Work with thought leaders and academic experts in Physics

Keiran Thompson is a machine learning and quantum chemistry researcher. Originally from Australia, he currently works as an AI research scientist at Stanford University where he transfers machine learning knowledge from the private sector to academic research which can then be reconverted back to private sector usage. He is experienced with large scale numerical computing and has led several startups as Chief Scientist.

I am a highly motivated individual with expertise in various artificial intelligence (AI) tools, including machine learning (ML) and natural language processing (NLP). Over the course of my academic and professional career, I have developed a strong skill set in these areas, and have applied them to various domains, including mental health. At Clarigent Health, I have played a key role in developing and improving machine learning models that analyze patient speech to identify mental health concerns such as depression, anxiety, and suicide risk. In addition, I have been the principal investigator on a NIMH SBIR grant investigating machine learning model performance across different patient characteristics and settings. Moreover, I have experience in developing and implementing customer-facing dashboards using tools like PowerBI, which enable clients to interact with and derive insights from complex data sets. Through my work at Clarigent Health, I have been able to leverage my expertise in ML, NLP, and other AI-related tools to drive innovation and improve mental health outcomes through data-driven solutions.

Katrina Webb is a highly educated and experienced physicist with a passion for teaching and research. She received her BS in Physics from Texas Tech University in 2022, followed by continuing in the physics PhD program at the same university in 2022. During her time at Texas Tech, she worked as a teaching assistant and student assistant, gaining valuable experience in the classroom and laboratory settings. She also conducted research in various areas, including quantum pedagogy and astronomy, and participated in the High Scholars Research Program at the University of Texas Rio Grande Valley. Katrina's passion for physics and education led her to further her studies and research at the National High Magnetic Field Laboratory, where she worked as a Reu Student. She also gained experience as an astronomical observer at CTMO and continued her research at Texas Tech University. With a strong background in both theoretical and experimental physics, Katrina is a well-rounded and knowledgeable physicist. She is dedicated to sharing her knowledge and skills with others, and her experience in teaching and research has prepared her to effectively communicate complex concepts and ideas. Katrina is committed to making a positive impact on the field of physics and inspiring the next generation of scientists.

Her research covers a broad range of topics in polymer- and nano-based materials engineering and transport modeling, which has been published in 110+ refereed technical manuscripts and presented at numerous national and international conferences. Dr. Walters’ work has been sponsored by government agencies including NSF, DOE, and DOD, and by industry partners.

Computational materials, imaging and microscopy scientist with **8 years combined experience** in industry and national laboratories. Expert in physics-based imaging and characterization with X-rays and optical probes, high-performance computing for light-matter interaction and materials data analysis. Experienced in machine learning for materials discovery. Previous experience at the National Energy Technology Laboratory, Argonne National Laboratory and KLA Corporation. <br>

Jim Samuel is an Associate Professor of Practice and Executive Director of the Informatics Program at the Bloustein School. He is an information and artificial intelligence (AI) scientist, with significant industry experience in finance, technology, entrepreneurship and data analytics. Dr. Samuel’s primary research covers human intelligence and artificial intelligences interaction and information philosophy.  Dr. Samuel’s applied research focuses on the optimal use of big data and smart data driven AI applications, textual analytics, natural language processing and artificially intelligent public opinion informatics. His expertise extends to socioeconomic implications of AI, applied machine learning, social media analytics, AI education and AI bias. Dr. Samuel completed his Ph.D. from the Zicklin School of Business, Baruch College – City University of New York, and he also has M.Arch and M.B.A (International Finance) degrees.  Dr. Samuel has worked with large multinational financial services corporations, and advises businesses and organizations on data analytics and AI driven value creation strategies. He is passionate about research driven thought leadership in AI, information philosophy, analytics and informatics. 

Dr. Ryan Howell is an Associate Professor at San Francisco State University. His research interests include the psychology of goals and how people pursue and achieve them. Dr. Howell received his PhD in Social/Personality Psychology from the University of California, Riverside in 2005.

I hold a PhD in astrophysics, with expertise in mathematics, data analysis, Python programming, SQL database management. My experience includes presenting at conferences, publishing in international journals, and conducting outreach talks. I've taught at both high school and university levels. More details in the resume. My pursuit is driven by a great curiosity about fundamental aspects of the universe. I narrowed that curiosity to the Space and Artificial Intelligence areas. Currently, I am looking for job opportunities (remote or on-site) or postdoctoral opportunities abroad, to advance my research and contribute scientifically in space exploration and AI. I am open to explore diverse career paths, but in excellent working environments, collaborative, and with professionals who share my dedication.

I am interested in new technology in optics, photonics, and quantum systems. I am motivated to find new solutions and develop new applications, constantly learning and improving my skills. I have a strong background in Physics and experience solving Multiphysics problems numerically with commercial software and using in-house code. I have experience with experimental equipment and automation. • Optics and Photonics. Design, modeling, and theoretical simulations and experiments for photonics components from UV to IR (high-speed photodetectors, metasurface structures, APD, SPAD, laser, VCSEL, lenses, interconnectors, sensors, fibers) for communications, sensors, image sensors, and quantum applications. Signal and image processing, temporal and special noise, stochastic process, stochastic resonance. Digital holography, wavefront reconstruction. Digital holography. Multi-physics simulation, non-linear optics. • Numerical methods (Monte-Carlo, FDTD, FEM, FFT, RCWA, BPM, TMM) for modeling complex multi-physics simulations, sparse matrices, ordinary and partial differential equations. Signal processing algorithms, machine learning, optimization, modeling probability distribution, time-dependent density functional theory, non-equilibrium Green’s functions. C++, Python, Matlab , Commercial software: Lumerical, Comsol, Zemax, Silvaco, LabVIEW, computer clusters parallel coding with CPU, parallel programming with CUDA and GPU. • New materials: quantum dots, photonic crystals, 2D materials, negative index materials, chirality, broken RT-symmetry, and non-Hermitian structures. Si, Ge, and III-V semiconductors. Turbulence, Mie and Raman scattering. • Quantum communication and quantum computing. Quantum to a classical interface. Quantum networks and Quantum Key Distribution system with noise and crosstalk. Quantum communication protocoles. • Experience with optical lab equipment, experiment automation, experimental setup, and alignment, data processing. Electrical and optical testing and data analysis. • Automation of a variety of systems, optical, cryogenic fuel loading, and storage systems. Physics-based models for a variety of physical systems (optics, photonics, fluid and gas dynamics, two-phase flow, cavitation). Prognostics for automated systems for NASA, using machine learning in combination with physics-based models. • Patents and publications in peer-reviewed journals: over 150 publications, conferences presentations at IEEE, APS, Optics https://scholar.google.com/citations?user=wpEXwjwAAAAJ&hl=en • Supervising students

Bartlett is a PhD student in applied physics at Stanford University. He also has a master's degree in electrical engineering from Stanford, and a bachelor's degree in physics and computer science from the California Institute of Technology. He has experience as an undergraduate researcher at the SLAC National Accelerator Laboratory and the European Organization for Nuclear Research.

Experience, research and interests include: Additive manufacturing of bonded permanent magnets Additive manufacturing of hybrid Halbach arrays and design Additive manufacturing of brushless permanent magnet motor and magnetic properties simulations using EMworks software package Synthesized several (more than 15) types of inter-metallic single crystalline, poly-crystalline, and nano-materials samples Maintained and used Vibrating Sample Magnetometer (VSM) for 5 years to measure low (down to 50 K) and high temperature ( up to 1000 K) magnetic and transport properties Characterized these samples using XRD, SEM, resistivity, TGA, and DSC Published 10 peer-reviewed journals as a lead author and working for 4 more Co-inventor of the two provisional patents An experienced physics teacher, lab mentor for graduate and undergraduate students Interested in public speaking and social service A clean-handed experimentalist maintained lab safety and equipment properly Can design, plan, execute, data analyze, and write a manuscript of a research project independently Good research collaboration experience, adaptable to diverse working conditions Self-motivated, goals oriented researcher Enjoy critical thinking, instrumentation, and outdoor exercises Additive manufacturing of bonded permanent magnets Additive manufacturing of hybrid Halbach arrays and design Additive manufacturing of brushless permanent magnet motor and magnetic properties simulations using EMworks software package Synthesized several (more than 15) types of inter-metallic single crystalline, poly-crystalline, and nano-materials samples Maintained and used Vibrating Sample Magnetometer (VSM) for 5 years to measure low (down to 50 K) and high temperature ( up to 1000 K) magnetic and transport properties Characterized these samples using XRD, SEM, resistivity, TGA, and DSC Published 10 peer-reviewed journals as a lead author and working for 4 more Co-inventor of the two provisional patents An experienced physics teacher, lab mentor for graduate and undergraduate students Interested in public speaking and social service A clean-handed experimentalist maintained lab safety and equipment properly Can design, plan, execute, data analyze, and write a manuscript of a research project independently Good research collaboration experience, adaptable to diverse working conditions Self-motivated, goals oriented researcher Enjoy critical thinking, instrumentation, and outdoor exercises <br>

Mengying Li is an Assistant Professor at The Hong Kong Polytechnic University. She received her Ph.D. in Mechanical Engineering from University of California, San Diego in 2018. Her research interests include predictive modeling, design optimization, and machine learning. She is also an expert in passive cooling devices.