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.

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.

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.

I am an **AI and Data Science expert** with two decades of experience specializing in machine learning, data engineering, and generative AI (GenAI). I hold an **MSc in Data Science & Predictive Analytics from** **Northwestern University**, where I honed my skills in advanced analytics, predictive modeling, and data-driven decision-making. Throughout my career, I have transformed vast datasets into actionable insights that drive innovation and efficiency for organizations. I have successfully developed and implemented enterprise-level AI strategies, designed advanced machine learning models, and created data-driven solutions for a range of industries. With a deep understanding of customer behavior, perception, and quality of experience, I excel at uncovering patterns in data that unlock business potential. My work spans from predictive modeling to designing recommendation systems and search algorithms that improve user engagement and operational performance. I have a proven track record of delivering clear, compelling narratives to both technical and non-technical audiences, making complex concepts accessible and actionable. I am passionate about bridging the gap between academia and industry, helping organizations leverage cutting-edge AI technologies to solve real-world problems. My expertise is complemented by a collaborative approach, working closely with cross-functional teams to deliver impactful, scalable solutions. I look forward to partnering with organizations seeking to accelerate innovation through AI and data science.

Vartenie Aramali holds a PhD in Civil, Environmental, and Sustainable Engineering and has extensive experience in project and construction management. She has managed large-scale and complex projects worth billions of dollars across the power, oil, gas, and renewable energy sectors. Vartenie’s skills in project management, data analysis, machine learning, and AI have been applied in both academic and professional contexts. She has mentored students for several years as a faculty member and assistant professor.

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.

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.

I hold a Master of Technology in Computer Sciences & Engineering from Sikkim Manipal Institute of Technology, Sikkim, and earned my PhD in Computer Applications from Sikkim Manipal University. Currently, I am serving as an Assistant Professor – Selection Grade in the Department of Computer Applications at Sikkim Manipal Institute of Technology. In this role, I am deeply involved in teaching both Bachelor and Master degree students, guiding them through major projects, research initiatives, and publication efforts. I have been conferred with the “Excellence in Teaching” award by Sikkim Manipal University for the academic year 2020, awarded on February 06, 2021. My extensive academic journey also includes a Post-Doctoral Research position at the Graduate Program in Teleinformatics Engineering (PPGETI) at the Federal University of Ceará (UFC), Brazil, which commenced on July 30, 2023, and is ongoing. Overall, my job profile revolves around teaching, research, curriculum development, academic advising, and contributing to the academic community through various departmental activities and committees. **Google Scholar:** https://scholar.google.com/citations?user=v3vU7C0AAAAJ&hl=en **Scopus:** https://www.scopus.com/authid/detail.uri?authorId=57200761349 **Orc ID:** https://orcid.org/0000-0001-6728-5977