Mahdi Davari, Phd Email and Phone Number
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• Forward-thinking computational research scientist with a strong understanding of data structures, algorithms, and excellent coding ability leading to the successful development of novel methods based on evolutionary computation for predictive modeling and materials discovery. In addition to the implementation and development of scientific packages, e.g., FPTE, USPEX.• Data Scientist with a solid background in Machine Learning and Deep Learning algorithms, analytical and statistical methods, and data analysis. Deep understanding of data science, including big data technologies, distributed computing, and cognitive / cloud services, leading to many pilot implementation/POCs in a short time, which quickly led to pursue further on the idea and productionalize.• Co-authored a book chapter, published 18+ papers, 4 invited/contributed talks at conferences on materials discovery and modeling, leveraging evolutionary algorithm (i.e., a subset of AI)• Delivered projects ranging from OOPs, to advanced topics of deep learning/machine learning to meet client’s demands. Provided the best solutions / pilot implementations / POCs for various use-cases, ranging from implementation of image / document quality enhancement using ML, PDF annotation removal, document denoising for better OCRing, DPI estimate, to ML based deskew approach and probabilistic approach which resulted in overall >20% accuracy increase.• Creative and self-motivated individual with easy-integration in a multicultural environment with the unique combination of detail-oriented mindset, driven personality, analytical skills, and proven ability to meet tight deadlines by working in a fast-paced work environment.- Programming: Python, Bash, SQL, Java, R, MATLAB, HTML, CSS and working knowledge of C++- Machine Learning & Deep Learning: PyTorch, TensorFlow, XGBoost, Spark-ML, Scikit-learn, H2OAi. Computer Vision: OpenCV, Torchvision, MMCV, MMDet, CVAT- NLP: SpaCy, Gensim, NLTK Data Analysis & Processing: Pandas, DASK, Spark, Koalas, Polars- Databases: MongoDB, PostgreSQL, Neo4j IDE & VCS: PyCharm, IntelliJ IDEA, Visual Studio Code, RStudio, Git and GitHub- BI tools: Tableau, Dash - Plotly, PowerBI, Data Studio Others: Data Visualization, Modeling, Quantitative Analysis and Statistics- Cloud platforms & services: Azure ML, Databricks, Amazon Web Services (SageMaker, EMR, EC2, S3, Lambda, Lightsail)
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Lead Data Scientist And DeveloperNyseNew York, Ny, Us -
Lead Data Scientist / DeveloperNyse Jun 2022 - PresentNew York, Ny, Us
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Data ScientistCiti Aug 2020 - May 2022New York, New York, Us• Created multiple ML/DL pipelines in Python, RESTful API with Flask, webapps with Dash. Containerized 8 services (ML models + API) with Docker, Jenkins, OpenShift and deployed them to Citi OpenShift container platform.• Trained multiple computer vision models for end-to-end table detection and table content extraction incorporating pattern recognition on images. Leveraged Cascade mask RCNN (a SOTA multi-stage architecture, with HRNet backbone), using PyTorch, MMCV, MMDet, TorchVision, ONNX, transfer learning from OpenTable dataset and fine tuned on custom datasets, ended up in detection accuracy of >97% .• Created multiple ML services ranging from Named Entity Recognition (NER) for determining labels/tags of contents/entities, e.g., in unstructured documents, to CRF based address parsing, initially trained on OpoenSteetMap and OpenAdresses and fine tuned as per the customer requirements. Used SpaCy, TensorFlow, DeepPavlov, Libpostal, Typer and Flask.• Created multiple end-to-end pipelines (from data pipeline to deploy pipeline through Jenkins). Deployed variety of ML services in OpenShift clusters. Created multiple python based custom Machine Leaning frameworks and packaged them using Python Package Index standards and deployed to client’s PyPi repo. -
Data ScientistEdgeverve Apr 2020 - Aug 2020Bangalore, Karnataka, In• Led a computer vision project for GUI element extraction and managed a team of data scientists. A patent filing has been initiated for our end-to-end solution.• Provided continues improvements for the application release and deployment management process, workflow and post implementing monitoring and evaluation.• Developed a user activity tracking model using traditional computer vision in addition to the state-of-the-art deep learning models (used Keras, OpenCV, CI/CD with GitLab & Jenkins pipeline) -
Data ScientistInfosys Nov 2019 - Apr 2020Bangalore, Karnataka, In• Business use case implementation of graph data science/graph database with Neo4j (Cypher) andMarkLogic (XQuery) and demonstrated performance, reliability, and integrity of the flexible data modelwith connected data.• Pilot implementation of recommender systems e.g., collaborative filtering (SVD, SVD++, ALS, NMF), content based (KNN, TF-IDF), context aware, session based, hybrid methods (LightFM), etc.• POC on text summarization with implementing a reusable pipeline for abstractive and extractive text summarization approaches, leveraging BERT, T5, and BART NLP models.• POV on distributed learning/big data technologies, covering variety of big data platforms, and distributed learning frameworks (Spark, DASK, RAPIDS (CuDF, CuML), XGBoost4j-Spark, MMLSpark, Koalas, Polars, etc.• Created multiple reusable pipelines in Python, RESTful APIs with Flask, interface with OpenAPI (Swagger), webapps with Dash.• Collaborated effectively within a small team environment as well as work tasks independently -
Postdoctoral Research AssociateStony Brook University Jan 2018 - Nov 2019Stony Brook, Ny, Us• Designed and implemented the Finite Pressure Temperature Elasticity (FPTE) package with more than 2000 lines of code used by researchers worldwide (available on my GitHub)• Helped PI recognize over $300,000 in grant funding by skillfully leading a team of scientists to complete all projects on time, resulting in grant renewal every year.Theoretical simulations based on quantum mechanics have played an increasingly important role in Earth sciences. Therefore, we apply the most advanced and recently developed tools in computational physics to address problems related to the mantle, core and core-mantle boundary region.My research focus is on materials of the most enigmatic regions of the Earth which will be studied using advanced quantum-mechanical simulations. Central to this study is our in-house developed evolutionary algorithm USPEX, interfaced with first-principles electronic structure calculations. This approach has produced many important predictions in mineralogy and physics, successfully confirmed by subsequent experiments. -
ResearcherInstitute For Advanced Computational Science Mar 2014 - Jan 2018Stony Brook, New York, UsMy primary research focus is to predict novel materials for energy storage, delivery, and technological applications. My research encompasses materials design, crystal structure prediction, superconducting properties calculation using ab initio quantum mechanical simulations. The goal of my work is to guide experimentalists towards achieving the high-Tc superconductivity. -
Graduate Research AssistantStony Brook University Graduate School Jun 2014 - Dec 2017Stony Brook, Ny, Us• Co-authored a book chapter, published 15+ peer reviewed journals including a Nature Communication article, 4 invited/contributed talks at conferences.• Designed and modeled new inorganic materials with desired properties using ab initio methods.• Predicted novel compounds under high pressure conditions (borides, hydrides, oxynitrides, etc).My research will address the needs of future energy systems by discovering novel materials for energy storage, delivery, and technological applications. The main aspect of my research is to develop a new methodology for the prediction of novel polyhydride materials with key properties specifically enhanced for application to future energy technologies. By combining theoretical predictions and experiments at various thermodynamic and kinetic conditions, a targeted material design strategy will be developed. This will be realized through scientific discoveries of novel materials and demonstrations of successful synthesis of polyhydrides with enhanced superconducting and hydrogen storage properties. -
Graduate Teaching AssistantStony Brook University Graduate School Aug 2013 - May 2014Stony Brook, Ny, Us
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Scientific Software DeveloperCenter For Materials By Design Jan 2014 - Dec 2017• Designed and developed crystal structure prediction algorithms, and integrated it in USPEX code (used by over 4000 researchers worldwide)• Implemented a descriptor of structure (fingerprint function), capable of the quantification of energy landscapes of 2D materials• Developed a method for two-parameter optimizations, enabled the search for materials with desired properties along with the stability
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Hpc System Administrator And WebmasterCenter For Materials By Design Jan 2014 - Oct 2017• Designed, developed and managed USPEX website and user data on SQL databases• Configured and managed high performance computing cluster on Red Hat/Debian platform• Collaborated effectively within a small team environment as well as work tasks independently
Mahdi Davari, Phd Skills
Mahdi Davari, Phd Education Details
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Stony Brook UniversityComputational Phys/Chem -
DatacampData Science Career Track
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Isfahan University Of TechnologyCondensed Matter And Materials Physics
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University Of IsfahanCondensed Matter Physics
Frequently Asked Questions about Mahdi Davari, Phd
What company does Mahdi Davari, Phd work for?
Mahdi Davari, Phd works for Nyse
What is Mahdi Davari, Phd's role at the current company?
Mahdi Davari, Phd's current role is Lead Data Scientist and Developer.
What is Mahdi Davari, Phd's email address?
Mahdi Davari, Phd's email address is ma****@****ook.edu
What schools did Mahdi Davari, Phd attend?
Mahdi Davari, Phd attended Stony Brook University, Datacamp, Isfahan University Of Technology, University Of Isfahan.
What skills is Mahdi Davari, Phd known for?
Mahdi Davari, Phd has skills like Computational Materials Science, Condensed Matter Physics, Matlab, Python, Data Science, Programming, Git, Statistics, Simulations, Research, R, Machine Learning.
Who are Mahdi Davari, Phd's colleagues?
Mahdi Davari, Phd's colleagues are Tom Heebner, Maeve Connors, Kees Tamminga, Vladimir Afanasyev, Sean Quigley, Gabriel Paz-Soldan, Husan Turdiev.
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