Vin Vashishta

AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

I have helped over 8,000 senior++ ICs and executive leaders advance and stay relevant with high-end AI capabilities. Courses & Certifications: https://datascience.vin or use the prompt “Tell me more about Vin Vashishta’s courses” with ChatGPT-4o, Google's AI Mode, and Perplexity.

Upskill your workforce to be AI-ready in 3 months: seminars@v2ds.com

Conference talks, brand partnerships, and thought leadership: partnerships@vinvashishta.com

Over 85% of AI projects fail to deliver a return on investment because they start with technology, not a business problem. I’m fixing that by empowering companies and training people to stop experimenting with AI and start monetizing it.

Define, align, and manage the business and technical transformations that turn C-level AI visions into products, productivity, and revenue. Since 2012, V Squared AI has worked with large enterprises to deliver over $3.7 billion in value.

My frameworks are built from working in the trenches. My practitioner-to-strategist journey allows me to bridge the critical gap between technical teams and the boardroom, ensuring that AI initiatives are not only technically sound but commercially viable.

Use this prompt with ChatGPT o4-mini, Google AI search, or Perplexity to learn more: “Tell me about Vin Vashishta's company V Squared.”

Partners: Dell Pro Max Ambassador & Gartner Ambassador.

Speaker’s Bio:

Vin Vashishta is an award-winning AI thought leader, best-selling author of ‘From Data to Profit,’ 3X founder with a successful exit, and CEO of V Squared AI, one of the first pure play data and AI consulting firms. The Wall Street Journal, Fast Company, and The Information have showcased Vin’s AI expertise.

He brings 30 years of experience in leadership, technical strategy, and hands-on technical product delivery. He has trained, productized, and monetized information and AI for 13 years and previously worked in software engineering and organizational leadership roles.

Founder & AI Advisor

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

Mercury Data Science

Product strategy and execution across multiple AI platforms serving customers in healthcare and the life sciences.

VP of Platforms

Teknoir

Developed a customer-centric data science strategy to address key needs in the areas of process automation, workplace safety, and sustainability.

Organized and executed sales and marketing campaigns to expand business and gather customer insights to identify new opportunities and focus areas.

Collaborated with other executives to assess and refine product-market fit through a customer feedback and category design lens.

Developed scopes of work for edge AI projects spanning use cases across multiple industrial application areas at the intersection of hardware and software.

Developed a strategic hiring plan and ran recruitment efforts to grow our technical and product teams.

Chief Data Scientist

Developed a collection of click-to-train and -deploy AI applications that run on resource-constrained devices to address various customer use cases in the field. Applications include: 
- People tracking (based on BlazePose, YOLOv7 Pose)
- Face blurring (based on BlazeFace)
- Personal protective equipment detection (based on YOLOv7)
- Vibration analysis and anomaly detection (Custom Autoencoder)
- Object segmentation (Based on Mask R-CNN)

Developed monitoring and alerting logic for Health Safety and Environmental (HSE) applications to help customers mitigate risk and promote safe, sustainable work environments.

Architected continuous learning solutions for deployed IoT devices that leverage advanced stream processing techniques to dynamically update running applications based on changes in their surroundings.

Director of Data Science

Created SOWs and corresponding project plans to expand opportunities for MDS and its clients. 

Collaborated with clients to develop analytics strategies and solution architectures that answer critical questions and drive business value across a wide variety of domains. 

Managed teams of data scientists, data engineers, and machine learning engineers in the development of full-stack, end-to-end machine learning applications with an emphasis on neuroscience and clinical trial analytics. 

Architected and oversaw the following software development initiatives:
- A MLOps Platform based on open-source tooling to standardize company-wide data science development and deployment practices.
- A scalable workflow for multimodal video analytics to help clients better understand human behavior and health.
- A scalable, knowledge graph-baed NLP platform for parsing and analyzing millions of academic papers to help end users identify novel drug targets among other use cases.

Principal Data Scientist, Director of Technology 

Led internal and client-facing projects that drive business value. 

Built MLOps infrastructure to support data science projects from conception through production and enforce best practices. 

Advised on and contributed to projects involving cloud infrastructure & MLOps, image & signal processing, biophysics, and video analytics. 

Worked hands-on with data science teams to develop and validate ML models across a wide variety of domains and applications.

Planned and managed R&D initiatives and fellowship opportunities to expand company capabilities and expertise including:
- ML model bias mitigation
- Emotion recognition based on voice and video datasets, and 
- MLOps product market research

Senior Data Scientist

Planned, constructed, and delivered transformative machine learning solutions across multiple industries with a particular emphasis on the consumer packaged goods (CPG) vertical.

Projects required experience in many areas including:
- Computer Vision, 
- Natural Language Processing,
- Dimension reduction and unsupervised clustering techniques, and 
- Time series analysis and classification techniques.

Contributed to preliminary project research and helped estimate project timelines.

Data Scientist

Gulf Coast Consortia - Research

[Project 1: Specimen Motion Correction]

Obtained transmission electron microscopy (TEM) data from state-of-the-art direct detection devices (DDD) that capture high-resolution images at high frame rates, enabling the measurement and correction of specimen motion during imaging.

Performed research to understand specimen drift in electron microscopes at cryogenic temperatures with the objective of minimizing motion blurring in collected data to obtain the highest resolution information possible.

Developed drift analysis and correction routines and comparatively analyzed widely-used drift correction algorithms. 

[Project 2: Protein-Lipid Nanodiscs]

Performed biophysics research on synthetic protein-lipid nanodiscs and high-density lipoproteins (HDL) to understand how their structure relates to heart disease states and how these particles could be used as vehicles for drug delivery.

Used image and signal processing techniques combined with mathematical modeling and machine learning to measure individual particle geometry and investigate observable differences.

Houston Area Molecular Biophysics (HAMBP) Fellow

Baylor College of Medicine

Developed algorithms and high-throughput workflows for transforming terabyte-scale electron cryo-microscopy (cryoEM) datasets into 3D protein structures via "Single Particle Analysis" techniques. 

Performed synthetic biology research to better understand the structure of protein-lipid nanodiscs.

Collaboratively developed machine learning and computer vision workflows for the detection and segmentation of macromolecular structures and cellular features in 3D electron cryo-tomography (cryoET) datasets.

Graduate Researcher

Provided guidance to students in context of the following courses:
- Introduction to Programming for Scientists
- Macromolecular Structures and Interactions
- Computational Mathematics for Quantitative Biomedicine

Graduate Teaching Assistant - Computational Mathematics and Biophysics

Millsaps College

Developed a series of over 200 lecture videos

Lectured about the theories of special and general relativity for an introductory astronomy course

Presented gravitational wave research at university-sponsored symposia

Ford Teaching Fellow - Physics Curriculum Development

Nikhef

Collaborated with NIKHEF scientists as part of a NSF IREU grant via the University of Florida

Worked on algorithms that analyze gravitational wave signals from neutron stars and black holes

Developed software to distribute gravitational wave simulations onto high-performance computing clusters and analyzed the results.

International REU Researcher - Gravitational Waves

I am passionate about collaborating across technical and business teams to conceptualize, build, and deliver AI-driven products that address challenging problems with a positive social impact.

I have engaged in numerous aspects of AI solution development, from R&D to scaling deployments and developing products. I have worked with companies of all sizes and at varying levels of technical maturity to design and develop AI solutions, and I know what it takes to integrate AI into real-world applications.

I have a PhD in Quantitative and Computational Biosciences from Baylor College of Medicine. My research focused on the creation and optimization of high throughput computer vision workflows to process large amounts of data to study protein structures and other biological entities.

I currently serve as VP of Platforms at Mercury Data Science where I am responsible for product strategy and execution.

Prior to this, I served as Chief Data Scientist at Teknoir where I guide AI strategy and the architecture and development of turn-key operational AI solutions that help our customers enhance workplace safety and productivity.

And prior to Teknoir, I worked as a Principal Consultant at Mercury Data Science (MDS) where I led projects spanning multimodal AI, bias mitigation for facial recognition models, and the architecture and development of platforms for MLOps, biomarker discovery, and biomedical research.

LEADERSHIP STYLE ►
• I serve as a thought partner to establish and continually refine practical data and AI strategies based on market research and relevant technology.
• I work to foster an encouraging, collaborative atmosphere built on a foundation of trust and respect.
• I coach technical (and non-technical) teams to address challenges at the intersection of business, technology, data, and stakeholder needs.

SKILLS & EXPERTISE ►
• I am skilled instructor who can teach data and technical skills to diverse audiences.
• I have PhD-level expertise in computer vision solution development, signal processing, machine learning, and MLOps.
• I am a motivated learner with over ten years of computational experience in application areas spanning physics, mathematics, biology, and neuroscience.

CONSULTING & SPEAKING ►
• I offer consulting and advisory services to organizations on a case-by-case basis and enjoy speaking about AI at conferences and related events. Please contact me if you would like to discuss related opportunities.

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Vin Vashishta

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