Work with thought leaders and academic experts in Computer Science Applications

Companies can benefit from collaborating with academic researchers in Computer Science Applications in several ways. These researchers have deep knowledge and expertise in areas such as artificial intelligence, data analysis, software development, and more. By working with them, companies can drive innovation, solve complex problems, and stay ahead of the competition. Researchers can provide valuable insights and solutions to optimize business processes, develop cutting-edge technologies, and improve customer experiences. They can also help companies leverage big data and implement machine learning algorithms to gain actionable insights and make data-driven decisions. Additionally, academic researchers can assist in developing secure and robust software systems, ensuring the company's technology infrastructure is efficient and protected.

Researchers on NotedSource with backgrounds in Computer Science Applications include Christos Makridis, Ping Luo, Burcu Vitrinel, Ph.D., PhD.Heydy Castillejos, IQRAM HUSSAIN, Ph.D., Elvira Forte, Aruna Ranaweera, Keiran Thompson, Jerry Schnepp, Ph.D., Dr. David Siderovski, Ph.D., Daniel Milej, Ph.D., Ajay Badhan, and Siddharth Maddali.

Christos Makridis

Nashville, TN
Web3 and Labor Economist in Academia, Entrepreneurship, and Policy
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (15)
Finance
Economics and Econometrics
Accounting
Pharmacology (medical)
Law
And 10 more
About
Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](https://www.dainamic.ai/), a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](https://www.livingopera.org/), a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.
Most Relevant Publications (2+)

25 total publications

Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans

Computers in Biology and Medicine / Jun 01, 2021

Makridis, C. A., Zhao, D. Y., Bejan, C. A., & Alterovitz, G. (2021). Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans. Computers in Biology and Medicine, 133, 104354. https://doi.org/10.1016/j.compbiomed.2021.104354

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs

BMJ Health & Care Informatics / Jun 01, 2021

Makridis, C. A., Strebel, T., Marconi, V., & Alterovitz, G. (2021). Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs. BMJ Health & Care Informatics, 28(1), e100312. https://doi.org/10.1136/bmjhci-2020-100312

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Ping Luo

Toronto, Ontario, Canada
Bioinformatics Specialist at Princess Margaret Cancer Centre with experience in deep learning
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (21)
single-cell genomics
deep learning
complex network analysis
Genetics (clinical)
Genetics
And 16 more
About
8 years of science and engineering experience integrating multi-omics data to identify biomarkers for cancer studies. Seeking to apply data analytics expertise to develop new diagnosis and treatment strategies.
Most Relevant Publications (5+)

23 total publications

Enhancing the prediction of disease–gene associations with multimodal deep learning

Bioinformatics / Mar 02, 2019

Luo, P., Li, Y., Tian, L.-P., & Wu, F.-X. (2019). Enhancing the prediction of disease–gene associations with multimodal deep learning. Bioinformatics, 35(19), 3735–3742. https://doi.org/10.1093/bioinformatics/btz155

CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization

Neurocomputing / Jan 01, 2018

Tian, L.-P., Luo, P., Wang, H., Zheng, H., & Wu, F.-X. (2018). CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization. Neurocomputing, 275, 2031–2040. https://doi.org/10.1016/j.neucom.2017.10.039

Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics / May 01, 2021

Li, Y., Luo, P., Lu, Y., & Wu, F.-X. (2021). Identifying cell types from single-cell data based on similarities and dissimilarities between cells. BMC Bioinformatics, 22(S3). https://doi.org/10.1186/s12859-020-03873-z

Ensemble disease gene prediction by clinical sample-based networks

BMC Bioinformatics / Mar 01, 2020

Luo, P., Tian, L.-P., Chen, B., Xiao, Q., & Wu, F.-X. (2020). Ensemble disease gene prediction by clinical sample-based networks. BMC Bioinformatics, 21(S2). https://doi.org/10.1186/s12859-020-3346-8

Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets

Computational and Structural Biotechnology Journal / Jan 01, 2022

Mahalanabis, A., Turinsky, A. L., Husić, M., Christensen, E., Luo, P., Naidas, A., Brudno, M., Pugh, T., Ramani, A. K., & Shooshtari, P. (2022). Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets. Computational and Structural Biotechnology Journal, 20, 6375–6387. https://doi.org/10.1016/j.csbj.2022.10.029

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PhD.Heydy Castillejos

Weston, Florida, United States of America
Research professor, Universidad Autónoma del Estado de Hidalgo
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (10)
Image and signal processing
boimedical signals
segmentation
classification
CAD
And 5 more
About
Results-oriented professional with over 15 years of experience in research and teaching. Skilled in Python, MATLAB programming, image processing, and telecommunications. Demonstrated ability to work under pressure, manage time effectively, and solve complex problems. Successfully advised doctoral candidates on data analysis methods, authored multiple peer-reviewed journal articles, and secured funding for future research initiatives. Developed innovative curricula for advanced mathematics courses and utilized technology to enhance learning experiences. A fast learner with excellent written and verbal communication skills.
Most Relevant Publications (2+)

15 total publications

Written Documents Analyzed as Nature-Inspired Processes: Persistence, Anti-Persistence, and Random Walks—We Remember, as Along Came Writing—T. Holopainen

Applied Sciences / Sep 12, 2020

López-Ortega, O., Pérez-Cortés, O., Castillejos-Fernández, H., Castro-Espinoza, F.-A., & González-Mendoza, M. (2020). Written Documents Analyzed as Nature-Inspired Processes: Persistence, Anti-Persistence, and Random Walks—We Remember, as Along Came Writing—T. Holopainen. Applied Sciences, 10(18), 6354. https://doi.org/10.3390/app10186354

Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics

Applied Sciences / Oct 11, 2022

Gonzalez-Islas, J.-C., Dominguez-Ramirez, O.-A., Lopez-Ortega, O., Peña-Ramirez, J., Ordaz-Oliver, J.-P., & Marroquin-Gutierrez, F. (2022). Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics. Applied Sciences, 12(20), 10197. https://doi.org/10.3390/app122010197

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IQRAM HUSSAIN, Ph.D.

New York City, New York, United States of America
Weill Cornell Medicine, Cornell University, NY, USA
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (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 (2+)

43 total publications

Measuring technological patent scope by semantic analysis of patent claims – An indicator for valuating patents

World Patent Information / Sep 01, 2019

Wittfoth, S. (2019). Measuring technological patent scope by semantic analysis of patent claims – An indicator for valuating patents. World Patent Information, 58, 101906. https://doi.org/10.1016/j.wpi.2019.101906

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

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Elvira Forte

New York, New York, United States of America
Scientific strategist • Senior Scientist • Senior Scientific Editor
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (26)
fibrosis
inflammation
cardiomyopathies
Physiology
Cardiology and Cardiovascular Medicine
And 21 more
About
Motivated and growth-driven biomedical scientist with over 15 years of experience in the cardiovascular field. Strong background in cell and molecular biology, data analysis, and interpretation. Seeking #newopportunities to deliver value as a Senior Scientist, Associate Principal Scientist, Scientific Liaison, or Scientific Consultant within a company that combines new technologies such as single-cell omics and AI for drug discovery and personalized medicine. <br> Throughout my career, I've used various in vivo and in vitro models to investigate the molecular and cellular mechanisms of fibrosis and inflammation in the heart, and how these mechanisms are affected in mice with different genetic backgrounds. My experience includes project management and mentoring. I completed three projects involving international collaborations, supervised two junior researchers, and taught cellular and molecular techniques to at least six professionals. As one of the launching editors and senior editor at Nature Cardiovascular Research, I oversaw the quality of the content published in the journal and the editorial process. I collaborated with authors, reviewers, and editors to ensure the highest standards of scientific rigor, relevance, and innovation. I also contributed to the journal's vision, strategy, and outreach, promoting the latest advances and discoveries in the cardiovascular and hematology fields. This experience has sharpened my analytical and communication skills and broadened my understanding of the field, covering a wide range of studies, from basic research to clinical, epidemiological, and public health research. My mission is to advance the knowledge and practice of cardiovascular medicine and to bridge the gap between research and clinical applications. Keywords: cardiovascular, #fibrosis, inflammation, cardioimmunology, RNA, single-cell biology, #transcriptomics, imaging, animal models, small animal surgery, and scientific writing.
Most Relevant Publications (1+)

63 total publications

Ex uno, plures–From One Tissue to Many Cells: A Review of Single-Cell Transcriptomics in Cardiovascular Biology

International Journal of Molecular Sciences / Feb 19, 2021

Forte, E., McLellan, M. A., Skelly, D. A., & Rosenthal, N. A. (2021). Ex uno, plures–From One Tissue to Many Cells: A Review of Single-Cell Transcriptomics in Cardiovascular Biology. International Journal of Molecular Sciences, 22(4), 2071. https://doi.org/10.3390/ijms22042071

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Keiran Thompson

Palo Alto, California, United States of America
Stanford University
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (5)
Physical and Theoretical Chemistry
Colloid and Surface Chemistry
Biochemistry
Catalysis
Library and Information Sciences
About
Keiran Thompson is a machine learning and quantum chemistry researcher. Originally from Australia, he currently works as an AI research scientist at Stanford University where he transfers machine learning knowledge from the private sector to academic research which can then be reconverted back to private sector usage. He is experienced with large scale numerical computing and has led several startups as Chief Scientist.
Most Relevant Publications (2+)

29 total publications

Large-Scale Functional Group Symmetry-Adapted Perturbation Theory on Graphical Processing Units

Journal of Chemical Theory and Computation / Jan 18, 2018

Parrish, R. M., Thompson, K. C., & Martínez, T. J. (2018). Large-Scale Functional Group Symmetry-Adapted Perturbation Theory on Graphical Processing Units. Journal of Chemical Theory and Computation, 14(3), 1737–1753. https://doi.org/10.1021/acs.jctc.7b01053

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

Journal of Chemical Information and Modeling / Apr 08, 2020

Seritan, S., Thompson, K., & Martínez, T. J. (2020). TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations. Journal of Chemical Information and Modeling, 60(4), 2126–2137. https://doi.org/10.1021/acs.jcim.9b01152

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Dr. David Siderovski, Ph.D.

Fort Worth
Professor of Computational Pharmacology; Chair of HSC SBS Dept. of Pharmacology & Neuroscience
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (25)
Regulator of G protein Signaling (RGS) proteins
Pharmacology
Molecular Biology
Cellular and Molecular Neuroscience
Molecular Medicine
And 20 more
About
Dr. David Siderovski is a renowned scientist and academic, with a career spanning over two decades. He received his Ph.D. in Medical Biophysics from the University of Toronto in 1997, where he specialized in signal transduction and cellular signaling pathways. After completing his doctorate, Dr. Siderovski held various faculty positions at prestigious universities, including the University of North Carolina at Chapel Hill, West Virginia University School of Medicine, and the University of North Texas Health Science Center. At these institutions, Dr. Siderovski has made significant contributions to the field of pharmacology through his research on G protein-coupled receptors (GPCRs) and RGS proteins, which are key regulators of GPCR cellular signaling. His work has helped to advance the understanding of RGS proteins and their roles in various diseases, including cancer, cardiovascular disorders, and neurological disorders. In addition to his research, Dr. Siderovski is also a dedicated educator and mentor. He has taught and mentored numerous undergraduate, graduate, and medical students, and has served as a mentor for postdoctoral fellows and junior faculty members. He is known for his passion and enthusiasm for science and his ability to inspire and guide the next generation of scientists. Dr. Siderovski has received numerous awards and honors for his contributions to the scientific community. He was the recipient of the Abel Award in 2004 from the American Society of Pharmacology & Experimental Therapeutics for his pioneering discoveries of the RGS proteins and the GoLoco motif. He has also served on editorial boards of several scientific journals (including a decade at *J.Biol.Chem.*) and has been a member of various scientific committees, NIH study section panels, and pharma/biotech advisory boards, including for Inspire, Wyeth, and BellBrook Labs. Overall, Dr. David Siderovski is a highly accomplished and respected scientist and educator, whose research has had a significant impact on the field of pharmacology. His dedication and passion for science continue to inspire and influence the next generation of researchers in this field.
Most Relevant Publications (3+)

94 total publications

Established and Emerging Fluorescence-Based Assays for G-Protein Function: Ras-Superfamily GTPases

Combinatorial Chemistry &amp; High Throughput Screening / Jun 01, 2003

Rojas, R., Kimple, R., Rossman, K., Siderovski, D., & Sondek, J. (2003). Established and Emerging Fluorescence-Based Assays for G-Protein Function: Ras-Superfamily GTPases. Combinatorial Chemistry &amp; High Throughput Screening, 6(4), 409–418. https://doi.org/10.2174/138620703106298509

Established and Emerging Fluorescence-Based Assays for G-Protein Function: Heterotrimeric G-Protein Alpha Subunits and Regulator of G-Protein Signaling (RGS) Proteins

Combinatorial Chemistry &amp; High Throughput Screening / Jun 01, 2003

Kimple, R., Jones, M., Shutes, A., Yerxa, B., Siderovski, D., & Willard, F. (2003). Established and Emerging Fluorescence-Based Assays for G-Protein Function: Heterotrimeric G-Protein Alpha Subunits and Regulator of G-Protein Signaling (RGS) Proteins. Combinatorial Chemistry &amp; High Throughput Screening, 6(4), 399–407. https://doi.org/10.2174/138620703106298491

A High Throughput Fluorescence Polarization Assay for Inhibitors of the GoLoco Motif/G-alpha Interaction

Combinatorial Chemistry &amp; High Throughput Screening / Jun 01, 2008

Kimple, A., Yasgar, A., Hughes, M., Jadhav, A., Willard, F., Muller, R., Austin, C., Inglese, J., Ibeanu, G., Siderovski, D., & Simeonov, A. (2008). A High Throughput Fluorescence Polarization Assay for Inhibitors of the GoLoco Motif/G-alpha Interaction. Combinatorial Chemistry &amp; High Throughput Screening, 11(5), 396–409. https://doi.org/10.2174/138620708784534770

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Daniel Milej, Ph.D.

London, Ontario, Canada
Ph.D. in biomedical engineering
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (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

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Ajay Badhan

Lethbridge, Alberta, Canada
Research Biologist, Lethbridge Research Center, Canada
Most Relevant Research Expertise
Computer Science Applications
Other Research Expertise (26)
Animal nutrition
cell wall biosynthesis and its deconstruction
biofuels
Waste Management and Disposal
Renewable Energy, Sustainability and the Environment
And 21 more
About
I am a proficient researcher with valuable research and teaching experience acquired at distinguished institutes like Complex Carbohydrate Research Center, US, University of Alberta, Canada, and Lethbridge Research Center (AAFC), Canada. I have been working for past 15 years on multiple projects focused on the economical, environmental and social sustainability of agricultural production. Improvement in livestock performance, productivity, and health by unlocking the microbiome, development of clean technologies, improving agriculture environmental performance, and Increase agro-ecosystem resilience are prime objectives for my research.
Most Relevant Publications (1+)

29 total publications

Mechanistic insights into the digestion of complex dietary fibre by the rumen microbiota using combinatorial high-resolution glycomics and transcriptomic analyses

Computational and Structural Biotechnology Journal / Jan 01, 2022

Badhan, A., Low, K. E., Jones, D. R., Xing, X., Milani, M. R. M., Polo, R. O., Klassen, L., Venketachalam, S., Hahn, M. G., Abbott, D. W., & McAllister, T. A. (2022). Mechanistic insights into the digestion of complex dietary fibre by the rumen microbiota using combinatorial high-resolution glycomics and transcriptomic analyses. Computational and Structural Biotechnology Journal, 20, 148–164. https://doi.org/10.1016/j.csbj.2021.12.009

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Siddharth Maddali

Fremont, California, United States of America

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Example Computer Science Applications projects

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

Optimizing Supply Chain Management

An academic researcher in Computer Science Applications can help companies optimize their supply chain management processes. By analyzing data and applying algorithms, they can identify bottlenecks, streamline operations, and reduce costs. They can also develop predictive models to forecast demand and optimize inventory management, ensuring efficient and timely delivery of products.

Enhancing Cybersecurity Measures

Collaborating with a Computer Science Applications researcher can enhance a company's cybersecurity measures. They can analyze the company's existing security infrastructure, identify vulnerabilities, and develop robust solutions to protect against cyber threats. Researchers can also develop advanced encryption algorithms and implement secure authentication systems to safeguard sensitive data and prevent unauthorized access.

Implementing Machine Learning for Personalized Recommendations

By collaborating with an academic researcher in Computer Science Applications, companies can implement machine learning algorithms for personalized recommendations. Researchers can analyze customer data, identify patterns, and develop recommendation systems that provide personalized product suggestions, improving customer satisfaction and driving sales. These algorithms can also be used for targeted marketing campaigns, increasing customer engagement and retention.

Developing Natural Language Processing Applications

Academic researchers in Computer Science Applications can help companies develop natural language processing applications. They can build chatbots and virtual assistants that can understand and respond to customer queries, improving customer support and reducing response time. Researchers can also develop sentiment analysis algorithms to analyze customer feedback and sentiment on social media, helping companies understand customer preferences and improve their products and services.

Optimizing Data Analysis and Visualization

Collaborating with a Computer Science Applications researcher can optimize a company's data analysis and visualization processes. Researchers can develop algorithms and tools to process and analyze large datasets, extract meaningful insights, and visualize data in a clear and intuitive manner. This can help companies make data-driven decisions, identify trends, and uncover hidden patterns, leading to improved business strategies and outcomes.