CompaniesLinkedIn
Product category (jev): unsure; best guess “Other software” at 45% confidence, so it isn't used in filters or charts
Org type (jev): End-user enterprise 62% confident
Also listed as: LinkedIN · LinkedIn AI · LinkedIn Local Los Angeles · LinkedIn focused on Big Data · Linkedin
Conferences sponsored9
Events sponsored19
Speaker slots127101 distinct people
Conferences present at32sponsor or speaker
Years active2019–2027
Circuit mix
Where LinkedIn shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Research24%14 events
- Software engineering & trading tech16%16 events
- Modern data stack & practitioner15%13 events
- Open-source data infrastructure13%12 events
- Streaming13%13 events
- AI engineering & MLOps7%6 events
- Python & R data science7%4 events
- Enterprise, analyst & AI expos4%4 events
- Vendor flagships & roadshows2%1 event
- Cloud providers1%1 event
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
16
6
3
7
7
11
11
8
1
Distinct speakers
People from this company on a program that year, each counted once.
16
7
2
8
22
20
22
20
1
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled
5
1
2
3
2
2
4
Where they've grown or pulled back
UpSponsored 4 conferences in 2026, up from 3 in 2022.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. Tiers use the best tier each year; swatches show the path, oldest first. A conference counts as pulled out only when a later event published a complete sponsor list, since most events don’t say whether their list is complete.
New 2
First sponsored in 2025 or later.
- AI Engineer (World's Fair, Summit and regional editions)first sponsored 2026, unranked tier
- Iceberg Summitfirst sponsored 2026, Bronze
Moved up 2
Best tier rose between the first and latest year with a ranked tier.
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)Gold 2019 → Platinum 2026
- NeurIPS (Conference on Neural Information Processing Systems)Silver 2024 → Platinum 2026
Moved down 2
Best tier fell between the first and latest year with a ranked tier.
- PyCon (national Python conferences)Gold 2019 → Bronze 2021
- VLDB (International Conference on Very Large Data Bases)Platinum 2019 → Gold 2022
Pulled out 1
Not sponsored since 2024 or earlier, though a later event published a complete sponsor list without them.
- Data Con LAlast sponsored 2019; the conference ran again in 2020–26
Once or on and off 2
No clear direction, or a gap we can’t confirm because later sponsor lists are incomplete.
- Data Councilonce, 2022, Bronze
- Tableau Conferenceonce, 2019, Bronze
Lookalikes
Companies with the most similar conference footprint: they sponsor and speak at the same conferences. Bars are cosine similarity, from 0 (nothing shared) to 1 (identical).
11 Cluster: NeurIPS · ACM SIGKDD circuit →
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 19 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| 15 | 79% | |
| Amazon Web Services | 14 | 74% |
| Microsoft | 13 | 68% |
| Meta | 12 | 63% |
| Two Sigma | 12 | 63% |
| Bloomberg | 11 | 58% |
| Capital One | 11 | 58% |
| Alibaba Cloud | 10 | 53% |
| Apple | 9 | 47% |
| Netflix | 9 | 47% |
| Springer Nature | 9 | 47% |
| Baidu | 8 | 42% |
| Salesforce | 8 | 42% |
| Snowflake | 8 | 42% |
| Ant Group | 7 | 37% |
Neighborhood: companies with nearby conference footprints
LinkedIn (ringed) and the 30 companies closest to it on the company map. Close together means they sponsor and speak at many of the same conferences. Distances are only meaningful locally; the axes have no meaning. Dot size: conferences present at. Color: cluster.
Sponsorships 19
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| NeurIPS 2026 (Sydney) | Dec 6–12, 2026 | Sydney, Australia | Platinum | Platinum |
| KDD 2026 (32nd ACM SIGKDD Conference) | Aug 9–13, 2026 | Jeju, South Korea | Platinum | Platinum |
| AI Engineer World's Fair 2026 | Jun 29, 2026 – Jul 2, 2026 | San Francisco, United States | Supporting Partners | Other |
| Iceberg Summit 2026 | Apr 8–9, 2026 | San Francisco, United States | Bronze | Bronze |
| NeurIPS 2025 (San Diego) | Dec 2–7, 2025 | San Diego, United States | Gold | Gold |
| KDD 2025 (31st ACM SIGKDD Conference) | Aug 3–7, 2025 | Toronto, Canada | Platinum | Platinum |
| NeurIPS 2024 | Dec 10–15, 2024 | Vancouver, Canada | Silver | Silver |
| KDD 2024 (30th ACM SIGKDD Conference) | Aug 25–29, 2024 | Barcelona, Spain | Silver | Silver |
| VLDB 2022 (48th) | Sep 5–9, 2022 | Sydney, Australia | Gold | Gold |
| KDD 2022 (28th ACM SIGKDD Conference) | Aug 14–18, 2022 | Washington, United States | Gold | Gold |
| Data Council Austin 2022 | Mar 23–24, 2022 | Austin, United States | Bronze | Bronze |
| KDD 2021 (virtual, Singapore) | Aug 14–18, 2021 | Singapore, Singapore | Gold | Gold |
| PyCon US 2021 (Online) | May 12–18, 2021 | — | Supporting | Bronze |
| PyCon US 2020 (Online) | Apr 15–23, 2020 | — | Platinum | Platinum |
| Tableau Conference 2019 (TC19) | Nov 12–15, 2019 | Las Vegas, United States | Kilobyte | Bronze |
| VLDB 2019 (45th) | Aug 26–30, 2019 | Los Angeles, United States | Platinum | Platinum |
| Data Con LA 2019 | Aug 17, 2019 | Los Angeles, United States | Partner Sponsors | Community |
| KDD 2019 (25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) | Aug 4–8, 2019 | Anchorage, United States | Gold | Gold |
| PyCon US 2019 | May 1–9, 2019 | Cleveland, United States | Gold | Gold |
Speakers 127
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Alborz Geramifard | Data Innovation Summit 2027 | featured | — | — |
| Min Chen | QCon AI New York 2026 | speaker | High-Signal AI Code Review That Adapts to Your Codebase at Scale | — |
| Ronak Nathani | QCon AI New York 2026 | speaker | Inside LinkedIn's Kubernetes Ops Agent: Skills, Tools, and Guardrails | — |
| Weiqing Yang | AWS re:Invent 2026 | lightning talk | Beyond chatbots: Event-driven AI agents on Apache Flink | — |
| Ronak Nathani | QCon San Francisco 2026 | speaker | Beyond the Kubernetes API: Building LinkedIn’s Compute Platform | — |
| Sanjay Singh | QCon San Francisco 2026 | speaker | When Everyone Can Code, Who Owns Production? | — |
| Sudarshan Srinivasa Ramanujam | QCon San Francisco 2026 | speaker | Bridging Retrieval and Ranking: From LLM Embeddings and Graph Features to a GPU-Served DLRM | — |
| Pooja Pal | Airflow Summit 2026 | talk | Stabilizing LinkedIn Continuous Deployment on Airflow | Video |
| Sandeep Dhillon | Airflow Summit 2026 | talk | Airflow Autopilot: The Generate-Verify-Refine Loop That Makes Pipeline Authoring Truly AI-Native | Slides · Video |
| Wensi Hu | Airflow Summit 2026 | talk | Stabilizing LinkedIn Continuous Deployment on Airflow | Video |
| Yifan (Stefan) Wang | Airflow Summit 2026 | talk | Airflow Autopilot: The Generate-Verify-Refine Loop That Makes Pipeline Authoring Truly AI-Native | Slides · Video |
| Sakshi Jain | KDD 2026 (32nd ACM SIGKDD Conference) | ADS invited talk | — | — |
| Ajay Prakash | AI Engineer World's Fair 2026 | speaker | 500 Skills, Zero Fine-Tuning: LinkedIn's Playbook for AI Agents That Actually Know Your Codebase | — |
| Ajay Prakash | QCon AI Boston 2026 | speaker | Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP | — |
| Sundara Raman Ramachandran | QCon AI Boston 2026 | speaker | Scaling LLM-Based Ranking Systems for Latency-Critical Search & Recommendation Workloads | — |
| Ankur Gupta | Budapest Data+AI Forum 2026 | speaker | Why ML Deployments Fail in Production and How to Engineer Reliability at Scale | — |
| Abhishek Das | Iceberg Summit 2026 | speaker | Live Storage Rebalancing of Iceberg Tables at Exabyte Scale | Video |
| Aditya Gupta | Iceberg Summit 2026 | speaker | The Invisible Switch: Zero-Downtime Hive to Iceberg Migrations via Views | Video |
| Brian DeWilde | Iceberg Summit 2026 | speaker | Live Storage Rebalancing of Iceberg Tables at Exabyte Scale | Video |
| Manak Pandey | Iceberg Summit 2026 | speaker | What Actually Breaks in Zero-Copy Iceberg Migrations (and How We Operated Through It) | Video |
| Prateek Agarwal | Iceberg Summit 2026 | speaker | The Invisible Switch: Zero-Downtime Hive to Iceberg Migrations via Views | Video |
| Walaa Moustafa | Iceberg Summit 2026 | speaker | Materialized Views in Iceberg: Towards a Portable, Engine-Agnostic Specification | Video |
| Karthik Ramgopal | QCon London 2026 | speaker | Beyond Context Windows: Building Cognitive Memory for AI Agents | — |
| Karthik Ramgopal | QCon AI New York 2025 | speaker | Platform Teams Enabling AI - MCP/Multi-Agentic Tools Across Linkedin | — |
| Prince Valluri | QCon AI New York 2025 | speaker | Platform Teams Enabling AI - MCP/Multi-Agentic Tools Across Linkedin | — |
| Nishant Lakshmikanth | QCon San Francisco 2025 | speaker | Modernizing Relevance at Scale: LinkedIn’s Migration Journey to Serve Billions of Users | — |
| Sayantan Ghosh | Data Con LA 2025 | speaker | Data Debt : The Hidden Malaise Impacting Your Business | — |
| Archit Goyal | Flink Forward Barcelona 2025 | speaker | Charting New Territory: LinkedIns Early Bet on Flink Batch for Large-Scale Workloads | — |
| Pravesh Gupta | Flink Forward Barcelona 2025 | speaker | Powering Stateful Joins at Scale with Flink SQL at LinkedIn | — |
| Venkata Krishnan Sowrirajan | Flink Forward Barcelona 2025 | speaker | Charting New Territory: LinkedIns Early Bet on Flink Batch for Large-Scale Workloads | — |
| Weiqing Yang | Flink Forward Barcelona 2025 | speaker | Powering Stateful Joins at Scale with Flink SQL at LinkedIn | — |
| Arthur Chen | Airflow Summit 2025 | talk | Lessons learned from migrating to Airflow @ LinkedIn | Slides · Video |
| Arun Kumar | Airflow Summit 2025 | talk | Linkedin's journey on scaling Airflow | Video |
| Rahul Gade | Airflow Summit 2025 | talk | Linkedin's journey on scaling Airflow | Video |
| Trevor DeVore | Airflow Summit 2025 | talk | Lessons learned from migrating to Airflow @ LinkedIn | Slides · Video |
| Yifan (Stefan) Wang | Airflow Summit 2025 | talk | DAGnostics: Shift-Left Airflow Governance with Policy Enforcement Framework | Slides · Video |
| Onur Karaman | Data Streaming Summit San Francisco 2025 | keynote | Data Streaming Summit Keynote | Video |
| Deepak Agarwal | KDD 2025 (31st ACM SIGKDD Conference) | ADS invited talk | — | — |
| Sanil Jain | Flink Forward Asia Singapore 2025 | speaker | Secret Sauce of Stream & Batch Convergence at Linkedin | — |
| Venkata krishnan Sowrirajan | Flink Forward Asia Singapore 2025 | speaker | Charting New Territory: LinkedIn’s Early Bet on Flink Batch for Large-Scale Workloads | — |
| Hamed Firooz | AI Engineer World's Fair 2025 | speaker | 360Brew LLM-based Foundation Model for Personalized Ranking and Recommendation | Video |
| Maziar Sanjabi | AI Engineer World's Fair 2025 | speaker | 360Brew LLM-based Foundation Model for Personalized Ranking and Recommendation | Video |
| Daniel Olmedilla | Data Council 2025 | talk | Guardrails for the Future: AI Safety and Responsible AI in Practice | — |
| Sumedh Sakdeo | Data Council 2025 | talk | Optimizing Iceberg Table Layouts at Scale: A Multi-Objective Approach | — |
| Daniel Olmedilla | Data & AI Warsaw Tech Summit 2025 | speaker | Building LinkedIn member trust in the new age of Generative AI | — |
| Sumedh Sakdeo | Iceberg Summit 2025 | speaker | Optimizing Iceberg Table Layouts at Scale: A Multi-Objective Approach | Video |
| Daniel Hewlett | QCon London 2025 | speaker | Lessons Learned From Building LinkedIn’s First Agent: Hiring Assistant | — |
| Karthik Ramgopal | QCon London 2025 | speaker | Lessons Learned From Building LinkedIn’s First Agent: Hiring Assistant | — |
| Xiaofeng Wang | AI Engineer Summit 2025 (New York) | leadership track | Lessons from Building LinkedIn's GenAI Platform | Video |
| Albert Chen | Trino Summit 2024 | speaker | Empowering self-serve data analytics with a text-to-SQL assistant at LinkedIn | Slides · Video |
| Gaurav Ahlawat | Trino Summit 2024 | speaker | Empowering self-serve data analytics with a text-to-SQL assistant at LinkedIn | Slides · Video |
| Manas Bundele | Trino Summit 2024 | speaker | Empowering self-serve data analytics with a text-to-SQL assistant at LinkedIn | Slides · Video |
| Pratham Desai | Trino Summit 2024 | speaker | Hassle-free dynamic policy enforcement in Trino | Slides · Video |
| Ramanathan Ramu | Trino Summit 2024 | speaker | Hassle-free dynamic policy enforcement in Trino | Slides · Video |
| Jiangjie (Becket) Qin | QCon San Francisco 2024 | speaker | Stream and Batch Processing Convergence in Apache Flink | — |
| Dre Olgiati | 5th Annual MLOps World and Generative AI World Conference 2024 | speaker | Large Language Model Training and Serving at LinkedIn | — |
| Keshav Tyagi | Airflow Summit 2024 | talk | Linkedin's Continuous Deployment | Slides · Video |
| Rahul Gade | Airflow Summit 2024 | talk | Linkedin's Continuous Deployment | Slides · Video |
| Jinjing Bi | Beam Summit 2024 | speaker | Usage Billing with BEAM @ LinkedIn | Slides · Video |
| Hao Xu | SIGMOD/PODS 2024 | industry paper | Bouncer: Admission Control with Response Time Objectives for Low-latency Online Data Systems | — |
| Aastha Agrrawal | Iceberg Summit 2024 | speaker | Harnessing Coral and Iceberg for Advanced Incremental View Maintenance | Video |
| Walaa Eldin Moustafa | Iceberg Summit 2024 | speaker | Harnessing Coral and Iceberg for Advanced Incremental View Maintenance | Video |
| Alex Gorelik | Data Innovation Summit 2024 | featured | — | — |
| Felix GV | QCon London 2024 | speaker | Lessons Learned From Building LinkedIn’s AI Data Platform | — |
| Karthik Ramgopal | QCon London 2024 | speaker | gRPC Migration Automation at LinkedIn | — |
| Min Chen | QCon London 2024 | speaker | gRPC Migration Automation at LinkedIn | — |
| Daniel Olmedilla | Data Council Austin 2024 | talk | Building Responsible and Trustworthy Generative AI Products at LinkedIn | — |
| Ryanne Dolan | Data Council Austin 2024 | talk | Is Kubernetes a Database? | — |
| Raghavan Muthuregunathan | Data Innovation Summit APAC 2024 | speaker | — | — |
| Ramanathan Ramu | Trino Summit 2023 | speaker | Trino upgrade at exabytes scale | Video |
| Kapil Surlaker | Open Source Data Summit 2023 | panelist | Panel: The growing role of open source technology in today's data architectures | Video |
| Becket Qin | Flink Forward Seattle 2023 | keynote | The Vision and Practice of Stream and Batch Unification - LinkedIn's Story | — |
| Weiqing Yang | Flink Forward Seattle 2023 | speaker | Powering Stateful Applications with Managed Flink SQL Platform at LinkedIn | — |
| Jia Guo | Community Over Code North America 2023 | speaker | Intelligent Query Routing And Runtime Query Killing in Apache Pinot | Slides |
| Vivek Iyer | Community Over Code North America 2023 | speaker | Intelligent Query Routing And Runtime Query Killing in Apache Pinot | Slides |
| Alex Dubrouski | QCon San Francisco 2023 | speaker | The Journey to a Million Ops / Sec / Node in Venice | — |
| Gaojie Liu | QCon San Francisco 2023 | speaker | The Journey to a Million Ops / Sec / Node in Venice | — |
| Heather McKelvey | QCon San Francisco 2023 | keynote | Mission, Culture, and Values: Using Them to Guide Your Company Through Good and Challenging Times | — |
| Scott Meyer | QCon San Francisco 2023 | speaker | LIquid: A Large-Scale Relational Graph Database | — |
| Ali Naqvi | Ray Summit 2023 | speaker | Inference Graphs at Linkedin Using Ray-Serve | — |
| Frank Gu | Ray Summit 2023 | speaker | Hyperparameter Tuning with Ray[Tune] for Next-Gen training platform at LinkedIn | — |
| Mingyuan Tian | Ray Summit 2023 | speaker | Hyperparameter Tuning with Ray[Tune] for Next-Gen training platform at LinkedIn | — |
| Sasha Ovsankin | Ray Summit 2023 | speaker | Inference Graphs at Linkedin Using Ray-Serve | — |
| Wei-Yu Yen | Ray Summit 2023 | speaker | Hyperparameter Tuning with Ray[Tune] for Next-Gen training platform at LinkedIn | — |
| Ya Xu | Ray Summit 2023 | keynote | — | — |
| David Shao | Beam Summit 2023 | speaker | Power Realtime Machine Learning Feature Engineering with Managed Beam at LinkedIn | Video |
| Mathew Teoh | QCon New York 2023 | speaker | Needle in a 930M Member Haystack: People Search AI @LinkedIn | — |
| Prateek Maheshwari | Beam Summit 2023 | speaker | Managed Stream Processing through Apache Beam at LinkedIn | Slides · Video |
| Xinyu Liu | Beam Summit 2023 | speaker | Managed Stream Processing through Apache Beam at LinkedIn | Slides · Video |
| Yanan Hao | Beam Summit 2023 | speaker | Power Realtime Machine Learning Feature Engineering with Managed Beam at LinkedIn | Video |
| Felix GV | QCon London 2023 | speaker | What is Derived Data? (And do You Already Have Any?) | — |
| Akhilesh Gupta | QCon San Francisco 2022 | speaker | The Secret to Finding Impactful Projects to Land a Staff-Plus Engineer Role | — |
| Akhilesh Gupta | QCon San Francisco 2022 | speaker | The Staff-Plus Engineer Path Panel | — |
| Keerthi Selvaraj | KDD 2022 (28th ACM SIGKDD Conference) | ADS invited talk | — | — |
| Sonam Mandal | Flink Forward San Francisco 2022 | speaker | Building a fully managed stream processing platform on Flink at scale for LinkedIn | — |
| Weiqing Yang | Flink Forward San Francisco 2022 | speaker | Building a fully managed stream processing platform on Flink at scale for LinkedIn | — |
| Yixing Zhang | Flink Forward San Francisco 2022 | speaker | Building a fully managed stream processing platform on Flink at scale for LinkedIn | — |
| Shangjin Zhang | Beam Summit 2022 | speaker | Unified Streaming and Batch Pipelines at LinkedIn using Beam | Slides · Video |
| Yuhong Cheng | Beam Summit 2022 | speaker | Unified Streaming and Batch Pipelines at LinkedIn using Beam | Slides · Video |
| Josh Shinavier | Data Day Texas 2022 | panelist | Panel: How do you scale a knowledge Graph when there is no consensus | — |
| Josh Shinavier | Data Day Texas 2022 | speaker | Transpilers Gone Wild - announcing Hydra | — |
| Akshay Rai | Trino Summit 2021 | speaker | Trino at LinkedIn | — |
| Jithesh Thekkarikottil Rajan | Trino Summit 2021 | speaker | Trino at LinkedIn | — |
| Renu Tewari | Flink Forward Global 2020 | keynote | Stream Processing at Linkedin: Then and Now | — |
| Kishore Gopalakrishna | PrestoCon 2020 | panelist | Panel Discussion: The Presto Ecosystem | — |
| Mars Lan | Budapest Data Forum 2020 | speaker | DataHub: Powering LinkedIn's Metadata | — |
| Daniel Chen | Beam Digital Summit 2020 | speaker | Scaling Python Portable Pipelines in Linkedin | Video |
| Becket Qin | Flink Forward Virtual Conference 2020 | speaker | AI made easy with Flink + AIFlow | — |
| Akhilesh Gupta | QCon London 2020 | speaker | Streaming a Million likes/second: Real-time Interactions on Live Video | Video |
| Hien Luu | QCon London 2020 | speaker | Speeding Up ML Development with MLFlow | — |
| Avik Das | PyData Los Angeles 2019 | speaker | Dynamic programming for machine learning: Hidden Markov Models | Video |
| Michael Kehoe | QCon San Francisco 2019 | speaker | Building Production-Ready Applications | Video |
| Zhe Zhang | O'Reilly Artificial Intelligence Conference London 2019 | keynote | Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond | — |
| Hai Lu | Beam Summit North America 2019 | speaker | Samza Portable Runner for Beam | Video |
| Romer Rosales-Delmoral | KDD 2019 (25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) | ADS invited talk | Product Ecosystem Optimization at LinkedIn | — |
| Ya Xu | KDD 2019 (25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) | ADS invited talk | Data Science Challenges @LinkedIn | — |
| Andrew Carter | SIGMOD/PODS 2019 | industry paper | Nanosecond Indexing of Graph Data With Hash Maps and VLists | — |
| Celia Kung | QCon New York 2019 | speaker | A Dive Into Streams @LinkedIn With Brooklin | — |
| Hien Luu | QCon New York 2019 | panelist | Panel: ML for Developers/SWEs | — |
| Walaa Eldin Moustafa | Presto Summit 2019 | speaker | Transport UDFs and Presto | Slides |
| Xinyu Liu | Beam Summit Europe 2019 | speaker | Streaming Pipelines at Scale with Apache Beam and Samza | Video |
| Szilard Pafka | Berlin Buzzwords 2019 | talk | Better than Deep Learning: Gradient Boosting Machines (GBM) | Slides · Video |
| Anthony Hsu | DataWorks Summit Washington, DC 2019 | speaker | Scaling Deep Learning on Hadoop at LinkedIn | — |
| krishnan raman | DataWorks Summit Washington, DC 2019 | speaker | Obfuscating LinkedIn Member Data | — |
| Krishnan Raman | Data Summit 2019 | speaker | Tackling Data Ingestion Challenges at LinkedIn With Apache Gobblin | — |
| Durgam Vahia | Data Council San Francisco 2019 | talk | Building a Data-Powered Sales Intelligence Platform | — |
| Ahmed H Metwally | ICDE 2019 (35th IEEE International Conference on Data Engineering) | industry paper | Scalable Similarity Joins of Tokenized Strings | — |