• Lead research on AI, Machine Learning, and other technology trends that will impact Fidelity 3 to 5 years out.• Influence leadership with actionable research reports and presentations that shape innovation• Co-leading enterprise-wide AI Ethics Business and Market-Focused Knowledge Group• Organized enterprise-wide No-Code Challenge for no-code tool exploration with over 700 participants• Mentor for Fidelity's intrapreneurship program• Intrapreneur for Fidelity's intrapreneurship program, exploring a new flexible work paradigm• Media Mentions: (links below with some of my articles)- CNBC article on a Fast Company Innovation Festival 2023 talk (Sep 2023)- Marketplace article on AI and investing (Aug 2023)- VentureBeat articles summarizing a VentureBeat Transform 2023 talk. (Jul 2023)- Barron’s article on trends that will change how people invest (Jun 2023) - Bloomberg TV (Apr 2023)- Bloomberg Radio (starting at 33:00) (Apr 2023)- Bloomberg article on AI and wealth management (Apr 2023)- The Wall Street Journal on ChatGPT (Dec 2022)

• AI Advisor for ChalkSense's RegulatoryGenie initiative• Provide Natural Language Processing related literature reviews• Develop testing frameworks to benchmark the startup’s tools against industry standard NLP and ML algorithms and tools• Participate in technology design and implementation decisions• Present AI educational and training programs to alpha-testers and institutions

- Head of FactSet's global 30-person Machine Learning Team, which specializes in Information Extraction and Classification, Intelligent Searching, and Predictive Analytics- Chair of FactSet's Machine Learning Steering Committee- Strategic evaluations of third party machine learning technologies and companies- Collaboration with C-suite executives - Presentations/trainings to FactSet employees at all levels to explain machine learning concepts and the power of Machine Learning/AI- Incorporated internal ML solutions to reduce operating costs by 50% for some of our content collection efforts- Increased operations productivity by 35% through strategic third party machine learning partnerships

Managed FactSet's 20 person Machine Learning and Language Technology TeamChair of FactSet's Machine Learning Steering Committee

Managed FactSet's 20-person Machine Learning and Language Technology Team, which specializes in Natural Language Processing, Machine Translation, and Machine LearningExpanded scope of team to new areas of Machine Learning

• Managed a team of Natural Language Processing/Machine Learning Engineers• Technical Lead for NLP for all research reports related projects• Presented on Machine Learning and Natural Language Processing to engineers at FactSet • Received department recognition for code reviewing abilities

• Led and co-developed first FactSet Machine Learning project to create automated preview content for research reports from 1000+ contributors• Led project to parse MS Excel financial models• Wrote code to extract information from research reports

1.Incorporated Sphinx-4 for speech recognition for a calendar system developed by the NLP group at Stony Brook2.Helped with transcriptions and verification of a speech-based rate-a-course system

Course: Introduction to Computer Science

1. Automatically extracted corporate subsidiary information from corporate reports to be inserted into a database or analyzed as a graph2. Created database tables and queries for querying the subsidiary information

Course: Business Statistics I

1. Co-developed a site for retrieval of client information2. Created and maintained a contact management system by customizing Outlook contact and appointment forms3. Optimized stored procedures for website and contact management system improving response time by up to 50%4. Generated test scripts with LoadRunner to assess application load5. Conducted technical support for facilitating company-wide reorganization following Sept 11, 2001 while going through training

Using XRunner, a TSL-based automated testing tool, tested the Broker Booth Support System, discovering approximately three fatal errors per release

1. Reconciled New York information on census data with information in central offices2. Automated benefit calculations for pension plans of various companies, improving response time by hours over manual procedure