We just announced our $3M Pre-Seed. Watch our — launch video.
Amazon Supply Chain Research - Machine Learning/ Artificial Intelligence Principal Applied Scientist
With 13+ years of extensive experience in AI and applied sciences, I've utilized foundational language/image/video models (Google) to improve ranking, developed ML-based recommendation and ranking systems (Facebook), implemented computational digital marketing, and applied ML-based operational research across industries such as vacation rentals (Expedia), airlines (Sabre), retailing (Staples), telecom (MTN), and massive multiplayer online game technology (MZ).
Proven entrepreneurial leadership, taking 25+ business ideas to test and bringing 15+ products to life.
Robust understanding of financial principles and business law, with a working knowledge of Chartered Financial Accountant (CFA), Financial Risk Management (FRM), and Certified Public Accountant (CPA) concepts.
Equipped with a solid academic background, including a PhD in Management Science focused on quantitative management/marketing analytics and an M.Sc. degree in Marketing Science from UT Dallas. Additional credentials include an M.Sc. in Computer Engineering, an MBA, and a B.Sc. in Computer Engineering and Information System/Technology Engineering.
Spent four years immersed in academic applied data and business science, machine learning, and statistics research. Presented findings at renowned conferences and authored research papers published on the Social Science Research Network (SSRN), achieving a top 10% author ranking in 2019. Published papers in ICML, TPDP, and POMS, and Recsys, conferences, and MSOM, JRPM journals.
Chat with Clera and we'll introduce you to the right opportunities.
This profile is based on publicly available information. Meisam is not affiliated with or endorsed by Clera. Privacy policy.
Driving innovation in supply chain optimization technology (SCOT) through advanced agentic LLM, machine learning, and reinforcement learning to enhance coding, reasoning, math, and writing use cases, for audit at scale, and root cause analysis.
• Reduced the performance gap from 15% to 4% in a Multi-Party Computation privacy-preserving machine learning prototype via strategic hyperparameter tuning. • Demonstrated feasibility and potential of Federated Learning (FL) for Ad ranking in a simulated environment, achieving ~54% recovery of ad value loss, resulting in increased investment for production. • Validated the concept of user-level differential privacy in an FL solution for ad ranking by implementing and analyzing it, and demonstrating comparable performance to FL without differential privacy on multiple ad models. • Led a cross-functional team to achieve a ~2% improvement in online experiment ad value in an FL solution, extending the model's capabilities to real mobile devices. • Proved the potential for ~0.5% increase in online experiment ad value through the design and implementation of Federated Ensemble Learning and Federated Split Learning (accepted as a patent). • Crafted an external industry proposal outlining the application and system design details of Federated Learning in Ad ranking, presented to industry leaders and platforms at W3C meetings. • Spearheaded and implemented Federated Learning strategies across Statistics and Privacy, Ad Business Platform, Ranking, and AI departments. • Initiated and secured support for two early-stage research projects on Private Generative Adversarial Networks/Private transformer-based networks and Private Contextual Multi-arm Bandit, leading to a publication in ICML. • Interviewed and assessed over 40 senior and management candidates, sourced 30+ top-tier candidates, and successfully led various internal privacy-preserving machine learning summits and events, per audience feedback. Also, mentored/coached 3 interns and 6 individual contributors.
Sparx innovation lab 4th US online Retail at the time • Created a prototype experimentation platform to measure the performance of recommendation systems, utilizing a Hierarchical Bayesian Logit model in Python and a data pipeline in Apache Spark. • Decreased experiment sample size requirements by 20% through the design of an algorithm integrating the Mann-Whitney-Wilcoxon semi-parametric test, mixture log-normal model, and Monte Carlo methods, resulting in a white paper publication. • Formulated a detailed analysis and road map to enhance the measurement of promotion effects across online and offline channels. • Cut the sample size requirement for all experiments by 40% by devising an adaptive Bayesian sequential testing algorithm for continuous monitoring and control. • Designed an algorithm to power a personalized recommendation system, optimizing incremental revenue for cross-selling and up-selling by leveraging recency, frequency, monetary value, and other models.
Claim it to keep it up to date, or request removal. We're happy to help either way.