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Work with thought leaders and academic experts in Artificial Intelligence

Companies can benefit from working with experts in Artificial Intelligence in various ways. These researchers can help develop and implement AI solutions to improve efficiency, automate processes, and enhance decision-making. They can also assist in data analysis and predictive modeling to identify patterns and trends. Additionally, AI researchers can provide insights and recommendations for optimizing business strategies and customer experiences. By collaborating with these experts, companies can gain a competitive edge, drive innovation, and achieve their business goals.

Researchers on NotedSource with backgrounds in Artificial Intelligence include Dr. Pantaleon Fassbender, ENG. BRYAN ASEGA, Artem Timoshenko, Ph.D., Dr. Antonio Pagliaro, Ph.D., Ping Luo, Tyler Streeter, Dr. Andrew Raij, Ph.D., Dr. Vartenie Aramali, Ph.D., Keiran Thompson, Christos Makridis, Dr. Wolfgang Messner, Nicolangelo Iannella, IQRAM HUSSAIN, Ph.D., and Matthew Deuschle.

Dr. Pantaleon Fassbender

Gainesville, Florida, United States of America
Highly experienced global leadership and corporate health management advisor
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (7)
Applied Psychology
Communication
Experimental and Cognitive Psychology
Human-Computer Interaction
Philosophy
And 2 more
About
With over 25 years of experience in corporate health management, talent management, and crisis management consulting, I am passionate about helping leaders and organizations overcome challenges and achieve their goals. As a Managing Director at Twisters Management Consulting LLC, I leverage my expertise in evidence-based management, applied psychology, data storytelling, and analytical skills to provide innovative and effective solutions for my clients. One of my unique offerings is horse-assisted coaching, which is a powerful and experiential method to enhance personal and professional development. By working with a horse and a coach on the ground, clients can gain insights into their body language, communication style, emotional intelligence, and leadership presence. I also specialize in personality assessment-at-a-distance, which is a valuable tool for due diligence, ghost negotiation, fraud investigation, and other high-stakes situations. Additionally, I have a strong background in investigative psychology and forensics, which enables me to apply behavioral analysis and profiling techniques to various security contexts.
Most Relevant Publications (1+)

6 total publications

Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types

AI & SOCIETY / Jun 02, 2023

Klein, U., Depping, J., Wohlfahrt, L., & Fassbender, P. (2023). Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types. AI & SOCIETY. https://doi.org/10.1007/s00146-023-01699-w

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ENG. BRYAN ASEGA

Nairobi
Software Engineer - Python, PHP, AI Developer, Web3 | Geologist - Seismology, Stratigraphy, Hydrogeologist
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (5)
Seismology
Hydrogeology
Blockchain
Stratigraphy
DevOps
About
I am a Full-Stack Software Engineer with 3 years of Industrial development experience. I am also a graduate Geologist with 1 year of experience in Hydrogeology, Stratigraphy, and Seismology. I am currently innovating the use of Blockchain Technology, Python, and Machine Learning in Stratigraphy, Hydrogeology, and Seismology.

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Artem Timoshenko, Ph.D.

Chicago, Illinois, United States of America
Professor at Kellogg School of Management
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (3)
Marketing
Management
Innovation
About
I am a Professor of Marketing at Kellogg School of Management at Northwestern University. My research and consulting focus on the business applications of AI, including innovation, product management, marketing, and sales. Happy to collaborate!

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Dr. Antonio Pagliaro, Ph.D.

Palermo
Senior Scientist at Istituto di Astrofisica Spaziale e Fisica cosmica di Palermo with publications on AI and expertise in Machine and Deep Learning, Data Driven Finance.
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (3)
High Energy Astrophysics
Machine and Deep Learning
Data Driven Finance
About
My vision is to achieve **greater generality in artificial intelligence**: empower computers to perform advanced tasks, such as **learning from experience, adapting seamlessly, and discovering new knowledge**, mainly in the fields of high energy astrophysics and astroparticles, but not only. To address the overwhelming volume of information, we require computers that can extract **meaningful patterns from vast amounts of data.** This presents a variety of intriguing scientific challenges: how can a computer determine the most effective representation? How can it differentiate between genuine patterns and random occurrences? How can it leverage prior knowledge? And, finally, how can it present its findings in a way that humans can comprehend?

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Ping Luo

Toronto, Ontario, Canada
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (21)
single-cell genomics
deep learning
complex network analysis
Genetics (clinical)
Genetics
And 16 more
About
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.
Most Relevant Publications (1+)

23 total publications

CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization

Neurocomputing / Jan 01, 2018

Tian, L.-P., Luo, P., Wang, H., Zheng, H., & Wu, F.-X. (2018). CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization. Neurocomputing, 275, 2031–2040. https://doi.org/10.1016/j.neucom.2017.10.039

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Tyler Streeter

Iowa City, Iowa, United States of America
Theoretical Machine Learning / Statistics Researcher: Bayes, Information Theory, Boltzmann Machines
Most Relevant Research Expertise
artificial intelligence
Other Research Expertise (43)
machine learning
undirected graphical models
Boltzmann machines
Markov random fields
Ising models
And 38 more
About
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.

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Dr. Andrew Raij, Ph.D.

Orlando, Florida, United States of America
Principal Research Scientist and Lab Director | VR, AR, MR, XR, Spatial Computing, Immersive Technologies
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (13)
Human-Computer Interaction
Virtual Reality
Augmented Reality
Human Factors and Ergonomics
Modeling and Simulation
And 8 more
About
Dr. Andrew Raij, Ph.D. is a principal researcher at the Charles Stark Draper Laboratory, where he leads research on human-centered computing and virtual / augmented / mixed reality. For over 20 years, Dr. Raij has been helping companies unlock the transformative power of immersive technologies through **rigorous user research, strategic guidance, and creative problem-solving**. With a deep understanding of human behavior in immersive environments, he bridges the gap between cutting-edge technology and real-world needs. His career began in academia, where he was an Assistant Professor and Director of the Powerful Interactive Experiences (PIE) Lab at the University of South Florida. Later, he joined Universal Creative, where he applied his expertise to the challenge of using immersive, wearable technologies in the theme park environment. At Draper Labs, his research focuses on applying immersive technologies to training and situational awareness. Dr. Raij earned his Ph.D. in the Department of Computer & Information Science & Engineering from the University of Florida in 2009. Prior to this, he completed his M.S. in Computer Science at the University of North Carolina at Chapel Hill in 2003 and his B.S. in Computer Science at Northwestern University in 2001.
Most Relevant Publications (1+)

54 total publications

A Large-Scale Study of Surrogate Physicality and Gesturing on Human–Surrogate Interactions in a Public Space

Frontiers in Robotics and AI / Jul 07, 2017

Kim, K., Nagendran, A., Bailenson, J. N., Raij, A., Bruder, G., Lee, M., Schubert, R., Yan, X., & Welch, G. F. (2017). A Large-Scale Study of Surrogate Physicality and Gesturing on Human–Surrogate Interactions in a Public Space. Frontiers in Robotics and AI, 4. https://doi.org/10.3389/frobt.2017.00032

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Dr. Vartenie Aramali, Ph.D.

Tempe, Arizona, United States of America
Unknown affiliation
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (6)
Project Management
Earned Value Management
Machine Learning
Safety
Construction
And 1 more
About
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.

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Keiran Thompson

Palo Alto, California, United States of America
Stanford University
Research Expertise (6)
Physical and Theoretical Chemistry
Colloid and Surface Chemistry
Biochemistry
Catalysis
Computer Science Applications
And 1 more
About
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.

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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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Dr. Wolfgang Messner

Columbia, SC
Professor in International Business with expertise in Data Analytics and Machine Learning
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (14)
International Business
International Marketing
International Management
Strategy and Management
Business and International Management
And 9 more
About
Results-oriented and internationally experienced project manager, consultant, and researcher with a passion for leveraging machine learning and advanced statistical techniques to solve intricate challenges in international marketing and consumer behavior. Demonstrated track record of driving strategic initiatives, cultivating cross-border partnerships, and delivering tangible impacts on revenue generation. Highly adaptable to rapidly evolving technologies and market trends. Aiming to apply my expertise to lead transformative projects and elevate organizational success on a global scale. **Research and publication overview** · Authored 36 peer-reviewed journal publications (data analytics, international business, marketing) · Authored and edited 8 business books, published by *Palgrave Macmillan* and *Springer* · Published 5 teaching cases with *SAGE* and *Ivey* · Research impact (Google Scholar): h-index of 17 with 1,000+ citations **Competences in data analysis (selected)** · Supervised: Neural networks, deep learning · Unsupervised: Kohonen self-organizing maps · Frequentist and Bayesian regression analysis · Multilevel (hierarchical) modeling · Exploratory and confirmatory factor analysis · fs/QCA \| HLM \| SPSS \| JASP \| Python\, incl\. Keras\, Dalex
Most Relevant Publications (1+)

65 total publications

Improving the cross-cultural functioning of deep artificial neural networks through machine enculturation

International Journal of Information Management Data Insights / Nov 01, 2022

Messner, W. (2022). Improving the cross-cultural functioning of deep artificial neural networks through machine enculturation. International Journal of Information Management Data Insights, 2(2), 100118. https://doi.org/10.1016/j.jjimei.2022.100118

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Nicolangelo Iannella

Oslo
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (18)
Neuromorphic circuits
Neural networks, Neural learning and applications
Theoretical and Mathematical neuroscience
Computational neuroscience
Cognitive Neuroscience
And 13 more
About
Following pre-doctoral studies in Mathematics and Theoretical Physics, I received a PhD in Computational Neuroscience from the University of Electro-Communications, Japan in 2009. From 2009, I was a Postdoctoral Researcher in RIKEN BSI. In 2010, I won the prestigious Australian Research Council (ARC) Australian Postdoctoral Award (APD) fellowship, based at the University of Adelaide from 2010–2014. In 2012 he completed a Graduate Certificate in Education (Higher Education) (GCEHE) from the University of Adelaide. From 2014–2017 he was an adjunct research fellow at the University of South Australia. From 2016–2018, he was a Cascade (Marie Curie) Research Fellow in Mathematical Sciences at the University of Nottingham. From 2018- a research fellow at the University of Oslo. His research interests include AI, Artificial and spiking neural networks and learning algorithms, synaptic plasticity, neuronal dynamics, and neuromorphic engineering. Dr. Iannella is a member of SFN and a Senior member of the IEEE.
Most Relevant Publications (2+)

47 total publications

A spiking neural network architecture for nonlinear function approximation

Neural Networks / Jul 01, 2001

Iannella, N., & Back, A. D. (2001). A spiking neural network architecture for nonlinear function approximation. Neural Networks, 14(6–7), 933–939. https://doi.org/10.1016/s0893-6080(01)00080-6

A neuromorphic VLSI design for spike timing and rate based synaptic plasticity

Neural Networks / Sep 01, 2013

Rahimi Azghadi, M., Al-Sarawi, S., Abbott, D., & Iannella, N. (2013). A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Networks, 45, 70–82. https://doi.org/10.1016/j.neunet.2013.03.003

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IQRAM HUSSAIN, Ph.D.

New York City, New York, United States of America
Weill Cornell Medicine, Cornell University, NY, USA
Most Relevant Research Expertise
Artificial Intelligence
Other Research Expertise (32)
Biomedical & Medical Physics
AI (Machine & Deep Learning)
Anesthesiology
Sleep Medicine
Human Gait & brain
And 27 more
About
Iqram Hussain works at the Department of Anesthesiology, Weill Cornell Medicine, Cornell University, NY, USA. Earlier, he was a postdoctoral researcher at the Medical Research Center, Department of Biomedical Engineering, Seoul National University. He pursued a Ph.D. degree in Medical Physics from the University of Science and Technology (UST), South Korea. He worked as a Research Associate with the Korea Research Institute of Standards and Science (KRISS), Daejeon, South Korea. He worked on the Knowledgebase Super Brain (KSB) project at the Electronics and Telecommunication Research Institute (ETRI), Daejeon. He received a B.Sc. degree in mechanical engineering from the Khulna University of Engineering & Technology, Bangladesh, in 2007. He has ten years of work experience in power plant operation and maintenance and power plant project management. His research interests include wearable sleep monitoring, neuroscience, medical physics, human factors, and ergonomics. He has experience in healthcare research, project management, power plant operation, and maintenance. He is a reviewer in IEEE Access, Sensors, Applied Sciences, Biomedical Signal Processing and Control, IEEE Transactions, Science of the Total Environment, Neuroscience Informatics, Brain Sciences, etc. He is a guest editor in special issues of several Journals. Website: https://sites.google.com/view/iqram/home
Most Relevant Publications (1+)

43 total publications

Tracking Trajectory Planning of Space Manipulator for Capturing Operation

International Journal of Advanced Robotic Systems / Sep 01, 2006

Huang, P., Xu, Y., & Liang, B. (2006). Tracking Trajectory Planning of Space Manipulator for Capturing Operation. International Journal of Advanced Robotic Systems, 3(3), 31. https://doi.org/10.5772/5735

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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
Artificial Intelligence
Other Research Expertise (6)
GenAI
Data Science
Machine Learning
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 Artificial Intelligence projects

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

AI-powered Customer Service Chatbot

An AI researcher can develop a chatbot that uses natural language processing and machine learning algorithms to provide personalized customer support. This chatbot can handle customer inquiries, resolve issues, and even make product recommendations, improving customer satisfaction and reducing the workload of customer service teams.

Automated Fraud Detection System

By collaborating with an AI researcher, companies can build an automated fraud detection system that analyzes large volumes of data in real-time. This system can identify suspicious patterns and anomalies, flagging potential fraudulent activities and minimizing financial losses.

Predictive Maintenance for Manufacturing

An AI expert can develop predictive maintenance models that leverage machine learning algorithms to analyze sensor data from manufacturing equipment. By predicting equipment failures in advance, companies can schedule maintenance activities, reduce downtime, and optimize production efficiency.

AI-powered Marketing Campaign Optimization

Working with an AI researcher, companies can optimize their marketing campaigns using AI techniques. These researchers can analyze customer data, segment audiences, and recommend personalized content and targeting strategies to maximize campaign effectiveness and ROI.

AI-driven Supply Chain Optimization

Collaborating with an AI expert can help companies optimize their supply chain operations. These researchers can develop AI models to analyze historical data, predict demand, optimize inventory levels, and streamline logistics, leading to cost savings and improved operational efficiency.