Work with thought leaders and academic experts in Computational Mathematics

Companies can greatly benefit from working with experts in Computational Mathematics. These researchers have a deep understanding of data analysis, optimization, and machine learning techniques. By collaborating with them, companies can enhance their decision-making processes, improve efficiency, and gain a competitive edge. Computational Mathematics experts can help companies solve complex problems, develop innovative algorithms, and optimize various processes. They can also assist in developing predictive models, improving risk management strategies, and identifying patterns and trends in large datasets. Overall, partnering with a Computational Mathematics researcher can lead to improved data-driven decision-making, increased productivity, and better business outcomes.

Researchers on NotedSource with backgrounds in Computational Mathematics include Nicolangelo Iannella, Siddharth Maddali, Ping Luo, Jeffrey Townsend, Emmanouil Mentzakis, Hector Klie, Tim Osswald, Dmitry Batenkov, Ph.D., Edoardo Airoldi, Ariel Aptekmann, Baidurya Bhattacharya, and Oguzhan Kulekci.

Nicolangelo Iannella

Oslo
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (18)
Neuromorphic circuits
Neural networks, Neural learning and applications
Theoretical and Mathematical neuroscience
Computational neuroscience
Artificial Intelligence
And 13 more
About
Following pre-doctoral studies in Mathematics and Theoretical Physics, I received a PhD in Computational Neuroscience from the University of Electro-Communications, Japan in 2009. From 2009, I was a Postdoctoral Researcher in RIKEN BSI. In 2010, I won the prestigious Australian Research Council (ARC) Australian Postdoctoral Award (APD) fellowship, based at the University of Adelaide from 2010–2014. In 2012 he completed a Graduate Certificate in Education (Higher Education) (GCEHE) from the University of Adelaide. From 2014–2017 he was an adjunct research fellow at the University of South Australia. From 2016–2018, he was a Cascade (Marie Curie) Research Fellow in Mathematical Sciences at the University of Nottingham. From 2018- a research fellow at the University of Oslo. His research interests include AI, Artificial and spiking neural networks and learning algorithms, synaptic plasticity, neuronal dynamics, and neuromorphic engineering. Dr. Iannella is a member of SFN and a Senior member of the IEEE.
Most Relevant Publications (1+)

47 total publications

Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices

Current Bioinformatics / Jun 28, 2019

Wong, K. K. L. (2019). Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices. Current Bioinformatics, 14(5), 374–375. https://doi.org/10.2174/157489361405190628122355

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Siddharth Maddali

Fremont, California, United States of America
Computational physicist with a specialization in X-ray and optical imaging and microscopy for condensed matter and materials systems.
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (21)
Computational microscopy
Fourier/physical optics
signal processing
physics
HPC
And 16 more
About
Computational materials, imaging and microscopy scientist with **8 years combined experience** in industry and national laboratories. Expert in physics-based imaging and characterization with X-rays and optical probes, high-performance computing for light-matter interaction and materials data analysis. Experienced in machine learning for materials discovery. Previous experience at the National Energy Technology Laboratory, Argonne National Laboratory and KLA Corporation. <br>
Most Relevant Publications (2+)

29 total publications

9Cr steel visualization and predictive modeling

Computational Materials Science / Oct 01, 2019

Krishnamurthy, N., Maddali, S., Hawk, J. A., & Romanov, V. N. (2019). 9Cr steel visualization and predictive modeling. Computational Materials Science, 168, 268–279. https://doi.org/10.1016/j.commatsci.2019.03.015

Topology-faithful nonparametric estimation and tracking of bulk interface networks

Computational Materials Science / Dec 01, 2016

Maddali, S., Ta’asan, S., & Suter, R. M. (2016). Topology-faithful nonparametric estimation and tracking of bulk interface networks. Computational Materials Science, 125, 328–340. https://doi.org/10.1016/j.commatsci.2016.08.021

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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
Computational Mathematics
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 (1+)

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

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Jeffrey Townsend

New Haven, CT, Connecticut, United States of America
Professor of Biostatistics and Ecology & Evolutionary Biology
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (52)
Evolutionary Genomics
Microbiology
Infectious Diseases
Genetics
Cell Biology
And 47 more
About
Jeffrey Townsend is a Professor of Organismic and Evolutionary Biology at Yale University. He received his Ph.D. from Harvard University in 2002 and his Sc.B. from Brown University in 1994. He has been a teacher at St. Ann's School and an Assistant Professor at the University of Connecticut. He is currently the Elihu Professor of Biostatistics at Yale University.
Most Relevant Publications (4+)

207 total publications

PathScore: a web tool for identifying altered pathways in cancer data

Bioinformatics / Aug 08, 2016

Gaffney, S. G., & Townsend, J. P. (2016). PathScore: a web tool for identifying altered pathways in cancer data. Bioinformatics, 32(23), 3688–3690. https://doi.org/10.1093/bioinformatics/btw512

H-CLAP: hierarchical clustering within a linear array with an application in genetics

Statistical Applications in Genetics and Molecular Biology / Jan 01, 2015

Ghosh, S., & Townsend, J. P. (2015). H-CLAP: hierarchical clustering within a linear array with an application in genetics. Statistical Applications in Genetics and Molecular Biology, 14(2). https://doi.org/10.1515/sagmb-2013-0076

AuthorReward: increasing community curation in biological knowledge wikis through automated authorship quantification

Bioinformatics / Jun 03, 2013

Dai, L., Tian, M., Wu, J., Xiao, J., Wang, X., Townsend, J. P., & Zhang, Z. (2013). AuthorReward: increasing community curation in biological knowledge wikis through automated authorship quantification. Bioinformatics, 29(14), 1837–1839. https://doi.org/10.1093/bioinformatics/btt284

LOX: inferring Level Of eXpression from diverse methods of census sequencing

Bioinformatics / Jun 10, 2010

Zhang, Z., López-Giráldez, F., & Townsend, J. P. (2010). LOX: inferring Level Of eXpression from diverse methods of census sequencing. Bioinformatics, 26(15), 1918–1919. https://doi.org/10.1093/bioinformatics/btq303

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Emmanouil Mentzakis

London
Health Economist, Professor at City University of London
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (21)
Pulmonary and Respiratory Medicine
Pediatrics, Perinatology and Child Health
Economics and Econometrics
Finance
Accounting
And 16 more
About
Senior academic and policy advisor. Public and private sector consultant with remit ranging from health ministries and public organizations to insurance and pharmaceutical companies. Cross-institutional leader in research and admin roles focusing on excellence, efficiency, innovation, and community. Strategic and proactive thinker with clear vision and plan, approaching challenges with creativity and adaptability. Highly motivational manager with strong communication skills and impeccable project management track-record.   Expert scholar and educator in health economics, discrete choice experiments, research study design and observational epidemiology. Long experience setting-up and coordinating multi-disciplinary teams into delivering high quality research.
Most Relevant Publications (1+)

46 total publications

Characterizing dynamic communication in online eating disorder communities: a multiplex network approach

Applied Network Science / Apr 17, 2019

Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2019). Characterizing dynamic communication in online eating disorder communities: a multiplex network approach. Applied Network Science, 4(1). https://doi.org/10.1007/s41109-019-0125-4

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Hector Klie

Houston, Texas, United States of America
CEO @ DeepCast.ai | AI-driven Industrial Solutions, Technical Innovation
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (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 (3+)

81 total publications

On optimization algorithms for the reservoir oil well placement problem

Computational Geosciences / Aug 17, 2006

Bangerth, W., Klie, H., Wheeler, M. F., Stoffa, P. L., & Sen, M. K. (2006). On optimization algorithms for the reservoir oil well placement problem. Computational Geosciences, 10(3), 303–319. https://doi.org/10.1007/s10596-006-9025-7

Reduced-order modeling for thermal recovery processes

Computational Geosciences / Sep 01, 2013

Rousset, M. A. H., Huang, C. K., Klie, H., & Durlofsky, L. J. (2013). Reduced-order modeling for thermal recovery processes. Computational Geosciences, 18(3–4), 401–415. https://doi.org/10.1007/s10596-013-9369-8

A family of physics-based preconditioners for solving elliptic equations on highly heterogeneous media

Applied Numerical Mathematics / Jun 01, 2009

Aksoylu, B., & Klie, H. (2009). A family of physics-based preconditioners for solving elliptic equations on highly heterogeneous media. Applied Numerical Mathematics, 59(6), 1159–1186. https://doi.org/10.1016/j.apnum.2008.06.002

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Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (44)
Polymer Engineering
Advanced Manufacturing
Composites
Additive Manufacturing
Materials Chemistry
And 39 more
About
T. Osswald is Hoeganaes Professor of Materials at the University of Wisconsin-Madison, where he has been a faculty member since 1989. Osswald received the PhD in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 1987, the MS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1982, and the BS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1981. Before joining the UW-Madison faculty, Osswald was a Humboldt Fellow at the Rheinisch Westfalische Technische Hochschule Aachen. Osswald’s research interests are in the areas of processing-structure-property relationships for metals and composites, with a focus on powder metallurgy and metal injection molding. His research has been supported by the National Science Foundation, the Department of Energy, the US Army Research Office, and industry. Osswald is a Fellow of ASM International and the American Academy of Mechanics, and he has received the Extrusion Division Award, the Powder Metallurgy Division Award, and the Distinguished Teaching Award from TMS.
Most Relevant Publications (1+)

117 total publications

Analysis of fiber damage mechanisms during processing of reinforced polymer melts

Engineering Analysis with Boundary Elements / Jul 01, 2002

Hernandez, J. P., Raush, T., Rios, A., Strauss, S., & Osswald, T. A. (2002). Analysis of fiber damage mechanisms during processing of reinforced polymer melts. Engineering Analysis with Boundary Elements, 26(7), 621–628. https://doi.org/10.1016/s0955-7997(02)00018-8

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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 Expertise
Computational Mathematics
Other Research Expertise (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 (5+)

50 total publications

Algebraic Fourier reconstruction of piecewise smooth functions

Mathematics of Computation / Jan 01, 2012

Batenkov, D., & Yomdin, Y. (2012). Algebraic Fourier reconstruction of piecewise smooth functions. Mathematics of Computation, 81(277), 277–318. https://doi.org/10.1090/s0025-5718-2011-02539-1

Complete algebraic reconstruction of piecewise-smooth functions from Fourier data

Mathematics of Computation / Feb 19, 2015

Batenkov, D. (2015). Complete algebraic reconstruction of piecewise-smooth functions from Fourier data. Mathematics of Computation, 84(295), 2329–2350. https://doi.org/10.1090/s0025-5718-2015-02948-2

Accuracy of Algebraic Fourier Reconstruction for Shifts of Several Signals

Sampling Theory in Signal and Image Processing / May 01, 2014

Batenkov, D., Sarig, N., & Yomdin, Y. (2014). Accuracy of Algebraic Fourier Reconstruction for Shifts of Several Signals. Sampling Theory in Signal and Image Processing, 13(2), 151–173. https://doi.org/10.1007/bf03549577

On inverses of Vandermonde and confluent Vandermonde matrices

Numerische Mathematik / Dec 01, 1962

Gautschi, W. (1962). On inverses of Vandermonde and confluent Vandermonde matrices. Numerische Mathematik, 4(1), 117–123. https://doi.org/10.1007/bf01386302

Sampling, Metric Entropy, and Dimensionality Reduction

SIAM Journal on Mathematical Analysis / Jan 01, 2015

Batenkov, D., Friedland, O., & Yomdin, Y. (2015). Sampling, Metric Entropy, and Dimensionality Reduction. SIAM Journal on Mathematical Analysis, 47(1), 786–796. https://doi.org/10.1137/130944436

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Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (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 (2+)

106 total publications

Quantitative visualization of alternative exon expression from RNA-seq data

Bioinformatics / Jan 22, 2015

Katz, Y., Wang, E. T., Silterra, J., Schwartz, S., Wong, B., Thorvaldsdóttir, H., Robinson, J. T., Mesirov, J. P., Airoldi, E. M., & Burge, C. B. (2015). Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics, 31(14), 2400–2402. https://doi.org/10.1093/bioinformatics/btv034

A Network Analysis Model for Disambiguation of Names in Lists

Computational and Mathematical Organization Theory / Jul 01, 2005

Malin, B., Airoldi, E., & Carley, K. M. (2005). A Network Analysis Model for Disambiguation of Names in Lists. Computational and Mathematical Organization Theory, 11(2), 119–139. https://doi.org/10.1007/s10588-005-3940-3

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Ariel Aptekmann

Bioinformatician at Hackensack Meridian Hospital Center for Discovery and Innovation
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (21)
Computational biology
bioinformatics
metagenomics
evolution
Genetics
And 16 more
About
I am a bioinformatician and researcher in computational biology. My research focuses on the application of machine learning and data analysis to understand the molecular basis of disease. I also work on developing novel software tools for data management and analysis.
Most Relevant Publications (1+)

23 total publications

mebipred: identifying metal-binding potential in protein sequence

Bioinformatics / May 27, 2022

Aptekmann, A. A., Buongiorno, J., Giovannelli, D., Glamoclija, M., Ferreiro, D. U., & Bromberg, Y. (2022). mebipred: identifying metal-binding potential in protein sequence. Bioinformatics, 38(14), 3532–3540. https://doi.org/10.1093/bioinformatics/btac358

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Baidurya Bhattacharya

Computational mechanics, probabilistic risk analysis, statistical inference, Monte Carlo simulations
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (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 (5+)

91 total publications

Pareto-optimal analysis of Zn-coated Fe in the presence of dislocations using genetic algorithms

Computational Materials Science / Sep 01, 2012

Rajak, P., Ghosh, S., Bhattacharya, B., & Chakraborti, N. (2012). Pareto-optimal analysis of Zn-coated Fe in the presence of dislocations using genetic algorithms. Computational Materials Science, 62, 266–271. https://doi.org/10.1016/j.commatsci.2012.05.002

Phases in Zn-coated Fe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms

Computational Materials Science / Jun 01, 2011

Rajak, P., Tewary, U., Das, S., Bhattacharya, B., & Chakraborti, N. (2011). Phases in Zn-coated Fe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms. Computational Materials Science, 50(8), 2502–2516. https://doi.org/10.1016/j.commatsci.2011.03.034

Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

Computational Materials Science / Oct 01, 2009

Bhattacharya, B., Dinesh Kumar, G. R., Agarwal, A., Erkoç, Ş., Singh, A., & Chakraborti, N. (2009). Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms. Computational Materials Science, 46(4), 821–827. https://doi.org/10.1016/j.commatsci.2009.04.023

Multi-Objective Materials Design by Genetic Algorithms—Generalized for B1 and B2 Ionic Structures

Journal of Computational and Theoretical Nanoscience / Apr 01, 2009

Sreevathsan, R., Bhattacharya, B., Dinesh Kumar, G., & Chakraborti, N. (2009). Multi-Objective Materials Design by Genetic Algorithms—Generalized for B1 and B2 Ionic Structures. Journal of Computational and Theoretical Nanoscience, 6(4), 849–856. https://doi.org/10.1166/jctn.2009.1117

Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms

Computational Materials Science / Mar 01, 2009

Chakraborti, N., Sreevathsan, R., Jayakanth, R., & Bhattacharya, B. (2009). Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms. Computational Materials Science, 45(1), 1–7. https://doi.org/10.1016/j.commatsci.2008.03.057

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Oguzhan Kulekci

Algorithm Engineer, Security/Privacy Researcher, Combinatorial Problem Solver
Most Relevant Research Expertise
Computational Mathematics
Other Research Expertise (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 (5+)

61 total publications

A System Architecture for Efficient Transmission of Massive DNA Sequencing Data

Journal of Computational Biology / Nov 01, 2017

Sağiroğlu, M. Ş., & Külekcİ, M. O. (2017). A System Architecture for Efficient Transmission of Massive DNA Sequencing Data. Journal of Computational Biology, 24(11), 1081–1088. https://doi.org/10.1089/cmb.2017.0016

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

A Two-Level Scheme for Quality Score Compression

Journal of Computational Biology / Oct 01, 2018

Voges, J., Fotouhi, A., Ostermann, J., & Külekci, M. O. (2018). A Two-Level Scheme for Quality Score Compression. Journal of Computational Biology, 25(10), 1141–1151. https://doi.org/10.1089/cmb.2018.0065

BLIM: A New Bit-Parallel Pattern Matching Algorithm Overcoming Computer Word Size Limitation

Mathematics in Computer Science / Apr 13, 2010

Külekci, M. O. (2010). BLIM: A New Bit-Parallel Pattern Matching Algorithm Overcoming Computer Word Size Limitation. Mathematics in Computer Science, 3(4), 407–420. https://doi.org/10.1007/s11786-010-0035-4

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Example Computational Mathematics projects

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

Optimizing Supply Chain Management

A Computational Mathematics expert can develop algorithms to optimize supply chain management, reducing costs and improving efficiency. By analyzing data on inventory levels, transportation routes, and demand patterns, they can identify bottlenecks and suggest strategies to streamline operations.

Predictive Maintenance in Manufacturing

By analyzing sensor data and historical maintenance records, a Computational Mathematics researcher can develop predictive models to identify potential equipment failures in manufacturing processes. This can help companies schedule maintenance activities proactively, minimizing downtime and reducing maintenance costs.

Fraud Detection in Financial Services

Using advanced machine learning techniques, a Computational Mathematics expert can develop models to detect fraudulent activities in financial transactions. By analyzing patterns and anomalies in large datasets, they can help financial institutions identify and prevent fraudulent transactions, protecting both the company and its customers.

Optimizing Energy Consumption

A Computational Mathematics researcher can analyze energy consumption data and develop optimization algorithms to minimize energy usage in various industries. This can lead to significant cost savings and environmental benefits by identifying energy-efficient practices and optimizing resource allocation.

Improving Healthcare Analytics

By analyzing healthcare data, including patient records, medical imaging, and genomic data, a Computational Mathematics expert can develop models to improve disease diagnosis, treatment planning, and patient outcomes. This can help healthcare companies provide personalized and effective care to their patients.