Damien Benveniste, PhD

Damien Benveniste, PhD Damien Benveniste, PhD is an influencer

Founder @ TheAiEdge | Follow me to learn about Machine Learning Engineering, Machine Learning System Design, MLOps, and the latest techniques and news about the field.

Long Beach, California, United States
171K followers 500+ connections

About

Machine Learning expert with strategic business perspective and 10+ years of expertise in developing production ML systems. Specialized in LLMs and LLM pipeline orchestration with proven track record of delivering significant business impact through ML innovations. Uniquely positioned to lead high-impact ML projects.

Articles by Damien

Activity

Join now to see all activity

Experience

  • The AiEdge Graphic

    Founder

    The AiEdge

    - Present 2 years 6 months

    United States

    Led development and execution of comprehensive AI/ML education platform, establishing market leadership in specialized ML training with focus on advanced ML fundamentals, LLM fine-tuning, deployment pipelines, and agentic AI systems.
    • Built profitable education business from concept, developing multi-channel business model across newsletter subscriptions, educational courses and technical content that reached 130K subscribers and 4,300 students across 50 US states and 195 countries.
    •…

    Led development and execution of comprehensive AI/ML education platform, establishing market leadership in specialized ML training with focus on advanced ML fundamentals, LLM fine-tuning, deployment pipelines, and agentic AI systems.
    • Built profitable education business from concept, developing multi-channel business model across newsletter subscriptions, educational courses and technical content that reached 130K subscribers and 4,300 students across 50 US states and 195 countries.
    • Designed and implemented in-depth technical training covering transformer architecture internals, advanced fine-tuning methodologies, distributed training strategies, production ML systems including RAG pipeline architectures, inference optimization, agentic frameworks, and scalable deployment strategies that address real-world performance constraints.
    • Successfully identified market gap in practical ML and LLM education, developing targeted curriculum that addressed specific industry needs, resulting in consistent 15% monthly subscriber growth in a highly competitive market.

    https://newsletter.theaiedge.io/

  • Intelligent Engineering Systems Graphic

    Machine Learning Consulting

    Intelligent Engineering Systems

    - Present 2 years 9 months

    I specialize in building large scale end to end Machine Learning capabilities. I can help you assess the AI readiness of your data and data infrastructures. I can architect the solutions, set KPIs, timelines, milestone and help in establishing a budget and headcount. I can train the models, deploy them and architect the infrastructures to support the Machine Learning solutions. I can coordinate the teams collaborations and train the teams as needed to maximize the success of the endeavors.

  • Motivee Graphic

    CTO and Founder

    Motivee

    - 1 year 1 month

    California, United States

  • Meta Graphic

    Machine Learning Tech Lead

    Meta

    - 1 year 1 month

    California, United States

    • Generated ~$40M in annualized additional revenue by developing and implementing low-fidelity evaluation, meta-learning (MAML), and joint feature-model optimization capabilities that improved predictive performance by ~1% across ~500 Ads ranking ML models responsible for ~96% of Meta's total revenue ($40B in 2021).
    • Accelerated model development cycles by leading a team of 5 ML Engineers specializing in model development automation, reducing latency, and enhancing predictive performance…

    • Generated ~$40M in annualized additional revenue by developing and implementing low-fidelity evaluation, meta-learning (MAML), and joint feature-model optimization capabilities that improved predictive performance by ~1% across ~500 Ads ranking ML models responsible for ~96% of Meta's total revenue ($40B in 2021).
    • Accelerated model development cycles by leading a team of 5 ML Engineers specializing in model development automation, reducing latency, and enhancing predictive performance across the organization's advertising infrastructure.

  • Medallia

    Medallia

    1 year 7 months

    • Medallia Graphic

      Head of Data Science and Machine Learning, Sense360

      Medallia

      - 9 months

      California, United States

    • Medallia Graphic

      Director of Machine Learning (Sense360 acquired Sept 2020 by Medallia)

      Medallia

      - 11 months

      Culver City

      • Drove $44M acquisition of Sense360 by Medallia by developing a new AI product (Intelligence) that leveraged Foot Traffic, Transaction, and Survey data for market research in Food and Retail industries.
      • Delivered actionable insights to industry leaders by leading a team of data scientists to build data and ML products that transformed raw location and transaction data into strategic business intelligence.

  • Albert Graphic

    Machine Learning and AI Lead

    Albert

    - 6 months

    Culver City, California

    •Automated financial advising services by leading a team to develop advanced language models for chatbot-like systems, reducing customer service costs while maintaining quality of financial guidance.

  • Rackspace Graphic

    Lead Data Scientist

    Rackspace

    - 7 months

    Greater Los Angeles Area

    I lead a small team of Data Scientists, Data Engineers and Software Engineers for a new Data Science initiative in the Private Cloud Department. I have assessed the business inefficiencies and designed Machine Learning solutions applied to customer ticket routing and failure predictions.

  • OPSkins Graphic

    Senior Data Scientist

    OPSkins

    - 6 months

    Greater Los Angeles Area

    I am heading the Data Science initiative at OPSkins. I have built Machine Learning models for pricing optimization, product recommendation and Natural Language Processing. I have identified the deployment patterns for the Machine Learning products and I was leading the effort to construct a data warehouse for data analytics.

  • Zenalytics a division of Bluestem Brands, Inc. Graphic

    Senior Data Scientist

    Zenalytics a division of Bluestem Brands, Inc.

    - 2 years 1 month

    Greater Los Angeles Area

    I have joined Bluestem as a Senior Data Scientist to partake in the brand-new Data Science initiative that has been key for the company in generating $2B in annual revenue. This includes identifying inefficiencies and building predictive models for business optimization across domains such as marketing, credit analysis or sales. I have had the opportunity to develop and deploy numerous models using machine learning techniques and big data technologies. Some of my accomplishments are:
    •…

    I have joined Bluestem as a Senior Data Scientist to partake in the brand-new Data Science initiative that has been key for the company in generating $2B in annual revenue. This includes identifying inefficiencies and building predictive models for business optimization across domains such as marketing, credit analysis or sales. I have had the opportunity to develop and deploy numerous models using machine learning techniques and big data technologies. Some of my accomplishments are:
    • Increasing by 10% the new customer acquisition rate through mailing campaigns using binary classification algorithms. • Enhancing customer experience with personalized product recommendation during call offers.
    • Improving sales profit with price optimization across ~200,000 different products and inventory forecast.
    • Developing new ECOA and FCRA compliant Machine learning techniques for adverse actionable credit worthiness prediction and credit line assignment.
    • Deploying highly accurate sales forecast software for promotion campaigns.

  • California State University-Long Beach Graphic

    Part-time Data Science Faculty

    California State University-Long Beach

    - 5 months

    Long Beach California

    I have joined the California State University-Long Beach faculty to teach master students about Data Science. They have learned about Python programming, statistical inference and Machine Learning. We have explored different subjects such as Natural Language Processing, Monte Carlo simulations, Image analysis and Deep Learning. It has been a truly enriching experience to be able to guide students to become data scientists!

  • EMC Graphic

    Senior data scientist researcher

    EMC

    - 10 months

    Greater Los Angeles Area

    I have worked in a research team applying machine learning techniques to optimize products and create new technological advances:

    • I have developed predictive models for HDD disks failure and file systems crash (R, python). I have extracted, analyzed and modeled data from a multi-terabyte Greenplum relational database (PostgreSQL) and from a Hadoop file system (PIG).
    • I have designed new methods of feature selection for non-linear mixed data with a high number of missing values…

    I have worked in a research team applying machine learning techniques to optimize products and create new technological advances:

    • I have developed predictive models for HDD disks failure and file systems crash (R, python). I have extracted, analyzed and modeled data from a multi-terabyte Greenplum relational database (PostgreSQL) and from a Hadoop file system (PIG).
    • I have designed new methods of feature selection for non-linear mixed data with a high number of missing values using information theory techniques.
    • I have designed new semi-supervised techniques of clustering to unmix data.
    • I have applied Hidden Markov Modeling to backup file systems data labeling.

  • Lead Data Scientist

    Transfuse Solutions, Inc.

    - 1 year 3 months

    San Francisco Bay Area

    • Developed machine learning algorithms for optimization of blood management (Java, R, Python, Hadoop).
    • Developed data visualization tool for business intelligence (D3.js, Google App Engine, SQL).
    • Developed iOS and Android mobile apps (Objective C, Android, AWS, DynamoDB, S3).

  • Constellation Graphic

    Quantitative Analyst Intern for Energy Valuation

    Constellation

    - 5 months

    Baltimore, Maryland Area

    • Development of Energy dispatch algorithm integrated into a Monte Carlo Machine for energy valuation (C++, Python/Cython). • Development of derivatives pricing methods in energy markets Development and enhancement and maintenance of stochastic models for demand and price, network congestion, gas storage and transportation.

  • Johns Hopkins University, Physics & Astronomy / Applied Mathematics & Statistics Departments

    Johns Hopkins University, Physics & Astronomy / Applied Mathematics & Statistics Departments

    7 years

    • Johns Hopkins University, Physics & Astronomy / Applied Mathematics & Statistics Departments Graphic

      Research Assistant in Theoretical Physics

      Johns Hopkins University, Physics & Astronomy / Applied Mathematics & Statistics Departments

      - 7 years

      Baltimore, Maryland Area

      • Development of Monte Carlo machines for stochastic differential equations and statistical physics.
      • Data mining and treatment of high dimensional data in a multi-terabytes database cluster
      • Testing of statistical arbitrage strategies using machine learning techniques for equity trading.
      • Development of diffusion, stochastic and probabilistic models for turbulent flows.
      • Study of algebraic models for quantum matter.
      (C/C++, Matlab, Fortran)

    • Johns Hopkins University, Department of Physics & Astronomy Graphic

      Teaching Assistant

      Johns Hopkins University, Department of Physics & Astronomy

      - 5 years 9 months

      Région de Baltimore, Maryland, États-Unis

  • University of Oxford, Clarendon Laboratory Graphic

    Research assistant

    University of Oxford, Clarendon Laboratory

    - 3 months

    Oxford, Royaume-Uni

  • Royal Holloway, University of London, Quantum fluids and solids Laboratory Graphic

    Research assistant

    Royal Holloway, University of London, Quantum fluids and solids Laboratory

    - 5 months

    London, Royaume-Uni

  • Teaching assistant

    Lycée Louis le Grand

    - 4 months

    Région de Paris, France

  • École normale supérieure, Laboratoire de Physique Statistique Graphic

    Research assistant

    École normale supérieure, Laboratoire de Physique Statistique

    - 3 months

    Région de Paris, France

Education

Skills

Publications

  • Backwards Two-Particle Dispersion in a Turbulent Flow

    Phys. Rev. E 89, 041003(R) (2014)

    We derive an exact equation governing two-particle backwards mean-squared dispersion for both
    deterministic and stochastic tracer particles. For the deterministic trajectories, we probe conse-
    quences of our formula for short time and arrive at approximate expressions for the mean squared
    dispersion which involve second order structure functions of the velocity and acceleration fields. For
    the stochastic trajectories, we analytically compute an exact t3 contribution to the squared…

    We derive an exact equation governing two-particle backwards mean-squared dispersion for both
    deterministic and stochastic tracer particles. For the deterministic trajectories, we probe conse-
    quences of our formula for short time and arrive at approximate expressions for the mean squared
    dispersion which involve second order structure functions of the velocity and acceleration fields. For
    the stochastic trajectories, we analytically compute an exact t3 contribution to the squared sepa-
    ration of stochastic paths. We argue that this contribution appears also for deterministic paths at
    long times and present direct numerical simulation (DNS) results for incompressible Navier-Stokes
    flows to support this claim. We also numerically compute the probability distribution of particle
    separations for the deterministic paths and the stochastic paths and show their strong self-similar
    nature.

    Other authors
    • Theodore Drivas
    See publication
  • Diffusion approximation in turbulent two-particle dispersion

    Phys. Rev. E Rapid Communication

    We solve an inverse problem for fluid particle pair statistics: we showthat a time sequence of probability density functions (PDFs) of separations can be exactly reproduced by solving the diffusion equation with a suitable time-dependent diffusivity. The diffusivity tensor is given by a time integral of a conditional Lagrangian velocity structure function, weighted by a ratio of PDFs. Physical hypotheses for hydrodynamic turbulence (sweeping, short memory, mean-field) yield simpler integral…

    We solve an inverse problem for fluid particle pair statistics: we showthat a time sequence of probability density functions (PDFs) of separations can be exactly reproduced by solving the diffusion equation with a suitable time-dependent diffusivity. The diffusivity tensor is given by a time integral of a conditional Lagrangian velocity structure function, weighted by a ratio of PDFs. Physical hypotheses for hydrodynamic turbulence (sweeping, short memory, mean-field) yield simpler integral formulas, including one of Kraichnan and Lundgren (K-L).We evaluate the latter using a space-time database from a numerical Navier-Stokes solution for driven turbulence. The K-L formula reproduces PDFs well at root-mean-square separations, but growth rate of mean-square dispersion is overpredicted due to neglect of memory effects. More general applications of our approach are sketched.

    Other authors
    • G. L. Eyink
  • Suppression of particle dispersion by sweeping effects in synthetic turbulence

    Phys. Rev. E

    Synthetic models of Eulerian turbulence like so-called kinematic simulations (KS) are often used as
    computational shortcuts for studying Lagrangian properties of turbulence. These models have been criticized by Thomson and Devenish (2005), who argued on physical grounds that sweeping decorrelation effects suppress pair dispersion in such models.We derive analytical results for Eulerian turbulence modeled by Gaussian random fields, in particular for the case with zero mean velocity. Our…

    Synthetic models of Eulerian turbulence like so-called kinematic simulations (KS) are often used as
    computational shortcuts for studying Lagrangian properties of turbulence. These models have been criticized by Thomson and Devenish (2005), who argued on physical grounds that sweeping decorrelation effects suppress pair dispersion in such models.We derive analytical results for Eulerian turbulence modeled by Gaussian random fields, in particular for the case with zero mean velocity. Our starting point is an exact integrodifferential equation for the particle pair separation distribution obtained from the Gaussian integration-by-parts identity. When memory times of particle locations are short, a Markovian approximation leads to a Richardson-type diffusion model.We obtain a time-dependent pair diffusivity tensor of the form Kij (r,t ) = Sij(r)τ(r,t ), where Sij(r) is the structure-function tensor and τ(r,t ) is an effective correlation time of velocity increments. Crucially, this is found to be the minimum value of three times: the intrinsic turnover time τ_{eddy}(r) at separation r, the overall evolution time t, and the sweeping time r/v_0 with v_0 the rms velocity. We study the diffusion model numerically by a Monte Carlo method. With inertial ranges like the largest achieved in most current KS (about 6 decades long), our model is found to reproduce the t^{9/2} power law for pair dispersion predicted by Thomson and Devenish and observed in the KS. However, for much longer ranges, our model exhibits three distinct pair-dispersion laws in the inertial range: a Batchelor t^2 regime, followed by a Kraichnan-model-like t^1 diffusive regime, and then a t^6 regime. Finally, outside the inertial range, there is another t 1 regime with particles undergoing independent Taylor diffusion. These scalings are exactly the same as those predicted by Thomson and Devenish for KS with large mean velocities, which we argue hold also for KS with zero mean velocity.

    Other authors
    • G. L. Eyink

Patents

  • Systems And Methods For Improving The Interpretability And Transparency Of Machine Learning Models

    Issued US 16/137200

    Embodiments herein provide for a machine learning algorithm that generates models that are more interpretable and transparent than existing machine learning approaches. These embodiments identify, at a record level, the effect of individual input variables on the machine learning model. To provide those improvements, a reason code generator assigns monotonic relationships to a series of input variables, which are then incorporated into the machine learning algorithm as metadata. In some…

    Embodiments herein provide for a machine learning algorithm that generates models that are more interpretable and transparent than existing machine learning approaches. These embodiments identify, at a record level, the effect of individual input variables on the machine learning model. To provide those improvements, a reason code generator assigns monotonic relationships to a series of input variables, which are then incorporated into the machine learning algorithm as metadata. In some embodiments, the reason code generator creates records based on the monotonic relationships, which are used by the machine learning algorithm to generate predicted values. The reason code generator compares an original predicted value from the machine learning model to the predicted values from the machine learning model.

    See patent

Projects

Languages

  • English

    Native or bilingual proficiency

  • French

    Native or bilingual proficiency

  • Spanish

    Elementary proficiency

Organizations

  • JHU Economics and Finance Club

    President

    -
  • The JHU Quant Trading Group

    President

    -
  • 1st Trading Competition in Johns Hopkins University

    Co-chair

    -
  • Princeton – UChicago Quant Trading Conference 2013

    Member of the organizing committee

    -
  • JHU Brazilian Jiu-jitsu club

    Instructor

    -

Recommendations received

More activity by Damien

View Damien’s full profile

  • See who you know in common
  • Get introduced
  • Contact Damien directly
Join to view full profile

Other similar profiles

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Add new skills with these courses