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.

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 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.

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.

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.

Yuan Yang is an Assistant Professor of Materials Science and Engineering at Columbia University. He received his Ph.D. in Materials Science and Engineering from Stanford University in 2012. His research interests include nanomaterials and optoelectronic devices. He has published over 50 papers in peer-reviewed journals and has been awarded several patents.

Based in Boston, MA, I am a recent PhD graduate in Physical Chemistry from Boston University with experience across multiple disciplines such as mathematics, statistics, physics, and chemistry. My Ph.D. research focused on the spectroscopy studies of 2D quantum materials.

Issam Mudawar is a professor of mechanical engineering at Purdue University. He received his Ph.D. from the Massachusetts Institute of Technology in 1984. His research interests include microelectronics cooling, heat transfer, and fluid mechanics. He is also the Founder and Director of the Purdue University International Electronic Cooling Alliance