Eric Glover Ph.D.

E-mail: eric_rez8@ericglover.com, Web: http://www.ericglover.com/, LinkedIn: https://www.linkedin.com/in/erglover/

 

__________________________________________________________________

 

About:

Technical leader and seasoned AI/ML expert with deep experience turning the latest advancements in AI into impactful products. Skilled in fine-tuning LLMs and delivering robust, end-to-end ML solutions with a track record of launching successful AI-driven products in search, generative AI, and mobile applications. Known for a practical, results-oriented approach, I bring broad ML expertise and proven success in creating high-value, business-driven outcomes.

Highlights:

·      Chip Scan: Designed, developed, and deployed an ion-prem, LLM-based tool to convert structural Verilog code to readable HDL, ensuring that the model generates code without introducing errors and operates in a secure environment.

·      AppliedIngenuity.AI: Created a blog dedicated to demystifying advanced AI/ML technology, focusing on practical applications of LLMs and ChatGPT. Published insights help bridge complex AI concepts for a broader technical audience, equipping readers with an intuitive understanding of cutting-edge tools and techniques.

·      Sage.guide: Co-founded and serve as CTO, leading the development of a personalized benefits search platform that combines user data with fine-tuned, domain-specific semantic search. Platform provides customized answers based on individual health plans and coverage, extracting knowledge from both PDF tables and textual data, including safeguards against LLM risks like hallucinations and bias.

·      Advised on creation of what became an FDA authorized Machine Learned algorithm to predict if a young child had autism or needed further evaluation. (Cognoa)

·      Key designer and implementer of a Natural Language Processing Engine for a mobile search product covering parsing, understanding, Elasticsearch query generation. (Branch)

·      Designed and implemented core enhancement to multi-Billion user-identity graph which probabilistically predicts multiple identifiers from the same user through scanning > 1 TB of events daily. (Branch)

·      Improved ultra-short query remote-powered app-search CTR by 3X by developing a (multi-locale - language, region) predictive model of intended app for queries as small as one character. (Branch)

·      On-Device App Search: Led key aspects of an on-device, personalized app-search algorithm deployed on over 50M devices, combining private and server-synced data to rank app results. The success of this solution contributed to adoption by multiple OEMs and carriers, handling hundreds of millions of queries daily. (Branch)

·      Microservice for Query Prediction: Designed and implemented a microservice to predict, in under 100 microseconds, if the NLP system could answer a query. (Quixey)

·      Defined DCG guidelines and assisted in the design of the Quixey Relevance Testing system - the basis of our MLR training and evaluation. (Quixey)

·      Designed and implemented a large-scale system for non-engineers to execute and manage automated web-scale categorization using active learning - ran on Billions of web pages and covered more than 1000 categories. (SearchMe)

·      Designed and drove engineering effort of an automated smart-answer system based on Wikipedia data that could handle synonyms - covered more than 15% of Ask.com traffic at peak.

Experience:

Chip Scan                                                                               Remote/NYC

Machine Learning Specialist                                             June 2023 – Present

Chip Scan pioneers Zero Trust techniques for hardware security, assuming that no component or user is inherently trustworthy. My role involves leveraging my expertise in hardware, software, and AI/ML, particularly in LLMs, to enhance the interpretability of hardware systems. I lead the development of a secure, private-cloud-based LLM service that translates anonymous structural Verilog into human-readable HDL Verilog, ensuring accuracy and reliability in code recovery.

 

Key Contributions:

·      Developed a secure, in-house service using private LLMs for error-free conversion of structural Verilog to human-readable HDL.

·      Applied GNNs for graph embeddings and LSI for text embeddings to optimize hardware analysis.

·      Utilized private cloud GPU infrastructure for scalable, high-performance model deployment.

Technologies: LLMs (including fine-tuning), GNNs, Verilog, Python, Docker, private cloud GPUs.

 

AppliedIngenuity.ai Substack/blog

                                                                                             Remote Creator/Content                                                       April 2023 – Present

Created AppliedIngenuity.AI a blog/consulting firm dedicated to making the latest AI accessible to all with a focus on maximizing Business’ ability to effectively utilize AI/MI for user-facing products. Specializing in LLMs, Prompt Engineering, Search, and the latest AI topics.

Sage.guide                                                                                                Remote

CTO/Co-founder                                                               Sep 2022 – June 2023

Sage.guide provides comprehensive employee benefits guidance and decision support for employers. As co-founder and CTO, I oversee the technology vision, enhancing employee experience through an AI-powered platform combining LLMs and knowledge graphs. I led the design and implementation of a flexible, knowledge-graph-based system for benefit search and personalized recommendations, collaborating with a remote team to bring the vision to life.

 

Key Contributions:

 

Technologies: LLMs (ChatGPT, OpenAI API with fine-tuning), document/concept embeddings (BERT), semantic/vector search, key-phrase extraction, knowledge graphs, Python, AWS, MongoDB.

 

Finny  (Sold to Origin)                                                                        Remote

CTO                                                                               Sep 2022 – May 2023

Finny, sold to Origin, is a financial wellness platform, empowering employees to make better and more confident money decisions. We work with employers to solve the #1 stress employees bring to work: their finances.

Branch Metrics                                                                          Palo Alto, CA

Principal Data Scientist                                                      July 2016 – July 2022

As Principal Data Scientist, I was involved with several different parts of the business. My primary responsibilities were to support other data scientists and engineers as well as design and implement to POC novel algorithms or solutions in areas related to attribution, and mobile search and discovery. I created the Data Science Guild and ran the first Data Science Meetup (https://www.youtube.com/watch?v=rQAL02Hdkws) as well as advised and mentored multiple data scientists.

Highlights:

•       Designed, implemented, deployed, and patented the first probabilistic matching produced a true probability score. Built and deployed years before Apple’s ITP and IOS 14.

•       Designed and implemented core logic of a multi-vertical natural language mobile search system which powered Billions of queries, including on Samsung S10 devices. System could answer queries like: "things to do", "vegetarian near me", "New York Pizza", "Portland Hardware Stores", “311” (the band) with ability to detect and resolve geographic,  entity and vertical ambiguity.

•       Improved CTR of remote mobile app search by 3X by explicitly predicting probabilities of intended apps, with more than 75% accuracy for single character queries, > 95% for 3+. Effective for multiple regions and languages. 

•       Led a small team of engineers and data scientists to create the first on-device personalized, real-time, app-search algorithm (SQLite query). Production live with over 50M devices. Algorithm leveraged both local (private) and remote-synced data, and could handle typos/alternatives, with extremely high accuracy for even one character.

•       On first and second place teams in company pitch day. Led 2nd place team by proposing shifting to a more modular - use-case driven business model. Within 6 months, parts of pitch have begun to bear fruit throughout the org.

•       Advised on the Media Mix Modeling project (MMM) - Assisted in developing business materials, direction and modeling to predict ROI for each channel without individual attribution.

•       Created Branch Data Science Guild – focused on educating everyone (esp. non-DS) about data science/machine learning

•       Facilitated and participated in the first Data Science Meetup

(https://www.youtube.com/watch?v=rQAL02Hdkws) 

•       Advised and mentored multiple data scientists

 

Cognoa Inc                                                                                        Palo Alto, CA

Adviser Aug 2016 –2021 

Cognoa is a behavioral health company that develops digital diagnostic and therapeutic products with the goal of enabling earlier and more equitable care for behavioral health conditions.

As an adviser for Cognoa, I assisted in the early work on what would later become their FDA Authorized algorithm for predicting childhood Autism. I advised on improving model accuracy by creating a third class of result corresponding to uncertain or recommend further medical evaluation. I also assisted and advised on techniques for being able to create effective models with extremely small initial labeled data – working directly with their Chief AI officer. This work resulted in multiple medical-journal publications.

Quixey                                                                                    Mountain View, CA

Engineering Fellow : Aug 2011 – May 2016

 

Quixey is an app-search and discovery platform company. Known for functional search and deep-app search and app-mining. With major funding from Alibaba and a strong presence in the Chinese market.

·      Founding advisor – advised the founders, convincing them they could build app search 

·      Responsible for early development of ML relevance model for app-search

·      Developed app search in English, Korean, Spanish, and assisted in Chinese App Search

·      Developed app-to-app similarity technology, launched by Microsoft to convert users from Android/IOS

·      Co-developing early prototype deep-app content search

·      Numerous patents (see below)

·      Developed (and prototyped in Python) knowledge-based query routing engine (sub 1 ms)

·      Designed and lead development of very high-performance knowledge-based query parsing tech < 20 microseconds per query

·      Designed and implemented automated content aggregation system

Intelligent Search Solutions                                        San Francisco, CA

CEO and Co-Founder                                                 Sep 2009 - Aug 2011

 

Intelligent Search Solutions was a small angel funded media search company with an interactive Flash-based UI

 

Designed and mostly implemented back-end search and personalization engine, including autosuggest, search, ranking, autocorrect (domain-specific). Designed large-scale crawling system that refreshed over 1M videos per day.

 

SearchMe                                                                   Mountain View, CA

Principal Scientist/Classification Architect                        Mar 2007 – July 2009

 

SearchMe was a visual multimedia search engine with its own crawler and auto-categorization system for real-time query disambiguation across over 1,000 categories.

 

Key Contributions:

·      Web-Scale Categorization System (CHOCO): Led development of a large-scale, web-based categorization system handling billions of documents across 1,000+ categories, featuring active learning, error checking, and ontology management.

·      Vertical Suggestions & Query Intention Mining: Designed real-time vertical suggestion ranking by leveraging a document-to-category matrix.

·      Multimedia Blending: Developed AI-driven multimedia ranking and indexing, incorporating content from sources like YouTube, Hulu, and Flickr, with automated feed processing and indexing.

·      Competitive Analysis System (TORGO): Created a flexible, web-based tool for competitive analysis and MLR training with scrapers, caching, and a customizable UI.

·      MLR Feature Design: Defined and implemented XPATH/Perl-based system for rapid addition of new features, utilizing structured data.

·      Near-Real-Time Feed Processing (El Rapido): Built a feed processing system for near-instantaneous inclusion of RSS feeds, boosting perceived relevance within three weeks of proposal.

·      Presented project results at board meetings and contributed to strategic business discussions.

 

Ask.com                                                                                                         Edison, NJ 

Research Engineer                                 May 2004 – Feb 2007, Manager Feb 2007 – Mar 2007

 

Ask.com is a leading search engine and mobile ads company, recognized for its expertise in question answering.

Key Contributions:

·      Presenter at NATO MMDSS Conference: Invited to present at NATO's MMDSS conference in Gazzada, Italy (2007), showcasing advancements in search and classification technology.

·      Core Search and Classification Technologies: Developed several high-performance systems used by millions daily.

·      Person Name Classifier: Created a name classification tool with sub-3-microsecond processing.

·      Automatic Smart Answers: Designed a Wikipedia-based answer system covering over 15% of Ask.com's traffic.

·      SQL-Based Question Answering Engine: Demonstrated a question-answering system that effectively handled over half of user-generated questions in sports, specifically baseball.

 

NEC Laboratories America                                                 Princeton, NJ

Research Staff Member                                                          Nov 2002 – Apr

2004

NEC Laboratories America is part of NEC Corporation’s global R&D network, renowned for breakthroughs in fields such as computer science, semiconductors, and AI.

Key Contributions:

·       Enterprise Search Technology: Led a team in developing a modular enterprise search architecture and demonstration system capable of learning new categories in minutes. The system performed category-specific searches across diverse unstructured data sources, showcasing flexibility and adaptability.

·       Prototype Development (Inquirus 2): Created a prototype enterprise search system integrating advanced technologies, including:

o   Rapid category learning through active learning,

o   SVM-based classification with feature selection,

o   Automated query modifications and expansion,

o   Intelligent resource routing and multi-source search capabilities.

 

Education:

 

·      BSE Electrical Engineering, Magna Cum Laude from University of Michigan, Ann Arbor, MI 4/1994

·      MSE Electrical Engineering, University of Michigan, Ann Arbor, MI 5/1997

·      Ph.D. Computer Science Engineering, University of Michigan, Ann Arbor, MI (my dissertation)  8/2001

 

Dissertation:

 

Eric J. Glover, Using Extra-Topical User Preferences to Improve Web-Based Metasearch, Ph.D. Dissertation, University of Michigan, 2001. PDF 

 

Patents:

Please contact for a full list – over 75 issued

 

 

Publications:

 

Halim Abbas,  Ford Garberson,  Eric Glover,  Dennis P Wall, Machine learning approach for early detection of autism by combining questionnaire and home video screening, Journal of the American Medical Informatics Association, Volume 25, Issue 8, 1 August 2018, Pages 1000– 1007, https://doi.org/10.1093/jamia/ocy039

Eric Glover, The "Real World" Web Search Problem, MMDSS NATO Conference, Gazzada, Italy, September 2007. Video Presentation. Please e-mail for an electronic copy of the actual paper.

Eric J. Glover, David M. Pennock, Steve Lawrence, and Robert Krovetz. Inferring hierarchical descriptions, Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM'02), November 2002. PS

David M. Pennock, Sandip Debnath, Eric J. Glover, and C. Lee Giles. Modeling information incorporation in markets with application to detecting and explaining events, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI-2002), pp. 405-413, August 2002. PS | PDF

Eric J. Glover, Kostas Tsioutsiouliklis, Steve Lawrence, David M. Pennock, and Gary W. Flake.

Using web structure for classifying and describing web pages, Proceedings of the Eleventh International World Wide Web Conference, pp. 562-569, May 2002. PS | PDF | HTML

Gary Flake, Eric Glover, Steve Lawrence, C. Lee Giles Extracting Query Modifications from

Nonlinear SVMs , Proceedings of the Eleventh International World Wide Web Conference, May 2002. HTML PS.Z PS.gz PS PDF BibTeX ©

David M. Pennock, Gary W. Flake, Steve Lawrence, Eric J. Glover, and C. Lee Giles. Winners don't take all: Characterizing the competition for links on the web, Proceedings of the National Academy of Sciences (PNAS), Volume 99, Issue 8, pp. 5207-5211, April 2002. PS | PDF | abstract | HTML | more info

Steve Lawrence, David M. Pennock, Gary William Flake, Robert Krovetz, Frans M. Coetzee, Eric Glover, Finn Årup Nielsen, Andries Kruger, and C. Lee Giles. Persistence of web references in scientific research. Computer, 34(2): 26-31, 2001 PS | PDF

Steve Lawrence, Frans Coetzee, Eric Glover, David Pennock, Gary Flake, Finn Nielsen, Robert

Krovetz, Andries Kruger, and C. Lee Giles.  Persistence of Web References in Scientific Research, IEEE Computer, vol 34, no 2, pp 26--31, 2001 

Eric J. Glover, Gary W. Flake, Steve Lawrence, William P. Birmingham, Andries Kruger, C. Lee

Giles, David M. Pennock. Improving Category Specific Web Search by Learning Query Modifications, Symposium on Applications and the Internet, SAINT 2001, San Diego, California, January 8--12, 2001.

Frans Coetzee, Eric Glover, Steve Lawrence, and C. Lee Giles. Feature selection in web applications using ROC inflections. In Symposium on Applications and the Internet, SAINT, San Diego, CA, January 8--12 2001.

Andries Kruger, C. Lee Giles, Frans Coetzee, Eric Glover, Gary Flake, Steve Lawrence, and

Cristian Omlin. DEADLINER: Building a new niche search engine. In Ninth International Conference on Information and Knowledge Management, CIKM 2000, Washington, DC, November 6-- 11 2000.

Eric J. Glover, Steve Lawrence, Michael D. Gordon, William P. Birmingham, C. Lee Giles, "Web Search -- Your Way," Accepted to Communications of the ACM

Eric J. Glover, Steve Lawrence, William P. Birmingham, C. Lee Giles, "Architecture of a

Metasearch Engine that Supports User Information Needs," Eighth International Conference on Information and Knowledge Management (CIKM 99), Kansas City, MO, November, 1999

Eric J. Glover, Steve Lawrence, Michael D. Gordon, William P. Birmingham, C. Lee Giles,

"Recommending Web Documents Based on User Preferences," in ACM SIGIR 99 Workshop on Recommender Systems, Berkeley, CA, August, 1999

E. J. Glover, S.R. Lawrence, K.D. Bollacker, C.L. Giles, W.P. Birmingham, G.W. Flake, "A

Metasearch Engine Architecture That Supports Individual Information Needs," NEC Research Institute Technical Report, TR# 99-063, May 13, 1999

E. J. Glover, W. P. Birmingham, and M. D. Gordon, "Improving Web Search Using Utility

Theory," in Proceedings of the First International Workshop on Web Information and Data Management, WIDM 98. Bethesda, Maryland, 1998

Eric J. Glover, Sunju Park, Anil Arora, Daniel Kiskis and Edmund Durfee, "A case study on the evolution of software tools selection and development in a large-scale multi-agent system," in

Workshop on Software Tools for Developing Agents, AAAI 1998. Madison, WI: AAAI

E. J. Glover and W. P. Birmingham, "Using Decision Theory To Order Documents," in Digital Libraries 98, Pittsburgh, PA, 1998: ACM

D. E. Atkins, W. P. Birmingham, E. H. Durfee, E. J. Glover, T. Mullen, E. A. Rundensteiner, E.

Soloway, J. M. Vidal, R. Wallace, and M. P. Wellman, "Toward Inquiry-Based Education Through Interacting Software Agents," IEEE Computer, vol. 29, pp. 69-76, 1996

Updated November 14, 2024