About

E-mail: marx.akl@asdrp.org

Education:
  · Ph.D. in Physics, Rensselaer Polytechnic Institute (RPI)

·  Overall GPA: 3.95/4.00

·  Master of Science in Physics, Rensselaer Polytechnic Institute (RPI)

·  Overall GPA: 3.95/4.00

·  Master of Science in Mechanical Engineering, University of Southern California

·  Overall GPA: 3.93/4.00

·  Bachelor of Science in Mathematics, Western Michigan University

·  Major GPA: 3.90/4.00

·  Bachelor of Science in Physics, Western Michigan University

·  Overall GPA: 3.75/4.00

·  Bachelor of Science in Aeronautical Engineering, Western Michigan University

·  Overall GPA: 3.70/4.00

Research Principal Investigator Profile: Marx Akl, Physics Ph.D.

About Marx Akl: Welcome to the research group led by Dr. Marx Akl, a distinguished Physics PhD and two Master’s degrees in Physics and Mechanical Engineering and two Bachelor degrees in Aeronautical Engineering and Mathematics and Physics. With over 10 years of extensive research/work experience Dr. Akl specializes in the exciting intersection of physics, material science, and cutting-edge machine learning and data science applications. With a proven track record of groundbreaking contributions to the field, Dr. Akl is passionate about mentoring aspiring researchers and creating an innovative and collaborative environment.

Research Focus: Dr. Akl's research focuses on advancing our understanding of materials at the nanoscale using a multidisciplinary approach. From molecular dynamics simulations and density functional theory calculations to the development of machine learning models, the group explores the mechanical responses of nanoparticles, epitaxial methods, deposition simulations, and more.

Key Areas of Expertise:

• Machine Learning in Materials Science/Physics and Machine Learning in Data Science: Dr. Akl leads pioneering research in applying machine learning techniques to characterize material properties, construct precise energy band structures, and analyze intricate phonon dispersion curves.

• Nanoscale Mechanics and Solid-state Physics: The group investigates the mechanical responses of nanoparticles under compression, revealing trends and critical transitions that contribute to a deeper understanding of nanomechanical behavior. We also characterize materials to reveal their mechanical properties such as stiffness hardness fracture energy viscosity etc. We inspect a multitude of factors that can influence this mechanical behavior such as size effect, temperature, simulation variables etc.

• Advanced Modeling and Simulation Techniques DFT and MD: Leveraging state-of-the-art tools such as LAMMPS, Quantum ESPRESSO, and more, Dr. Akl's team conducts simulations in areas ranging from molecular dynamics to quantum mechanics.

• Epitaxial methods exfoliation regime of thin film/substrate perovskites crystals:

We design build and run DFT calculations on heterogenous perovskite systems to investigate exfoliation. It is an invaluable means of detaching epitaxial layers from substrates to produce membranes that are essential in various applications such as optoelectronics and high speed computing. 

Experience prior to ASDRP:

Professor of Physics, De Anza Community College, Cupertino, CA | 08/23 - present 

·  Teaching Physics.

Data Scientist / Project Manager, ZAN Compute, Santa Clara, CA ( 01/2014 – 08/2018)

• Orchestrated the comprehensive planning and execution of a pioneering startup project, from concept inception to final product realization, demonstrating unwavering commitment throughout the developmental journey.

• Designed and built partitioned and indexed data pipelines in Python for Cluster Analysis on real-time and batch data from live installed sensors. Performed ETL processing, data extraction, joining, manipulation cleaning, analysis of large volume of data in S3 buckets.

• Lead the end-to-end data science project implementation including training, testing, and deploying Machine Learning models for optimization of Sensor performance and management plan generation with CI/CD deployment.

• Applied supervised machine learning techniques to forecast sensor data inputs, contributing to the optimization of custodial services management.

• Played an integral role in the advancement of the patented Smart Facility Management Platform (SFMP), leading a multidisciplinary team in the integration of cutting-edge wireless power sensors.

• Spearheaded extensive investigations into energy conversion strategies, yielding profound insights that underpinned the optimization of custodial services.

• Distinguished by my presence as a featured presenter at esteemed industry conferences, securing critical acclaim and earning funding accolades for our innovative endeavors.

• Pioneered the successful deployment of the SFMP across diverse client sites, including industry giants such as Facebook, Intel, and BMW, delivering consistent average savings of 30% across custodial processes and HVAC energy management systems.

Research Assistant @ RPI (08/2018– 07/2023):

• Applied MD Simulations and DFT calculations on supercomputer (Quantum Espresso) to characterize materials, deepening understanding of nanomechanical behavior.

• Investigated nanoparticle mechanics, uncovering trends in compressive strengthening and shear increase, published as first author with funding.

• Pioneered critical radius identification for brittle-to-ductile transition in nanoparticles, aligned with Griffith energy criteria, published as first author.

• Characterized exfoliation regimes in perovskite systems, aligning energies with experimental results, published in Nature with high funding.

• Employed advanced techniques (Raman, AFM, TERS) to analyze 2-D materials, securing funding and prestigious conference slot.

Machine Learning Modeling using LAMMPS and DFT:

·      Spearheaded the development of machine learning (ML) surrogate models for quantum mechanics (QM) potential energy functions.

·     Curated extensive datasets comprising small atomic configurations and QM-calculated energies and forces.

·   Trained regression models to map local atomic environments to atomic energies and forces.

·      Implemented ML potentials into LAMMPS for seamless integration.

· Conducted simulations on large-scale supercomputers, enabling the accurate representation of systems with millions of particles.

·  Achieved QM-level accuracy in simulating complex systems through innovative ML approaches.

Founder/Instructor, Marx Academy of Sciences, Los Angeles, CA | 1/2008 – 08/2018

• Established and scaled a tutoring company from inception, leading to the recruitment and management of a 30-member tutor team.

• Strategically recruited and managed a diverse team of tutors, leading to a significant increase in revenue and profit margins.

• Created and managed a YouTube channel dedicated to providing accessible and engaging science content, garnering substantial views and positive feedback.

Data Engineer, Asahi Inc, Atlanta, GA (01/2007 – 1/2008)

• Conducted comprehensive data analysis using SQL queries to optimize packaging techniques for glass transportation.

• Utilized SQL queries to access inventory data, improving load building processes and creating interactive dashboards for informed decision-making.

• Led efforts to enhance routing guide compliance, resulting in reduced operating costs and optimized shipping routes.

• Executed Extract, Transform, and Load (ETL) processes for data-driven cost-saving and equipment distribution analysis projects.

• Achieved 15% cost savings at the glass packaging center.Top of Form

Data Analyst, Fuel Industries, Los Angeles, CA (01/2010 – 12/2014)

• Led quantitative modeling efforts for Planning and Forecasting Quarterly Revenue in the Marketing Finance Ads Team, utilizing Python and SQL to consider accounts, revenue, spend phasing, and seasonality for forecasting Rolling and In-Quarter Revenue.

• Collaborated with cross-functional teams in Engineering and Finance to build reports and dashboards, conducting concept-testing for new products within Internal Marketing teams.

• Developed Python code to analyze marketing influence on channels, monitored acquisitions, migrated reporting systems, and generated weekly, EoQ, and SoQ reports for Marketing Monetization by Channels and Regions.