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Work with thought leaders and academic experts in Signal Processing

Companies can benefit from working with Signal Processing experts in various ways. These experts can provide innovative solutions to complex problems, optimize signal processing algorithms for better performance, develop advanced signal processing techniques for data analysis, and design efficient communication systems. They can also contribute to the development of cutting-edge technologies such as image and speech recognition, radar and sonar systems, and biomedical signal processing. By collaborating with Signal Processing researchers, companies can gain a competitive edge, improve product quality, enhance data processing capabilities, and accelerate technological advancements.

Researchers on NotedSource with backgrounds in Signal Processing include Nicolangelo Iannella, Siddharth Maddali, Aruna Ranaweera, David J. Lilja, Edoardo Airoldi, Vladimir Shapiro, Ph.D., Dmitry Batenkov, Ph.D., Tim Osswald, Lee Weinstein, Dhritiman Das, Ph.D., Vivek Singh, Dr. Haikun Huang, Ph.D., Anit Kumar Sahu, and Ayse Oktay.

Nicolangelo Iannella

Oslo
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Most Relevant Research Expertise
Signal Processing
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

Finding iterative roots with a spiking neural network

Information Processing Letters / Sep 01, 2005

Iannella, N., & Kindermann, L. (2005). Finding iterative roots with a spiking neural network. Information Processing Letters, 95(6), 545–551. https://doi.org/10.1016/j.ipl.2005.05.022

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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
signal processing
Other Research Expertise (21)
Computational microscopy
Fourier/physical optics
physics
HPC
Electrical and Electronic Engineering
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>

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Aruna Ranaweera

Colombo
Professor at University of Kelaniya, PhD(Kyung Hee University, South Korea)
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (16)
Wireless Power Transfer
Metamaterials
Supercapacitor Assisted Power Electronics
Electronic, Optical and Magnetic Materials
Surfaces, Coatings and Films
And 11 more
About
I am dedicated and passionate about inspiring and engaging my students in an effective learning process to generate new knowledge, do innovations, engage in technology transfer, and enhance human capital through interdisciplinary and collaborative research for the well-being of academia, industry, and society.
Most Relevant Publications (1+)

30 total publications

Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing

Electronics / Oct 04, 2021

Piyumal, K., Ranaweera, A., Kalingamudali, S., & Kularatna, N. (2021). Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing. Electronics, 10(19), 2422. https://doi.org/10.3390/electronics10192422

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David J. Lilja

Minneapolis, Minnesota, United States of America
Professor Emeritus of Electrical and Computer Engineering, University of Minnesota
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (15)
Computer architecture
high-performance parallel processing
computer systems performance analysis
approximate computing
Hardware and Architecture
And 10 more
About
**Research Expertise** Computer architecture, high-performance parallel processing, computer systems performance analysis, approximate computing, computing with emerging technologies, and storage systems. **Biographical summary** David J. Lilja received a Ph.D. and an M.S., both in Electrical Engineering, from the [University of Illinois at Urbana-Champaign,](http://www.uiuc.edu/) and a B.S. in Computer Engineering from [Iowa State University](http://www.iastate.edu/) in Ames. He is Professor Emeritus of [Electrical and Computer Engineering](http://www.ee.umn.edu/) at the [University of Minnesota](http://www.umn.edu/) in Minneapolis. He previously served as a member of the graduate faculties in [Computer Science](http://www.cs.umn.edu/), [Scientific Computation](http://www.scicomp.umn.edu/), and [Data Science](http://datascience.umn.edu//).  He served ten years as the head of the ECE department at the University of Minnesota, worked as a research assistant at the Center for Supercomputing Research and Development at the [University of Illinois,](http://www.uiuc.edu/) and as a development engineer at [Tandem Computers Incorporated](http://www.tandem.com/) in Cupertino, California.  He received a [Fulbright](http://www.fulbright.org/) Senior Scholar Award to visit the University of Western Australia and was a visiting Professor at the University of Canterbury in Christchurch, New Zealand. He has chaired and served on the program committees of numerous conferences.  He was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the American Association for the Advancement of Science (AAAS) for contributions to the statistical analysis of computer performance. He also is a registered Professional Engineer.
Most Relevant Publications (4+)

99 total publications

Dynamic task scheduling using online optimization

IEEE Transactions on Parallel and Distributed Systems / Jan 01, 2000

Lilja, D. J., Lau Ying Kit, & Hamidzadeh, B. (2000). Dynamic task scheduling using online optimization. IEEE Transactions on Parallel and Distributed Systems, 11(11), 1151–1163. https://doi.org/10.1109/71.888636

An effective processor allocation strategy for multiprogrammed shared-memory multiprocessors

IEEE Transactions on Parallel and Distributed Systems / Jan 01, 1997

Yue, K. K., & Lilja, D. J. (1997). An effective processor allocation strategy for multiprogrammed shared-memory multiprocessors. IEEE Transactions on Parallel and Distributed Systems, 8(12), 1246–1258. https://doi.org/10.1109/71.640017

The potential of compile-time analysis to adapt the cache coherence enforcement strategy to the data sharing characteristics

IEEE Transactions on Parallel and Distributed Systems / May 01, 1995

Mounes-Toussi, F., & Lilja, D. J. (1995). The potential of compile-time analysis to adapt the cache coherence enforcement strategy to the data sharing characteristics. IEEE Transactions on Parallel and Distributed Systems, 6(5), 470–481. https://doi.org/10.1109/71.382316

Coarse-grained thread pipelining: a speculative parallel execution model for shared-memory multiprocessors

IEEE Transactions on Parallel and Distributed Systems / Sep 01, 2001

Kazi, I. H., & Lilja, D. J. (2001). Coarse-grained thread pipelining: a speculative parallel execution model for shared-memory multiprocessors. IEEE Transactions on Parallel and Distributed Systems, 12(9), 952–966. https://doi.org/10.1109/71.954629

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Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Expertise
Signal Processing
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 (1+)

106 total publications

SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series

IEEE Transactions on Signal Processing / Oct 01, 2017

Han, Q., Ding, J., Airoldi, E. M., & Tarokh, V. (2017). SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Transactions on Signal Processing, 65(19), 4994–5005. https://doi.org/10.1109/tsp.2017.2716898

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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 Expertise
Signal Processing
Other Research Expertise (14)
Computer Vision and Pattern Recognition
Hardware and Architecture
Computer Science Applications
Software
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 (4+)

37 total publications

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

Motion analysis via interframe point correspondence establishment

Image and Vision Computing / Mar 01, 1995

Shapiro, V., Backalov, I., & Kavardjikov, V. (1995). Motion analysis via interframe point correspondence establishment. Image and Vision Computing, 13(2), 111–118. https://doi.org/10.1016/0262-8856(95)93152-i

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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
Signal Processing
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 (3+)

50 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

Decimated Prony's Method for Stable Super-Resolution

IEEE Signal Processing Letters / Jan 01, 2023

Katz, R., Diab, N., & Batenkov, D. (2023). Decimated Prony’s Method for Stable Super-Resolution. IEEE Signal Processing Letters, 30, 1467–1471. https://doi.org/10.1109/lsp.2023.3324553

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

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Tim Osswald

Polymers Professor - University of Wisconsin
Most Relevant Research Expertise
Signal Processing
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

Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method

International Journal of Automation and Smart Technology / Dec 01, 2018

Jiang, C.-P. (2018). Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method. International Journal of Automation and Smart Technology, 8(4), 173–178. https://doi.org/10.5875/ausmt.v8i4.1840

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Lee Weinstein

STEM Educator
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (21)
Energy conversion
solar energy
thermoelectrics
Energy Engineering and Power Technology
Fuel Technology
And 16 more
About
Weinstein is a Principal Scientist at Brilliant.org, where he focuses on engineering education. He holds a Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology, and his research interests include the design of educational technology and the development of educational content.
Most Relevant Publications (1+)

22 total publications

Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows

Smart Materials and Structures / Mar 14, 2012

Weinstein, L. A., Cacan, M. R., So, P. M., & Wright, P. K. (2012). Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows. Smart Materials and Structures, 21(4), 045003. https://doi.org/10.1088/0964-1726/21/4/045003

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Dhritiman Das, Ph.D.

Postdoc at MIT | Deep Learning | Computer Vison | Neuroinformatics
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (14)
Machine Learning
Medical Image Analysis
Computer Vision
Electronic, Optical and Magnetic Materials
Condensed Matter Physics
And 9 more
About
Postdoctoral Researcher at the Massachusetts Institute of Technology and freelance machine-learning consultant for startups. Strong background in Deep Learning, Computer Vision, Signal Processing and Neuroinformatics. My work has involved building pipelines for integrating machine learning technologies for medical imaging research and clinical applications. Key focus areas: self-supervised learning, multimodal learning, generative models, open source ML software development, image & signal processing, large-scale datasets (2D, 3D, time-series), reproducible research. Skilled in Python (TensorFlow/Keras/Pytorch), JAX, C++, Pandas, Docker, Matlab. Previously, I was a Marie Skłodowska-Curie PhD Fellow in Computer Science at the Technical University of Munich and GE Healthcare. My thesis focused on Data Driven Methods (using Machine Learning) for Accelerated Analysis of 3D Neuroimaging Data. Strong track record of multi-site collaborations across academia and industry along with high-impact publications.

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Vivek Singh

Rutgers Professor, MIT alum, CS PhD, AI expert
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (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 (2+)

95 total publications

New Signals in Multimedia Systems and Applications

IEEE MultiMedia / Jan 01, 2018

Cesar, P., Singh, V., Jain, R., Sebe, N., & Oliver, N. (2018). New Signals in Multimedia Systems and Applications. IEEE MultiMedia, 25(1), 12–13. https://doi.org/10.1109/mmul.2018.011921231

Predicting Loneliness through Digital Footprints on Google and YouTube

Electronics / Nov 29, 2023

Ahmed, E., Xue, L., Sankalp, A., Kong, H., Matos, A., Silenzio, V., & Singh, V. K. (2023). Predicting Loneliness through Digital Footprints on Google and YouTube. Electronics, 12(23), 4821. https://doi.org/10.3390/electronics12234821

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Dr. Haikun Huang, Ph.D.

Chief Technology Officer at Great Victory Legends
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (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

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Anit Kumar Sahu

PhD from CMU working in ML/AI
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (19)
Federated Learning
Stochastic Optimization
Data Selection
Electrical and Electronic Engineering
Applied Mathematics
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 (7+)

59 total publications

Federated Learning: Challenges, Methods, and Future Directions

IEEE Signal Processing Magazine / May 01, 2020

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/msp.2020.2975749

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

IEEE Transactions on Signal and Information Processing over Networks / Jan 01, 2016

Sahu, A. K., Kar, S., Moura, J. M. F., & Poor, H. V. (2016). Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. IEEE Transactions on Signal and Information Processing over Networks, 1–1. https://doi.org/10.1109/tsipn.2016.2618318

$\mathcal {CIRFE}$: A Distributed Random Fields Estimator

IEEE Transactions on Signal Processing / Sep 15, 2018

Sahu, A. K., Jakovetic, D., & Kar, S. (2018). $\mathcal {CIRFE}$: A Distributed Random Fields Estimator. IEEE Transactions on Signal Processing, 66(18), 4980–4995. https://doi.org/10.1109/tsp.2018.2863646

Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization

IEEE Transactions on Signal Processing / Jan 01, 2022

Wang, J., Sahu, A. K., Joshi, G., & Kar, S. (2022). Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization. IEEE Transactions on Signal Processing, 70, 5208–5221. https://doi.org/10.1109/tsp.2022.3212536

Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference

IEEE Transactions on Signal Processing / Jan 01, 2023

Petrović, N., Bajović, D., Kar, S., Jakovetić, D., & Sahu, A. K. (2023). Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference. IEEE Transactions on Signal Processing, 71, 1319–1333. https://doi.org/10.1109/tsp.2023.3263254

Guest Editorial Inference and Learning over Networks

IEEE Transactions on Signal and Information Processing over Networks / Dec 01, 2016

Matta, V., Richard, C., Saligrama, V., & Sayed, A. H. (2016). Guest Editorial Inference and Learning over Networks. IEEE Transactions on Signal and Information Processing over Networks, 2(4), 423–425. https://doi.org/10.1109/tsipn.2016.2615526

Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing

IEEE Transactions on Signal Processing / Jan 01, 2016

Sahu, A. K., & Kar, S. (2016). Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing. IEEE Transactions on Signal Processing, 64(1), 89–103. https://doi.org/10.1109/tsp.2015.2478737

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Ayse Oktay

Assoc. Prof., Yildiz Technical University
Most Relevant Research Expertise
Signal Processing
Other Research Expertise (34)
Medical image analysis
Computer vision
Machine learning
Pattern recognition
Data science
And 29 more
About
Experienced academic and researcher specializing in data science and computer vision, with a proven track record in academia and industry. Proficient in Computer Vision, Machine Learning, and Image Processing, with published many works recognized by over 1000 citations in peer-reviewed publications. Proven ability to collaborate effectively within multidisciplinary teams to tackle complex problems and deliver innovative solutions.  Advocate for teamwork, effective communication, and mentorship.
Most Relevant Publications (2+)

45 total publications

Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks

Biomedical Signal Processing and Control / Feb 01, 2020

Oktay, A. B., & Kocer, A. (2020). Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks. Biomedical Signal Processing and Control, 56, 101683. https://doi.org/10.1016/j.bspc.2019.101683

Human identification with dental panoramic radiographic images

IET Biometrics / Nov 06, 2017

Oktay, A. B. (2017). Human identification with dental panoramic radiographic images. IET Biometrics, 7(4), 349–355. Portico. https://doi.org/10.1049/iet-bmt.2017.0078

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Example Signal Processing projects

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

Optimizing Signal Processing Algorithms for Image Recognition

A company in the computer vision industry can collaborate with a Signal Processing expert to optimize their image recognition algorithms. By leveraging advanced signal processing techniques, the expert can improve the accuracy and speed of image recognition systems, enabling the company to develop more efficient and reliable computer vision solutions.

Developing Advanced Signal Processing Techniques for Data Analysis

A data analytics company can partner with a Signal Processing researcher to develop advanced signal processing techniques for data analysis. These techniques can help the company extract valuable insights from complex datasets, identify patterns and trends, and make data-driven decisions. By leveraging the expertise of the researcher, the company can enhance their data analysis capabilities and gain a competitive advantage in the market.

Designing Efficient Communication Systems

A telecommunications company can collaborate with a Signal Processing expert to design efficient communication systems. The expert can develop signal processing algorithms and protocols that optimize data transmission, reduce noise and interference, and improve overall system performance. By working with the researcher, the company can enhance the reliability and efficiency of their communication networks, leading to improved customer satisfaction and business growth.

Advancing Biomedical Signal Processing

A healthcare technology company can partner with a Signal Processing specialist to advance biomedical signal processing techniques. The researcher can develop algorithms and methods for analyzing physiological signals, such as ECG and EEG, to detect abnormalities, monitor patient health, and improve medical diagnosis. By collaborating with the expert, the company can enhance their healthcare solutions and contribute to the development of innovative medical technologies.

Improving Radar and Sonar Systems

A defense contractor can collaborate with a Signal Processing researcher to improve radar and sonar systems. The expert can develop signal processing algorithms that enhance target detection, tracking, and classification capabilities, improving the performance and accuracy of these systems. By leveraging the expertise of the researcher, the company can strengthen their defense technologies and gain a competitive edge in the market.