Work with thought leaders and academic experts in Big Data

Companies can benefit from collaborating with academic researchers in the field of Big Data in several ways. These experts can provide valuable insights and analysis to optimize operations, identify patterns and trends, develop predictive models, and drive innovation. By leveraging their expertise, companies can make data-driven decisions, improve efficiency, enhance customer experience, and gain a competitive edge in the market. Academic researchers can also help companies in data collection, data cleaning, and data integration, ensuring the accuracy and reliability of the data. Additionally, collaboration with academic researchers can lead to the development of new algorithms, methodologies, and tools that can further enhance data analysis and decision-making processes.

Researchers on NotedSource with backgrounds in Big Data include Jim Samuel, Konstantinos Tsavdaridis, Mark Ryan, Beth Egan, Enrico Capobianco, Dr. Abdussalam Elhanashi, José Luis Jiménez Márquez, Bernd Stahl, Xihao Xie, Marcin Wylot, PhD, Michelle Espinoza, and Weixian Liao.

Jim Samuel

11 Years Experience
Associate Professor at Rutgers University
Education

Baruch College

PhD, Information Systems / 2007

New York, New York, United States of America
Experience

Rutgers University

Associate Professor / 2021 — Present

Axivest

CIO / 2012 — Present

Research Expertise (22)
Analytics
Artificial Intelligence
Informatics
Machine Learning
NLP NLU NLG Behavioral Finance
And 17 more
About
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. 

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Konstantinos Tsavdaridis

London
12 Years Experience
Full Professor of Structural Engineering, Chartered Civil and Structural Engineer
Education

City University London

PhD in Civil/Structural Engineering, Department of Engineering and Mathematics / May, 2010

London

Imperial College London

MSc (DIC) in General Structural Engineering, Department of Civil and Environmental Engineering / September, 2006

London
Experience

City University of London

Professor of Structural Engineering / October, 2021 — Present

University of Leeds

Associate Professor of Structural Engineering / January, 2016 — Present

Assistant Professor in Structural Engneering / September, 2012 — December, 2015

City University

Lecturer in Civil Engineering / September, 2010 — August, 2012

Research Expertise (29)
Mechanics of Materials
Safety, Risk, Reliability and Quality
Building and Construction
Architecture
Civil and Structural Engineering
And 24 more
About
civil engineering, steel structures, composite structures, structural engineering, design optimisation, modular buildings, nonlinear analysis

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Mark Ryan

7 Years Experience
Digital Ethics Researcher at Wageningen Economic Research
Education

National University of Ireland, Galway

PhD, Ethics - AI / 2015

Galway

National University of Ireland, Galway

MA, Philosophy / 2010

Galway

Carlow University

BA, Philosophy / 2008

Pittsburgh, Pennsylvania, United States of America
Experience

Wageningen University and Research Wageningen Economic Research

Digital Ethics Researcher at Wageningen Economic Research / August, 2020 — Present

He specialises in the ethics of AI, data-sharing, and robotics. In his work at Wageningen, his research gives a special attention to ethical and societal concerns of developing and implementing digital technologies

KTH Royal Institute of Technology

Postdoctoral Researcher / 2019 — 2020

In this position at KTH Royal Institute of Technology, I worked in the Department of Philosophy alongside Dr. Barbro Fröding on the ethics of technology, specifically focusing on the ethics of artificial intelligence. As part of my role, I published several international peer-reviewed articles, attended international conferences to disseminate my work, organised internal workshops, and applied for grant funding.

University of Twente

Postdoctoral Researcher / 2018 — 2019

In my position at University of Twente, I worked in the Department of Philosophy alongside Prof. Brey and Dr. Macnish on the prestigious and exciting 3 ½ - year international SHERPA Project that was funded through the European Union Horizon 2020 “Science with and for Society” program with a budget of 3 million euros. It had eleven participating organizations, ranging from universities to industry to human rights organizations, amongst others. The SHERPA project investigated, analyzed and synthesized our understanding of the ways in which Smart Information Systems (SIS - involving big data analytics and artificial intelligence) impact ethics and human rights issues. It developed novel ways of understanding and addressing their challenges, evaluated with stakeholders, and advocated the most desirable and sustainable solutions. My role in this project focused on the ethical evaluation of particular SIS technologies, the companies and organizations that use and implement them, and the societal implications of their widespread adoption and integration. As part of my role, I liaised with international organizations from an array of societal domains and evaluate their use of these emerging technologies.

Research Expertise (34)
Digital Ethics
Philosophy of Technology
Environmental Ethics
AI Ethics
Data Ethics
And 29 more
About
Ryan’s primary research focuses on the ethical issues surrounding artificial intelligence and digital technology. He has published numerous papers on the topic, and has presented his work at various international conferences. He is also a member of the Association for Computing Machinery’s (ACM) Committee on Professional Ethics (COPE). Mark was previously a researcher at KTH University (Stockholm), the University of Twente (the Netherlands), and the National University of Ireland, Galway (Ireland). While at Twente, he worked on an interdisciplinary  project (SHERPA), involving 11 partners from 6 European countries. This project was a European Union Horizon 2020 project (2018-2021, budget €3 million) and focused on the ethical, social and human rights implications of smart information systems (data analytics and artificial intelligence) within a European context. He has published on topics, such as the ethics of smart cities, self-driving vehicles, agricultural data analytics, social robotics, and AI. In his previous research, he has also published a 2016 monograph: Human Values, Environmental Ethics and Sustainability.

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Beth Egan

14 Years Experience
Associate Professor of Advertising, Syracuse University, Author of Media Planning Essentials, an online courseware and published in areas related to data, data privacy, native advertising and general advertising investment topics
Education

Southern Methodist University

MBA, Business / July, 2000

Dallas, TX

Syracuse University

Bachelor of Science, Advertising, Advertising / May, 1988

Syracuse, New York, United States of America
Experience

Syracuse University

Associate Professor / August, 2013 — Present

MEC (now Wavemaker)

Managing Director / July, 2009 — May, 2013

Looked after the media planning strategy for the AT&T Mobility account ($1Bn) and Campbell's Globally ($400M)

Most Relevant Research Expertise
big data
Other Research Expertise (7)
media
advertising
branded content
native advertising
Human-Computer Interaction
And 2 more
About
Beth is an Associate Professor of Advertising at Syracuse University’s S.I. Newhouse School of Public Communication, author and TEDx speaker. As a 25-year veteran of the advertising media industry she leads the media planning and data & analytics curricula. She is also the author of Media Planning Essentials, the first online, digital-first media planning courseware. Beth’s research interests include applying machine learning techniques to develop predictive models of television audience retention. These models can be used to optimize ad curation and provide television networks with tools to maximize their revenue models. She is also working on employing psychophysiological research techniques to understand how people perceive branded content implicitly. This research can help inform both the creation and placement of branded content.

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Enrico Capobianco

25 Years Experience
Expertise in network science and special interest in cancer domain. Scientific Leader, Advisor. Quant, Computational & Digital Biomedical & Health research.
Education

Stanford University

Post-doctoral Fellowship, AI, Machine & Statistical Learning, Neural Networks / 1998

Stanford, California, United States of America

University of Padua

PhD, Statistical Sciences / 1995

Padova
Experience

The Jackson Laboratory

Associate Director, Computational Systems / 2018 — 2024

Most Relevant Research Expertise
Big Data
Other Research Expertise (34)
Networks
Machine Learning
Systems Biology & Medicine
Statistics
Molecular Biology
And 29 more
About
Enrico Capobianco is a highly experienced and accomplished expert in the fields of artificial intelligence, machine learning, and statistical learning. He holds a Post-doctoral Fellowship in AI, Machine & Statistical Learning, Neural Networks from Stanford University, which he completed in 1998. Prior to that, he received his PhD in Statistical Sciences from the University of Padua in 1995. With over 25 years of experience, Capobianco has held various positions in academia, research, and industry. Most recently, he served as the Associate Director of Computational Systems at The Jackson Laboratory, a leading non-profit research institute focused on genetics and genomics. In this role, he oversaw the development and implementation of computational systems and tools for genetic and genomic research. Throughout his career, Capobianco has published numerous articles and book chapters on topics such as machine learning, artificial intelligence, and computational biology. He has also been a keynote speaker at various international conferences and has received numerous awards and grants for his research. In addition to his professional achievements, Capobianco is known for his collaborative and innovative approach to problem-solving. He is constantly seeking new ways to apply advanced computational techniques to solve complex problems in various industries, from healthcare to finance. Overall, Enrico Capobianco is a highly respected and sought-after expert in the fields of AI, machine learning, and statistical learning. His education and experience have equipped him with the knowledge and skills to make significant contributions to the advancement of these fields.

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Dr. Abdussalam Elhanashi

4 Years Experience
Researcher at University of Pisa
Education

University of Pisa

PhD, Department of Information Engineering / February, 2023

Pisa

University of Nicosia

MBA, Department of Business Management / March, 2018

Nicosia

University of Glasgow

Master of Science, Department of Electronics and Electrical Engineering / January, 2018

Glasgow
Experience

University of Pisa

Researcher / July, 2019 — Present

Research Expertise (21)
Machine learning Deep learning and Imaging processing IoT devices Object detection Embedded system
Information Systems
Electrical and Electronic Engineering
Computer Science Applications
Modeling and Simulation
And 16 more
About
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.

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José Luis Jiménez Márquez

2 Years Experience
Expert in Machine/Deep Learning, NLP, LLMs, Prompt engineering, and advanced chatbot development
Education

Universidad Carlos III de Madrid - Campus de Leganes

PhD in Computer Sciences and Technology, Computer Science / March, 2019

Leganes
Experience

Universidad Autonoma de Madrid

Visiting Researcher / June, 2023 — December, 2023

• Developed and optimized prompts for OpenAI LLMs using Chain-of-thought techniques, leading to a substantial improvement in chatbot conversational fluency.

The Villa Group

Data Scientist / November, 2022 — June, 2023

• Showcased a working knowledge of database management by optimizing databases using SQL, Python, and Azure SQL. Played a key role in a successful customer unification process, eliminating duplicates.

Most Relevant Research Expertise
Big Data
Other Research Expertise (13)
Machine Learning
Cloud Computing
Natural Language Processing
Library and Information Sciences
Information Systems
And 8 more
About
José Luis Jiménez Márquez is a highly skilled computer scientist with a PhD in Computer Sciences and Technology from Universidad Carlos III de Madrid. He completed his doctoral studies in 2019, specializing in the field of computer science. During his time as a student, José Luis excelled academically and was recognized for his exceptional research and problem-solving abilities. After completing his PhD, José Luis gained valuable experience as a Visiting Researcher at Universidad Autonoma de Madrid. He worked on several projects related to data analysis and machine learning, further honing his skills in these areas. Currently, José Luis works as a Data Scientist at The Villa Group, where he utilizes his expertise in data analysis and machine learning to develop innovative solutions and drive business growth. He has a proven track record of delivering high-quality results and has received praise from his colleagues for his strong work ethic and dedication to his work. José Luis is a passionate and driven individual, always seeking opportunities to expand his knowledge and skills in computer science. He is a valuable asset to any team and is committed to making a positive impact in the field of computer science through his work.

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Bernd Stahl

36 Years Experience
Director of the Centre for Computing and Social Responsibility
Education

De Montfort University

DSc, Centre for Computing and Social Responsibility

Leicester

De Montfort University

LLM in Business Law / October, 2007

Leicester

University Witten / Herdecke

PhD, Philosophy and Economics / November, 2002

Witten
Experience

University of Nottingham

Professor of Critical Research in Technology / October, 2022 — Present

De Montfort University

Director / June, 2003 — September, 2023

University College Dublin

Lecturer in Information Systems / September, 2000 — March, 2003

Research Expertise (53)
critical theory
information systems
computer ethics
information ethics
responsible innovation
And 48 more

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Xihao Xie

6 Years Experience
AI and big data practitioner with research and industrial experience in software engineering, recommender systems, service computing, big data
Education

Southern Methodist University

Ph.D., Computer Science / May, 2024

Dallas, Texas, United States of America

University of Chinese Academy of Sciences

Mater of Engineer, Software Engineering / July, 2013

Beijing

University of Science and Technology of China

Bachelor of Science, Computer Science / July, 2010

Hefei
Experience

Litigiven, LLC

R&D Manager - AI & Big Data / May, 2023 — September, 2024

FAW-Volkswagen

Big Data Architect / June, 2020 — July, 2021

Tencent

Senior Software Engineer / April, 2018 — March, 2020

Most Relevant Research Expertise
Big Data
Other Research Expertise (4)
Recommender System
Data Science
Software Engineering
Service Computing

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Marcin Wylot, PhD

Most Relevant Research Expertise
Big Data
Other Research Expertise (8)
artificial intelligence
Internet of Things
distributed databases
Linked Data
Theoretical Computer Science
And 3 more
About
**Dedicated to empowering organizations through data-driven and artificial intelligence solutions to optimize workflows and enhance revenue streams.** With over two decades of comprehensive experience in Computer Science across industry and academia, I specialize in devising innovative solutions that streamline operations and boost profitability, adhering to the principle of simplicity (KISS paradigm). My expertise spans the entire spectrum of the data processing pipeline – from data collection and modeling to analysis, insight extraction, and the deployment of AI solutions in production environments. I offer strategic guidance to companies embarking on AI-powered initiatives and assist them in navigating complex data transformations. Throughout my career, I’ve led dynamic teams within startup environments, leveraging my expertise to drive innovation and optimize performance. As a trusted consultant and advisor, I’ve provided strategic guidance to companies navigating complex challenges and embarking on transformative AI initiatives. Additionally, I’ve played a pivotal role in mentoring and developing talent, guiding individuals through the intricacies of industry-specific projects and fostering their professional growth. With a knack for distilling complex concepts into actionable insights, I excel in delivering impactful presentations at industry conferences and driving the adoption of cutting-edge technologies. My commitment to excellence, coupled with a keen eye for efficiency, has consistently delivered tangible results, propelling organizations toward success in the ever-evolving landscape of data-driven solutions.

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Michelle Espinoza

Most Relevant Research Expertise
big data
Other Research Expertise (4)
online fraud detection
platforms
complexity science
cybercrime

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Weixian Liao

Education

Case Western Reserve University

Research Expertise (0)

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Example Big Data projects

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

Optimizing Supply Chain Management

An academic researcher in Big Data can analyze supply chain data to identify bottlenecks, optimize inventory levels, and improve delivery times. By leveraging advanced analytics techniques, companies can reduce costs, enhance efficiency, and improve customer satisfaction.

Personalized Marketing Campaigns

Collaborating with a Big Data expert can help companies analyze customer data to create personalized marketing campaigns. By understanding customer preferences, behavior, and demographics, companies can target their marketing efforts more effectively, increase conversion rates, and improve ROI.

Fraud Detection and Prevention

Academic researchers in Big Data can develop algorithms and models to detect and prevent fraud in various industries. By analyzing large volumes of data in real-time, companies can identify suspicious patterns, flag potential fraud cases, and take proactive measures to mitigate risks.

Predictive Maintenance

By collaborating with a Big Data researcher, companies can leverage predictive analytics to optimize maintenance schedules and reduce downtime. By analyzing sensor data and historical maintenance records, companies can predict equipment failures, schedule maintenance activities, and avoid costly unplanned downtime.

Healthcare Analytics

Academic researchers in Big Data can help healthcare organizations analyze patient data to improve outcomes and reduce costs. By leveraging machine learning and predictive modeling, companies can identify high-risk patients, optimize treatment plans, and enhance healthcare delivery.