15 years of experience as a researcher and consultant on machine learning problems (predictive analytics, regression, classification, scoring, feature extraction, dimensionality reduction, NLP). Neural Networks, SVM, RMB, Deep Learning. Experience on Python, R, Matlab, SQL, Weka. More than 50 papers published in international journals
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I joined IBM T.J. Watson Research, Industry Solution group in Spring 2012. I worked as a J2EE software engineer from 2006 to 2008. I obtained a Ph.D of Computer Science from University of California, Los Angeles, with major in Machine Learning and minors in Artificial Intelligence and Data Mining. I have been awarded the Most Outstanding Ph.D Graduate Award, the Northrup-Grumman Outstanding Graduate Student Research Award, the Chancellor Award for Most Outstanding Applicants, all from Computer Science Department, UCLA and the Chinese Government Award for Outstanding Chinese Students Overseas, 2010. I am interested in developing efficient algorithms and machine learning techniques to solve real world problems. I am also interested in problems on combinatorial optimization, data mining and information retrieval. I had worked as a consultant for many start-ups on various projects and I have solid background on both research and development. My home page: www.cs.ucla.edu/~danhe
I have experience of 3+ years of data mining in different fields like: computer vision, pattern recognition, text mining, natural language processing etc. For last year I took part in some big data projects using hadoop+java and map/reduce pattern. In my work I use python, java, R, c# and sql.
I am an expert data scientist. I have academic and industrial experience in machine learning and applied statistics. I hold 2 MSc degrees (awarded with distinction), and I am currently finishing a PhD in machine learning and applied statistics from one of the best institutions in Europe. I am familiar with both statistical methodologies (such as linear regression, generalized linear model and logistic regression, clustering, mixed effects, models for time series such as ARIMA) as well as machine learning/data mining techniques (such as neural networks, decision trees, support vector machines, Gaussian processes and deep learning). Throughout my 5 year experience with the sector I have analyzed tons of datasets from various different domains (from business, to sports to text). There are two things that I believe set me apart from the competition: 1) My breadth of knowledge. 2) My communication and reporting skills. Feel free to contact me about any data-related problem.
PhD in machine learning/data science/bioinformatics, with 4+ years of professional experience in industrial R&D (see www.zaslavskiy.org and my kaggle profile https://www.kaggle.com/users/1996/mikhail for more information). Some examples of problems I can help with: * Building a predictive statistical/machine learning model (gradient boosted trees, svm, elastic nets, neural nets etc.) * Proper statistical data analysis and visual representation * Design and planning of custom data collection protocols Most of the time I use R for fast prototyping/data analysis/automatic report generation and C++ to devolop the final product.
Accomplishments: I published eight peer reviewed journal papers including publications in prestigious journals such as transactions on Signal Processing and transactions on Geoscience and Remote Sensing. I authored, presented and published over ten conference papers at international technical conferences including IEEE Radar conference 2014 at Cincinnati. I am the first author for a US patent application on a new concept/invention named “Weighted OFDM modulation technology for Radar parameter estimation.” Below is a summary of my work experience: In my PHD work, I was also involved with two NASA funded projects at Geo-system Research Institute at Mississippi state university. My overall objective was to develop novel pattern recognition applications for analysis and improvement of existing satellite based hydrological datasets. As a postdoc at MSU, I was involved in a US Department of Defense funded project where the goal is to detect buried radioactive waste. I worked on a semi supervised learning approach for buried depleted uranium detection. In an another project on image processing, I proposed and implemented an advanced image processing algorithm for building detection in high resolution satellite imagery of an urban area. At the University of Maryland Eastern Shore, I participated in a DoD and National Science Foundation funded research on the adaptive radar. My responsibilities include development and implementation of cutting edge adaptive radar signal processing algorithms. List of specialized skills: § Machine Learning: Support Vector Machines, Feed Forward Neural Networks with Back Propagation, and Clustering Methods. § Regression : Multivariate Linear Regression § Optimization: Ant Colony Optimization § Feature Extraction: Current methods in Feature Construction, Selection and Reduction Processes. Relevance ranking etc. § Parameter Estimation: Sequential Bayesian Estimation, Variational Bayes. § Radar Waveform design: Mutual Information based techniques, OFDM, Water-filling Algorithms. § Spectral Analysis: Singular Value Decomposition (SVD), Multivariate Multi-taper SVD, Wavelet-based techniques
I am a passionate Software Engineer with a strong background in Machine Learning, Data Mining and high performance Web programming - I am writing Machine Learning Systems for production environments. To deliver the absolute best software quality is always my top priority. I write code with the intension for anyone to read and understand it as quickly as possible.
You should only hire me if you want a Data Scientist to create a solution to your problem. Math & Statistics: machine learning, statistical modeling, experiment design, Bayesian inference, Supervised learning: (decision tees, random forests, logistic regression), Unsupervised learning: (clustering, dimensionality reduction), Optimization: (gradient descent and variants) Programing & Database: Computer science fundamentals, Python, R, SQL, NoSQL, relational algebra, parallel databaes and parallel query processing, MapReduce concepts, Hadoop and Hive/Pig, custom reducers Domain Knowledge & Soft Skills: Passionate about the business, curious about data, influence without authority, hacker mindset, problem solver, strategic, proactive, creative, innovative and collaborative Communication & Visualization: Able to engage with senior management, story telling skills, translate data-driven insights into decisions and actions, visual art design, visualization (D3.js, ggplot, Tableau)