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, Ranjit Panigrahi, Keiran Thompson, Joshua Cohen, Christos Makridis, Ping Luo, Matthew Deuschle, Tyler Streeter, and Alexander Bowler.

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
About
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.

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Ranjit Panigrahi

Gangtok
Assistant Professor at Sikkim Manipal Institute of Technology
Most Relevant Research Expertise
Machine Learning
Other Research Expertise (3)
Pattern Recognition
Biomedical Data Analysis
Prediction Model Design
About
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

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Christos Makridis

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

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Matthew Deuschle

Kansas City, Missouri, United States of America
Data Science and AI Strategist with Over 20 Years of Expertise in Machine Learning, Generative AI, and Large-Scale Data Solutions
Most Relevant Research Expertise
Machine Learning
Other Research Expertise (6)
Artificial Intelligence
GenAI
Data Science
Deep Learning
Algorithms
And 1 more
About
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.

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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.