Work with thought leaders and academic experts in Machine learning

Companies can greatly benefit from collaborating with academic researchers in the field of Machine learning. Here are some reasons why: 1. Enhanced Data Analysis: Academic researchers have advanced knowledge and expertise in data analysis techniques, allowing companies to gain deeper insights from their data. 2. Innovative Solutions: Researchers can develop cutting-edge algorithms and models to solve complex business problems, leading to innovative solutions and competitive advantages. 3. Stay Ahead of the Competition: By collaborating with academic researchers, companies can stay updated with the latest advancements in Machine learning, ensuring they remain ahead of their competitors. 4. Access to Research Facilities: Academic researchers often have access to state-of-the-art research facilities and resources, which can be leveraged by companies for their projects. 5. Talent Acquisition: Collaborating with academic researchers provides companies with opportunities to identify and recruit top talent in the field of Machine learning.

Researchers on NotedSource with backgrounds in Machine learning include Hakob Tamazyan, David J. Hamilton, PhD, Christopher Timms, Keiran Thompson, Joshua Cohen, Christos Makridis, Ping Luo, Tyler Streeter, Suhas Chelian, Serena Booth, Jim Samuel, and Edoardo Airoldi.

David J. Hamilton, PhD

Fairfax, Virginia, United States of America
PhD Neuroscience focused on computational modeling of biologically plausible neuronal circuits.
Most Relevant Research Expertise
Machine Learning
Other Research Expertise (5)
Cognitive Neuroscience
Biomedical Engineering
Artificial Intelligence
Cellular and Molecular Neuroscience
Modeling and 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.

See Full Profile

Christos Makridis

Nashville, TN
Web3 and Labor Economist in Academia, Entrepreneurship, and Policy
Research Expertise (16)
Economics and Econometrics
Pharmacology (medical)
And 11 more
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](, 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](, 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.

See Full Profile

Jim Samuel

Associate Professor at Rutgers University
Most Relevant Research Expertise
Machine Learning
Other Research Expertise (21)
Artificial Intelligence
NLP NLU NLG Behavioral Finance
Pharmacology (medical)
And 16 more
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. 

See Full Profile

Example Machine learning projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Machine learning?

Predictive Maintenance in Manufacturing

An academic researcher can develop a predictive maintenance model using Machine learning algorithms to identify potential equipment failures in manufacturing processes. This can help companies reduce downtime, optimize maintenance schedules, and improve overall operational efficiency.

Customer Segmentation in E-commerce

By collaborating with an academic researcher, companies can develop a customer segmentation model using Machine learning techniques. This can enable personalized marketing strategies, targeted promotions, and improved customer satisfaction.

Fraud Detection in Financial Services

Academic researchers can assist companies in developing robust fraud detection systems using Machine learning algorithms. This can help identify fraudulent transactions, minimize financial losses, and enhance security measures.

Medical Diagnosis and Treatment

Collaborating with academic researchers in Machine learning can lead to the development of advanced medical diagnosis and treatment models. This can improve accuracy, speed up diagnosis, and enable personalized treatment plans.

Demand Forecasting in Retail

By leveraging the expertise of academic researchers, companies can develop accurate demand forecasting models using Machine learning. This can optimize inventory management, reduce costs, and improve customer satisfaction.