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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.

Researchers on NotedSource with backgrounds in theoretical computer science include Edoardo Airoldi, Dmitry Batenkov, Ph.D., Athul Prasad, Anit Kumar Sahu, Shubham Gupta, Denys Dutykh, Mark Ryan, Krzysztof Wolk, Vivek Singh, Baidurya Bhattacharya, Oguzhan Kulekci, Osaye Fadekemi, PhD, and Hector Klie.

Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (43)
Statistics
Causal Inference
Network Science
Cell Biology
Molecular Biology
And 38 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

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Dmitry Batenkov, Ph.D.

New York City, New York, United States of America
A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science.
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (30)
Applied Harmonic Analysis
Sparse Representations
Numerical Analysis
Approximation Theory
Inverse Problems
And 25 more
About
I am passionate about solving big problems with scientific and computational tools. A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science. A highly-skilled software engineer and analyst/architect with 6+ years of experience as a technical lead in professional software development.
Most Relevant Publications (4+)

51 total publications

Moment inversion problem for piecewise D -finite functions

Inverse Problems / Sep 16, 2009

Batenkov, D. (2009). Moment inversion problem for piecewise D -finite functions. Inverse Problems, 25(10), 105001. https://doi.org/10.1088/0266-5611/25/10/105001

Accurate solution of near-colliding Prony systems via decimation and homotopy continuation

Theoretical Computer Science / Jun 01, 2017

Batenkov, D. (2017). Accurate solution of near-colliding Prony systems via decimation and homotopy continuation. Theoretical Computer Science, 681, 27–40. https://doi.org/10.1016/j.tcs.2017.03.026

Stable soft extrapolation of entire functions

Inverse Problems / Dec 07, 2018

Batenkov, D., Demanet, L., & Mhaskar, H. N. (2018). Stable soft extrapolation of entire functions. Inverse Problems, 35(1), 015011. https://doi.org/10.1088/1361-6420/aaedde

Open BEAGLE: a generic framework for evolutionary computations

Genetic Programming and Evolvable Machines / Mar 29, 2011

Batenkov, D. (2011). Open BEAGLE: a generic framework for evolutionary computations. Genetic Programming and Evolvable Machines, 12(3), 329–331. https://doi.org/10.1007/s10710-011-9135-4

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Athul Prasad

5G / 6G Technology and Ventures at Samsung; D.Sc. (Tech), MBA
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (35)
Machine Learning
Mobility Management
5G / New Radio
Dynamic Resource Allocation
Electrical and Electronic Engineering
And 30 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

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Anit Kumar Sahu

PhD from CMU working in ML/AI
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (19)
Federated Learning
Stochastic Optimization
Data Selection
Electrical and Electronic Engineering
Signal Processing
And 14 more
About
Anit Kumar Sahu completed his PhD from Carnegie Mellon University in 2018, focusing on statistical machine learning and stochastic optimization. During his time at CMU, Anit worked on numerous research projects and published several papers in top-tier conferences and journals. After completing his PhD, Anit joined Amazon Services LLC as a Senior Applied Scientist. In this role, he is responsible for developing and implementing machine learning models and algorithms to enhance the performance of Amazon's services and products. Prior to joining Amazon, Anit worked as a Machine Learning Research Scientist at Bosch Center for Artificial Intelligence. He was actively involved in developing cutting-edge machine learning solutions for various industrial applications, including autonomous vehicles, smart homes, and industrial automation. Anit is a passionate and driven individual, constantly seeking new challenges and opportunities to further his knowledge and expertise in the field of electrical and computer engineering. With his strong educational background, extensive experience, and innovative mindset, he is a valuable asset to any organization.
Most Relevant Publications (1+)

62 total publications

Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise

SIAM Journal on Optimization / May 16, 2023

Jakovetić, D., Bajović, D., Sahu, A. K., Kar, S., Milos̆ević, N., & Stamenković, D. (2023). Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise. SIAM Journal on Optimization, 33(2), 394–423. https://doi.org/10.1137/21m145896x

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Shubham Gupta

With over seven years of extensive experience in 3GPP standards, 5G core networks, satellite communication, post-quantum cryptography, and cybersecurity, I possess profound expertise in the telecommunications and IoT sectors.
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (14)
5G Network Security
Quantum Cryptography
Cybersecurity
Computer Science Applications
Electrical and Electronic Engineering
And 9 more
About
Expertise in investigation of key security issues of mobile authentication protocols (LTE, 5G) and designing efficient and secure key agreement protocols. Expertise in verification and proving the correctness of security protocols by software simulation and mathematical modelling Advanced knowledge of public key infrastructure (PKI), digital signatures, hash functions, and key management Understanding of the effects of quantum computing on cryptography, along with proficiency in post-quantum cryptographic (PQC) algorithms and Quantum Key Distribution (QKD) Led a research project on integrating QKD into 5G networks for secure communication and designed an API for QKD data exchange, introducing a QKD-secured 5G service between satellites and ground devices. Participating and leading in various European Union projects, including SNS-JU, Horizon, as well as national projects
Most Relevant Publications (1+)

33 total publications

ICT – a surviving tool for economy in the phase of social distancing: a systematic literature review

Kybernetes / Feb 25, 2022

Gupta, S., Gupta, S., Kataria, S., & Gupta, S. (2022). ICT – a surviving tool for economy in the phase of social distancing: a systematic literature review. Kybernetes, 52(9), 3136–3160. https://doi.org/10.1108/k-05-2021-0374

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Denys Dutykh

Professional Applied Mathematician, Modeller, and Advisor
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (50)
Applied mathematics
fluid mechanics
scientific computing
numerical methods
Fluid Flow and Transfer Processes
And 45 more
About
Dr. Denys Dutykh initially comes from the broad field of Applied Mathematics. He did his Master's degree in numerical methods applied to the problems of Continuum Mechanics and a Ph.D. thesis at Ecole Normale Supérieure de Cachan (France) on the mathematical modeling of tsunami waves. After this, he was hired as a permanent research scientist at the Institute of Mathematics (INSMI) at the Centre National de la Recherche Scientifique (CNRS). His research activities have been conducted in the following years at the picturesque University Savoie Mont Blanc (USMB, France) in the field of mathematical methods applied to the modeling and simulation of nonlinear waves (mostly in Fluid Dynamics). The Habilitation thesis of Dr. Dutykh was defended there on the topic of the mathematical methods in the environment. Since then, his research interests have significantly broadened to include the Dimensionality Reduction methods in Machine Learning, modeling of PV panels, and even some more theoretical questions in the Number Theory.
Most Relevant Publications (3+)

186 total publications

On the Galerkin/Finite-Element Method for the Serre Equations

Journal of Scientific Computing / Feb 05, 2014

Mitsotakis, D., Ilan, B., & Dutykh, D. (2014). On the Galerkin/Finite-Element Method for the Serre Equations. Journal of Scientific Computing, 61(1), 166–195. https://doi.org/10.1007/s10915-014-9823-3

Derivation of dissipative Boussinesq equations using the Dirichlet-to-Neumann operator approach

Mathematics and Computers in Simulation / Sep 01, 2016

Dutykh, D., & Goubet, O. (2016). Derivation of dissipative Boussinesq equations using the Dirichlet-to-Neumann operator approach. Mathematics and Computers in Simulation, 127, 80–93. https://doi.org/10.1016/j.matcom.2013.12.008

Tsunami generation by dynamic displacement of sea bed due to dip-slip faulting

Mathematics and Computers in Simulation / Dec 01, 2009

Dutykh, D., & Dias, F. (2009). Tsunami generation by dynamic displacement of sea bed due to dip-slip faulting. Mathematics and Computers in Simulation, 80(4), 837–848. https://doi.org/10.1016/j.matcom.2009.08.036

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Mark Ryan

Digital Ethics Researcher at Wageningen Economic Research
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (33)
Digital Ethics
Philosophy of Technology
Environmental Ethics
AI Ethics
Data Ethics
And 28 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

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Krzysztof Wolk

Professor
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (27)
Machine Learning
AI
NLP
Multimedia
Control and Systems Engineering
And 22 more
About
I hold a PhD Eng. degree in computer science. I am a graduate of the Polish-Japanese Academy of Information Technology in Warsaw, POLAND. Currently, I am associate professor at the Cathedral of Multimedia at the same university. I lead and conduct scientific projects and research related to natural language processing and machine learning based on statistical methods and neural networks. I eagerly take up IT challenges and engage in interesting interdisciplinary projects, in particular related to HCI, UX, medicine, and psychology. In addition, as my profession, I have worked as a lecturer at the Warsaw School of Photography, and as an IT trainer. My specialties as a teacher are primarily deep learning, machine learning, natural language processing, computational linguistics, multimedia, HCI, UX, mobile applications, HTML 5, Adobe applications, and server products from Apple and Microsoft. As far as my didactic work is concerned, I lead classroom studies at the faculty of computer science and at the new media art department of the Polish-Japanese Academy of Information Technology and, in the past, I have also directed classes and lectures at the Warsaw School of Photography & Graphic Design. I am also an expert at the Polish National Agency for Academic Exchange, a member of the Polish Information Processing Society, and a member of the Polish Telemedicine and eHealth Society with Bene Meritus honor. Finally, I am a certified Microsoft, Apple, Adobe, w3schools, and EITCA specialist as well as being the Author of many scientific monographs and specialized IT books related to machine learning, administration of servers, and multimedia. I also engage as an editor of various specialized IT web portals such as in4.pl, pclab.pl, and e-biotechnologia.pl where I am author of training materials, guides, and hardware reviews. Some of my articles have also been published in iCoder Magazine and Komputer Świat magazine.
Most Relevant Publications (1+)

59 total publications

Advanced social media sentiment analysis for short‐term cryptocurrency price prediction

Expert Systems / Nov 21, 2019

Wołk, K. (2019). Advanced social media sentiment analysis for short‐term cryptocurrency price prediction. Expert Systems, 37(2). Portico. https://doi.org/10.1111/exsy.12493

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Vivek Singh

Rutgers Professor, MIT alum, CS PhD, AI expert
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (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 (1+)

95 total publications

Social Bridges in Urban Purchase Behavior

ACM Transactions on Intelligent Systems and Technology / Dec 11, 2017

Dong, X., Suhara, Y., Bozkaya, B., Singh, V. K., Lepri, B., & Pentland, A. ‘Sandy.’ (2017). Social Bridges in Urban Purchase Behavior. ACM Transactions on Intelligent Systems and Technology, 9(3), 1–29. https://doi.org/10.1145/3149409

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Baidurya Bhattacharya

Computational mechanics, probabilistic risk analysis, statistical inference, Monte Carlo simulations
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (43)
computational materials science
probabilistic mechanics
Mechanical Engineering
Industrial and Manufacturing Engineering
Mechanics of Materials
And 38 more
About
Baidurya Bhattacharya is a highly accomplished and respected civil engineer with over 20 years of experience in the field. He was born in Kolkata, India and completed his B.Tech (hons.) in Civil Engineering from the prestigious Indian Institute of Technology Kharagpur in 1991. He then went on to pursue his PhD in Civil Engineering from Johns Hopkins University, which he completed in 1997. After completing his PhD, Bhattacharya started his academic career as a Visiting Professor at the University of Delaware. He then moved on to become an Assistant Professor at the same university, where he taught for several years and mentored numerous students. In 2005, he returned to his alma mater, Indian Institute of Technology Kharagpur, as a Professor in the Department of Civil Engineering. He has been a valuable member of the faculty and has made significant contributions to the department through his research and teaching. Bhattacharya's research interests lie in the areas of structural engineering, earthquake engineering, and soil dynamics. He has published numerous papers in reputable journals and has also presented his work at various international conferences. His research has been recognized and funded by prestigious organizations such as the National Science Foundation and the American Society of Civil Engineers. Aside from his academic career, Bhattacharya is also actively involved in consulting and has worked on various projects in collaboration with government agencies and private firms. He is known for his expertise and has received several awards and honors for his contributions to the field of civil engineering. Bhattacharya is a dedicated educator and mentor, and he continues to inspire and guide young engineers through his teaching and research. His passion for the field and his dedication to his students make him a highly respected figure in the academic community.
Most Relevant Publications (1+)

91 total publications

Performance metrics in a hybrid MPI–OpenMP based molecular dynamics simulation with short-range interactions

Journal of Parallel and Distributed Computing / Mar 01, 2014

Pal, A., Agarwala, A., Raha, S., & Bhattacharya, B. (2014). Performance metrics in a hybrid MPI–OpenMP based molecular dynamics simulation with short-range interactions. Journal of Parallel and Distributed Computing, 74(3), 2203–2214. https://doi.org/10.1016/j.jpdc.2013.12.008

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Oguzhan Kulekci

Algorithm Engineer, Security/Privacy Researcher, Combinatorial Problem Solver
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (20)
algorithms
pattern matching
data compression
bioinformatics
security & privacy
And 15 more
About
My main expertise is in solving computational challenges with an innovative algorithm engineering approach. For more than two decades, I have been studying on such challenges originating from different fields mainly in cryptography and data security, natural language processing, information retrieval, computational biology, data compression and coding, massive data management, and most recently focusing on scalability and security aspects of ML/AI algorithms. I have been devising efficient innovative solutions and/or improving current state-of-art in terms of resource usage, e.g., time, memory, energy, communication costs. I would like to provide a summary of my previous achievements in engineering, research, and administration. Engineering Expertise: After spending around two years on programming point-of-sales devices and regular database programming, I have spent 10+ years in cryptography, where the main focus had been efficient implementation and cryptanalysis of the security&privacy algorithms and protocols both in hardware and software. During those years, despite gaining experience on how to develop programs that run fast and/or with small memory footprint, I had the chance to work with talented mathematicians and hardware engineers, that gave me the opportunity to widen my knowledge on different dimensions, including reverse engineering and FPGA/ASIC design. I also learned a lot on how to develop projects with a team of talent coming from different disciplines. I have observed, and today strongly believe, that theoretical knowledge is vital, but never enough to built efficient systems in practice. The platform that the solution will be executed on and the properties of the input data should always be considered for ground-breaking progress in practical performance. Theory without practice, or vice versa, is akin to trying to fly with one wing. In that sense, the development of the fastest pattern matching solutions and innovating patents that are licensed to companies have been exemplary outcomes of my perspective. Academic Expertise: Following my 15+ years in industry, I joined academia and have been serving as a professor of computer sci- ence. I succeeded to get several research grants and have been also serving in the committees of conferences. Actually, I started publishing in scientific venues when I was with the industry as well. I did my phd on natu- ral language processing, after which I got more engaged with combinatorial algorithms. I mostly published on data compression, combinatorial pattern matching and applications of them on computational biol- ogy/bioinformatics. Most recently, I have been studying scalablity and security aspects in ML/AI systems as well as in information retrieval. I have also experience in massive data management and analysis. I have been teaching courses on algorithms, security/privacy, and related topics. Administrative Expertise: After engineering cryptography for many years, I changed my focus to computational biology, particularly the genomics area. I have served as the deputy director of the National Institute of Genetics and Biotechnology of Turkey for two years, during which I was responsible for the establishment of the first high-throughput DNA sequencing facility of the country. That leadership equipped me with a unique experience of leading an interdisciplinary project with people from computing and life sciences disciplines. The establishment of the lab was supported with more than 2 million dollars grant by the government and was successfully completed in two years. Another leadership experience I had was being the program coordinator of the graduate programs in my university for more than four years. I was responsible by curriculum development and hiring new faculty. I have also served previously as principal investigator in research projects, lead research labs, and delivered project lead positions in industry projects.
Most Relevant Publications (6+)

62 total publications

I/O-efficient data structures for non-overlapping indexing

Theoretical Computer Science / Feb 01, 2021

Hooshmand, S., Abedin, P., Oğuzhan Külekci, M., & Thankachan, S. V. (2021). I/O-efficient data structures for non-overlapping indexing. Theoretical Computer Science, 857, 1–7. https://doi.org/10.1016/j.tcs.2020.12.006

A Survey on Shortest Unique Substring Queries

Algorithms / Sep 06, 2020

Abedin, P., Külekci, M., & Thankachan, S. (2020). A Survey on Shortest Unique Substring Queries. Algorithms, 13(9), 224. https://doi.org/10.3390/a13090224

Applications of Non-Uniquely Decodable Codes to Privacy-Preserving High-Entropy Data Representation

Algorithms / Apr 17, 2019

Külekci, M. O., & Öztürk, Y. (2019). Applications of Non-Uniquely Decodable Codes to Privacy-Preserving High-Entropy Data Representation. Algorithms, 12(4), 78. https://doi.org/10.3390/a12040078

Range selection and predecessor queries in data aware space and time

Journal of Discrete Algorithms / Mar 01, 2017

Külekci, M. O., & Thankachan, S. V. (2017). Range selection and predecessor queries in data aware space and time. Journal of Discrete Algorithms, 43, 18–25. https://doi.org/10.1016/j.jda.2017.01.002

A simple yet time-optimal and linear-space algorithm for shortest unique substring queries

Theoretical Computer Science / Jan 01, 2015

İleri, A. M., Külekci, M. O., & Xu, B. (2015). A simple yet time-optimal and linear-space algorithm for shortest unique substring queries. Theoretical Computer Science, 562, 621–633. https://doi.org/10.1016/j.tcs.2014.11.004

Fast and flexible packed string matching

Journal of Discrete Algorithms / Sep 01, 2014

Faro, S., & Külekci, M. O. (2014). Fast and flexible packed string matching. Journal of Discrete Algorithms, 28, 61–72. https://doi.org/10.1016/j.jda.2014.07.003

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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 (11)
Graph Theory
Network Modeling
Disease Modeling
Discrete Mathematics and Combinatorics
Applied Mathematics
And 6 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

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Hector Klie

CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Interests
Theoretical Computer Science
Other Research Interests (23)
Artificial Intelligence
Machine Learning
Data Science
optimization
Computational Theory and Mathematics
And 18 more
About
**Results-driven AI leader with 20+ years of success spearheading model development and optimization initiatives in the energy industry and academia. Proven track record in leveraging computational data science, scientific machine learning, and AI to drive breakthrough physics-data solutions and deliver tangible business value. Adept at translating complex scientific concepts into robust AI models. Skilled in numerical simulation, scientific machine learning, and bilingual communication to optimize project outcomes.**
Most Relevant Publications (2+)

81 total publications

Towards a rigorously justified algebraic preconditioner for high-contrast diffusion problems

Computing and Visualization in Science / Mar 27, 2008

Aksoylu, B., Graham, I. G., Klie, H., & Scheichl, R. (2008). Towards a rigorously justified algebraic preconditioner for high-contrast diffusion problems. Computing and Visualization in Science, 11(4–6), 319–331. https://doi.org/10.1007/s00791-008-0105-1

A neural stochastic multiscale optimization framework for sensor-based parameter estimation

Integrated Computer-Aided Engineering / May 13, 2007

Banchs, R. E., Klie, H., Rodriguez, A., Thomas, S. G., & Wheeler, M. F. (2007). A neural stochastic multiscale optimization framework for sensor-based parameter estimation. Integrated Computer-Aided Engineering, 14(3), 213–223. https://doi.org/10.3233/ica-2007-14302

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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.