Amir Behbehani Email & Phone Number

San Francisco, CA, US

• As the founder of Memra, I spent the first year and a half developing Large Action Models (LAMs), the core of our AI-driven platform. These digital employees go beyond task automation by managing entire jobs.
• Built LAMs to autonomously manage entire workflows and system interactions.🔹AI System Architecture:
• Built a proprietary vector DB to store knowledge graphs in vector space, modeling context beyond RAG.
• Created an info-theoretic model for task reasoning and decision-making.🔹 Strategic Growth & Market Positioning:
• Lead efforts to integrate our digital employees into client operations, focusing on strategic partnerships and market expansion.🔹 Thought Leadership:
• Promoting Memra’s solutions through podcasts and industry papers.As a founder, I combine deep AI expertise with strategic initiatives to bring Memra’s groundbreaking digital employee technology to market.

• Serial Metrics, founded in 2010, specialized in developing core AI intellectual property. The company partnered with early-stage startups. At Serial Metrics, I played a key role in both the technical development and.
• Levo
• Leaf Logistics
• Orion
• Medibio
• CloudLending (Acquired by Q2)

San Francisco Bay Area

• In an advisory role with Levo, I achieved the following key milestones:
• LevoSearch Creation: Spearheaded the development of a natural language query engine to enhance user experience and streamline data access for healthcare professionals.
• Technical Leadership: Led a team and coded a C++ data pipeline, achieving seamless integration with enterprise databases and ERP systems and enabling real-time analytics.
• NLP Deployment: Utilized natural language processing to convert user queries to SQL in real-time, delivering actionable insights into employee performance and engagement with 95% accuracy.

• Helped define a forward derivatives model that doubled freight market liquidity and reduced deadweight- loss by 50%, optimizing pricing and network efficiencies for the freight logistics industry.
• Employed machine learning techniques (SVMs, Random Forests, and GBT) to analyze transportationdata and forecasted supply and demand variables with 90% accuracy, yielding a 20% efficiency gain.
• Helped grow company value by 150% from $100MM to $250MM after implementing a forward derivativepricing structure, improving revenue stability for thousands of shippers and carriers.
• Achieved 10x YoY net revenue growth through an economic study using Structured Equation Modeling.

San Francisco Bay Area

• Launched an auto-ETL platform and directed the company to rapid growth, successful enterprise sales, and a 1000x return on initial seed investment.
• Created an NLP-based ETL system for real-time data analysis, reducing query creation times by 90%.
• Devised a neural network-based language model that transformed databases into knowledge graphs, enabling real-time analysis and NLP queries, reducing analysis time by 50%.
• Played a key technical role in enterprise sales efforts, contributing to a 300% YOY increase in revenue.
• Increased adoption by 25% and satisfaction by 40% via cross-functional collaboration to improve the platform.

• Pioneered an AI platform using abnormal, arrhythmic cardiac signals collected from wearable devices to aid in clinical diagnosis, resulting in an average of 8 years earlier diagnosis.
• Boosted Alzheimer's diagnosis accuracy by 73% with an AI platform that analyzed cardiac signals fromwearables, reducing inter-diagnostic variance from 55% to over 95% using random forest models.
• Coded a C++ application to convert EDF binary data to CSV format, enabling faster and more accuratedata preparation and analysis, improving model development speed by 2x.
• Published ML results in a peer-reviewed neurology journal, contributing to $17M in venture financing.

• Spearheaded the launch of a transformative AI-powered lending service, reducing the loan process from months to minutes, resulting in the company's acquisition value of $105 million.
• Predicted default likelihood using non-parametric models, improving risk management by 30%.
• Integrated ML models to Salesforce using REST API and Apex, increasing market reach by 100%.
• Deployed ETL flows for financial platform interactions, reducing customer onboarding time by 50%.

San Francisco Bay Area

Developed an AI-assisted targeting system that resulted in a 10x return on ad spend and generated a new pipeline of $3 million in potential revenue.· Designed and developed a data-driven system that merges CRM sales data with Google ad campaigns to identify effective campaign attributes for driving sales· Implemented real-time data integration and analysis.

Launched an initial product offering that hastened the path to securing a 2.5 million dollar round of funding.· Developed and implemented a cross-label music recommendation engine utilizing machine learning algorithms and data analysis, resulting in improved user music discovery and increased engagement across different music labels.· Enhanced the system.

• Discovered, patented, and sold a successful home automation system to Google, resulting in a 40x increase in company valuation from $500,000 to $20 million.
• Designed 99% accurate presence detection system for adjusting home appliances by user location.
• Prototyped machine learning code in Python and ported code to Objective C for the Zuli iPhone app.
• Optimized algorithm for 2x power and memory efficiency while streaming gyroscopic data.

Developed an automated valuation system using deep learning models on publicly available data, including the number of bedrooms, sqft, county, yard size, year built, etc., to estimate property values for portfolio acquisition. This eliminated the need for “walkthrough” property inspections, where – for example – a property inspector evaluates worn kitchen.

San Francisco Bay Area

Developed a unique matching algorithm, leading to a quick sale to Monster.com with a 10x valuation increase and a $12.5 million dollar exit.· Developed a matching algorithm to optimally surface “Tinder-like” matches between job seekers and recruiters.· Created a random forest model to match job seekers and job posters by presenting candidates and.

San Francisco Bay Area

Developed core music recommendation engine, driving 8tracks from start-up to $50M valuation.· Engineered a recommendation system using R composed of three different models and modeling approaches resulting in a system that recommends playlists to users.· Category 1 Model: Utilized the dplyr and tidyr libraries to pull and organize user data by location.

Developed machine learning video recommendation system with clustering, enhancing IP and aiding $1.5 Billion dollar sale to LinkedIn. · Implemented a video recommendation system using machine learning to analyze usage data from both active and canceled users· Utilized clustering algorithms such as K-means to group users based on their viewing patterns and.

Developed the Geo-Inferencing system for Identified.com for user location detection, boosting IP and contributing to sale of the company to Workday.· Created a geo-inferencing system to detect where users live, down to the metropolitan statistical area. The technology helped spur the sale of the company to Workday in 2014.· Developed a geo-inferencing.

Developed a recommendation engine using R, leveraging topic modeling methods to increase user engagement by 85% by recommending academic journals based on browsing history.· Designed and developed a topic modeling framework using the R programming language· Utilized R libraries such as gensim, tm, and LDAvis to implement Latent Dirichlet Allocation (LDA).

Designed a job recommendation system that pairs lexical features (using LDA) extracted from resumes with job postings. Conducted an experiment to validate a proprietary scoring algorithm for matching job seekers to employers.· Designed the experiment to identify differences between human classification and machine learning classifier· Hired recruiters to.

Engineered innovative enterprise software module for automatic, optimal data visualization.· Developed advanced BI tools for data visualization and analysis, utilizing machine learning algorithms to automatically select and optimize chart types.· Utilized C++ and Java to analyze data and identify patterns, leveraging MPI libraries such as Boost.MPI and.

San Francisco Bay Area

Developed an AI-based system to classify 1.5 million food items in conjunction with USDA guidelines into three user-friendly categories: Red, Yellow, and Green. · Contributed to pre-formative phase of company by collaborating with CEO, my next-door neighbor, to apply machine learning and analytics to enhance health and wellness, resulting in the creation.

Developed an ML-based solution to ensure legal compliance of organizations' electronic communications· Designed and implemented a legal privilege determination system to comply with different jurisdictions' regulations.· Utilized JAVA programming language to build the system and Perl to perform data analysis.· Gathered legal requirements by reviewing.

• Engineered a recommendation engine in PLSQL, which sold to Facebook for $83 Million.
• Derived a Bayesian filtering algorithm based on conditional entropy to remove low-signal content from the large-scale Facebook feed, while ensuring linearly efficient big O complexity, and personalization.
• Ported algorithm from PL/SQL to JAVA for Facebook's recommendation engine, optimizingscalability, performance, user engagement, and revenue.

• Implemented an intelligent pricing algorithm to optimize revenue for online ticket sales, yielding a 700X improvement in ticket sales.
• Developed an adaptive ticket pricing system that optimizes revenue in real-time, resulting in a 30%increase in gross margin and ensuring events sell out at profitable prices.
• Applied nonlinear dynamics, feedback control theory, data mining, and optimization to determinerevenue-maximizing prices for each ticket, resulting in a patented ticket pricing methodology.
• Collaborated with the engineering team to seamlessly integrate the adaptive pricing system into aproduction environment.
• Continuously optimized pricing strategies and increased revenue by monitoring and evaluating systemperformance through A/B testing and multivariate analysis.

Developed the System Dynamics-based, predictive, software simulator to dynamically forecast operating cost as a function of various macro-economic, and micro- economic variables.

Co-developed a software product to adaptively change a targeted email message within an email campaign, by dynamically measuring the level of recipient engagement.

Created the System Dynamics models, causal loop diagrams, and corresponding System Dynamics system of differential equations, for the IBM-Portland Smarter Cities Initiative

· Constructed forecasts to predict long-term Federal Reserve Bank prime rates.· Analyzed the impact of price changes on demand for Wells Fargo interest-bearing accounts. · Determined the optimal interest rate to pay Wells Fargo customers, minimizing account closures and CD tenure rollovers. · Conducted a variance analysis using SAS and SQL to assess yield.

· Served as an internal consultant on key business initiatives, assessing the economic impacts of process improvements.· Developed a dynamic marketing attribution model based on the Markov chain algorithm as an alternative to traditional methodology (static, linear, rules-based attribution)· Developed a sales lead grading model to determine the probability.