Principal AI Scientist | Mathematician | Engineer | Consultant with 20+ years of solving complex problems.
- Big data forecasting (Celsius Holdings): Developed a generic probabilistic solution for inventory forecasting across 2K+ locations (PyMC 5, PySpark, Amazon SageMaker).
- Intelligent automation (Deloitte / ServiceNow): Researched and developed for production symmetric & asymmetric semantic search on multiple NLP tasks (PyTorch, spaCy, Transformers, Faiss, Dataiku).
- AI for complex systems (AES Corp.): Researched and prototyped: several probabilistic forecasting models for gas prices (Prophet, Edward / TensorFlow), a framework for full simulation on the Southern California energy market (multi-agents, Bayesian Nets), a latent reinforcement learning algorithm, and a multi-agent reinforcement learning for competitive games model for the Colombian hydro energy market (PyTorch). Developed for production an automatic gas trader for the Southern California energy market.
- Nonparametric probabilistic inference (Scientific Systems Company, Inc. / DARPA): Researched and developed two models for nonparametric inference for simulated trajectories with noise: 1) a parametric approximation for LSTM models based on a variational autoencoder and probabilistic classifier (TensorFlow Probability), and 2) nonparametric one based on the Gutmann's likelihood-free inference of the simulator-based statistical models.
- Intelligent advertising (ADΣXT Corp.): Analyzed data streams from different platforms and restored their integrity with algorithmic constraints. Developed a reinforcement learning algorithm for optimal advertising strategies on FB (TensorFlow). Designed and prototyped a probabilistic multi-agent test framework for simulation and analysis of the users' behavior on FB.
- An intelligent virtual agent (agent.ai): Researched automatically generated responses for IVAs. Developed a model and all algorithms based on deep adversarial learning (Torch7 / Lua).
- Scalability for an enterprise system (Earley Information Science / Ford): Analyzed, solved, and developed a solution for a scalability problem for Ford's Virtual Assistant for Vehicles project.
- Clinical trials (BBCR Consulting): Designed and developed an adaptive computational framework for CTs (Julia).
- NLP / NLU (noHold): Developed several NLP / NLU algorithms for production with spaCy and scikit-learn. Prototyped unsupervised deep embedding for text clustering (word2vec, MXNet) and several semantic transformations on the texts based on the dependency parser (spaCy).
- Intelligent mortgage (Boston Consulting Group): Researched and designed a framework for mortgage loan automation.
- An intelligent online dating paltform (Likeli): Researched and designed a conceptual model for the platform. Designed a personality similarity metric between users and implemented in Theano / GPU library. Developed an intelligent multi-agent framework based on Scala,
Breeze, Akka, and Redis to scale computations for many thousands simultaneous users in real-time. Developed computing personality profiles for FB users based on the Big Five
Personality Traits Model. Developed a real-time personalization system based on users’ feedbacks for several types of bandit algorithms modified for a vector space: Epsilon-Greedy,
Softmax, UCB1, Bayesian, and Bayes-UCB1. Improved Bayes-UCB1 for balance multifactor learning. Solved scalability and performance issues for 1M users and put the system into production.
- Marketing (MutualMind): Developed for production various algorithms such as word sense disambiguation, Twitter hashtag decomposition (used Norvig’s algorithm), topic modeling, sentiment analysis, etc. (Python, NLTK, Gensim).
- A marketing platform (SocialExtract): Developed many NLP algorithms to clean, transform, and integrate data streams from different sources. Prototyped a library of NLP and ML primitives and a framework for the quick composition of computational pipelines based on
Python functional and streaming libraries. Prototyped a relevance engine in this framework. After the client was satisfied with the accuracy, I programmed it in a distributed environment
(Hadoop / MapReduce, MongoDB, and Redis) for scalable production with mrjob / Python and Java. Implemented the distributed Sparse Proximal SVM based on publications.
- A recommender (IEEE): Researched novelty detection in scientific articles and Deep NLP as a framework for NLP problems. Designed and developed a multi-label classification for a
large label set (> 7K) (Mulan, MEKA, scikit-learn) and a similarity metric as a core for the client's recommender system.
- Intelligent real-time logistics (Weft): Researched and designed a conceptual framework for a real-time logistics platform based on DARPA’s Cougaar architecture. Developed several
heuristics based on ant-colony optimization for adaptive routing optimization (Java).
- A courier management system (Deliverator): Researched and developed a solution based on ant-colony optimization for delivery scheduling and optimization, which led to a grant from a
federal agency.
- A personalized cognitive assistant for mobile (Knowtle): Researched, evaluated, and prototyped a model based on the DARPA's CALO project.
- A social network (PopCircle): Researched, designed, and developed for production a composable approach to collaborative filtering with several social regularizations (Octave).
- Personalized marketing (FocusKPI): Researched and designed a marketing model based on a recommender system (collaborative filtering and matrix factorization).
- E-learning (Junyo): Researched and designed a model for adaptive learning. Developed the
concept map extraction from documents and a fuzzy matching algorithm (Java, Stanford NLP). Found significant patterns in the data for sales prediction (Weka).
- A semantic search engine (Boston-based start-up): Developed semantic add-on for Apache Solr / Lucene.
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, Statistica).
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