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Work with thought leaders and academic experts in theoretical computer science

Companies can greatly benefit from collaborating with experts in Theoretical Computer Science. These researchers have a deep understanding of algorithms, complexity theory, and cryptography, which can be applied to various industries. They can help companies optimize their processes, develop efficient algorithms, enhance data security, and solve complex computational problems. Additionally, their expertise can drive innovation, improve decision-making, and provide valuable insights for developing cutting-edge technologies. By working with Theoretical Computer Science researchers, companies can gain a competitive edge, improve efficiency, and achieve breakthrough solutions.

Experts on NotedSource with backgrounds in theoretical computer science include Edoardo Airoldi, Osaye Fadekemi, PhD, Athul Prasad, Suhang Wang, Mark Ryan, Dr. Abdussalam Elhanashi, Asst. Prof. Eng. Davide Verzotto, Ph.D., Kayvan Najarian, Bernd Stahl, Upavan Gupta, Ph.D., Marian Grendar, Ph.D., and Piotr Sobolewski.

Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (57)
Statistics
Causal Inference
Network Science
Statistical Machine Learning
Computational Biology
And 52 more
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 (1+)

106 total publications

Scalable estimation strategies based on stochastic approximations: classical results and new insights

Statistics and Computing / Jun 11, 2015

Toulis, P., & Airoldi, E. M. (2015). Scalable estimation strategies based on stochastic approximations: classical results and new insights. Statistics and Computing, 25(4), 781–795. https://doi.org/10.1007/s11222-015-9560-y

Osaye Fadekemi, PhD

Assistant Professor of Mathematics at Alabama State University with expertise in Graph Theory and Network Modeling
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (16)
Graph Theory
Discrete Mathematics
Network Modeling
Disease Modeling
Discrete Mathematics and Combinatorics
And 11 more
About
Dr Fadekemi Janet Osaye is a mathematician whose primary research interest is in graph theory and network modeling. In particular, she is interested in distance measures in graphs and their applications to solving many real-world problems. Her interest in discrete mathematics was inspired by her research project carried out at AIMS Ghana. She has published several articles in reputable journals and has presented in several conferences across the globe. In 2019, she became the first black female to be awarded a PhD in Mathematics by the University of Johannesburg, South Africa, in its 116 years of its existence. Since June 2021, she has been an Assistant Professor of Mathematics at Alabama State University and was previously a Visiting Assistant Professor at Auburn University. She is the founder of GirlsMatics Foundation, a STEM non-governmental organisation for girls in Nigeria which was motivated by her involvement with AIMS Ghana’s outreach programs for high school students in Biriwa, Ghana. She is also the co-founder of FadNna Partners, an analytics and management firm based in Lagos, Nigeria.
Most Relevant Publications (2+)

7 total publications

Average eccentricity,k-packing andk-domination in graphs

Discrete Mathematics / May 01, 2019

Dankelmann, P., & Osaye, F. J. (2019). Average eccentricity,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e86" altimg="si36.gif"><mml:mi>k</mml:mi></mml:math>-packing and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e91" altimg="si36.gif"><mml:mi>k</mml:mi></mml:math>-domination in graphs. Discrete Mathematics, 342(5), 1261–1274. https://doi.org/10.1016/j.disc.2019.01.004

The average eccentricity of a graph with prescribed girth

Discrete Mathematics / Dec 01, 2022

Osaye, F. J. (2022). The average eccentricity of a graph with prescribed girth. Discrete Mathematics, 345(12), 113066. https://doi.org/10.1016/j.disc.2022.113066

Athul Prasad

5G / 6G Technology and Ventures at Samsung; D.Sc. (Tech), MBA
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (41)
Machine Learning
Mobility Management
5G / New Radio
Dynamic Resource Allocation
Electrical and Electronic Engineering
And 36 more
About
Dr. Athul Prasad received his MBA from MIT where he was a Sloan Fellow, M.Sc. (Tech.) (with distinction) and D.Sc. (Tech) from Aalto University, B.Tech (with distinction) from University of Kerala, and is also a graduate of the year-long executive management (LEAD) program from Stanford University's Graduate School of Business. He was with Nokia from 2014-2023 and is currently with Samsung based out of Mountain View, CA. He has coauthored over 40 peer reviewed scientific publications and has written a book on 5G "End-to-End Mobile Communications: Evolution to 5G," McGraw-Hill, Aug. 2020. He's also the co-inventor of over 90 patents.
Most Relevant Publications (1+)

75 total publications

Quasi-universal k-regular sequences

Theoretical Computer Science / Nov 01, 2021

Honkala, J. (2021). Quasi-universal k-regular sequences. Theoretical Computer Science, 891, 84–89. https://doi.org/10.1016/j.tcs.2021.08.028

Suhang Wang

Professor at Pennsylvania State University
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (28)
Machine learning
data mining
social media mining
deep learning
Graph Mining
And 23 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 (6+)

92 total publications

Feature Selection

ACM Computing Surveys / Dec 06, 2017

Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature Selection. ACM Computing Surveys, 50(6), 1–45. https://doi.org/10.1145/3136625

A Generative Model for category text generation

Information Sciences / Jun 01, 2018

Li, Y., Pan, Q., Wang, S., Yang, T., & Cambria, E. (2018). A Generative Model for category text generation. Information Sciences, 450, 301–315. https://doi.org/10.1016/j.ins.2018.03.050

Disentangled Variational Auto-Encoder for semi-supervised learning

Information Sciences / May 01, 2019

Li, Y., Pan, Q., Wang, S., Peng, H., Yang, T., & Cambria, E. (2019). Disentangled Variational Auto-Encoder for semi-supervised learning. Information Sciences, 482, 73–85. https://doi.org/10.1016/j.ins.2018.12.057

Random-Forest-Inspired Neural Networks

ACM Transactions on Intelligent Systems and Technology / Oct 29, 2018

Wang, S., Aggarwal, C., & Liu, H. (2018). Random-Forest-Inspired Neural Networks. ACM Transactions on Intelligent Systems and Technology, 9(6), 1–25. https://doi.org/10.1145/3232230

Understanding and Identifying Rhetorical Questions in Social Media

ACM Transactions on Intelligent Systems and Technology / Jan 10, 2018

Ranganath, S., Hu, X., Tang, J., Wang, S., & Liu, H. (2018). Understanding and Identifying Rhetorical Questions in Social Media. ACM Transactions on Intelligent Systems and Technology, 9(2), 1–22. https://doi.org/10.1145/3108364

Semi-supervised anomaly detection in dynamic communication networks

Information Sciences / Sep 01, 2021

Meng, X., Wang, S., Liang, Z., Yao, D., Zhou, J., & Zhang, Y. (2021). Semi-supervised anomaly detection in dynamic communication networks. Information Sciences, 571, 527–542. https://doi.org/10.1016/j.ins.2021.04.056

Mark Ryan

Digital Ethics Researcher at Wageningen Economic Research
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (39)
Digital Ethics
Philosophy of Technology
Environmental Ethics
AI Ethics
Data Ethics
And 34 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.
Most Relevant Publications (1+)

40 total publications

In defence of digital contact-tracing: human rights, South Korea and Covid-19

International Journal of Pervasive Computing and Communications / Aug 06, 2020

Ryan, M. (2020). In defence of digital contact-tracing: human rights, South Korea and Covid-19. International Journal of Pervasive Computing and Communications, 16(4), 383–407. https://doi.org/10.1108/ijpcc-07-2020-0081

Dr. Abdussalam Elhanashi

Researcher at University of Pisa
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (23)
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 18 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.
Most Relevant Publications (1+)

28 total publications

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Algorithms / Oct 07, 2020

Fedele, R., & Merenda, M. (2020). An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data. Algorithms, 13(10), 254. https://doi.org/10.3390/a13100254

Asst. Prof. Eng. Davide Verzotto, Ph.D.

Assistant Professor of Computer Science, Centre for Higher Defence Studies (CASD) - School of Advanced Studies, Italian Defence General Staff, Rome
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (20)
Algorithms & Data Structures
Bioinformatics & Computational Biology
Machine Learning
Scalable
Multidisciplinary
And 15 more
About
Algorithms & Information Intelligence, Pattern Discovery Bioinformatics & Computational Biology/Genomics Scalable Data Mining & Machine Learning
Most Relevant Publications (2+)

40 total publications

Editorial: Special Issue on Algorithms for Sequence Analysis and Storage

Algorithms / Mar 25, 2014

Mäkinen, V. (2014). Editorial: Special Issue on Algorithms for Sequence Analysis and Storage. Algorithms, 7(1), 186–187. https://doi.org/10.3390/a7010186

Filtering Degenerate Patterns with Application to Protein Sequence Analysis

Algorithms / May 22, 2013

Comin, M., & Verzotto, D. (2013). Filtering Degenerate Patterns with Application to Protein Sequence Analysis. Algorithms, 6(2), 352–370. https://doi.org/10.3390/a6020352

Kayvan Najarian

Professor of Comp Med and Bioinf, Emergency Med, and Electrical and Comp Engineering
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (59)
biomedical inforamtics
bioinformatics
singal processing
image processing
machine learning
And 54 more
Most Relevant Publications (1+)

106 total publications

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

ACM Computing Surveys / Aug 25, 2020

Wood, A., Najarian, K., & Kahrobaei, D. (2020). Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. ACM Computing Surveys, 53(4), 1–35. https://doi.org/10.1145/3394658

Bernd Stahl

Director of the Centre for Computing and Social Responsibility
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (67)
critical theory
information systems
computer ethics
information ethics
responsible innovation
And 62 more
Most Relevant Publications (3+)

145 total publications

The Ethics of Computing

ACM Computing Surveys / Feb 22, 2016

Stahl, B. C., Timmermans, J., & Mittelstadt, B. D. (2016). The Ethics of Computing. ACM Computing Surveys, 48(4), 1–38. https://doi.org/10.1145/2871196

Responsible Innovation

ITNOW / Aug 14, 2014

Stahl, B. C., Jirotka, M., Eden, G., Timmermans, J., & Hartswood, M. (2014). Responsible Innovation. ITNOW, 56(3), 20–22. https://doi.org/10.1093/itnow/bwu068

Tomorrow’s ethics and today’s response: An investigation into the ways information systems professionals perceive and address emerging ethical issues

Information Systems Frontiers / Mar 29, 2014

Wakunuma, K. J., & Stahl, B. C. (2014). Tomorrow’s ethics and today’s response: An investigation into the ways information systems professionals perceive and address emerging ethical issues. Information Systems Frontiers, 16(3), 383–397. https://doi.org/10.1007/s10796-014-9490-9

Upavan Gupta, Ph.D.

Analytics Consulting, Data Science, Risk and Prospecting Models, Credit, Mortgage, Property, and Rental Data Expert
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (15)
Game theory
multi-metric optimization
risk minimization
fuzzy logic
stochastic and linear programming
And 10 more
Most Relevant Publications (1+)

12 total publications

Multievent Crisis Management Using Noncooperative Multistep Games

IEEE Transactions on Computers / May 01, 2007

Gupta, U., & Ranganathan, N. (2007). Multievent Crisis Management Using Noncooperative Multistep Games. IEEE Transactions on Computers, 56(5), 577–589. https://doi.org/10.1109/tc.2007.1023

Marian Grendar, Ph.D.

Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (66)
Gastroenterology
Hepatology
Cancer Research
Oncology
Physical Therapy, Sports Therapy and Rehabilitation
And 61 more
Most Relevant Publications (1+)

117 total publications

The Pólya information divergence

Information Sciences / Nov 01, 2010

Grendár, M., & Niven, R. K. (2010). The Pólya information divergence. Information Sciences, 180(21), 4189–4194. https://doi.org/10.1016/j.ins.2010.06.031

Piotr Sobolewski

Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (3)
Artificial Intelligence
Computational Theory and Mathematics
Control and Systems Engineering
Most Relevant Publications (1+)

2 total publications

SCR: simulated concept recurrence - a non-supervised tool for dealing with shifting concept

Expert Systems / Nov 22, 2013

Sobolewski, P., & Woźniak, M. (2013). SCR: simulated concept recurrence - a non-supervised tool for dealing with shifting concept. Expert Systems, 34(5), e12059. Portico. https://doi.org/10.1111/exsy.12059

Example theoretical computer science projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on theoretical computer science?

Optimizing Supply Chain Management

A Theoretical Computer Science expert can develop algorithms to optimize supply chain management, reducing costs and improving efficiency. By analyzing complex data and considering various factors such as demand, inventory, and transportation, they can create models that minimize delays, optimize routes, and streamline operations.

Enhancing Data Security

With their knowledge of cryptography and data encryption, Theoretical Computer Science researchers can help companies enhance their data security measures. They can develop robust encryption algorithms, design secure communication protocols, and identify vulnerabilities in existing systems to prevent data breaches and unauthorized access.

Machine Learning and AI

Theoretical Computer Science experts can contribute to the development of machine learning and AI algorithms. They can design efficient algorithms for training models, improve the accuracy of predictions, and optimize computational resources. Their expertise can help companies leverage the power of AI to automate processes, make data-driven decisions, and improve customer experiences.

Optimizing Financial Trading Strategies

By applying algorithms and mathematical models, Theoretical Computer Science researchers can optimize financial trading strategies. They can analyze market data, identify patterns, and develop algorithms that maximize returns and minimize risks. Their expertise can help companies make informed investment decisions and achieve better financial outcomes.

Solving Complex Computational Problems

Theoretical Computer Science experts excel in solving complex computational problems. They can develop algorithms and mathematical models to tackle challenges in various domains, such as optimization, scheduling, and network design. By collaborating with them, companies can find innovative solutions to their most challenging problems.