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20+ Years in Artificial Intelligence | 100+ Patents in Machine Learning | 1 of the Top 10 Data Scientists in India
J+O=Y Joy Mustafi is the Founder Chairman of MUST Research. MUST is dedicated to promote excellence and competence in the field of artificial intelligence, to build an ecosystem to enable interaction between academia and enterprise, help them in resolving problems, as well make them aware of the latest developments in the cognitive era to provide solutions. The most exciting feature of MUST is the advanced research on cutting-edge technologies offering solutions and products on artificial intelligence for business and services bundled with data engineering, innovative algorithms, end-to-end coding, deployment and intellectual properties. https://must.co.in/ Had worked as Principal Researcher at Salesforce, primarily responsible for Salesforce Einstein, Principal Applied Scientist at Microsoft - Artificial Intelligence and Research, Data Science and Machine Learning and with IBM for a decade as a Data Scientist involved in Watson Solutions. Got the Research Fellowship Award in Computer and Communication Sciences from Indian Statistical Institute, Kolkata. Collaborated with around twenty leading universities in India as visiting faculty (e.g. Indian Institute of Technology, Kharagpur and Indian School of Business, Hyderabad etc.), mentor, project supervisor, academic board member, curricula moderator, etc. Having more than eighty patents and forty publications in the machine learning space. https://scholar.google.com/citations?user=xpZ8wXkAAAAJ https://patents.google.com/?q=%22Joy+Mustafi%22&scholar&oq=%22Joy+Mustafi%22 Recognized among the Top Ten Data Scientists in India (Analytics India Magazine). Having two decades of experience in the corporate, research, and academic world. Managed and led more than five hundred practitioners in various organizations with respect to technical and professional guidance in the recent past. Recognized as One of the Top Dat
Indian Institute of Technology, Kharagpur
Doctor of Philosophy - PhD, Computational and Data Sciences
August 1, 2021 – Present
MUST Research Labs
Founder
November 1, 2020 – Present
Salesforce
Principal Researcher - Artificial Intelligence
September 1, 2018 – December 1, 2019
Hyderabad
Microsoft
Principal Applied Scientist - Artificial Intelligence and Research
May 1, 2016 – August 1, 2018
Hyderabad
IBM
Senior Data Scientist - IT Operations Analytics
October 1, 2014 – March 1, 2016
IBM
Analytics Consultant - Watson
February 1, 2013 – October 1, 2014
IBM
Data Specialist - Advanced Analytics
August 1, 2006 – January 1, 2013
Indian Statistical Institute, Kolkata
Research Fellow
August 1, 2003 – August 1, 2006
Kolkata
Einstein Platform - Unsupervised Object Localization
September 1, 2018 – December 1, 2019
Invented and built an unsupervised object localization technique which takes the image and provides the approximate bounding box for the object(s) present in it and can be used for various applications in computer vision for multiple domains.
Einstein Platform - Next Best Offer (Recommendation)
September 1, 2018 – December 1, 2018
Invented and developed a platform on recommendation engine for industry customers with a wide range of data with the objective of predicting one or more items a user should be shown as the next best offer based on his previous buying behavior, other users buying behavior and potentially user profile information.
Multi-Modal Personal Assistant (Softie)
July 1, 2018 – August 1, 2018
Invented and built a physical robot equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc. Formed the team from various business units and encouraged to explore research applications on artificial intelligence and related fields. Won first runner-up prize in hackathon.
STCI - Data Science and Machine Learning (DSML)
May 1, 2016 – August 1, 2018
• CogniMaths - Solving Arithmetic Word Problems • Academic Search Enrichment and Dash-boarding • Auto Suggestion System • Ranking of Subject Matter Expertise using Social (Linkedin) Data
STCI - Data Science and Machine Learning (DSML)
May 1, 2016 – August 1, 2018
• Took the Leadership of AI School in Microsoft India, to promote the excellence in artificial intelligence education for all Microsoft employees. • Organized India Data Sciences Meet 2016 and 2017 by introducing DSML Studio (data science and machine learning studio), shared data science research knowledge and expertise by taking technical sessions. • Mentored colleagues in data mining, machine learning and natural language processing. • Supported Microsoft academic accelerator and hiring team by taking interviews and becoming ML Ambassador from DSML Studio. • Collaborated with seniors and peers from IDC, MSR, MSIT, MSGD, MCS and worked together as One Microsoft with common vision.
Digital India Cloud and Enterprise (DICE)
May 1, 2016 – August 1, 2018
Engaged with potential customers to prototype AI SaaS by integrating various customer data, generating universal model and visualization and introducing innovative solutions that match the customer requirements. • Healthcare (Apollo and Multiple Eye-Care Customers for AI SaaS) • Education (Mid-Day Meals Services - MHRD India) • Agriculture (ICRISAT) • Retail (Flipkart) • Transport and Communications (Indian Railway) • Finance (HDFC)
Automatic IT Service Management Data Categorization
May 1, 2015 – December 1, 2015
Invented and developed an integrated and automated system which can analyze textual description fields of service management tickets; transform data by combining rows and columns based on the patterns; cleanse data by removing unwanted fields and phrases; extract keywords from relevant transformed but unstructured data field using natural language processing; categorize defects into groups using machine learning by extracting features; auto-generate rules from machine learning output; combine extracted information, rules and reuse for future; produce integrated metric report from each and every step stated above without human intervention to reduce effort, time and cost.
Automatic Outlier and Early Warning Detection in Infrastructure Operations
October 1, 2014 – April 1, 2015
Invented and developed an integrated and automated system which can parse any type of machine generated log files; convert raw textual semi-structured file into tabular format; identify patterns in specific log; aggregate based on key attributes; determine time interval based on range of the data set dynamically; move time frames by iterations and by selecting or deselecting specific messages by count; retrieve anomalies or unusual events from historical log data and correlate with unusual early log messages to major events of failure automatically using statistical methods (e.g. control limits); recommend early warning and corrective actions based on certain log messages - without human intervention to reduce effort, time and cost.
Analytics for IT Operations Agent Self Assist (Watson for IT)
March 1, 2014 – March 1, 2016
Involved as Analytics Consultant for IBM SPSS solutions. Built statistical models which can automatically identify the root cause of poor performance of IT products, along with providing next best actions by leveraging existing knowledge within the enterprise. Introduced intelligent mapping between the factors and performance to be able to quickly detect problematic layer.
Solving Arithmetic Word Problems using Natural Language Processing (Watson - x)
January 1, 2014 – October 1, 2015
Invented the method of solving word problem in natural language, which was an open problem with IBM Watson. Contributed significantly to develop the solution (Java), lead the team and deliver the solution. Demonstrated to VP, DEs, STSMs and other senior researchers in IBIM (RTE 2014). Highly recommended by IBM Research (IRL) for further collaboration. The system can understand an arithmetic or algebraic problem stated in natural language and provide a solution in real-time as a natural language answer, with the following key steps: Get the input problem statements and question to be answered; Convert the input sentences to a sequence of sentences which are well-formed from a mathematical perspective; Convert the well-formed sentences into mathematical equations; Solve the set of equations; Narrate the mathematical result in natural language.
Information Retrieval from Statistical Diagrams and Charts using Computer Vision (Watson - i)
May 1, 2013 – December 1, 2014
Leveraged the ISL innovation program, took the leadership of assembling the team across ISL, managed and delivered the project. Contributed technically with Java programming and SPSS analytics solutions for computer vision, natural language processing, user interface and system integration. Selected as Top Innovator at ISL for first ever solution with IBM vision and recognition. Demonstrated live project to RGM ISA, VP ISL, DEs and other leaders in IBM (ConnectIn 2014). The solution is for automatic data interpretation and answering analytical questions with tables and charts. The prototype a) extracts and classifies images from documents; b) retrieves and restructures information from charts and statistical diagrams; c) represents structured information in database format; d) answers natural language queries from charts, data tables, and databases.
Survival Analysis for Cancer Patient
February 1, 2013 – February 1, 2014
Primarily took the ownership of the entire IBM Watson / SPSS solution for MDACC. Analyzed the client data along with unstructured information and built the statistical models (Kaplan Meier Estimation) to predict the overall survival for the CLL (Chronic Lymphocytic Leukemia) patients and to prescribe the right treatment for the same.
Telecom Predictive Analytics
August 1, 2012 – January 1, 2013
Involved as Analytics Consultant for IBM SPSS Modeler and IBM SPSS Statistics. Developed statistical model for churn prediction of telecom industry using Classification Tree(s). Made innovative statistical transformations for feature vector selection. Built segmentation using K-Means algorithm.
IBM Support Portal Advisor (Watson Deep Insight)
June 1, 2012 – August 1, 2012
Contributed significantly in the implementation of Watson Deep Q&A Solution as a service for human machine interaction through natural language in different industry domains. Created ontology and semantic web for super computing solution known as IBM Support Portal Adviser.
Content Storage Optimizer (Unstructured Data)
April 1, 2011 – December 1, 2011
Involved as SME for IBM Content Analytics. Designed and built appliance-like solution that is capable to discover duplicated or redundant information across multiple data silos, shared drives, email systems etc.
Customer Portfolio Optimization - Industry Index and Feature Vector
July 1, 2010 – January 1, 2012
Involved as SME for IBM SPSS products. Provided a look ahead into industry trends and a more meaningful benchmark for judging performance. This allowed distinguishing between results that are up just because the in economy as a whole is doing better or worse. These results were reported in a monthly newsletter. The Retail index is processed once a month after the release of Monthly Trade Report by the census bureau. Established and maintained a library of feature vectors developed for various clients.
Compliance Call Monitoring - Unstructured Data Analytics
July 1, 2010 – August 1, 2010
Created statistical model on text search for inappropriate words or derogatory use. Analysis of marketing messages actually delivered and compared against directives by marketing. Did the interpretation of text against compliance rules, where rules are defined by local regulatory. Unstructured text string analytics provided a reference and white paper which the organization took to other clients.
Bangla Transliteration System
August 1, 2005 – August 1, 2006
Introduced a standard for English to Bangla transliteration system and UNICODE based editor having the following features i) plain text storage (platform, encoding, font independent); ii) one-to-one character mapping (using lower [a~z] and upper [A~Z] cases of English to map 50 basic characters of Bangla); iii) phonetic character chart (English characters are chosen very close to the phonetics of Bangla characters); iv) simple representation of character clusters (easy to parse the input text); v) morphological word construction (to overcome ambiguities).
Computational Linguistic-Based Approach to Verb-Driven Morpho-Syntactic Processing for English to Bangla Machine Translation
August 1, 2004 – August 1, 2006
English sentences were automatically translated to Bangla sentences using linguistic-based machine translation methodology by parsing the English sentences to get the constituent structure and part-of-speech tags; by analysis of feature vector of the tags; and by the Bangla morphological generation of the words to form the Bangla sentences.
Bangla Off-Line Handwritten Numeral Recognition System
January 1, 2003 – June 1, 2003
Developed for optical character recognition system, where Bangla numeral characters [0~9] written in a plain paper are scanned, digitized and the generated feature vectors of the individual cluster of numerals are automatically recognized by the machine using statistical machine learning and multilayer perceptron (artificial neural network).
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for roles requiring innovation, research, and cross-functional collaboration. Their involvement in founding a research lab, leading AI schools, and mentoring colleagues suggests a proactive, knowledge-sharing, and leadership-oriented mindset. The breadth of projects, from academic research to industry applications, indicates a versatile individual who can thrive in diverse technical environments. The focus on solving real-world problems across various sectors aligns with a results-driven culture.
Soft Skills & Operational Fit
The candidate's project descriptions highlight leadership roles, team collaboration (e.g., 'lead the team', 'assembled the team'), and mentorship, suggesting strong soft skills. The diversity of projects across different domains (healthcare, education, agriculture, retail, transport, finance) indicates adaptability and a broad operational fit. Experience in organizing data science meets and promoting AI education also points to a proactive and collaborative work attitude.