Work with thought leaders and academic experts in Information Systems

Companies can benefit from working with an academic researcher in Information Systems in several ways. Firstly, they can gain valuable insights and knowledge about the latest trends and advancements in the field. Researchers can help companies solve complex problems by applying their expertise and conducting in-depth analysis. Additionally, collaborating with researchers can provide companies with access to cutting-edge technology and tools. By working with an academic researcher, companies can stay ahead of the competition and make informed decisions based on data-driven research.

Researchers on NotedSource with backgrounds in Information Systems include Ping Luo, Jerry Schnepp, Ph.D., Martin Tsui, David J. Lilja, Dr Leandra Jordaan, Edoardo Airoldi, Jeffrey Townsend, Jim Samuel, Catherine Tucker, Hussein Al-Hussein, Vivek Singh, Shion Guha, Suhang Wang, Naveen Adusumilli, Anit Kumar Sahu, and Dr. KEHINDE ADEWALE ADESINA, Ph.D.

Ping Luo

Toronto, Ontario, Canada

Assistant Professor at Algoma University

Most Relevant Publications (1+)

23 total publications

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets

Briefings in Bioinformatics / Dec 30, 2022

Christensen, E., Luo, P., Turinsky, A., Husić, M., Mahalanabis, A., Naidas, A., Diaz-Mejia, J. J., Brudno, M., Pugh, T., Ramani, A., & Shooshtari, P. (2022). Evaluation of single-cell RNAseq labelling algorithms using cancer datasets. Briefings in Bioinformatics, 24(1). https://doi.org/10.1093/bib/bbac561

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Jerry Schnepp, Ph.D.

Chicago, Illinois, United States of America

Chair of Computer Science, Judson University

Most Relevant Research Expertise

Information Systems

Other Research Expertise (18)

Human Computer Interaction

User Experience

Interactive Media

Computer Graphics

Accommodations for the Deaf

And 13 more

About

As a technologist, designer, and creative problem-solver, I'm passionate about teaching people to embrace new technology and explore. I am the Chair of the Computer Science department at Judson University. Before my appointment, I served as an Associate Professor in the College of Technology, Architecture and Applied Engineering at Bowling Green State University (BGSU). I teach courses in Programming, Data Structures and Algorithms, Software Design Patterns, Interactive Media, Usability, User Experience, and Augmented/Virtual Reality. I was the founding director of the Collab Lab, a hands-on, creative space for students and faculty to engage in collaborative work. My research efforts are directed in several areas: AI Supported Individualized Learning, Learner Experience Design, Technology for Online Assessment, Interactive Mobile Learning, and Computerized Sign Language Synthesis. I enjoy collaborating on projects involving cutting-edge technology and new applications.

Most Relevant Publications (2+)

20 total publications

An automated technique for real-time production of lifelike animations of American Sign Language

Universal Access in the Information Society / May 14, 2015

McDonald, J., Wolfe, R., Schnepp, J., Hochgesang, J., Jamrozik, D. G., Stumbo, M., Berke, L., Bialek, M., & Thomas, F. (2015). An automated technique for real-time production of lifelike animations of American Sign Language. Universal Access in the Information Society, 15(4), 551–566. https://doi.org/10.1007/s10209-015-0407-2

Special issue: recent advances in sign language translation and avatar technology

Universal Access in the Information Society / Jun 02, 2015

Wolfe, R., Efthimiou, E., Glauert, J., Hanke, T., McDonald, J., & Schnepp, J. (2015). Special issue: recent advances in sign language translation and avatar technology. Universal Access in the Information Society, 15(4), 485–486. https://doi.org/10.1007/s10209-015-0412-5

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Martin Tsui

San Francisco, California, United States of America

University of California, San Francisco

Most Relevant Research Expertise

Information Systems

Other Research Expertise (15)

Molecular Assembly and Interaction

And 10 more

About

Martin Tsui is an accomplished scientist with a strong background in biochemistry. He received his Ph.D. in Molecular Biophysics from Florida State University in 2017, where he conducted research on the structure and function of CRISPR proteins. Prior to that, he obtained his B.S. in Chemistry from the University of California, San Diego in 2012. After completing his graduate studies, Martin founded his own company, Stealth, where he serves as a Founder & CEO. Under his leadership, the company has developed innovative solutions for the biotech industry and has gained recognition for its groundbreaking research. Before starting his company, Martin worked as a Senior Scientist at Amazon, where he applied his expertise in protein biochemistry and CRISPR to improve the company's product development processes and creating new products. He also gained valuable experience as a Postdoctoral Scholar at the University of California, San Francisco and Postdoctoral Fellow at the Van Andel Institute, where he studied cancer proteins, SARS-CoV-2, HIV proteins, and the role of proteins in neurodegenerative diseases, respectively. Martin is a highly driven and passionate individual who is dedicated to advancing the field of biotechnology. His impressive education and diverse experience have equipped him with the skills and knowledge to make significant contributions to the scientific community. He continues to pursue new opportunities to further his research and make a positive impact in the world of science.

Most Relevant Publications (1+)

16 total publications

Comparative host–pathogen protein–protein interaction analysis of recent coronavirus outbreaks and important host targets identification

Briefings in Bioinformatics / Sep 11, 2020

Khan, A. A., & Khan, Z. (2020). Comparative host–pathogen protein–protein interaction analysis of recent coronavirus outbreaks and important host targets identification. Briefings in Bioinformatics, 22(2), 1206–1214. https://doi.org/10.1093/bib/bbaa207

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David J. Lilja

Minneapolis, Minnesota, United States of America

Professor Emeritus of Electrical and Computer Engineering, University of Minnesota

Most Relevant Research Expertise

Information Systems

Other Research Expertise (15)

Computer architecture

high-performance parallel processing

computer systems performance analysis

approximate computing

Hardware and Architecture

And 10 more

About

**Research Expertise** Computer architecture, high-performance parallel processing, computer systems performance analysis, approximate computing, computing with emerging technologies, and storage systems. **Biographical summary** David J. Lilja received a Ph.D. and an M.S., both in Electrical Engineering, from the [University of Illinois at Urbana-Champaign,](http://www.uiuc.edu/) and a B.S. in Computer Engineering from [Iowa State University](http://www.iastate.edu/) in Ames. He is Professor Emeritus of [Electrical and Computer Engineering](http://www.ee.umn.edu/) at the [University of Minnesota](http://www.umn.edu/) in Minneapolis. He previously served as a member of the graduate faculties in [Computer Science](http://www.cs.umn.edu/), [Scientific Computation](http://www.scicomp.umn.edu/), and [Data Science](http://datascience.umn.edu//). He served ten years as the head of the ECE department at the University of Minnesota, worked as a research assistant at the Center for Supercomputing Research and Development at the [University of Illinois,](http://www.uiuc.edu/) and as a development engineer at [Tandem Computers Incorporated](http://www.tandem.com/) in Cupertino, California. He received a [Fulbright](http://www.fulbright.org/) Senior Scholar Award to visit the University of Western Australia and was a visiting Professor at the University of Canterbury in Christchurch, New Zealand. He has chaired and served on the program committees of numerous conferences. He was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the American Association for the Advancement of Science (AAAS) for contributions to the statistical analysis of computer performance. He also is a registered Professional Engineer.

Most Relevant Publications (4+)

99 total publications

Accelerating geoscience and engineering system simulations on graphics hardware

Computers & Geosciences / Dec 01, 2009

Walsh, S. D. C., Saar, M. O., Bailey, P., & Lilja, D. J. (2009). Accelerating geoscience and engineering system simulations on graphics hardware. Computers & Geosciences, 35(12), 2353–2364. https://doi.org/10.1016/j.cageo.2009.05.001

JaViz: A client/server Java profiling tool

IBM Systems Journal / Jan 01, 2000

Kazi, I. H., Jose, D. P., Ben-Hamida, B., Hescott, C. J., Kwok, C., Konstan, J. A., Lilja, D. J., & Yew, P.-C. (2000). JaViz: A client/server Java profiling tool. IBM Systems Journal, 39(1), 96–117. https://doi.org/10.1147/sj.391.0096

Exploring Performance Characteristics of the Optane 3D Xpoint Storage Technology

ACM Transactions on Modeling and Performance Evaluation of Computing Systems / Feb 04, 2020

Yang, J., Li, B., & Lilja, D. J. (2020). Exploring Performance Characteristics of the Optane 3D Xpoint Storage Technology. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 5(1), 1–28. https://doi.org/10.1145/3372783

High Quality Down-Sampling for Deterministic Approaches to Stochastic Computing

IEEE Transactions on Emerging Topics in Computing / Jan 01, 2021

Najafi, M. H., & Lilja, D. J. (2021). High Quality Down-Sampling for Deterministic Approaches to Stochastic Computing. IEEE Transactions on Emerging Topics in Computing, 9(1), 7–14. https://doi.org/10.1109/tetc.2017.2789243

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Dr Leandra Jordaan

I work at the intersection of information, wise judgement, and the social reality of people.

Most Relevant Research Expertise

Information Systems

Other Research Expertise (8)

Phronesis

Ethnography

Organisational research

Information architecture

Communication and Resource Optimization

And 3 more

About

I am an ICT and IS expert with experience in higher education support services, championing various complex projects, programmes and frameworks to improve cross-disciplinary communication, optimize the use of available resources, and ensure an excellent user experience when accessing information across all digital platforms. Servant leadership, organizational acumen, and innovative/analytical problem-solving combined with extensive ICT expertise and a blend of other relevant disciplines, equip me with a unique perspective to design and implement effective solutions where people and information converge. Professional Focus: Adding measurable value to any organisation seeking to navigate the complex and rapidly evolving landscape of digital transformation, implementing frameworks for effective communication, increased synergy, and organizational success through the resulting innovation.

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Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University

Most Relevant Research Expertise

Information Systems

Other Research Expertise (43)

About

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.

Most Relevant Publications (5+)

106 total publications

Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook

Information Systems Research / Dec 01, 2016

Cavusoglu, H., Phan, T. Q., Cavusoglu, H., & Airoldi, E. M. (2016). Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook. Information Systems Research, 27(4), 848–879. https://doi.org/10.1287/isre.2016.0672

A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks

Information Systems Research / Mar 01, 2019

Bhattacharya, P., Phan, T. Q., Bai, X., & Airoldi, E. M. (2019). A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks. Information Systems Research, 30(1), 117–132. https://doi.org/10.1287/isre.2018.0790

Confidence sets for network structure

Statistical Analysis and Data Mining / Sep 09, 2011

Airoldi, E. M., Choi, D. S., & Wolfe, P. J. (2011). Confidence sets for network structure. Statistical Analysis and Data Mining, 4(5), 461–469. https://doi.org/10.1002/sam.10136

Network sampling and classification: An investigation of network model representations

Decision Support Systems / Jun 01, 2011

Airoldi, E. M., Bai, X., & Carley, K. M. (2011). Network sampling and classification: An investigation of network model representations. Decision Support Systems, 51(3), 506–518. https://doi.org/10.1016/j.dss.2011.02.014

An entropy approach to disclosure risk assessment: Lessons from real applications and simulated domains

Decision Support Systems / Apr 01, 2011

Airoldi, E. M., Bai, X., & Malin, B. A. (2011). An entropy approach to disclosure risk assessment: Lessons from real applications and simulated domains. Decision Support Systems, 51(1), 10–20. https://doi.org/10.1016/j.dss.2010.11.014

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Jeffrey Townsend

New Haven, CT, Connecticut, United States of America

Professor of Biostatistics and Ecology & Evolutionary Biology

Most Relevant Research Expertise

Information Systems

Other Research Expertise (52)

Evolutionary Genomics

About

Jeffrey Townsend is a Professor of Organismic and Evolutionary Biology at Yale University. He received his Ph.D. from Harvard University in 2002 and his Sc.B. from Brown University in 1994. He has been a teacher at St. Ann's School and an Assistant Professor at the University of Connecticut. He is currently the Elihu Professor of Biostatistics at Yale University.

Most Relevant Publications (3+)

207 total publications

Identifying modules of cooperating cancer drivers

Molecular Systems Biology / Mar 01, 2021

Klein, M. I., Cannataro, V. L., Townsend, J. P., Newman, S., Stern, D. F., & Zhao, H. (2021). Identifying modules of cooperating cancer drivers. Molecular Systems Biology, 17(3). Portico. https://doi.org/10.15252/msb.20209810

Bringing Web 2.0 to bioinformatics

Briefings in Bioinformatics / Oct 08, 2008

Zhang, Z., Cheung, K.-H., & Townsend, J. P. (2008). Bringing Web 2.0 to bioinformatics. Briefings in Bioinformatics, 10(1), 1–10. https://doi.org/10.1093/bib/bbn041

A Bayesian method for analysing spotted microarray data

Briefings in Bioinformatics / Jan 01, 2005

Meiklejohn, C. D., & Townsend, J. P. (2005). A Bayesian method for analysing spotted microarray data. Briefings in Bioinformatics, 6(4), 318–330. https://doi.org/10.1093/bib/6.4.318

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Jim Samuel

Associate Professor at Rutgers University

Most Relevant Research Expertise

Information Systems

Other Research Expertise (21)

Analytics

Artificial Intelligence

Informatics

Machine Learning

NLP NLU NLG Behavioral Finance

And 16 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.

Most Relevant Publications (5+)

44 total publications

Regulation of data-driven market power in the digital economy: Business value creation and competitive advantages from big data

Journal of Information Technology / Feb 24, 2023

Fast, V., Schnurr, D., & Wohlfarth, M. (2023). Regulation of data-driven market power in the digital economy: Business value creation and competitive advantages from big data. Journal of Information Technology, 026839622211143. https://doi.org/10.1177/02683962221114394

Customized AI Readers: An Adaptive Framework for Flexible Human Handwriting Recognition of Numerical Digits with OCR Methods

Information / May 26, 2023

Jain, P. H., Kumar, V., Samuel, J., Singh, S., Mannepalli, A., & Anderson, R. (2023). Customized AI Readers: An Adaptive Framework for Flexible Human Handwriting Recognition of Numerical Digits with OCR Methods. Information, 14(6), 305. https://doi.org/10.3390/info14060305

Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations

International Journal of Information Management / Aug 01, 2022

Samuel, J., Kashyap, R., Samuel, Y., & Pelaez, A. (2022). Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations. International Journal of Information Management, 65, 102505. https://doi.org/10.1016/j.ijinfomgt.2022.102505

Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation

Decision Support Systems / May 01, 2021

Garvey, M. D., Samuel, J., & Pelaez, A. (2021). Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation. Decision Support Systems, 144, 113497. https://doi.org/10.1016/j.dss.2021.113497

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Information / Jun 11, 2020

Samuel, J., Ali, G. G. Md. N., Rahman, Md. M., Esawi, E., & Samuel, Y. (2020). COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information, 11(6), 314. https://doi.org/10.3390/info11060314

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Catherine Tucker

Advertising & Economics Professor at MIT

Most Relevant Research Expertise

Information Systems

Other Research Expertise (23)

Marketing

Economics

Regulation

Economics and Econometrics

And 18 more

About

Catherine Tucker is a Professor of Economics and Data Science at the Massachusetts Institute of Technology. She is also a member of the MIT Sloan School of Management's faculty. She is a world-renowned expert in the fields of big data, machine learning, and consumer behavior. She has worked extensively on the use of data science to improve public policy and has been a consultant to government agencies and businesses around the world. In addition to her academic work, she is a popular speaker and has been featured in many media outlets, including The Economist, The New York Times, The Wall Street Journal, and Wired.

Most Relevant Publications (1+)

94 total publications

Active Social Media Management: The Case of Health Care

Information Systems Research / Mar 01, 2013

Miller, A. R., & Tucker, C. (2013). Active Social Media Management: The Case of Health Care. Information Systems Research, 24(1), 52–70. https://doi.org/10.1287/isre.1120.0466

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Hussein Al-Hussein

Adjunct Faculty, Mission College & De Anza College, California

Most Relevant Research Expertise

Information Systems

Other Research Expertise (28)

About

Technical Skills: · **Programming Languages:** Python, OpenCV, C++ 11 and up, C, and Java. · **Image processing**: Image segmentation, alignment, registration, enhancement, filters, noise removal, binarization, object detection and identification, English & Arabic OCR, 2D barcode readers, watermarking, DCT (Discrete Cosine Transform), check cropping, industrial defect detection, and fault detection of assembled systems. · **Software Engineering Skills:** OOP, algorithms, data structures, SW processes, static and dynamic code analysis, algorithms performance, MISRA standards code compliance, Google C & C++ coding guidelines, git, and hg. Cryptography

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Vivek Singh

Rutgers Professor, MIT alum, CS PhD, AI expert

Most Relevant Research Expertise

Information Systems

Other Research Expertise (24)

Human-centered Data Science

Human-centered AI

Computational Social Science

Behavioral Informatics

Algorithmic Fairness

And 19 more

About

Vivek Singh is a highly accomplished computer scientist and researcher. He earned his Ph.D. in Information and Computer Science from the University of California Irvine in 2012. During his time at UC Irvine, he focused on research in the areas of natural language processing and machine learning. After completing his Ph.D., Singh joined Massachusetts Institute of Technology as a post-doctoral associate, where he worked on developing algorithms for large-scale data analysis and information retrieval. In 2014, Singh joined Rutgers University as a faculty member in Information Science and Computer Science. As a faculty member at Rutgers, he has published numerous papers in top computer science journals and conferences and has received several grants for his research. Singh's research interests include natural language processing, generative AI, and social computing. He has a particular interest in developing algorithms for analyzing large datasets and extracting valuable insights from them. His work has been applied to various domains, including social media, healthcare, and finance. He has multiple patents, and has experience consulting with early and late-stage (unicorn) startups. His work has led to multiple grants, awards, funding, patents, and deployed products.

Most Relevant Publications (6+)

95 total publications

Detecting fake news stories via multimodal analysis

Journal of the Association for Information Science and Technology / May 04, 2020

Singh, V. K., Ghosh, I., & Sonagara, D. (2020). Detecting fake news stories via multimodal analysis. Journal of the Association for Information Science and Technology, 72(1), 3–17. Portico. https://doi.org/10.1002/asi.24359

If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

International Journal of Information Security / Feb 20, 2016

Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V. K., Alsaleh, M., Alarifi, A., Alfaris, A., & Pentland, A. ‘Sandy.’ (2016). If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security, 15(5), 475–491. https://doi.org/10.1007/s10207-016-0321-5

Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms

Journal of the Association for Information Science and Technology / Jan 22, 2020

Singh, V. K., Chayko, M., Inamdar, R., & Floegel, D. (2020). Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms. Journal of the Association for Information Science and Technology, 71(11), 1281–1294. Portico. https://doi.org/10.1002/asi.24335

Phones, privacy, and predictions

Online Information Review / Oct 23, 2018

Ghosh, I., & Singh, V. (2018). Phones, privacy, and predictions: A study of phone logged data to predict privacy attitudes of individuals. Online Information Review, 44(2), 483–502. https://doi.org/10.1108/oir-03-2018-0112

Altrumetrics: Inferring Altruism Propensity Based on Mobile Phone Use Patterns

IEEE Transactions on Big Data / Jun 01, 2021

Bati, G. F., & Singh, V. K. (2021). Altrumetrics: Inferring Altruism Propensity Based on Mobile Phone Use Patterns. IEEE Transactions on Big Data, 7(2), 397–406. https://doi.org/10.1109/tbdata.2018.2873346

“Not all my friends are friends”: Audience‐group‐based nudges for managing location privacy

Journal of the Association for Information Science and Technology / Oct 14, 2021

Ghosh, I., & Singh, V. (2021). “Not all my friends are friends”: Audience‐group‐based nudges for managing location privacy. Journal of the Association for Information Science and Technology, 73(6), 797–810. Portico. https://doi.org/10.1002/asi.24580

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Shion Guha

Assistant Professor at University of Toronto

Most Relevant Research Expertise

Information Systems

Other Research Expertise (24)

Human-Centered Data Science

Public Interest Technology

Responsible AI

Human Computer Interaction

Computer Networks and Communications

And 19 more

About

Shion Guha is an accomplished scholar and researcher in the field of Information Science and Statistics. He earned his Ph.D. from Cornell University in 2016, after completing his M.S. in Information Science from the same institution in 2014. Prior to that, he obtained an M.S. in Information Science from the Indian Statistical Institute in 2010. Dr. Guha's research focuses on the intersection of information science and statistics, with a particular interest in data mining and machine learning. He has published numerous articles in top journals and presented his work at international conferences. His research has also been funded by prestigious organizations such as the National Science Foundation. In addition to his academic achievements, Dr. Guha has also gained valuable teaching experience. He has served as an Assistant Professor at both the University of Toronto and Marquette University, where he taught courses on data mining, information retrieval, and statistical methods. Dr. Guha is highly regarded by his colleagues and students for his expertise, dedication, and passion for his field. He continues to push the boundaries of information science and statistics through his research and teaching, and is a valuable asset to any academic institution.

Most Relevant Publications (3+)

89 total publications

Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?

Journal of the Association for Information Science and Technology / Apr 28, 2017

Baumer, E. P. S., Mimno, D., Guha, S., Quan, E., & Gay, G. K. (2017). Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? Journal of the Association for Information Science and Technology, 68(6), 1397–1410. Portico. https://doi.org/10.1002/asi.23786

Cross‐campus collaboration: A scientometric and network case study of publication activity across two campuses of a single institution

Journal of the American Society for Information Science and Technology / Dec 05, 2012

Birnholtz, J., Guha, S., Yuan, Y. C., Gay, G., & Heller, C. (2012). Cross‐campus collaboration: A scientometric and network case study of publication activity across two campuses of a single institution. Journal of the American Society for Information Science and Technology, 64(1), 162–172. Portico. https://doi.org/10.1002/asi.22807

Thwarting location privacy protection in location‐based social discovery services

Security and Communication Networks / Feb 04, 2016

Xue, M., Liu, Y., Ross, K. W., & Qian, H. (2016). Thwarting location privacy protection in location‐based social discovery services. Security and Communication Networks, 9(11), 1496–1508. Portico. https://doi.org/10.1002/sec.1438

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Suhang Wang

Professor at Pennsylvania State University

Most Relevant Research Expertise

Information Systems

Other Research Expertise (20)

Machine learning

Graph Mining

Theoretical Computer Science

Information Systems and Management

Computer Science Applications

And 15 more

About

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.

Most Relevant Publications (4+)

92 total publications

FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media

Big Data / Jun 01, 2020

Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3), 171–188. https://doi.org/10.1089/big.2020.0062

Exploring Hierarchical Structures for Recommender Systems

IEEE Transactions on Knowledge and Data Engineering / Jun 01, 2018

Wang, S., Tang, J., Wang, Y., & Liu, H. (2018). Exploring Hierarchical Structures for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1022–1035. https://doi.org/10.1109/tkde.2018.2789443

Facilitating Time Critical Information Seeking in Social Media

IEEE Transactions on Knowledge and Data Engineering / Oct 01, 2017

Ranganath, S., Wang, S., Hu, X., Tang, J., & Liu, H. (2017). Facilitating Time Critical Information Seeking in Social Media. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2197–2209. https://doi.org/10.1109/tkde.2017.2701375

Self-Supervised learning for Conversational Recommendation

Information Processing & Management / Nov 01, 2022

Li, S., Xie, R., Zhu, Y., Zhuang, F., Tang, Z., Zhao, W. X., & He, Q. (2022). Self-Supervised learning for Conversational Recommendation. Information Processing & Management, 59(6), 103067. https://doi.org/10.1016/j.ipm.2022.103067

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Naveen Adusumilli

Warner Bruner Endowed Professor, Associate Professor- Extension Economist -Louisiana State University

Most Relevant Research Expertise

Information Systems

Other Research Expertise (29)

Soil and Water Policy

Production Economics

Agronomy and Crop Science

Earth-Surface Processes

Water Science and Technology

And 24 more

About

Naveen Adusumilli is an accomplished agricultural economist with a strong background in conservation policy-related research and extension work. He completed his PhD in Agricultural Economics from Texas A&M University in 2012, focusing on the economics of water resource management in agriculture. During his doctoral studies, he conducted extensive research on the impacts of climate change on agricultural production and the role of water markets in mitigating these impacts. After completing his PhD, Naveen joined Louisiana State University as an Extension Economist. In this role, he worked closely with farmers, policymakers, and other stakeholders to provide economic analysis and recommendations for improving agricultural productivity and sustainability. He also conducted outreach programs to educate farmers about the latest research findings and best practices in the field of agricultural economics. Naveen's research has been published in numerous peer-reviewed journals, and he has presented at more than 100 farmer group meetings and national and international conferences. He has also received several awards and grants for his research contributions, including the Emerging Scholars Award in 2017 from the Southern Agricultural Economics Association and, in 2021, the Chair Leadership Award for his contributions to Soil and Water Conservation Society. from the Agricultural and Applied Economics Association. In addition to his work in academia, Naveen has also collaborated with various government agencies and international organizations on projects related to agricultural development and resource management. His expertise in agricultural economics has been sought after by organizations such as the Foundation for Agricultural Research, the National Institute for Water Resources, Foreign Agricultural Services, the Natural Resources Conservation Service, the Food and Agriculture Organization of the United Nations, and the World Bank. Naveen is a dedicated and passionate economist who is committed to finding solutions for the complex challenges facing the agricultural industry. Through his research and extension work, he continues to make valuable contributions to the field of agricultural economics and drive positive change in the world of farming.

Most Relevant Publications (1+)

35 total publications

The Biofuel Feedstock Genomics Resource: a web-based portal and database to enable functional genomics of plant biofuel feedstock species

Database / Jan 01, 2012

Childs, K. L., Konganti, K., & Buell, C. R. (2012). The Biofuel Feedstock Genomics Resource: a web-based portal and database to enable functional genomics of plant biofuel feedstock species. Database, 2012. https://doi.org/10.1093/database/bar061

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Anit Kumar Sahu

PhD from CMU working in ML/AI

Most Relevant Research Expertise

Information Systems

Other Research Expertise (19)

Federated Learning

Stochastic Optimization

Data Selection

Electrical and Electronic Engineering

Signal Processing

And 14 more

About

Anit Kumar Sahu is a highly accomplished researcher and engineer in the field of Electrical and Computer Engineering. He earned his PhD from Carnegie Mellon University in 2018, where he focused his research on statistical machine learning. During his time at CMU, he received the A.G. Jordan award for outstanding thesis. After completing his PhD, Anit joined Amazon Services LLC as a Senior Applied Scientist, where he works on developing innovative solutions for complex business problems using machine learning and artificial intelligence. Prior to joining Amazon, Anit worked at Bosch Center for Artificial Intelligence as a Machine Learning Research Scientist, where he developed cutting-edge algorithms for adversarial machine learning. He is currently Principal AI Scientist at GE Healthcare AI, where he is responsible for leading research and development efforts in the healthcare sector. With his extensive education and experience in both academia and industry, Anit has become a leading expert in the field of machine learning, computer vision, and artificial intelligence. He has published numerous research papers in top conferences and journals, and his work has been widely cited by other researchers in the field. Apart from his professional accomplishments, Anit is also passionate about mentoring and teaching the next generation of engineers and scientists. In his free time, Anit enjoys hiking, trying new restaurants, and traveling to new places. He also actively participates in various volunteer activities and is dedicated to giving back to his community.

Most Relevant Publications (3+)

59 total publications

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

IEEE Transactions on Signal and Information Processing over Networks / Jan 01, 2016

Sahu, A. K., Kar, S., Moura, J. M. F., & Poor, H. V. (2016). Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. IEEE Transactions on Signal and Information Processing over Networks, 1–1. https://doi.org/10.1109/tsipn.2016.2618318

Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise

IEEE Transactions on Information Theory / Aug 01, 2017

Sahu, A. K., & Kar, S. (2017). Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise. IEEE Transactions on Information Theory, 63(8), 4797–4828. https://doi.org/10.1109/tit.2017.2686435

Guest Editorial Inference and Learning over Networks

IEEE Transactions on Signal and Information Processing over Networks / Dec 01, 2016

Matta, V., Richard, C., Saligrama, V., & Sayed, A. H. (2016). Guest Editorial Inference and Learning over Networks. IEEE Transactions on Signal and Information Processing over Networks, 2(4), 423–425. https://doi.org/10.1109/tsipn.2016.2615526

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Dr. KEHINDE ADEWALE ADESINA, Ph.D

Harlow

Assistant Professor in engineering (food, industrial and process) with quantitative and qualitative research in food processing, safefy, efficiency management, optimization and data analysis

Most Relevant Research Expertise

Information Systems

Other Research Expertise (18)

Artificial Intelligence

Software

Management Science and Operations Research

Safety, Risk, Reliability and Quality

Computational Mathematics

And 13 more

About

Dr. KEHINDE ADEWALE ADESINA, Ph.D is a highly educated and experienced individual in the field of Industrial Engineering, Process, and Food Engineering. He obtained his PhD in Industrial Engineering from Eastern Mediterranean University in 2018, where he focused on research related to optimization and process improvement in manufacturing industries. Prior to his PhD, Dr. Adesina completed his Master's degree in Chemical Engineering from Obafemi Awolowo University in 2011 and his Bachelor's degree in Chemical Engineering from Ladoke Akintola University of Technology in 2003. Dr. Adesina has also gained valuable teaching experience as an Assistant Professor at Near East University Nicosia/KKTC and as a Lecturer at Rufus Giwa Polytechnic. He has taught a variety of courses related to industrial engineering, food engineering, chemical engineering, and research methodology. In addition to his teaching experience, Dr. Adesina has also worked as a Research Assistant at Eastern Mediterranean University, where he conducted research on various industrial engineering topics. Through his education and experience, Dr. Adesina has developed a strong understanding of industrial engineering principles and techniques, as well as a passion for research and teaching. He continues to contribute to the field through his academic work and is dedicated to helping students and industries improve their processes and operations.

Most Relevant Publications (2+)

27 total publications

A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment

Complex & Intelligent Systems / Mar 24, 2023

Li, H., Yazdi, M., Huang, H.-Z., Huang, C.-G., Peng, W., Nedjati, A., & Adesina, K. A. (2023). A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex & Intelligent Systems, 9(5), 5527–5553. https://doi.org/10.1007/s40747-023-01002-w

Probabilistic Risk Analysis of Process Systems Considering Epistemic and Aleatory Uncertainties: A Comparison Study

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems / Apr 01, 2021

Yazdi, M., Golilarz, N. A., Adesina, K. A., & Nedjati, A. (2021). Probabilistic Risk Analysis of Process Systems Considering Epistemic and Aleatory Uncertainties: A Comparison Study. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(02), 181–207. https://doi.org/10.1142/s0218488521500098

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Example Information Systems projects

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

Optimizing Supply Chain Management

An academic researcher in Information Systems can help companies optimize their supply chain management processes. By analyzing data and implementing advanced algorithms, researchers can identify bottlenecks, reduce costs, and improve overall efficiency.

Enhancing Cybersecurity Measures

With the increasing threat of cyber attacks, companies can benefit from collaborating with an academic researcher in Information Systems to enhance their cybersecurity measures. Researchers can develop innovative solutions, conduct vulnerability assessments, and provide recommendations to strengthen the company's security infrastructure.

Implementing Data Analytics Strategies

Data analytics is crucial for companies to make informed business decisions. An academic researcher in Information Systems can help companies implement data analytics strategies by developing predictive models, analyzing large datasets, and providing insights that can drive business growth.

Designing User-Friendly Interfaces

User experience is a key factor in the success of digital products and services. By collaborating with an academic researcher in Information Systems, companies can benefit from expertise in designing user-friendly interfaces. Researchers can conduct usability studies, gather user feedback, and optimize the user experience to enhance customer satisfaction.

Integrating Emerging Technologies

Emerging technologies such as artificial intelligence, blockchain, and Internet of Things (IoT) have the potential to transform industries. Academic researchers in Information Systems can help companies explore and integrate these technologies into their existing systems, enabling them to innovate and gain a competitive edge.