Work with thought leaders and academic experts in machine learning

I have machine learning and deep learning professional experience with over 6 years of experience, focusing mainly on computer vision and the development of cutting-edge technology. I have worked on numerous computer vision and generative AI related projects focusing on creating efficient and high-quality models with various architectures. I am in the final stages of completing my Ph.D. program in computer science. Throughout my academic and professional journey, I have been recognized for achievements in international mathematical and programming competitions.

Christopher Timms is a highly skilled computational physicist with a Ph.D in Computational Physics from The University of Texas at Dallas. He also holds a Master of Science in computational physics from the same institution. With a strong background in physics and computer science, Christopher has a deep understanding of how to use computational methods to solve complex problems in physics. He has experience working as a Technical Writer/Developer at Amazon Braket, where he was responsible for creating technical documents and developing software tools for quantum computing. Prior to that, he worked as a Research Assistant at The University of Texas at Dallas, where he conducted research in computational physics and published several papers in peer-reviewed journals. Christopher's extensive education and experience make him a valuable asset in any team working on challenging computational physics projects.

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.

Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](https://www.dainamic.ai/), a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](https://www.livingopera.org/), a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.

8 years of science and engineering experience integrating multi-omics data to identify biomarkers for cancer studies. Seeking to apply data analytics expertise to develop new diagnosis and treatment strategies.

I am a machine learning expert with a focus on easy-to-deploy inference engines. With a background in building efficient intelligent systems in my doctoral research, I have created innovative solutions for a major US airline and a leading auto parts supplier. My professional journey encompasses academic pursuits, a role as an accelerometer-systems engineer in Silicon Valley, CTO of a brokerage firm, CEO of two startups, software process improvement, and industrial controls.

I am a researcher and software engineer focused on making machine learning simpler, more general, and more effective. Having spent many years studying a wide range of existing models and algorithms, I now work on deriving new methods from elegant theoretical principles. I enjoy writing clean code and simple APIs, designing data visualizations to gain intuition about new domains, simulating physical processes with unexpected emergent behavior, building tangible objects from humble materials, and capturing big ideas with small math. My ideal project is one that lets me be a scientist, artist, and engineer.

David J. Hamilton, PhD, Neuroscience, successfully defended his dissertation in 2016 while attending George Mason University (GMU). His research (Ascoli lab) focused on molecular, morphological, and electrophysiological characterization of hippocampal formation neuron types to facilitate potential connectivity for biologically plausible computational modeling. Dr. Hamilton has extensive R&D experience in AI/ML 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 GMU (2007-present), 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.

Team lead in machine learning, neuroscience. <br> I have captured and executed projects for DARPA, NASA, and several international clients (GM, Toyota, Fujitsu). \* 12 projects transitioned--$10M revenue captured (31+ publications, 32+ patents) \* 9 awards including those from NASA, GM, and HRL Laboratories \* I also have startup, contracting and consulting experience \* US citizen (authorized to work) <br> Last updated: Aug 30, 2023

Serena Booth is a research scientist at the Massachusetts Institute of Technology. She received her Ph.D. in robotics from MIT in 2024 and her BS in computer science from Harvard University in 2016. Her research focuses on developing new methods for robotic control and learning.