Find Academic Supervisors

• Control Systems
• Machine Learning
• Electrical Engineering
• +4 more

Marko Tanasković (born December 6, 1986) is a researcher at Singidunum University with a PhD in Information Technology and Electrical Engineering from ETH Zurich (2015). His academic background includes master's (ETH Zurich, 2011) and bachelor's (University of Belgrade, 2009) studies in Electrical Engineering. Doctoral studies: Information Technology and Electrical Engineering, ETH Zurich (2011-2015) Master studies: Information Technology and Electrical Engineering, ETH Zurich (2009-2011) Basic studies: Electrical Engineering, University of Belgrade (2005-2009) Tanasković's research focuses on control systems , predictive modeling , and optimization algorithms for mechanical and electrical systems. His work addresses adaptive model predictive control (MPC), sensorless motor positioning, and data-driven approaches for nonlinear systems. Recent publications (2018-2024) demonstrate expertise in: Embedded control systems (rotor polarity detection) Drone forensics and autonomous navigation Industrial automation (LabVIEW applications) Biomedical sensor development ('Smart Anklet') Machine learning optimization (firefly algorithm)

• Audio Processing
• Machine Learning
• Sound Event Detection
• +3 more

Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.

MARIOROSARIO PRISTView profile

Researcher

• Artificial Intelligence
• Industrial Automation
• Machine Learning
• +4 more

Dr. MARIOROSARIO PRIST serves as a Researcher at the Department of Information Engineering within the Faculty of Engineering at Marche Polytechnic University (UNIVPM) in Ancona, Italy. His institutional affiliation is maintained through the Department of Information Engineering (quota 170) at Via Brecce Bianche, 60131 Ancona, with contact details including phone 071 220 4468 and email m.prist@staff.univpm.it. Dr. PRIST's research spans cutting-edge domains in artificial intelligence applications for industrial systems, with particular emphasis on neural network implementations, digital twin architectures, and Industry 4.0 technologies. His work demonstrates strong focus on lightweight AI frameworks for edge computing , anomaly detection in manufacturing processes , and resource optimization in production environments . The research portfolio reveals consistent innovation in adapting advanced machine learning techniques to practical industrial constraints, especially for small and medium enterprises. Analysis of his publication trends indicates a strategic shift toward implementing AI solutions on resource-constrained devices and bridging edge computing with cloud infrastructure for real-time industrial monitoring. Recent work emphasizes practical applications of Echo State Networks for process control and anomaly detection, while maintaining strong connections to additive manufacturing optimization and safety monitoring systems. His research consistently addresses the challenge of making advanced AI accessible for industrial implementation without requiring extensive computational resources. Dr. PRIST's work demonstrates significant contributions to the integration of cyber-physical systems in manufacturing environments, with particular expertise in translating theoretical AI concepts into practical industrial applications that enhance production efficiency, safety, and sustainability.

Preethi SrivathsaView profile

Assistant Professor

• Computer Architecture
• Internet of Things
• Cyber Security
• +4 more

Dr. Preethi Srivathsa is an Assistant Professor - Senior Scale in the School of Computer Engineering at Manipal Academy of Higher Education (MAHE), Bengaluru. She holds a B.Tech, M.Tech, and Ph.D. (awarded by Presidency University in 2022). Her academic career includes positions at Presidency University (2019-2023) and East Point College of Engineering (2008-2019). Her research focuses on: Computer architecture and low-power hardware design IoT applications and cyber-physical systems Cryptography and blockchain security Machine learning implementations in hardware FPGA-based accelerators and optimization techniques Her publication portfolio shows strong emphasis on hardware-efficient algorithms, cryptographic systems (especially elliptic curve applications in blockchain), and emerging IoT architectures. Recent work integrates machine learning with hardware acceleration for smart home systems and agricultural technology. Awards and recognitions: Best Paper Award at IEEE iSES-2021 for low-power sorter design Infosys Bronze Partner Faculty (2013) She has developed intellectual property including IoT-based monitoring systems and blockchain educational frameworks. Technical skills include Verilog, FPGA design, IoT platforms (Arduino/Raspberry Pi), and multiple programming languages.

• Language Acquisition
• Cognitive Science
• Computational Modeling
• +7 more

Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development

• Autonomous Systems
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems and Director of the Institute for Intelligent Systems at Esslingen University of Applied Sciences, Germany, within the Department of Computer Science and Engineering. His leadership role positions him at the forefront of intelligent systems research in applied academic settings. His research spans autonomous systems, computer vision, and robotics with emphasis on visual-inertial SLAM, semantic segmentation, and collective perception. Key contributions address real-world challenges in unstructured environments like agricultural fields and urban settings through efficient perception systems. Recent work focuses on lightweight monocular solutions, sensor fusion techniques, and computational efficiency optimization for autonomous vehicles. Analysis of his 2024-2025 publications reveals strong trends in collective perception infrastructure, NeRF/Gaussian Splatting integration for SLAM, and multi-sensor dataset development. His research consistently benchmarks computational costs against accuracy improvements while creating valuable resources like the OPNV public transportation dataset and Rover multi-season SLAM corpus. As Director of the Institute for Intelligent Systems, Enzweiler leads initiatives advancing autonomous driving technologies through practical implementations and industry-relevant research frameworks.

• Robotics
• Embedded Systems
• Autonomous Systems
• +3 more

Eric M. Schwartz is an Instructional Professor and Director of the Machine Intelligence Laboratory at the University of Florida's College of Engineering, Department of Computer and Information Science and Engineering. He holds a PhD in Electrical & Computer Engineering from the University of Florida (1995), along with MS and dual BS degrees in Electrical Engineering and Mechanical Engineering from the same institution (1989, 1984). His research focuses on Robotics, Embedded Systems, and Autonomous Agents, with particular emphasis on underwater and surface vehicles. He has led the development of multiple award-winning robotic systems, including the SubjuGator AUV and NaviGator maritime systems. His work spans control systems, sensor design, and collaborative robotics, often emphasizing modularity, cost-effectiveness, and real-world applications. Dr. Schwartz has been recognized for his teaching and mentorship, earning titles such as HWCOE Faculty Adviser/Mentor of the Year (2024), ECE Department Faculty of the Year (2022-2023), and multiple Teacher of the Year awards (2002, 2012). He has advised teams that won world championships in RoboSub (2005-2007), RoboBoat (2013), and the Maritime RobotX Challenge (2019). His advising and grant efforts have supported student-led projects in autonomous systems, robotics competitions, and educational outreach. He is affiliated with the Machine Intelligence Laboratory (MIL), which develops advanced robotics technologies and promotes STEM education through hands-on projects.

Abhinav BhateleView profile

Associate Professor

• High Performance Computing
• Systems
• Networking
• +4 more

Abhinav Bhatele is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, and directs the Parallel Software and Systems Group. He holds affiliate roles in the AIM Institute and AMSC Program. His research focuses on high-performance computing, systems, networking, and AI, particularly in parallel computing, distributed AI, and machine learning applications. He has contributed to frameworks like AxoNN and Hatchet, and his work spans performance modeling, network simulation, and scalable deep learning. Education: Ph.D. (2010), M.S. (2007), and B.Tech. (2005) in Computer Science from the University of Illinois at Urbana-Champaign and IIT Kanpur, respectively. Prior to UMD, he was at Lawrence Livermore National Laboratory (2011–2019). Research interests include parallel systems, network design, performance analysis tools, and applying machine learning to optimize HPC workflows. He has received notable awards, including the IEEE TCSC Award (2023), NSF CAREER (2021), and UIUC Early Career Alumni Award (2024). Key achievements include developing open-source tools like Hatchet and AxoNN, optimizing large-scale LLM training, and advancing GPU-based supercomputing. He advises over 10 students and has secured grants totaling millions, focusing on HPC software ecosystems and AI integration. Professional service includes roles at SC, ISC conferences, and editorial work for IEEE TPDS. His lab collaborates on projects like the Exascale Computing Project and DOE INCITE allocations.

Guillermo Payá VayáView profile

Professor

• Embedded Systems
• Processor Architecture
• FPGA Design
• +5 more

Prof. Dr.-Ing. Guillermo Payá Vayá leads the Chair for Chip Design for Embedded Computing at Technical University of Braunschweig's Faculty of Electrical Engineering, Information Technology, and Physics. His research focuses on processor architecture design, FPGA/ASIC implementations, and optimization techniques for embedded systems, particularly in high-performance, low-power, and radiation-hardened computing domains. Primary research interests include: Application-Specific Instruction Set Processors (ASIPs) and compiler co-design Radiation effects characterization and fault-tolerant hardware Ultra-low-power processor architectures for embedded AI Hardware acceleration of neural networks and computer vision algorithms Memory subsystem optimization and parallel computing techniques Recent publications demonstrate strong emphasis on radiation-hardened electronics (35% of recent works), AI accelerator design (27%), and ultra-low-power systems (20%), with growing interest in biomedical applications. Experimental validation through FPGA prototyping and semiconductor testing is a consistent methodology across research domains. Leads research team investigating: Radiation-tolerant FPGA architectures (Trumann, Weide-Zaage) Vector processor optimization (Gesper, Thieu) Nano-scale controller design (Weißbrich) AI-hardware co-design (Kautz, Beyer)

• Robotic Vehicles
• Computational Intelligence
• Cooperative Control and Optimization
• +3 more

Lefteris Doitsidis is an Associate Professor at the School of Production Engineering and Management, Technical University of Crete. He holds a PhD in Production and Management Engineering from the same institution (2008), with prior academic positions at the Department of Electronics, Hellenic Mediterranean University. His professional journey includes visiting scholar roles at the University of South Florida, USA. Research focuses on robotic systems, including autonomous navigation of UAVs/AUVs, multirobot teams, computational intelligence, and educational robotics. He leads the Intelligent Systems and Robotics Laboratory, developing tools like HYDRA for STEM education and frameworks for industry 4.0 applications such as bin-picking and precision agriculture. His work integrates control systems optimization, energy efficiency in manufacturing, and digital twin technologies. Over 65 publications span journals, conferences, and books, emphasizing practical implementations like ROS-based autonomous vehicle testbeds and energy management systems for electric vehicles. Key contributions include UAV path planning algorithms, swarm robotics coordination, and sensor fusion techniques. Current research trends emphasize sustainability in manufacturing, educational robotics platforms, and autonomous systems validation through advanced algorithms like Deep Deterministic Policy Gradient.

Bogdan GhitaView profile

Associate Professor

• Cybersecurity
• Network Security
• IoT Security
• +8 more

Dr. Bogdan Ghita is an Associate Professor and Associate Dean International in the Faculty of Science and Engineering at the University of Plymouth. His primary affiliation is with the School of Engineering, Computing and Mathematics. He leads network research within the Centre for Security, Communications, and Network Research. His research focuses on cybersecurity, IoT security, machine learning applications in networking, blockchain technology, network function virtualization, and disaster communication systems. He has supervised over 20 PhD students on topics ranging from smart grid optimization to network intrusion detection. Teaching responsibilities include undergraduate courses like Cyber Security and Networks, Ethical Hacking, and Network Monitoring, as well as postgraduate modules in Ethical Hacking. He actively contributes to curriculum development in online education and cybersecurity pedagogy. Key research themes include: Network slicing and NFV in disaster recovery systems Cybersecurity for IoT and industrial systems Machine learning for network anomaly detection Blockchain-based trust management for M2M services Forensic analysis of digital evidence His recent work explores AI-driven personalized education frameworks and quantum-resistant cryptographic schemes. He has published extensively in top-tier conferences/journals with a focus on applying computational methods to solve real-world cybersecurity challenges.

Mehrdad NouraniView profile

Professor

• Biometric signal/image processing
• Machine learning for healthcare
• VLSI/SoC design for reliability
• +4 more

Dr. Mehrdad Nourani is a Professor of Electrical and Computer Engineering and Senior Associate Provost for Faculty Affairs at The University of Texas at Dallas. He leads research in healthcare technology, VLSI testing, and embedded systems through the Predictive Analytics & Technologies (PAT) Lab and Quality of Life Technology (QoLT) Laboratories. His work focuses on biometric signal processing for patient monitoring (e.g., epilepsy, diabetes, sleep apnea) and VLSI reliability in nanometer-scale circuits. Education: PhD in Computer Engineering, Case Western Reserve University (1993) M.S. & B.S. in Electrical Engineering, University of Tehran (1986 & 1984) Research Interests: Integrates machine learning with biomedical engineering for wearable healthcare devices, develops novel VLSI testing methodologies, and explores data-driven diagnostics. Key areas include artifact removal in wearable sensors, personalized analytics for pressure ulcers, and SEEG-based seizure network modeling. Advising & Grants: Supervises 6 current PhD/Master’s students. Research supported by NSF, Texas Instruments, UT Southwestern, and others. Co-founded CICS and QoLT labs fostering interdisciplinary collaboration. Labs & Teams: Directs PAT Lab and collaborates through QoLT and CICS. Projects include smart bed systems for ulcer prevention, AI-driven camera platforms for ambulation assessment, and ESD stress analysis in semiconductor devices.

Shaon Bhatta ShuvoView profile

Assistant Professor

• Deep Learning
• Machine Learning
• Reinforcement Learning
• +4 more

Dr. Shaon Bhatta Shuvo is an Assistant Professor at the University of Windsor's School of Computer Science. He holds a Ph.D. from the University of Windsor (2020–2024), an M.Sc. from South Asian University (2013–2015), and a B.Sc. from Noakhali Science and Technology University, Bangladesh. Research Interests: Deep Learning & Reinforcement Learning: Development and application of advanced algorithms for computer vision and natural language processing. AI-Based Decision Support Systems: Designing AI-driven systems for healthcare and social network analysis. Modelling and Simulation: Multi-agent simulations and mathematical modeling for complex systems, including pandemic preparedness and healthcare optimization. Research Trends in Publications: His work focuses on AI-driven solutions for healthcare (e.g., pandemic modeling, PPE demand prediction), social network analysis (link/node classification, knowledge graphs), and computational epidemiology. He emphasizes hybrid simulation models and data-driven approaches for real-world challenges. Awards: No scientific awards explicitly mentioned. Advising & Grants: No student advisees or grants listed. Labs/Teams: Not specified in available information.

Antonio Calomarde PalominoView profile

Associate Professor

• Low Power Digital Circuits
• Soft Error Resilient Circuits
• Integrated Circuit Design
• +4 more

Antonio Calomarde Palomino is an Associate Professor at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC), located at the Higher Technical School of Industrial Engineering of Barcelona. He holds a B.S. in Telecommunications Engineering (UPC, 1986), an M.S. in Electronic Engineering (Universitat Autònoma de Barcelona, 1993), and a Ph.D. in Electronic Engineering (UPC, 2007) with honors. His research focuses on low-power/high-performance digital circuits and soft-error resilient designs, addressing challenges in semiconductor technology and radiation-hardened electronics. Key research areas include FinFET-based memory cells, RRAM modeling, and radiation mitigation strategies for advanced nanoelectronics. He contributes to the High Performance Integrated Circuits and Systems Design (HIPICS) Group, advancing circuit design methodologies and educational initiatives such as integrating autonomous vehicle systems into undergraduate curricula. His work bridges theoretical advancements with practical applications in automotive electronics and embedded systems. Publications span topics from memristor-based associative memory to approximation techniques in object detection systems, reflecting a balance between fundamental research and applied engineering. His recent work emphasizes variability-aware design and energy-efficient architectures, as seen in 2023-2024 studies on RRAM models and automotive case studies. Calomarde actively organizes academic events like the 2022 ETS conference and develops educational materials for digital electronics and audiovisual systems courses. His contributions to both technical research and pedagogical innovation solidify his role as a key figure in electronic engineering education and innovation.

Catherine SchumanView profile

Assistant Professor

• Neuromorphic Computing
• Artificial Intelligence
• Hardware-Software Co-Design
• +7 more

Catherine Schuman is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. Her work focuses on neuromorphic computing, spiking neural networks, and AI-driven hardware-software co-design. She holds a Ph.D. and B.S. in Computer Science and Mathematics from the University of Tennessee (2015 and 2010, respectively). Research interests include neuromorphic systems, energy-efficient computing architectures, stochastic devices, and applications of neuromorphic computing in scientific domains like materials science and quantum materials. She emphasizes benchmarking frameworks (NeuroBench) and real-world applications such as radiation detection and control systems. Her recent publications (2023-2025) highlight advancements in neuromorphic hardware-software co-design, spiking reinforcement learning frameworks, and neuromorphic implementations for scientific computing. Notable contributions include frameworks like SpikeRL, NeuroPong, and the RISP neuroprocessor, emphasizing open-source tools for embedded neuromorphic computing. No scientific awards are explicitly listed in the provided information. She actively explores cross-disciplinary applications, including combustion control and materials discovery through AI-enhanced device design.