Manish Gupta

AI Researcher

India18 yrs 8 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Published over 150 papers in top conferences.
Filed 12 patents in data mining and information retrieval.
Experienced in teaching complex concepts simply.

Stackforce AI infers this person is a Data Science expert with a focus on Machine Learning and Information Retrieval.

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Skills

Core Skills

Machine LearningData Mining

Other Skills

AlgorithmsComputer ScienceData StructuresDatabasesDistributed SystemsHadoopInformation RetrievalJavaScriptMapReducePythonXML

About

Goal: Quality research, and adapting research to make impact in adding value to users. Yet, another goal is to teach complex concepts in easy ways. Researcher, innovator, with a PhD in data mining. Working at Microsoft Bing as a principal applied scientist and as a faculty at IIIT-Hyderabad since May 2013. Also a visiting faculty at ISB since 2016. 2007-2009 at Yahoo!. Interned at Microsoft Research, IBM TJ Watson, NEC Labs. Research work and Innovation: Published more than 150 refereed papers at top conferences and journals in data mining and information retrieval including WWW, KDD, PKDD, SDM, ICDE, TKDE. Filed 12 patents. Authored 2 books: "Outlier Detection for Temporal Data" and "Information Retrieval with Verbose Queries". On program committees of KDD, WSDM, AAAI. Education: 2009-2013 PhD under Prof. Jiawei Han at Univ of Illinois at Urbana Champaign (UIUC), 2005-2007 MTech under Prof. Soumen Chakrabarti at IIT Bombay. Specialities and Interests: Deep Learning, Natural Language Processing, Information Retrieval and Web Mining, Machine Learning, Data Mining. Teaching: IIIT-Hyderabad, ISB, https://www.youtube.com/channel/UC_g2RbNMcraoOPc-vYJjgIA

Experience

Microsoft

3 roles

Principal Applied Scientist

Promoted

Sep 2017 – Present · 8 yrs 6 mos

Senior Applied Scientist

Promoted

Jun 2014 – Aug 2017 · 3 yrs 2 mos

1. Entity linking solutions for Microsoft Edge, and the Bing Snapshot Android App.
2. Mining information for projects from enterprise data.
3. Predicting post-operative outcomes for cataract surgeries.
4. Book: Information Retrieval with Verbose Queries.
5. Linking event mentions to set of entities in knowledge base, specifically for cricket.
6. Predicting Post-Operative Visual Acuity for LASIK Surgeries.
7. Prediction of refractive error using machine learning.
8. Extracting Social Lists from Twitter.
9. Improving Tweet Representations using Temporal and User Context
10. STWalk: Learning Trajectory Representations in Temporal Graphs.

Machine LearningData MiningInformation Retrieval

Applied Researcher

May 2013 – Jun 2014 · 1 yr 1 mo

1. Book: Outlier Detection for Temporal Data.
2. CharBoxes: A System for Automatic Discovery of Character Infoboxes from Books.
3. Cross Market Modeling for Query-Entity Matching.
4. Towards a Social Media Analytics Platform: Event Detection and User Profiling for Twitter.

Data MiningInformation Retrieval

Indian school of business

Visiting Faculty

Nov 2016 – Present · 9 yrs 4 mos · Hyderabad Area, India

1. Taught a full course on deep learning covering basics, perceptron, MLPs, CNNs, RNNs, GRUs, LSTMs, Attention-models, Auto-encoders, Memory networks.
2. Taught a few lectures on Internet of Things covering various use cases, examples using Raspberry Pi, smart cities, smart electric grid, smart water management, smart transportation, smart homes, smart bio-devices.
3. Taught a full course on data collection covering topics like Basics of Data Collection, Structured Data Collection and Pre-processing, Collecting and pre-processing Text Data, Web scraping, NLTK, Stanford CoreNLP, Crawling, Indexing, Graph data collection, Crowd-sourcing, Sensor Data Collection and IoT.
4. Mentoring multiple industry-ISB projects for the Certificate in Business Analystics (CBA) course.

International institute of information technology

2 roles

Adjunct Faculty

Dec 2013 – Present · 12 yrs 3 mos · Hyderabad

1. Taught a course on web mining to a class of 33 students.
2. Query-based Graph Cuboid Outlier Detection for Static and Dynamic Graphs.
3. Predicting Previous Version of a Product Entity.
4. Entity Linking for Tweets and Queries.
5. Predicting user profile attributes.
6. Structured Information Extraction from Natural Disaster Events on Twitter.
7. Doc2Sent2Vec: A Novel Two-Phase Approach for Learning Document Representation.
8. Author2Vec: Learning Author Representations by Combining Content and Link Information.
9. Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction Tasks.
10. Query-based Evolutionary Graph Cuboid Outlier Detection
11. Deep Learning for Hate Speech Detection in Tweets
12. SNEIT: Salient Named Entity Identification in Tweets
13. New Entity Identification Approaches in Entity Linking Systems
14. Interpretation of Semantic Tweet Representations
15. Simultaneous Inference of User Representations and Trust
16. Medical Persona Classification in Social Media
17. Hybrid MemNet for Extractive Summarization
18. SSAS: A Novel Metric for Abstractive Summarization
19. Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization.
20. Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets.
21. Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets.
22. User Profiling based Deep Neural Network for Temporal News Recommendation.
23. A Workbench for Rapid Generation of Cross-Lingual Summaries.
24. Attention-based Neural Text Segmentation.
25. Medical Forum Question Classification Using Deep Learning.
26. Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets.
27. Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets.
28. Believe It or Not! Identifying Bizarre News in Online News Media.

Visiting Faculty

Jul 2013 – Dec 2013 · 5 mos · Hyderabad

Taught a course on web mining to a class of 62 students, covering similarity search, relevance ranking, link analysis, LSI and EM, topic models, recommendation systems, social network analysis, social influence analysis, analysis of microblogs, computational advertising, mining structured information from the web, entity semantics mining, query log mining, and crowd sourcing.