Mikhail Gorelkin

1612 Worcester Rd, Apt. 204A, Framingham, MA 01702
mikhail@gorelkin.com

SUMMARY

Principal AI Scientist | Mathematician | Engineer | Consultant with 20+ years of solving complex problems.

KEY SKILLS

EXPERIENCE

Principal AI Scientist | Mathematician | Engineer | Consultant, Boston, MA 04.2005 - current

A Partial Project List (in various areas)

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). Prepared a paper for publication on discovering server scalability bottlenecks based on the Kruskal-Wallis test and modified Hodges-Lehmann estimators for statistical modeling (MS C++/STL, all major RDBs, Statistica).

Central Transport International, Inc., Sterling Heights, MI 03.1998 – 02.2000
Systems Architect

Developed an NT-based distributed enterprise architecture for 100+ terminals across the US, Canada, and Mexico using satellite communication. Managed several consulting teams to implement an ERP system on clusters.

Resource Technologies, Troy, MI 08.1997 – 03.1998
Software Consultant

Developed a scalable architecture for terminal operations based on MTS and MS SQL Server with up to 300 MS-DOS clients / hand-held computers using MS RPC (MS C++). After several months of work, the client hired me as a Systems Architect.

Advanced System & Designs, Inc., Troy, MI 01.1996 – 05.1997
Software Engineer

Researched and developed the DoES for Windows (the primary product for the Shainin approach to Statistical Design of Experiment) and the ANOVA-TM 2.x for Windows (the primary product for the Taguchi approach to DoE) (MS C++, Statistica).

DataNet Technologies, Inc., Troy, MI 02.1993 – 06.1994
Software Engineer

Researched and developed all statistics for the WinSPC (statistical process control software), including the non-normal capability analysis for all types of Pearson’s and Johnson’s distributions (MS C++).

EDUCATION

Voronezh State University, the Department of Algebra and Topological Analysis Methods, Voronezh, USSR
Master of Science, Mathematics

Non-linear functional analysis. The master's thesis: Diffeomorphisms in Banach spaces.

CONTINUING EDUCATION WITH CERTIFICATION

Train & Fine-Tune LLMs for Production, Intel Disruptor Initiative+, 2023
Quantum Computation using Qiskit v0.2X, 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, FutureLearn / University of Groningen, 2014
Data Mining with Weka. Parts 1 & 2, University of Waikato, 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 University, 2013
Algorithms: Design and Analysis. Parts 1 & 2, Coursera / Stanford University, 2013
Machine Learning, Coursera / Stanford University, 2012
Model Thinking, Coursera / University of Michigan - Ann Arbor, 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

MEMBERSHIPS

Integral Life