Home
How It Works
Solutions
Resources
Events
Blog
Case Studies
For Academic Experts
Sign Up
Login

Work with thought leaders and academic experts in software

Companies can benefit from working with an academic researcher in the field of Software in several ways. Firstly, researchers bring deep knowledge and expertise in the latest software technologies and methodologies. They can help companies stay ahead of the curve and adopt cutting-edge solutions. Secondly, researchers can provide valuable insights and analysis to solve complex software-related problems. They have the ability to conduct in-depth research, identify patterns, and propose innovative solutions. Thirdly, academic researchers often have access to state-of-the-art facilities and resources, which can be leveraged by companies for experimentation and prototyping. Lastly, collaborating with researchers can lead to valuable partnerships and networking opportunities, opening doors to new collaborations and potential funding sources.

Researchers on NotedSource with backgrounds in software include Stefano De Angelis, Ph.D., Jerry Schnepp, Ph.D., Dr. Wolfgang Messner, Daniel Milej, Ph.D., IQRAM HUSSAIN, Ph.D., Vladimir Shapiro, Ph.D., Edoardo Airoldi, Pranav Chandramouli, Dr. Aalok Thakkar, Dmitry Batenkov, Ph.D., Hector Klie, Dr. Haikun Huang, Ph.D., and Dr. KEHINDE ADEWALE ADESINA, Ph.D.

Stefano De Angelis, Ph.D.

Rome
Ph.D. computer scientist with interest in blockchains, cyber security, and applied cryptography. Strong expertise in secure protocols design and assessment, wirh publications on blockchains and distributed consensus security.
Most Relevant Research Interests
Software
Other Research Interests (7)
Cybersecurity
Blockchain
IoT
Distributed Systems
Computer Networks and Communications
And 2 more
About
Dr. Stefano De Angelis is a computer scientist with interest in distributed systems, blockchain, security, and verifiable computing. Currently, he is a visiting researcher at the University of Southampton, working on formal verification methods for smart contracts, and a researcher at the University of Salerno, working on privacy-preserving solutions for blockchain applications. Over his career, Dr. De Angelis worked for NATO HQ as security research scientist and for Algorand, a layer-1 blockchain, as principal scientist and solution architect. He holds a PhD in Computer Science and Cyber Security awarded in 2022 from the University of Southampton, with a thesis about methodologies and benchmarking procedures for assessing blockchain-based systems in realistic adversarial environments, and an MSc in Engineering of Computer Science awarded in 2018 from the University “Sapienza” of Rome. He is the author of several research papers in the field of blockchain and security published in peer-reviewed journals, international conferences and workshops.
Most Relevant Publications (1+)

4 total publications

Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults

International Journal of Parallel, Emergent and Distributed Systems / Oct 24, 2023

De Angelis, S., Lombardi, F., Zanfino, G., Aniello, L., & Sassone, V. (2023). Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults. International Journal of Parallel, Emergent and Distributed Systems, 1–21. https://doi.org/10.1080/17445760.2023.2272777

See Full Profile

Jerry Schnepp, Ph.D.

Chicago, Illinois, United States of America
Chair of Computer Science, Judson University
Most Relevant Research Interests
Software
Other Research Interests (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 (3+)

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

An improved articulated model of the human hand

The Visual Computer / May 01, 2001

McDonald, J., Toro, J., Alkoby, K., Berthiaume, A., Carter, R., Chomwong, P., Christopher, J., Davidson, M. J., Furst, J., Konie, B., Lancaster, G., Roychoudhuri, L., Sedgwick, E., Tomuro, N., & Wolfe, R. (2001). An improved articulated model of the human hand. The Visual Computer, 17(3), 158–166. https://doi.org/10.1007/s003710100104

See Full Profile

Dr. Wolfgang Messner

Columbia, SC
Professor in International Business with expertise in Data Analytics and Machine Learning
Most Relevant Research Interests
Software
Other Research Interests (14)
International Business
International Marketing
International Management
Strategy and Management
Business and International Management
And 9 more
About
Results-oriented and internationally experienced project manager, consultant, and researcher with a passion for leveraging machine learning and advanced statistical techniques to solve intricate challenges in international marketing and consumer behavior. Demonstrated track record of driving strategic initiatives, cultivating cross-border partnerships, and delivering tangible impacts on revenue generation. Highly adaptable to rapidly evolving technologies and market trends. Aiming to apply my expertise to lead transformative projects and elevate organizational success on a global scale. **Research and publication overview** · Authored 36 peer-reviewed journal publications (data analytics, international business, marketing) · Authored and edited 8 business books, published by *Palgrave Macmillan* and *Springer* · Published 5 teaching cases with *SAGE* and *Ivey* · Research impact (Google Scholar): h-index of 17 with 1,000+ citations **Competences in data analysis (selected)** · Supervised: Neural networks, deep learning · Unsupervised: Kohonen self-organizing maps · Frequentist and Bayesian regression analysis · Multilevel (hierarchical) modeling · Exploratory and confirmatory factor analysis · fs/QCA \| HLM \| SPSS \| JASP \| Python\, incl\. Keras\, Dalex
Most Relevant Publications (1+)

65 total publications

From black box to clear box: A hypothesis testing framework for scalar regression problems using deep artificial neural networks

Applied Soft Computing / Oct 01, 2023

Messner, W. (2023). From black box to clear box: A hypothesis testing framework for scalar regression problems using deep artificial neural networks. Applied Soft Computing, 146, 110729. https://doi.org/10.1016/j.asoc.2023.110729

See Full Profile

Daniel Milej, Ph.D.

London, Ontario, Canada
Ph.D. in biomedical engineering
Most Relevant Research Interests
Software
Other Research Interests (31)
Biomedical Optics
NIRS
fNIRS
Diffuse Correlation Spectroscopy
CBF
And 26 more
About
Dr. Daniel Milej is a multidisciplinary researcher with experience in medical biophysics, electronics, biocybernetics, biomedical optics and engineering. He is highly knowledgeable and experienced in a range of research techniques. He is currently a Research Associate at the Lawson Health Research Institute, leading the transition of multimodal optical imaging systems from a research setting to clinical use in an ICU and OR environment, working closely with teams of nurses, surgeons, doctors and respiratory therapists. Previously he was a postdoctoral fellow working on developing noninvasive modalities for brain activity monitoring in the Department of Medical Biophysics at Western University. Before that, Dr. Milej worked as a researcher at the Nalecz Institute of Biocybernetics and Biomedical Engineering. He obtained his Ph.D. in 2014 from the Polish Academy of Science, specializing in Electronics and Biomedical Engineering. He received his MSc from the Military University of Technology in 2008.
Most Relevant Publications (1+)

91 total publications

Analysis of estimation of optical properties of sub superficial structures in multi layered tissue model using distribution function method

Computer Methods and Programs in Biomedicine / Jan 01, 2020

Żołek, N., Rix, H., & Botwicz, M. (2020). Analysis of estimation of optical properties of sub superficial structures in multi layered tissue model using distribution function method. Computer Methods and Programs in Biomedicine, 183, 105084. https://doi.org/10.1016/j.cmpb.2019.105084

See Full Profile

IQRAM HUSSAIN, Ph.D.

New York City, New York, United States of America
Weill Cornell Medicine, Cornell University, NY, USA
Most Relevant Research Interests
Software
Other Research Interests (32)
Biomedical & Medical Physics
AI (Machine & Deep Learning)
Anesthesiology
Sleep Medicine
Human Gait & brain
And 27 more
About
Iqram Hussain works at the Department of Anesthesiology, Weill Cornell Medicine, Cornell University, NY, USA. Earlier, he was a postdoctoral researcher at the Medical Research Center, Department of Biomedical Engineering, Seoul National University. He pursued a Ph.D. degree in Medical Physics from the University of Science and Technology (UST), South Korea. He worked as a Research Associate with the Korea Research Institute of Standards and Science (KRISS), Daejeon, South Korea. He worked on the Knowledgebase Super Brain (KSB) project at the Electronics and Telecommunication Research Institute (ETRI), Daejeon. He received a B.Sc. degree in mechanical engineering from the Khulna University of Engineering & Technology, Bangladesh, in 2007. He has ten years of work experience in power plant operation and maintenance and power plant project management. His research interests include wearable sleep monitoring, neuroscience, medical physics, human factors, and ergonomics. He has experience in healthcare research, project management, power plant operation, and maintenance. He is a reviewer in IEEE Access, Sensors, Applied Sciences, Biomedical Signal Processing and Control, IEEE Transactions, Science of the Total Environment, Neuroscience Informatics, Brain Sciences, etc. He is a guest editor in special issues of several Journals. Website: https://sites.google.com/view/iqram/home
Most Relevant Publications (1+)

44 total publications

Tracking Trajectory Planning of Space Manipulator for Capturing Operation

International Journal of Advanced Robotic Systems / Sep 01, 2006

Huang, P., Xu, Y., & Liang, B. (2006). Tracking Trajectory Planning of Space Manipulator for Capturing Operation. International Journal of Advanced Robotic Systems, 3(3), 31. https://doi.org/10.5772/5735

See Full Profile

Vladimir Shapiro, Ph.D.

Boston, Massachusetts, United States of America
PRINCIPAL AI/COMPUTER VISION DATA SCIENTIST; EXPERIENCED SOFTWARE (PYTHON, C/C++, R) DEVELOPER; ADJUNCT UNIVERSITY PROFESSOR
Most Relevant Research Interests
Software
Other Research Interests (14)
Computer Vision and Pattern Recognition
Hardware and Architecture
Computer Science Applications
Signal Processing
Artificial Intelligence
And 9 more
About
• Expertise in image and video processing, machine vision, machine learning, digital signal processing, deep learning and pattern recognition algorithm development. • Expertise of production quality C/C++, Python language implementation including for real-time and multiple including embedded platforms. • Experience of working for start-ups and global companies. • Over 50 scientific publications and patents. Specialties: AI, image/video processing, computer vision, machine vision, deep learning, pattern recognition, machine learning, data science, software engineering, embedded software, real-time systems, motor control, Python, C/C++, R and MATLAB programming, software development, object oriented, Linux, Windows, algorithms, Agile development.
Most Relevant Publications (5+)

38 total publications

Towards a Multinational Car License Plate Recognition System

Machine Vision and Applications / May 25, 2006

Shapiro, V., Gluhchev, G., & Dimov, D. (2006). Towards a Multinational Car License Plate Recognition System. Machine Vision and Applications, 17(3), 173–183. https://doi.org/10.1007/s00138-006-0023-5

Handwritten document image segmentation and analysis

Pattern Recognition Letters / Jan 01, 1993

Shapiro, V., Gluhchev, G., & Sgurev, V. (1993). Handwritten document image segmentation and analysis. Pattern Recognition Letters, 14(1), 71–78. https://doi.org/10.1016/0167-8655(93)90134-y

Accuracy of the straight line Hough Transform: The non-voting approach

Computer Vision and Image Understanding / Jul 01, 2006

Shapiro, V. (2006). Accuracy of the straight line Hough Transform: The non-voting approach. Computer Vision and Image Understanding, 103(1), 1–21. https://doi.org/10.1016/j.cviu.2006.02.001

On the hough transform of multi-level pictures

Pattern Recognition / Apr 01, 1996

A. Shapiro, V. (1996). On the hough transform of multi-level pictures. Pattern Recognition, 29(4), 589–602. https://doi.org/10.1016/0031-3203(95)00116-6

On the reconstructive matching of multidimensional objects

IEEE Transactions on Image Processing / Apr 01, 1996

Shapiro, V. A. (1996). On the reconstructive matching of multidimensional objects. IEEE Transactions on Image Processing, 5(4), 653–661. https://doi.org/10.1109/83.491342

See Full Profile

Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Software
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

A Survey of Statistical Network Models

Foundations and Trends® in Machine Learning / Jan 01, 2009

Goldenberg, A. (2009). A Survey of Statistical Network Models. Foundations and Trends® in Machine Learning, 2(2), 129–233. https://doi.org/10.1561/2200000005

See Full Profile

Pranav Chandramouli

Graduate Student with expertise in Computer Vision, Deep Learning, and its applications, specifically autonomous remote sensor systems for object detection.
Most Relevant Research Interests
Software
Other Research Interests (3)
Computer Vision
Deep Learning
Computer Science Applications
About
I am an M.S. student specializing in Computer Vision for Remote Sensing for Wildlife Conservation. I am passionate about leveraging CV, Perception, and DL to build products and solutions that improve the world around us. I am a self-starter and collaborative problem-solver with a strong work ethic. Beyond technology, I spend my time on photography, bird watching, or in the great outdoors.
Most Relevant Publications (1+)

1 total publications

analyzeR: A SonarQube plugin for analyzing object-oriented R Packages

SoftwareX / Jul 01, 2022

Chandramouli, P., Codabux, Z., & Vidoni, M. (2022). analyzeR: A SonarQube plugin for analyzing object-oriented R Packages. SoftwareX, 19, 101113. https://doi.org/10.1016/j.softx.2022.101113

See Full Profile

Dr. Aalok Thakkar

Seattle, Washington, United States of America
Research Scientist focussed on integrating formal methods and artificial intelligence.
Most Relevant Research Interests
Software
Other Research Interests (8)
programming languages
verification
logic
synthesis
Computer Science Applications
And 3 more
About
My research focuses on integrating **formal methods** and **artificial intelligence**. In particular, I have worked on applying formal methods in the context of programming-by-examples (PBE) for relational queries, synthesis of reactive programs, bounded model checking, and verification of smart contracts. Currently, I work with Movement Labs on designing the Fractal interpreter will allow users to deploy Solidity smart contracts directly on Movement's M1 blockchain. Previously, I have worked with Aptos Labs, Amazon AWS, Nokia Bell Labs, and Adobe India.
Most Relevant Publications (2+)

12 total publications

Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates

Proceedings of the ACM on Programming Languages / Oct 16, 2023

Thakkar, A., Sands, N., Petrou, G., Alur, R., Naik, M., & Raghothaman, M. (2023). Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates. Proceedings of the ACM on Programming Languages, 7(OOPSLA2), 1394–1417. https://doi.org/10.1145/3622847

Synthesis of coordination programs from linear temporal specifications

Proceedings of the ACM on Programming Languages / Dec 20, 2019

Bansal, S., Namjoshi, K. S., & Sa’ar, Y. (2019). Synthesis of coordination programs from linear temporal specifications. Proceedings of the ACM on Programming Languages, 4(POPL), 1–27. https://doi.org/10.1145/3371122

See Full Profile

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
Software
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 (1+)

51 total publications

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

See Full Profile

Hector Klie

CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Interests
Software
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 (6+)

81 total publications

null

Computational Geosciences / Jan 01, 1997

Dawson, C. N., Klíe, H., Wheeler, M. F., & Woodward, C. S. (1997). Computational Geosciences, 1(3/4), 215–249. https://doi.org/10.1023/a:1011521413158

An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement

Cluster Computing / Oct 01, 2005

Bangerth, W., Klie, H., Matossian, V., Parashar, M., & Wheeler, M. F. (2005). An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement. Cluster Computing, 8(4), 255–269. https://doi.org/10.1007/s10586-005-4093-3

Models, methods and middleware for grid-enabled multiphysics oil reservoir management

Engineering with Computers / Sep 16, 2006

Klie, H., Bangerth, W., Gai, X., Wheeler, M. F., Stoffa, P. L., Sen, M., Parashar, M., Catalyurek, U., Saltz, J., & Kurc, T. (2006). Models, methods and middleware for grid-enabled multiphysics oil reservoir management. Engineering with Computers, 22(3–4), 349–370. https://doi.org/10.1007/s00366-006-0035-9

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

Application of Grid-enabled technologies for solving optimization problems in data-driven reservoir studies

Future Generation Computer Systems / Jan 01, 2005

Parashar, M., Klie, H., Catalyurek, U., Kurc, T., Bangerth, W., Matossian, V., Saltz, J., & Wheeler, M. F. (2005). Application of Grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation Computer Systems, 21(1), 19–26. https://doi.org/10.1016/j.future.2004.09.028

See Full Profile

Dr. Haikun Huang, Ph.D.

Chief Technology Officer at Great Victory Legends
Most Relevant Research Interests
Software
Other Research Interests (25)
Computational Design
Graphics
Vision
VR/AR/MR
Cognitive Science.
And 20 more
About
Dr. Haikun Huang holds a Ph.D. in Computer Science from UMass Boston and is a postdoctoral research fellow at George Mason University. His graduation thesis was titled AI-driven Computational Design Tools For Synthesizing Human-centric Design and won the Umass Boston year's graduate program award. With a strong background in AR/VR/MR, computational design, graphics, HCI, and vision, he is passionate about applying artificial intelligence techniques to create innovative 3D content creation tools and virtual experiences.   Dr. Huang has published his research in prestigious conferences such as IEEE VR and ACM CHI, and his work has been recognized with a Best Paper Honorable Mention Award at CHI 2019. He is also an active reviewer for IEEE VR and CHI, contributing to advancing these fields. From 2017 to 2023, he has successfully published 21 papers, which have been cited more than 470 times. In the first half of 2023 alone, it was cited more than 180 times. At the same time, his h-index is 12, and i10-index is 15.   In addition to his academic achievements, he has years of industry experience, particularly in the game development sector. He has also been a columnist for popular game development forums in China, where he shared his expertise and insights with fellow developers.   He has also served as a teaching assistant for various computer science courses, including Computer Games Programming, Computer Vision, Programming in C, and (Computer Architecture and Organization at UMB. These experiences have allowed him to hone his teaching skills and effectively communicate complex concepts to students.   As a co-founder and CTO of Great Victory Legends, he gained valuable experience leading technical teams and developing cutting-edge solutions. He is confident in bringing this expertise into the classroom and providing students with a comprehensive understanding of the subject matter.   He also runs his studio as a freelance and sells the tools on Unity Asset Store. The tools he develops are all about practical tools to improve development efficiency. UPython 3 Pro is his masterpiece. It provides real-time communication between Unity and Python. It was used in the research projects he was involved in. AR/VR researchers deeply love it.
Most Relevant Publications (4+)

34 total publications

Exercise Intensity-Driven Level Design

IEEE Transactions on Visualization and Computer Graphics / Apr 01, 2018

Xie, B., Zhang, Y., Huang, H., Ogawa, E., You, T., & Yu, L.-F. (2018). Exercise Intensity-Driven Level Design. IEEE Transactions on Visualization and Computer Graphics, 24(4), 1661–1670. https://doi.org/10.1109/tvcg.2018.2793618

Automatic Optimization of Wayfinding Design

IEEE Transactions on Visualization and Computer Graphics / Sep 01, 2018

Huang, H., Lin, N.-C., Barrett, L., Springer, D., Wang, H.-C., Pomplun, M., & Yu, L.-F. (2018). Automatic Optimization of Wayfinding Design. IEEE Transactions on Visualization and Computer Graphics, 24(9), 2516–2530. https://doi.org/10.1109/tvcg.2017.2761820

Synthesizing Personalized Construction Safety Training Scenarios for VR Training

IEEE Transactions on Visualization and Computer Graphics / May 01, 2022

Li, W., Huang, H., Solomon, T., Esmaeili, B., & Yu, L.-F. (2022). Synthesizing Personalized Construction Safety Training Scenarios for VR Training. IEEE Transactions on Visualization and Computer Graphics, 28(5), 1993–2002. https://doi.org/10.1109/tvcg.2022.3150510

Mood-Driven Colorization of Virtual Indoor Scenes

IEEE Transactions on Visualization and Computer Graphics / May 01, 2022

Solah, M., Huang, H., Sheng, J., Feng, T., Pomplun, M., & Yu, L.-F. (2022). Mood-Driven Colorization of Virtual Indoor Scenes. IEEE Transactions on Visualization and Computer Graphics, 28(5), 2058–2068. https://doi.org/10.1109/tvcg.2022.3150513

See Full Profile

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 Interests
Software
Other Research Interests (18)
Artificial Intelligence
Management Science and Operations Research
Safety, Risk, Reliability and Quality
Information Systems
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 (4+)

27 total publications

An improved lasso regression model for evaluating the efficiency of intervention actions in a system reliability analysis

Neural Computing and Applications / Jan 02, 2021

Yazdi, M., Golilarz, N. A., Nedjati, A., & Adesina, K. A. (2021). An improved lasso regression model for evaluating the efficiency of intervention actions in a system reliability analysis. Neural Computing and Applications, 33(13), 7913–7928. https://doi.org/10.1007/s00521-020-05537-8

Supportive emergency decision-making model towards sustainable development with fuzzy expert system

Neural Computing and Applications / Jun 22, 2021

Li, H., Guo, J.-Y., Yazdi, M., Nedjati, A., & Adesina, K. A. (2021). Supportive emergency decision-making model towards sustainable development with fuzzy expert system. Neural Computing and Applications, 33(22), 15619–15637. https://doi.org/10.1007/s00521-021-06183-4

An improved multi-criteria emergency decision-making method in environmental disasters

Soft Computing / May 19, 2021

Jiang, G.-J., Chen, H.-X., Sun, H.-H., Yazdi, M., Nedjati, A., & Adesina, K. A. (2021). An improved multi-criteria emergency decision-making method in environmental disasters. Soft Computing, 25(15), 10351–10379. https://doi.org/10.1007/s00500-021-05826-x

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

See Full Profile

Example software projects

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

Optimizing Software Performance

A company in the gaming industry can collaborate with a software academic researcher to optimize the performance of their gaming software. The researcher can analyze the code, identify bottlenecks, and propose optimizations to enhance the gaming experience for users.

Cybersecurity Solutions

A financial institution can partner with a software academic researcher to develop robust cybersecurity solutions. The researcher can conduct research on emerging threats, develop algorithms for threat detection, and propose strategies to mitigate risks.

Machine Learning Algorithms

An e-commerce company can work with a software academic researcher to develop machine learning algorithms for personalized product recommendations. The researcher can analyze customer data, build predictive models, and optimize the recommendation engine for better conversion rates.

Software Testing Automation

A software development company can collaborate with a software academic researcher to automate their testing processes. The researcher can develop testing frameworks, design automated test cases, and improve the overall efficiency and reliability of the software testing phase.

Data Analytics and Visualization

A healthcare organization can partner with a software academic researcher to leverage data analytics and visualization techniques. The researcher can analyze large healthcare datasets, develop algorithms for predictive analytics, and create interactive visualizations to aid in decision-making and patient care.