Forrest Laskowski Email & Phone Number

Bend, Oregon, United States

Leveraging First Principles, Continuum, and Machine Learning approaches to tackle challenges in solid-state batteries.

Bend, Oregon, United States

Predicting improved battery architectures via P2D and P4D Multiphysics simulations.Developing machine learning pipelines to accelerate data analysis and predict long-term cycling performance from beginning-of-life observables.Providing subject matter expertise for all electrochemical experiments (e.g. extended cycling, EIS, DRT, RPT, HPPC, symmetric cell.

Pasadena, California, United States

Discovery of next generation battery materials.

Eugene, Oregon Area

• Pursuing photoelectrochemical oxygen evolution research in the Boettcher Lab - specific emphasis on understanding of the energetic losses associated with semiconductor | catalyst | solution interfaces.
• Computationally modeling the semiconductor | catalyst interface in 1D via Mathematica & Python and in 3D via COMSOL multiphysics.
• Routinely use and well versed in various (photo)electrochemical techniques, Atomic Force Microscopy (AFM), thermal/E-beam deposition methods, SEM, photolithography techniques, optical/contact profilometry, X-ray.
• Trained on and proficient with TEM, small angle x-ray scattering (SAXS), and x-ray diffraction (XRD).

Richland/Kennewick/Pasco, Washington Area

• Contributed to six separate works on topics including quantum dots, biofouling, and trace analyte detection.
• Trained on chemical detection methods including Brunauer-Emmet-Teller, Thermo Gravimetric Analysis, Ion –Mobility Spectroscopy, Isothermal Titration Calorimetry, Matrix-Assisted Laser Desorption/Ionization, and HNMR.
• Coordinated multidisciplinary efforts while optimizing surface chemistries for low surface energy anti-biofouling.

Berkeley, California

• Worked under Dr. Tsu Jae King.
• Tasked with independent research to investigate the radiation hardness of micro-electromechanical relays.
• Created a mathematical model for this phenomenon and compared it to measured degradation after the devices had been run through a particle accelerator.
• Presented to peers in Berkeley and to the ACS regional committee in Chico, Montana, on micro-electromechanical devices.
• Wrote a senior thesis based on this work.

Postdoc - Machine Learning And Solid-State Batteries

Doctor Of Philosophy (Phd), Materials Chemistry

Cumulative GPA: 4.2 Classes Completed for Degree Requirements: • Intro to Device Processing • Semiconductor Device Physics • Semiconductor.

Master Of Science (M.S.), Materials Chemistry

Bs, Chemistry, Computer Science

Activities and Societies: President of Carroll College Talking Saints Cumulative GPA: 3.82 Chemistry GPA: 3.86 Computer Science GPA: 3.92