Work with thought leaders and academic experts in modeling simulation

David J. Hamilton, PhD Neuroscience, GMU, 2016. His current research focus is Efficient Generative AI leveraging biologically plausible computational circuits and spiking neural networks to implement transformer-based algorithms. Dr. Hamilton has extensive R&D experience in Generative AI and Machine Learning capability development. Specific projects include transformer-based LLM sensor parameter tuning, analytic prediction, Cyber Threat Analysis Platform R&D, US Treasury cyber defense, credit card fraud detection, sensor fusion/analysis, LIDAR signal characterization, and active/passive sonar signal detection/classification. Companies for which David has worked include Intelligent Mission Consulting Services (2020-2023), Northrop Grumman (2004-2020), NeuralTech/CardSystems (1994-2004), Raytheon (1980-1994), and AAI (1977-1980). Earlier in his career, David received his MSEE (1981) from Loyola University, Maryland, and his BSEE (1977) from PSU. He is well published, holds memberships in Society for Neuroscience (SfN), AAAS, IEEE, and continues to maintain his association with GMU as an Affiliate Faculty.

Results-oriented professional with over 15 years of experience in research and teaching. Skilled in Python, MATLAB programming, image processing, and telecommunications. Demonstrated ability to work under pressure, manage time effectively, and solve complex problems. Successfully advised doctoral candidates on data analysis methods, authored multiple peer-reviewed journal articles, and secured funding for future research initiatives. Developed innovative curricula for advanced mathematics courses and utilized technology to enhance learning experiences. A fast learner with excellent written and verbal communication skills.

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>

Dr. Maurice O’ Sullivan is a sensory scientist and senior lecturer in food science working in the School of Food and Nutritional Sciences, UCC. In the past Maurice has worked in the food industry as a sensory scientist and flavour chemist for Diageo Baileys and Guinness. He has worked as a food technologist and process/product development scientist for the processed foods sector in the 1990’s as well as more recently a product development consultant to the dairy, meat and beverage industries. He has a Ph.D. in Sensory and Consumer science from the Department of Food Science, University of Copenhagen, Denmark as well as MSc. and BSc. Degrees in Food Science and Technology from University College Cork (UCC). Currently Maurice works in the School of Food and Nutritional Sciences, University College Cork as a senior lecturer and sensory and product reformulation project coordinator on large scale multi-million euro research projects. For many years he has also managed the “Sensory Unit” at UCC working on sensory and flavour based projects involving a diverse selection of products. This sensory science based service (at UCC) includes testing the difference, affective and descriptive analysis of foods and beverages as well as assessing shelf-life and comparing sensory results to microbiological and analytical (instrumental, physic-chemical) data using multivariate data analysis (MVA). Sensory testing is critical for new product development/optimisation, ingredient substitution and devising appropriate packaging and comparing foods or beverages to competitor’s products. Again, the key to unlocking the hidden secrets of this multimodal data is with MVA (Multivariate Data Analysis). To date Maurice has secured significant research funding of €5,300,000 Gross, €3,843,329.30 to UCC. He has worked in the area of targeted ingredient (salt, fat, sugar) reduction in foods since 1997. The focus of this research has been reduction of negative ingredients linked to civilisation diseases (Diabetes, Obesity, Heart Disease, Anaemia) while maintaining sensory properties, but also improving nutritional quality of developed products. To date this research has produced numerous publications with many more in development. Other research areas include Cross Cultural Sensory Perception of Irish Dairy Products in Asia, Europe and North America as well as Foods for the Elderly, Packaging and Meat optimisation. Maurice has published more than 200 sensory or consumer research based articles (papers, book chapters, proceedings etc.).

Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.

Dr. Justin P. Whalley was educated in the UK (M.Sci. Mathematics, University of Bristol) and France (Ph.D. Bioinformatics, University of Évry). He moved to Spain to work as a postdoc at the National Center for Genomic Analysis (CNAG). During his time there, he ran the Quality Control working group for the Pan-Cancer Analysis of Whole Genomes project to assess the data coming in and reduce batch effects. This involved collaboration with researchers from the Broad Institute of MIT and Harvard, the German Cancer Research Center and the Wellcome Sanger Institute in the UK. He returned to the UK to work as a [Senior Bioinformatician at the University of Oxford](https://www.well.ox.ac.uk/people/jpw/). His time there coincided with the global pandemic and he was deeply involved in the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium as the lead for the Integration (Tensor) working group. Dr. Whalley became a member of the [faculty of the Chicago Medical School at Rosalind Franklin University of Medicine and Science](https://www.rosalindfranklin.edu/academics/faculty/justin-p-whalley/) in January 2023.

Jeffrey Townsend is a Professor of Organismic and Evolutionary Biology at Yale University. He received his Ph.D. from Harvard University in 2002 and his Sc.B. from Brown University in 1994. He has been a teacher at St. Ann's School and an Assistant Professor at the University of Connecticut. He is currently the Elihu Professor of Biostatistics at Yale University.

Dr. Andrea Corti holds a PhD in Biomedical Engineering from The City College of New York. His research interests include tissue mechanics, cellular transport, and biomechanics. He received his BS and MS in biomedical engineering with a specialization in Prosthesis and Implant Design from the Politecnico di Milano and the University of Groningen, respectively. He has experience working in both industry and academia, and has been a teaching assistant for several courses in his department.

I do first-principles computational research in materials science, that is, I adopt fundamental theories of physics at the atomistic scale to the computer to calculate and predict material properties. My interests include electron and thermal transport, lattice dynamics, and phase transitions. I have studied thermoelectrics, battery materials and interfaces, and shape-memory alloys, both independently and in collaboration with experimentalists.

Technical Skills: ·       **Programming Languages:** Python, OpenCV, C++ 11 and up, C, and Java. ·       **Image processing**: Image segmentation, alignment, registration, enhancement, filters, noise removal, binarization, object detection and identification, English & Arabic OCR, 2D barcode readers, watermarking, DCT (Discrete Cosine Transform), check cropping, industrial defect detection, and fault detection of assembled systems. ·       **Software Engineering Skills:** OOP, algorithms, data structures, SW processes, static and dynamic code analysis, algorithms performance, MISRA standards code compliance, Google C & C++ coding guidelines, git, and hg.   Cryptography

I hold a PhD Eng. degree in computer science. I am a graduate of the Polish-Japanese Academy of Information Technology in Warsaw, POLAND. Currently, I am associate professor at the Cathedral of Multimedia at the same university. I lead and conduct scientific projects and research related to natural language processing and machine learning based on statistical methods and neural networks. I eagerly take up IT challenges and engage in interesting interdisciplinary projects, in particular related to HCI, UX, medicine, and psychology. In addition, as my profession, I have worked as a lecturer at the Warsaw School of Photography, and as an IT trainer. My specialties as a teacher are primarily deep learning, machine learning, natural language processing, computational linguistics, multimedia, HCI, UX, mobile applications, HTML 5, Adobe applications, and server products from Apple and Microsoft. As far as my didactic work is concerned, I lead classroom studies at the faculty of computer science and at the new media art department of the Polish-Japanese Academy of Information Technology and, in the past, I have also directed classes and lectures at the Warsaw School of Photography & Graphic Design. I am also an expert at the Polish National Agency for Academic Exchange, a member of the Polish Information Processing Society, and a member of the Polish Telemedicine and eHealth Society with Bene Meritus honor. Finally, I am a certified Microsoft, Apple, Adobe, w3schools, and EITCA specialist as well as being the Author of many scientific monographs and specialized IT books related to machine learning, administration of servers, and multimedia. I also engage as an editor of various specialized IT web portals such as in4.pl, pclab.pl, and e-biotechnologia.pl where I am author of training materials, guides, and hardware reviews. Some of my articles have also been published in iCoder Magazine and Komputer Świat magazine.

Empowering innovation through 10+ years of dedicated expertise in Computational Research Science, I specialize in leveraging statistical techniques, mathematical modeling, and high-performance computing (HPC) to solve complex challenges across diverse domains. My expertise spans critical areas such as Theoretical/Computational Quantum Condensed Matter Physics, Computational Chemistry, Computational Material Science, Quantum Computing, and Data Analysis. With a proven track record in deploying advanced methodologies, including quantum teleportation (qubit transfer in spin networks), Quantum Monte Carlo Simulations, First-principles (ab-initio) calculations, and cutting-edge algorithms, I bring a robust foundation in both classical and quantum realms. I thrive on translating complex data into actionable insights, utilizing a spectrum of Machine Learning techniques—from supervised to unsupervised and reinforcement learning, encompassing Regression, Classification, Neural Networks, TensorFlow, and Recommender Systems.

Tim Leung is a professor of operations research and financial engineering at the University of Washington. He holds a PhD from Princeton University and a B.S. from Cornell University. He has held previous positions as an assistant professor at Columbia University and Johns Hopkins University.