Kirk Borne, Ph.D.

https://www.dataleadershipgroup.ai/

My services include: 
(1) Speaking at conferences, webinars, events.
(2) Teaching and Training Courses.
(3) Strategy Consulting.
(4) Social Promotions & Engagement for Brands.
(5) Career advising & mentoring.
(6) Content creation (blogs, book chapters, white papers).
(7) Expert Witness.

My areas of expertise include those listed here:
#AI #DataScience #BigData #MachineLearning #DigitalTransformation #IoT #IIoT #AIstrategy #AnalyticsStrategy #DataStrategy #DataLiteracy #DataStorytelling #Analytics
+more

Founder, Data Leadership Group (Data Scientist. Top Influencer. Speaker. Trainer. Consultant)

DataPrime, Inc.

As DataPrime’s inaugural Chief Science Officer and now a member of the DataPrime Advisory Board, I advise on activities and projects related to:
—Building the Data Science Profession for data pros.
—Expanding Data Literacy and Data Fluency across enterprises.
—Accelerating value creation and innovation from data in data-drenched organizations.

Advisory Board Member

Booz Allen Hamilton

I get great pleasure in finding solutions through data. The scientist in me loves to explore data (evidence), find new discoveries, develop a hypothesis to explain it, and then test those theories. As the Principal Data Scientist at Booz Allen Hamilton, I am privileged to exercise that Data Scientist role across numerous internal and external projects: human resources, organizational change, training and mentoring, marketing, customer engagement, behavior analytics, risk mitigation, novelty discovery (including fraud detection, anomaly detection, surprise discovery), thought leadership, socialization of data science across organizations and industries, data technologies (including machine learning, data management innovations, graph analytics), predictive and prescriptive analytics, geospatial-temporal modeling, and much more. I engage with clients within the federal sector, within numerous commercial industries, and with nonprofit organizations. And I enjoy mentoring, working with, and learning from some of the best and brightest young data scientists in the business.

Read more here: "A Growth Hacker's Journey - my mini-history of Data Science"
https://www.mapr.com/blog/growth-hackers-journey-right-place-right-time

Principal Data Scientist and Executive Advisor, First Data Science Fellow at Booz Allen

SYNTASA™

Kirk Borne is the first member of SYNTASA’s Advisory Board. Dr. Borne’s extensive experience in big data and data science, including expertise in scientific data mining and data systems, will help guide SYNTASA in providing focused Marketing Analytic solutions. SYNTASA will leverage the Advisory Board to grow expertise in marketing analytics, data science, predictive modeling, and management of big data sets. The Board will help steer the company on its path in creating big data analytic solutions using open source technology.
http://syntasa.com/president-and-ceo-jay-marwaha-appoints-kirk-d-borne-ph-d-as-first-syntasa-advisory-board-member/

Astroinformatics and Astrostatistics International Organizations

Contributing to the international and national efforts to promote astrostatistics and astroinformatics as robust research disciplines: organizing international research conferences on astrostatistics and astroinformatics, and contributed to the creation of three new international and national organizations:

(1) Vice President, International Astrostatistics and Astroinformatics (IAA) professional society.

(2) Senior Steering Committee member, AAS (American Astronomical Society) Working Group on Astroinformatics and Astrostatistics.

(3) Organizing Committee member, IAU (International Astronomical Union) Working Group in Astrostatistics and Astroinformatics.

In addition... (for 2014-2015):

(4) Secretary, SIAM SIAG/DMA (SIAM Activity Group on Data Mining & Analytics).

Executive Committee Member

ARIES Scientific

ARIES Scientific Inc. is a non-profit 501(c)(3) corporation, dedicated to the advancement of Science Research and STEM Education.

Kirk Borne has advised and consulted with numerous organizations in data science areas, including machine learning, artificial intelligence (AI), data strategy, data mining, and large-scale data management.

Vice President / Astronomer / Data Scientist

George Mason University

Conduct Research, Teach Courses, Advise Students (undergraduate, graduate) in these areas:  Astroinformatics, Data Science, Computational Astrophysics, Astronomy, Computational Science, Data Mining, Informatics, Scientific Databases.

Co-creator of Data Science BS degree program.

For over 30 years, Kirk Borne has studied the dynamics and evolution of galaxies within the context of colliding and merging galaxies, groups and clusters of galaxies, starbursting galaxies, active galaxies, and the mass assembly history of large and small scale structure in the Universe.  

Since 2000, he has developed & applied data mining & data science techniques on large astronomy data collections & sky survey databases, earth science remote sensing data collections, and more.  He is investigating new and existing algorithms for the discovery of patterns, correlations, associations, and outliers (surprises) within highly dimensional astrophysical parameter spaces.  These techniques are being used in several research areas, including: galaxy scaling relations, parameterization of the dynamics and evolution of galaxies and groups of galaxies, characterization and classification of time-varying astronomical events and targets of opportunity, and novelty detection within massive datasets. As part of this research, he is working with citizen science results from the Galaxy Zoo and Galaxy Merger Zoo projects.  

He is investigating research connections with other data-intensive disciplines, to explore opportunities for advancing research in a broad context, through the investigation and application of data science methods (informatics and statistics) in a manner that transcends discipline boundaries.

He is one of the originators of the field of Astroinformatics, which is data science for astronomy research and education.  

He is working with education researchers to develop new pedagogies for science learning that engage students through active experiences using Big Data in the classroom.

Professor of Astrophysics and Computational Science

Soft10 Inc.

Advisory Board member of Soft10 Inc., which brings fast Automatic Predictive Modeling Technology to any business that needs to build, validate, and refine multiple models from their data : http://soft10ware.com/about-us/our-team/

LSST Astroinformatics and Astrostatistics Research Collaboration Team

The Large Synoptic Survey Telescope (LSST) is planned to generate a 20-petabyte science database and a 100-petabyte astronomical image archive -- the analysis of such an enormous data collection requires novel research methods and algorithms.  Our collaboration team consists of astronomers, computer scientists, data mining experts, and statisticians --- we are working on solutions to the LSST petascale data challenges.

I was co-creator of the field of Astroinformatics, which is Data Science for Big Data Astronomy:
1) http://link.springer.com/article/10.1007%2Fs12145-010-0055-2
2) http://arxiv.org/abs/1301.3069
3) http://arxiv.org/abs/0909.3892

I also contributed to the revolution in scientific data mining and data science education, not only in Astronomy:
4) http://link.springer.com/referenceworkentry/10.1007%2F978-94-007-5618-2_9
5) http://arxiv.org/abs/0911.0505

Chairman

LSST Large Synoptic Survey Telescope

Advising the LSST project team regarding the education and public outreach programs, activities, goals, data products, data system design, interfaces, and learning objectives that will support the mission of the LSST and that will be based on the massive petascale scientific database and astronomical imaging data collection that LSST will produce.

Outreach Advisory Board

Worldwide top influencer since 2013. Data Scientist. Global Speaker. Consultant. Astrophysicist. Space Scientist.
Big Data & Data Science advisor, TedX speaker, researcher, blogger, Data Literacy advocate.
Social Reporter for your Data Event...
Speaker Requests for me to speak at your event/organization?...
...Please go here: https://www.chartwellspeakers.com/speaker/kirk-borne/
...or go here: https://www.bigspeak.com/speakers/kirk-borne/
2018-21: #1 Digital (Data Science, Big Data, AI) worldwide top influencer:
... http://ipfconline.fr/blog/2020/11/14/50-top-digital-influencers-to-follow-at-the-end-of-2020-and-in-2021/
2013-present: #1 or #2 or Top 10 influencer in many different categories and lists, including Big Data, Data Science, AI, Machine Learning, IoT, Data Mining, Digital Marketing, Tech Consulting, Data Security, Data Topics Blogging, and more.
2020: Elected Fellow of the American Astronomical Society for lifetime contributions to astronomy and the public's understanding of the Universe.

Data Scientist -- Transdisciplinary data science research, applications, and consulting across a variety of fields -- transcending traditional discipline boundaries.

Specialties: Public / Inspirational / Technical Keynote Speaking. Consulting / Adivising in many disciplines, including Data Science, Machine Learning, Data Mining, Data Analytics, Big Data, X-Informatics (Discovery Informatics, Science Informatics, and more), Scientific databases, Scientific data mining, Computational-X (Computational Science, and more), Astroinformatics** (Data Science for Astronomy), Observational Astronomy (ground-based and space-based), Computational Astrophysics, Higher Education, Science Education and Public Outreach, Project Management, Proposal-writing.

**co-creator of the field of Astroinformatics: see http://www.springerlink.com/content/007642412771162j/ and http://arxiv.org/abs/0909.3892

🛜New release from Packt Publishing ⤵️

"DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM"

💎 Get it at https://amzn.to/4imbryH 💎

🔷Discover DeepSeek's unique traits in the LLM landscape
🔷Compare DeepSeek's multimodal features with leading models
🔷Consume DeepSeek via the official API, Ollama, and llama.cpp
🔷Use DeepSeek for coding, document understanding, and creative ideation
🔷Integrate DeepSeek with third-party platforms like OpenRouter and Cloudflare
🔷Distill and deploy DeepSeek models into production environments
🔷Identify when and where to use DeepSeek
🔷Understand DeepSeek's open philosophy

LinkedIn Top Voice, Thinkers360 Top 25 Overall Thought Leader, Founder of Data Leadership Group (Data Scientist. Top Influencer. Speaker. Trainer. Consultant. Astrophysicist). Advisor to PrimeAI and other AI startups.

Anthony Alcaraz

Amazon Web Services (AWS)

Driving growth in the startup ecosystem by leveraging AWS's cutting-edge AI/ML technologies. My team helps startups accelerate their go-to-market strategies and technological development.

Key focus areas:
- Developing AI/ML startups
- Guiding high-potential startups through AWS's AI/ML offerings
- Fostering partnerships within the EMEA tech community
- Aligning AWS's AI/ML solutions with startup needs

Passionate about empowering the next wave of AI-driven innovation. 

Let's connect and explore how AWS can supercharge your startup's AI/ML journey!

Senior AI/ML Strategist Startups & VC EMEA Strategic Pursuits

Fribl

Advisor

GenAI x HR 
Designed AI strategy
Lead engineering team 

With recruiting topping the list of concerns for many CEOs and companies spending upwards of $4,000 on average per hire, the need for innovation is clear. 

All too often, the arduous recruiting process leaves both employers and applicants frustrated after 42 days of effort with uncertainty if the right match was made...

At Fribl , we are leading the charge to transform this status quo, starting with reinventing the screening process using our proprietary GenAI technology enhanced by symbolic AI. 

This powerful combination doesn't just accelerate hiring, but actually personalizes the experience to precisely match candidates with roles that align to the company's unique culture - a monumental undertaking previously thought impossible.

As innovations like video interviews, CV parsers and AI recommendations shake up recruitment, we remain laser focused on four seismic shifts: Diversity, Advanced Interviewing, Data Analytics and AI Adoption. 

Our mission? To disrupt outdated screening tactics by simplifying the complex, multidimensional analysis of applicant credentials at scale to surface the ideal talents that personify an organization's ethos.

Chief AI/ML Product Officer & Partner

University of Oxford

Guest lecturer to Ajit Joakar Course : Artificial Intelligence: Generative AI, Cloud and MLOps (online)
Subject : the intersection of LLMs and graphs

AI/ML Guest Lecturer

Aldecis

My client portfolio was L’Oreal Finance Transformation 
AI for BI 

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Chief AI/ML Product Officer

CNAF

AI for performance management and customer support

I served as a Senior Executive after a competitive exam (EN3S). 
In this multifaceted role, I spearheaded a range of initiatives across key sectors such as Finance, Fraud Detection, Quality Control, Accounting, and Data & Performance Management.

Signature Project in ML Ops:
Facing the pervasive challenge of managing recurring incoming flows, I took the lead in conceptualizing and executing an avant-garde Proof of Concept (POC). This POC employed Azure ML-based ML Ops solutions, setting a precedent by being the organization's inaugural foray into Machine Learning Operations. The result was a transformative improvement in operational efficiency and service delivery.

Influence on Pluriannual Strategy 2023-2027 :
My pioneering project had a profound impact on the organization's multi-year strategic plan, specifically in the following domains:

Predictive Analytics: My efforts catalyzed the adoption of predictive analytics, enabling the organization to preemptively identify and mitigate challenges in benefits delivery. This proactive methodology revolutionized our service approach.

Sentiment Analysis: The launch of the "Lab Parentalité" was a critical milestone. Through the use of sentiment analysis, we gained unprecedented insights into parent feedback. This initiative not only enriched our data but also helped us better comprehend and meet the needs of parents.

Chatbots & Virtual Assistants: I played a key role in overhauling our customer service paradigm. By integrating cutting-edge chatbots and virtual assistants, we were able to enhance customer engagement across multiple platforms, including phone support, online portals, and mobile apps.

Personalization Algorithms: To elevate the user experience, we incorporated algorithms designed to customize services and recommendations. This bespoke approach significantly increased user satisfaction by tailoring the experience to individual preferences and needs.

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

As AWS's Senior AI/ML Strategist for EMEA Startups, I specialize in helping early-stage AI companies accelerate their development and go-to-market strategies. My expertise spans:

Technical Domains:
- Large Language Models (LLM) and GenAI architectures
- Knowledge Graphs and Vector Spaces
- MLOps and production AI systems
- Multi-agent and agentic AI systems

Strategic Value-Add:
- Technical due diligence for  AI investments
- Architecture optimization for AWS AI/ML services
- GTM strategy development for AI startups
- Deep expertise in evaluating GenAI applications

Recent Achievements:
- Guest lecturer at Oxford on LLMs and graph-based systems
- Led AI strategy for multiple venture-backed startups
- Published thought leadership on agentic AI systems
- Built and scaled ML platforms for enterprise clients

I'm passionate about helping VCs and founders navigate the technical and strategic challenges of building transformative AI companies. Happy to provide technical evaluation, architectural guidance, and strategic insights to help portfolio companies succeed.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Your AI agents can't reason about what they can't structurally represent. ⭕ 

The gap between demos and deployable agents comes down to one thing: knowledge representation strategy.

Here's when knowledge graphs become non-negotiable for agentic systems:

🔗 Multi-hop inference is core to your reasoning tasks

Vector embeddings excel at semantic similarity but collapse on relationship queries. When your agent needs to traverse "Service → DEPENDS_ON → Database → PROTECTED_BY → SecurityGroup" to assess blast radius, you need graph topology. Property graphs give you O(1) relationship traversal. Document stores force you to reconstruct connections at query time.

⚡ You're hitting the relational complexity threshold

The 30-table heuristic is real. Beyond this, JOIN complexity explodes exponentially. More critically: when understanding connections requires traversing 3+ relationship types simultaneously, relational schema optimization becomes the bottleneck. Graph databases are optimized for exactly this access pattern.

🧠 Your agents must handle temporal reasoning and contradictory perspectives

Event-centric schema patterns capture causality. Multi-perspective patterns represent conflicting data sources with confidence scores and provenance. Try modeling "Terraform says X, AWS API says Y, confidence scores differ" in a relational schema without creating a maintenance nightmare.

🔄 Entity resolution across fragmented systems is blocking coherence

When the same entity appears as "Bob Jones" in CRM, "Robert Smith Jr" in fraud detection, and "R.J. Smith" in compliance, your agent needs evidence-based resolution using relationship patterns and contextual validation. Graph neighborhoods provide the evidence structure for identity inference.

⚙️ The three-graph architecture matters for production

Separate domain graph (trusted structured data), lexical graph (unstructured source text), and subject graph (LLM extractions). Entity resolution creates CORRESPONDS_TO edges between them. This prevents extraction artifacts from polluting canonical knowledge while maintaining full provenance.

❌ Skip graphs when:

Simple retrieval suffices, no multi-hop reasoning required, or relationship density is low. RAG with vector search handles these cases at 1/10th the infrastructure complexity.

The technical reality: RDF gives you formal reasoning semantics but slower performance. Property graphs give you traversal speed but require explicit inference logic. Hypergraphs handle n-ary relationships elegantly but lack mature tooling.

Choose based on your reasoning requirements, not architectural trends.

What's your biggest challenge in getting LLMs to reason over complex, interconnected enterprise data?

Senior AI/ML Strategist Startups @AWS | O’Reilly Author | Business Angel

AI agents are economically broken at scale without caching ❌ 

A single user goal triggers 5-10 LLM calls in loops. At 10K daily conversations, you're burning $547K/year on a single workflow pattern.

The obvious fix? Semantic caching with vector embeddings.

The problem? It fails catastrophically for agents.

🔴 Why Semantic Caching Breaks:

Agents execute context-dependent reasoning chains. Each step builds on previous steps.

"Analyze Tesla's Q3 performance" vs "Analyze Tesla's stock price"

Cosine distance: 0.28 → Cache hit 

Actual equivalence: False

Your agent retrieves a financial analysis plan when the user wanted technical specs. 

The entire workflow executes the wrong reasoning path.

Tune your threshold tighter → 5% hit rate (still burning $520K) Tune it looser → 65% hit rate but 40% false positives

The F1 optimization doesn't solve context drift.

🔧 The Architecture That Works:

Hybrid Cache = Vector Speed + Graph Intelligence

Layer 1: Vector DB for fuzzy matching (~30% of redundant queries) 

Layer 2: Knowledge Graph storing reasoning structures, not text
Explicit entity disambiguation (Apple fruit ≠ Apple stock)
Multi-hop traversal for validating context alignment 

Layer 3: Validation gate before serving cached plans

🎯 The Critical Shift: Granularity

Don't cache full responses. Cache atomic sub-queries.
"Compare platforms" decomposes into:
FetchFeatures(Platform A)
FetchPricing(Platform A)
FetchFeatures(Platform B)
CompareAttributes(A, B)

These canonical sub-tasks repeat at 40-70% rates vs 5-15% for full workflows.

The graph stores execution plan templates. 
Vector search finds intent. 

Graph traversal validates the reasoning path applies to current context.

📊 Production Results:

Hit rate: 65% (vs 8% with semantic-only)
Precision: 95% (vs 40% with loose thresholds)
Cost: $547K → $191K annually (65% reduction)
Latency: 60% faster (cold → hot state)

Graph adds 20ms per query, but prevents false positives that would cost 2000ms+ in LLM calls or failed workflows

The Mental Model:

Traditional cache: "Have I seen this text?" 
Semantic cache: "Have I seen something similar?" 
Agentic cache: "Do I know how to solve this class of problem?"

You're not storing what agents said. 
You're storing how agents think.

The cache evolves from a lookup table to a reasoning substrate, the agent's long-term memory of validated strategies.

Agentic needs graphs. 

At scale, the hybrid architecture : the difference between agents that burn capital and agents that generate ROI.

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