Work with thought leaders and academic experts in data mining

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.

Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.

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. 

Dr. Suhang Wang is an Assistant Professor of Computer Science and Engineering at Pennsylvania State University. He received his PhD in Computer Science from Arizona State University in 2018, and his Master's degree in Electrical Engineering: Systems from the University of Michigan in 2013. Before joining Penn State, Dr. Wang was a postdoctoral researcher at the University of California, Santa Barbara. His research interests include natural language processing, artificial intelligence, and machine learning. He was recognized for his work at the International Conference on Knowledge Discovery and Data Mining in 2017 and the Fifth ACM International Conference on Web Search and Data Mining in 2016.

Crowdsourcing,machine learning, databases,Online Labor Markets,Social Media Analytics,Data Mining,Information Retrieval

Dr Abdussalam is a researcher at the Università di Pisa, Italia. He received M.Sc. degree in Electronic Engineering from the University of Glasgow in Scotland in 2018. He authored and co-authored several scientific articles in international conferences and journals . He is a member IET , and a member of IEEE. His current research interests are Deep learning, imaging processing, medical images, embedded systems, Power optimization management and IoT devices.

Dr. Ali Y Expert is an experienced academic and researcher in the field of engineering and operations research. He obtained his Master of Science in Electrical Engineering from the University of Waterloo in 2021 and his PhD in Engineering (Operations Research) in Mechanical Engineering from KU Leuven in 2014. He has extensive teaching and research experience, most recently serving as an Assistant Professor at Babol Noshirvani University of Technology and as a Postdoctoral Researcher at Ghent University. He has also worked as a Research Analyst at NotedSource. Dr. Expert is highly knowledgeable in the fields of engineering and operations research and is passionate about using his expertise to help others.

Algorithms & Information Intelligence, Pattern Discovery Bioinformatics & Computational Biology/Genomics Scalable Data Mining & Machine Learning

Michal Kruczkowski is an accomplished professional with extensive experience in the implementation of AI algorithms in accordance with the fourth industrial revolution. With a strong background in business and academia, he has successfully led numerous projects related to digital transformation and education. His expertise is primarily focused on safety systems and predictive modelling in healthcare, and he currently leads a multidisciplinary data science research group that conducts cutting-edge research on applied artificial intelligence and predictive modelling. In addition, Michal is a dedicated educator who has introduced over 100 students each year to the most relevant techniques and applications of AI and predictive modelling. Follow Michal for insights into the latest developments in AI and predictive modelling, as well as updates on his ongoing research and educational initiatives.

Wide multiyear experience in computational biomedicine with specialty areas : Ai/ML/DL Big data Network Medicine Systems Medicine Personalized and Precision Medicine Oncoradiomics Cancer Complex systems inference