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• Avalara: Sr Director, AI-ML, Content Research & Engineering: Tax Code Classification, Compliance Content Discovery, Automatic Extraction from diverse sources and formats, Self-improving systems, System Architecture, Data Pipelines, Enterprise Content Hub, Event Architecture, Information Modeling. Vendor management.• AskVera: CTO: Conversational Machines, image analysis.• Drop (Fresco): Chief Data Science Officer: Automatic conversion of recipes in structured format.• ApolloFactor: CTO, Advisor (formerly Chief Data Science Officer): Data pipelines, Predictive modeling, Evaluation, Perf. • OpenTable: Sr Director, Data Science: Search, Recommendation, Discovery, Personalization, Optimization (ML, NLP-Natural Language Processing). • Amazon-A9.com: Sr Manager: Local Search. Principal Research Scientist: Product Search: Semantic Search & NLP.• Microsoft-Bing: Principal Dev Manager: Data & Relevance Measurement, Local Search.• J.D.Power and Associates/McGraw-Hill (Web Intelligence): Sr Director: Multilingual content analysis; Sentiment Analysis; Topic identification, categorization.• Umbria: Chief Scientist: Sentiment analysis for market intelligence.• IBM: Managed Multilingual Automatic Content Extraction-entity detection & tracking (English, Spanish, Italian, Farsi).• IBM: e-commerce: Dialog & recommendations.• IBM: Won Time Expression Recognition and Normalization NIST-run competition.• Uni Sussex, UK: Fellow: Robust parsing with wide-coverage grammars for English.• Uni Stuttgart, Germany (IMS): Visiting Researcher: Memoization-based surface realizer for lexicalized grammars.• Apple-BBS: Lead OS localization and translation.• LIMSI-CNRS, Paris: Visiting Researcher: Morphological generation for computer-aided language learning.• Univ. of Edinburgh (Dept of AI): PhD in Artificial Intelligence.See my ML tutorial @ http://www.slideshare.net/Nicolas_Nicolov/machine-learning-14528792Specialties: Managing R&D; Knowledge Management; Machine Learning (ML), Web Search, Unstructured Content Analysis, Natural Language Processing (NLP), Text Analytics, Computational Linguistics (CL), Natural Language Engineering (NLE), Sentiment/Opinion Analysis, Text Categorization, Clustering, Parsing (dependency), Semantic Search, Information Retrieval (IR), Dialog Systems, Business Intelligence, NL Generation, Unstructured Information Management Architecture.
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Senior Director, Ai & Ml, Content Research & EngineeringAvalara Aug 2020 - PresentDurham, Nc, UsFocusing on identifying the right problems to solve (strategy) and creating the path to the solution. Building and growing advanced technology teams developing end-to-end tax compliance solutions with novel AI-ML components providing measurable and lasting impact to Avalara and our clients.
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CtoAskvera Jul 2020 - PresentAskVera is your Virtual Environmental Recycling Assistant.We are working on initiatives at the crossroads of sustainability, AI/ML, image classification, object detection, text extraction, conversational dialog systems.
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Cto, AdvisorApollofactor™ Sep 2019 - PresentBoulder, Co, UsApollo is a compensation app, targeted to disrupt the $1.2 billion salary survey market. The focus is on the tech industry initially, and the system leverages Machine Learning engine and optimizes the financial efficiency of job offers.+ Setting vision, roadmap and execution. + Team building.+ System design.+ Predictive modeling: base/bonus/equity models (private and public companies).+ Overall quality.+ Model accuracy.+ Performance testing.+ Product, planning.+ Project management. -
Chief Data Science OfficerDrop May 2018 - Sep 2019Organize the world's recipes, enable users to cook them in a heterogeneous connected environment (IoT), and monetize through grocery integration. Drop is building the operating system for the kitchen — one unified solution that connects the whole cooking journey.• Managing Data Science and Content teams (consisting of data scientists, engineers, data science content manager, content manager, content editors, technical advisory board).• Dependency parsing for recipe domain. Using the modern Deep Dependency Links: Resulting system was better than using Google dependency parser!• Link generation (identifying recipes sites to crawl).• Categorization of crawled pages.• Domain exploration (identifying capabilities of connected kitchen devices to be extracted automatically).• Recipe field extraction and verification from crawled data.• Creation of Data Science Workbench for automatic conversion of recipes.• Recipe terms identification (mention detection, phrases and their semantic types).• Syntactic clause detection.• Analysis of ingredients: ingredient name, quantity, measure, preparation.• Recipe flow inference (splitting of incremental ingredients, implicit container identification).• Coreference of ingredient mentions in recipe instructions to recipe ingredients.• Coreference of containers.• Semantic role labeling: Source and target container relations for cooking actions.• Review and correction of auto converted recipes (both [1] semantic layer, and [2] end-user facing recipe creation and editing system).• Identifying missing resources for recipes and adding to system (ingredients, cooking actions, secondary actions, appliances, settings, containers, tools, utensils, …): 10X increase of ingredient database!• Recipe taxonomy induction and update (1K+ categories).• Recipe categorization into taxonomy nodes.• Creating pristine recipes for demos to investors.• Creating comprehensive user manuals and annotation guidelines.
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Senior Director, Data ScienceOpentable.Com Nov 2015 - Mar 2018San Francisco, California, UsLeading the Data Science team. Working on:+ Search and Discovery.+ Recommendations.+ Personalization.+ Advertizing.+ Optimization.+ Prediction.Using:+ ML: Machine Learning.+ NLP: Natural Language Processing. -
Senior Manager & Principal Research ScientistA9.Com (Amazon) Apr 2013 - Nov 2015Senior Manager, Local Search and Principal Research Scientist, Product Search.+ Launched Local Search beta version.+ Competitive analysis across Amazon and other Local Search Providers.+ Query understanding: Location extraction.+ Semantic Search & NLP (Natural Language Processing):++ Product type identification.++ Semantic types identification.+ Search results evaluation.+ Drove annotation efforts (both internally within Amazon and externally with an annotation vendor).++ Working with Legal team on Master Services Agreements and Work Orders.+ Product and Project management.+ Evaluation of startups for acquisition.
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Principal Development Manager & Principal ScientistMicrosoft (Bing) Oct 2010 - Apr 2013Redmond, Washington, UsLeading Bing Local Search Data Measurement and Local Search Relevance Measurement.+ Local Search attribute accuracy measurement.+ sNDCG: Signed, Normalized, Cummulative Gain for Local Search relevance.+ Local Entity conflation/matching.+ Local Entity annotation and categorization, clustering and taxonomy induction.+ Local Entity popularity modeling.+ Cross-lingual Local Search. -
Senior DirectorJ.D. Power And Associates / Mcgraw-Hill May 2005 - Oct 2010Troy, Mi, UsLeading the R&D team in the Web Intelligence Division. Team of chief architect, senior engineers, scientists, manager of linguistics team (data researchers, annotators/judges, outsourced labelers), interns, professors members of Technical Advisory Board (Stanford, Univ. of Pennsylvania, Univ. of Colorado) with end-to-end responsibility for content analysis (natural language processing, sentiment analysis and topic detection and categorization systems).• Discovery of topics of conversation:++ Topic modeling for clustering, topic tracking over time, new topic identification.++ Content Search (Information Retrieval): Specifying large queries of phrases (thousands vs. 30 terms in search engines) using non-recursive, context-free grammars.• Sentiment analysis:++ Machine learning techniques for mention detection, dependency parsing, coreference, meronymy. Targeting of sentiment expressions. Determining the semantic orientation of mentions and aggregating across all mentions in a coreference/meronymy chain.++ Sentiment corpus: 2 larger and more detailed than any other sentiment resource: Entities: mentions (16 semantic types|automotive, camera & consumer packaged goods (cpg) domains), coreference relations, meronymy relations; Sentiment expression, negation,intensifiers, comparisons; relation visualization; annotation; annotation guidelines, creating a license and working with the legal department.++ Chinese sentiment analysis (use of Amazon Mechanical Turk for creating machine learning training datasets).++ Structural analysis (dependency parsing); State-of-the-art accuracy and ecient decoder; managing CoNLL-style annotation for out-of-domain data. -
Chief ScientistUmbria, Inc. (Startup) May 2005 - Apr 2008UsManaged R&D team (scientists, chief architect, senior engineers, manager of linguistic annotation team, outsourced spam annotation team, interns, university professors members of the Technical Advisory Board).• Foundational NLP architecture: Crawling, de-htmlizing, tokenization (statistical and heuristic; emoticons), language identification (word-based n-gram models), token normalization; sentence detection; storing, indexing, retrieval (Lucene, UIMA).• Clustering for automatic topic discovery (phrases generalized through WordNet hypernyms; k-means).• Proximity-based sentiment analysis.• Developed fast, state-of-the-art spam detector (based on url segmentation and web content.• Part-of-speech tagger (efficient, Hidden Markov Model with tag pruning, and data-driven, semi-supervised, sffix tying model for unknown words; Transformation-Based Learning (TBL); Support Vector Machines - SVMs).• Phrase detector/chunker (Support Vector Machines (Yamcha); de-lexicalized, pos n-gram context model; fast finite-state chunking; Transformation-based Learning (fn-TBL).• Social Network Analysis (SNA): tribe analysis system (comparisons of clusters of content from two sets of authors, visualization{guess system).• Demographic analysis (gender and age) based on user handles, URL segmentation, patterns and text categorization.• Supported sales, marketing and thought leadership efforts through client presentations, industry speaking events, and press interviews.• Evaluated third party analytic components for licensing by Umbria Inc. -
Research Staff Member (Rsm)Ibm T.J. Watson Research Center 1999 - 2005Armonk, New York, Ny, Us• Winner, U.S. Government (NIST-run) competition on text analytics:TERN (Time Expression Recognition & Normalization):– Used Robust Risk Minimization and Maximum Entropy techniques;– Developed a “conservative” tokenizer;– Used distributed environment for quick experimentation;– Built system for visualizing mentions.• E-commerce: Web-based dialog recommendation:– IAAI award (Innovative Applications of Artificial Intelligence) for deploying a live system.– System shown to significantly reduce the length of interaction in terms of time and number of clicks in finding products.– Integrated Decision Tree-based (constituency) parser.– Internal semantic representation, mapping to SQL, choosing follow-up questions, relaxation of constraints, database back-end.• Lead cross-team effort on entity extraction (mention detection) between Information Extraction, Question Answering, Finite-State Processing teams within IBM:– Awarded IBM division-level achievement.– Showed higher accuracy of combined mention detection system which uses machine learning module, question answering module, and finite state module.– Liaised with different teams to achieve a common code base and ran individual components.– Performed stream synchronization (via token alignment) and built combined system.• ACE (Automatic Content Extraction - entity detection & tracking):– DecisionTreess, RRM & MaxEnt.– Explored n-gram features combining: lexical stems, capitalization, POS, chunking (induced from Penn TreeBank first-level constituent labels), dependency-based information (using English Slot Grammar), WordNet, gazetteers, also streams which are the output of other classifiers (HMM) trained on other data (MUC, CoNLL).– Created rule-based coreference system. -
Research FellowUniversity Of Sussex 1996 - 1999Brighton, East Sussex, GbRobust parsing with wide-coverage grammars for English: Designed and implemented an integrated environment for parsing and grammar development using a variant of Tree Adjoining Grammars; visualizing parse structures including feature structures and co-references. Parsing through compilation to Linear Prioritized Multiset Grammars. Developed and implemented algorithms for large-scale grammar development using a specialized language for describing non-monotonic inheritance hierarchies (DATR). Compiled feature structures to flat terms for efficient unification during lookup and parsing. Encoding semantic information. Finite-state parser which uses precompilation techniques in order to speed up on-line processing. Calculated compact form of expanded grammar from hierarchical encoding. Precompiled sets of trees associated with the same word class (computationally intensive). Built in a team effort the largest grammar of English with over 3500 constructions.
Nicolas Nicolov Skills
Nicolas Nicolov Education Details
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The University Of EdinburghComputer Science)
Frequently Asked Questions about Nicolas Nicolov
What company does Nicolas Nicolov work for?
Nicolas Nicolov works for Avalara
What is Nicolas Nicolov's role at the current company?
Nicolas Nicolov's current role is Sr Director, AI & ML, Content Research & Engineering.
What is Nicolas Nicolov's email address?
Nicolas Nicolov's email address is ni****@****hoo.com
What is Nicolas Nicolov's direct phone number?
Nicolas Nicolov's direct phone number is +141534*****
What schools did Nicolas Nicolov attend?
Nicolas Nicolov attended The University Of Edinburgh.
What skills is Nicolas Nicolov known for?
Nicolas Nicolov has skills like Natural Language Processing, Machine Learning, Information Retrieval, Artificial Intelligence, Algorithms, Computational Linguistics, Information Extraction, Data Mining, Text Analytics, Computer Science, Text Mining, Text Classification.
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