala, Coursera / École Polytechnique Fédérale de Lausanne, 2015
- Deep Learning for Natural Language Processing, Stanford University, 2015
- Neural Networks for Machine Learning, Coursera / University of Toronto, 2015
- Scalable Machine Learning (Spark / PySpark), edX / UC - Berkeley, 2015
- Artificial Intelligence. Part 1, edX / UC - Berkeley, 2014
- Machine Learning: Reinforcement Learning, Udacity / Georgia Tech, 2014
- Web Intelligence and Big Data, Coursera / Indian Institute of Technology - Delhi, 2012
CONFERENCES
- The Conference on Neural Information Processing Systems, 2020
- The International Conference on Probabilistic Programming, 2018
- O'Reilly Artificial Intelligence Conference, 2016-2018
- IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2007