About: AI, Engineering and Physics lab

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:

Lead Data Scientist, Technical and Engineering Team, Copeland, Atlanta, GA | 08/2024 -present

•      Lead experiment design, development of machine learning models, statistical models and analytics solutions using thermodynamics data from refrigeration cycles to optimize HVACR systems.

Senior Engineer R & D, Spectral Analysis & Data Analytics, Dominion Energy, Insbrook, VA | 04/2024 – 08/2024

•      Real time data series analysis, Signal Processing and analysis of synchrophasor and power grid real time data, Time series analysis and forecasting in both time and frequency domain

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)

• 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.

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.

Machine Learning Modeling using LAMMPS and DFT:

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

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.

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

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