جست‌وجوی استاد و پژوهشگر

• Statistical Machine Learning
• Bayesian Statistical Inference
• Statistical Ecology
• +۷ مورد دیگر

Jabed Tomal is an Associate Professor in Statistics and Data Science at Thompson Rivers University (TRU), Canada. Previously, he held positions as an Assistant Professor at TRU (2018–2023) and the University of Toronto Scarborough (2014–2018), and a Postdoctoral Fellow at the University of British Columbia (2014). He earned a Ph.D. in Statistics (2013) from UBC, specializing in statistical machine learning, and dual M.Sc. degrees in Statistics (University of Windsor, 2007) and Biostatistics (University of Dhaka, Bangladesh). His research focuses on ensemble methods, Bayesian inference, and statistical ecology, with applications in drug discovery, protein homology, and environmental modeling. Key research interests include developing ensemble models for high-dimensional data, Bayesian methods for breakpoints detection in housing markets and ecological systems, and statistical approaches in healthcare. Notable contributions include work on QSAR studies, Bayesian hierarchical modeling of pandemic impacts, and ecological threshold detection. Tomal has secured grants such as the NSERC Discovery Grant ($102,500) and TRU internal funds for projects in big data and environmental thresholds. He has advised numerous graduate and undergraduate students on topics ranging from machine learning in healthcare to single-cell RNA sequencing analysis. His teaching spans courses like Bayesian Machine Learning, Multivariate Statistics, and Theoretical Machine Learning at the graduate level, alongside foundational statistics and calculus courses at TRU and the University of Dhaka. Administrative roles include Chair of the Award and Scholarship Committee for TRU’s Master of Data Science program and membership in Senate Research Committee. Education: Ph.D., Statistics (2013), UBC Vancouver M.Sc., Statistics (2007), University of Windsor M.Sc., Biostatistics (2005), University of Dhaka Awards: NSERC Discovery Grant (2021–2026) Research Training Recognition Fund (TRU,多次) SSC 2013 Talk Honourable Mention Labs/Teams: Active in interdisciplinary projects at TRU’s Department of Mathematics and Statistics, focusing on data science applications in ecology, healthcare, and genetics.

Sangita Pokhrelمشاهده پروفایل

مدرس

• Machine Learning
• Deep Learning
• Data Science
• +۱۰ مورد دیگر

Sangita Pokhrel is a Lecturer in Computer Science and Data Science at York St John University's London Campus. She holds an MSc in Computer Science (Distinction) from York St John and an undergraduate degree in Electronics and Communication Engineering from Tribhuvan University, Nepal. Her research focuses on machine learning, AI applications in agriculture and healthcare, blockchain, and educational technology. She leads the Paragon Research Group, emphasizing interdisciplinary innovation in data science and Gen AI. Research Interests: Machine Learning, Deep Learning, AI in Agriculture, Mental Health Analysis, Blockchain, and Educational Technology. She actively collaborates with IBM UK and universities like St Mary’s University to bridge academia-industry gaps. Her funded projects include SAAR and QR initiatives, and she has published in IEEE journals and conferences. Awards include the Best Presentation Award at CDSBDA 2022. Teaching: She instructs master’s modules in Big Data, Agile Software Development, AI, and Data Visualization. Pedagogical focus on practical learning and real-world problem-solving. Engages students through initiatives like The Dock and MakerVerse, offering hands-on experience with VR and podcasting tools. Labs/Teams: Paragon Research Group (focusing on blockchain and Gen AI), collaborations with UK universities to enhance teaching practices. Part of diversity initiatives like U Connect Mentoring and YSJ London Women’s Network.

• Electrical and Electronic Engineering
• Communications Technologies
• Materials Engineering
• +۷ مورد دیگر

Dr. Amir Ebrahimi is a Senior Research Fellow at the School of Engineering, RMIT University. His research focuses on microwave-based sensing technologies, metamaterials, and their applications in biomedical engineering, environmental monitoring, and communications. He holds an ORCID identifier (0000-0002-0858-4958) and is open to supervising Masters and PhD students in areas such as microwave sensors for concrete structures, IoT antennas, and microfluidic devices. Research interests include Electrical Engineering, Communications Technologies, Materials Engineering, Optical Physics, and Analytical Chemistry . His work bridges theoretical design and practical implementation, addressing challenges in sensor sensitivity, material characterization, and reconfigurable RF systems. Recent projects include developing wearable sweat sensors, detecting honey adulteration via microwave techniques, and designing graphene-based terahertz absorbers. Though no scientific awards are explicitly mentioned, his contributions span over 100 peer-reviewed articles and innovative sensor prototypes. Dr. Ebrahimi supervises research projects on flexible electronics, microwave-enabled microfluidics, and metamaterial antennas. He collaborates on lab-on-a-chip technologies for cancer cell detection and nano-scale vacuum circuits for satellite communications.

Golnoush Abaeiمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Golnoush Abaei is a Lecturer in Industry Practice at RMIT University's School of Computing Technologies. She holds dual roles as Professional Practice Lead (since 2022) and Program Manager for the Bachelor of Software Engineering (since 2023). Her expertise bridges academia and industry, focusing on software engineering, machine learning, and data analytics. She coordinates the Software Testing Course and supervises capstone projects for software engineering undergraduates and IT postgraduates. Research interests include software quality, INS/GPS navigation systems, health-related recognition applications, and blockchain. Notable projects involve AI-powered digital lecturer twins for education and machine learning-driven visual defect detection in software interfaces. Teaching methodologies emphasize collaborative active learning (CAL), problem-based learning (PBL), and curriculum design innovation. She actively supports student internships and graduate job placements through industry partnerships. Over 25 peer-reviewed articles span topics like anomaly detection in IoT networks, medical image processing, and hybrid optimization algorithms. Key Projects: Machine Learning for Testing, AI in Education, BDD Test Automation Grants/Industry Collaboration: Multiple industry-sourced capstone projects Curriculum Development: PBL integration, CAL frameworks, and active learning strategies

Marco Zimmerlingمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Wireless Embedded Systems
• Battery-Free IoT
• +۱ مورد دیگر

Marco Zimmerling is a Full Professor in the Department of Computer Science at TU Darmstadt, specializing in cyber-physical systems with focus on wireless embedded systems. His research aims to design dependable, adaptive, and sustainable systems for the battery-free Internet of Things. Education: Ph.D. in Computer Engineering from ETH Zurich (2015), Diploma in Computer Science from TU Dresden. Research interests include battery-free IoT, wireless networked systems, concurrent transmissions, and dependable cyber-physical systems. His work bridges theoretical foundations with practical implementations in sustainable computing. Recent publications focus on energy-harvesting testbeds (Shepherd Nova), open-source platforms for battery-free devices (Riotee), and fault-tolerant networking. Key themes include energy efficiency, wireless protocols, and system reliability under constrained conditions. Honors include ACM SIGBED Early Career Researcher Award (2022), Test-of-Time Award (SenSys 2022), Best Paper Awards (EWSN 2022, ICCPS 2019), and DFG Emmy Noether Grant (2018). Leads research on sustainable IoT systems and wireless control networks, with projects spanning testbed development, protocol design, and real-world deployments.

Yiorgos Tsiatouhasمشاهده پروفایل

استاد

• Analog and Digital VLSI Design
• Integrated Circuit Reliability
• Secure Computing Systems
• +۴ مورد دیگر

Yiorgos Tsiatouhas is a Professor in the Department of Computer Science and Engineering at the University of Ioannina. He holds a B.Sc. in Physics (University of Athens, 1990), M.Sc. in Informatics and Telecommunications (University of Athens, 1993), and Ph.D. in Informatics and Telecommunications (University of Athens, 1999). His research focuses on VLSI circuit design, reliability engineering, and secure computing. He teaches courses such as 'Electronics' (MYY404), 'VLSI Circuits' (MYE018), and 'Reliable Integrated Systems' (Y2), emphasizing topics like CMOS technology, radiation-hardened electronics, and testing methodologies. His research interests span secure computing architectures, fault-tolerant systems, and aging monitoring in integrated circuits. Recent work includes developing radiation-hardened latches, PUF-based security solutions for SoCs, and novel testing algorithms for phase-change memories. He has also contributed to visible light communication (VLC) systems, addressing challenges like anti-reflective obstacle detection and adaptive signal decoding in non-line-of-sight (NLOS) environments. Teaching activities include supervising student projects, maintaining an active lab for SPICE-based circuit simulation, and integrating industry-standard tools like Cadence into coursework. His lab focuses on designing and testing analog/digital circuits, emphasizing practical skills in SPICE simulation and layout design.

Giovanni Steaمشاهده پروفایل

دانشیار

• Quality of Service
• Wireless Networks
• Network Calculus
• +۳ مورد دیگر

Giovanni Stea is an Associate Professor at the Department of Information Engineering within the College of Engineering at the University of Pisa. His research focuses on Quality of Service, resource allocation in wireline and wireless networks, performance evaluation through simulation and analytical techniques, and traffic engineering. Dr. Stea's research spans network calculus, wireless systems, and telecommunications infrastructure. His recent work demonstrates strong emphasis on 5G/6G networks, AI integration in telecommunications, and optimization of network performance. Key trends include: Advancements in network calculus methodologies Innovations in mobile edge computing applications Development of trustworthy AI systems for next-generation networks Cross-disciplinary approaches combining telecommunications and machine learning His publications consistently address computational efficiency in network analysis and practical implementations of theoretical models in real-world telecommunications environments.

Travis Mandelمشاهده پروفایل

دانشیار

• Human-In-the-Loop AI
• Computer Vision
• Human-Computer Interaction (HCI)
• +۳ مورد دیگر

Travis Mandel is an Associate Professor in the Department of Computer Science at the University of Hawaii at Hilo (UH Hilo), serving as the Data Science Program Coordinator within the College of Natural and Health Sciences. He holds a PhD from the University of Washington's Paul G. Allen School of Computer Science and Engineering and a Bachelor's degree from Carnegie Mellon University. His research focuses on Human-In-the-Loop AI, bridging AI and Human-Computer Interaction (HCI), with applications in Computer Vision for environmental and climate science. He leads NSF-funded initiatives such as the NSF CAREER Award (2020) and NSF EPSCoR CHANGE HI (2022). His prior work includes reinforcement learning for educational games and error correction in wireless networks. Teaching includes courses like Python for Data Analysis (DATA 172), Artificial Intelligence (CS 440), and Computer Vision (DATA/CS 483). He has advised numerous undergraduate and graduate students, emphasizing interdisciplinary collaboration. Key awards include the Pattern Recognition Editor's Choice Award (2023) and Best Paper nominations in educational data mining (2013). His contributions span over 20 peer-reviewed publications, with a focus on AI ethics, human-AI collaboration, and environmental applications. He actively develops data science programs at small institutions and explores AI's role in democratizing scientific tools.

• Structural Dynamics
• Experimental Dynamics
• Structural Health Monitoring
• +۴ مورد دیگر

Shinae Jang is a Professor in Residence at the University of Connecticut's School of Civil and Environmental Engineering within the College of Engineering. Her research focuses on structural dynamics, experimental dynamics, structural health monitoring algorithms, long-term bridge health monitoring, and wireless smart sensor technology. She holds a Ph.D. from the University of Illinois at Urbana-Champaign (2010). Professor Jang has pioneered innovative approaches in both structural engineering and civil engineering education. Her work combines advanced monitoring techniques with cutting-edge sensor technologies, emphasizing real-time data acquisition and machine learning applications. Recent research includes developing low-cost RFID-based crack detection systems and ANN-based structural assessment methods for infrastructure resilience. In education, she is recognized for reimagining statics courses to foster creativity and inclusion, particularly for neurodiverse learners. Her pedagogical innovations include integrating augmented reality tools and hybrid learning models to enhance engagement. Her over 100 publications span structural health monitoring systems, damage detection methodologies, and education reforms. Key projects include long-term bridge monitoring in Connecticut and comparative studies on global construction policies. She maintains an active research lab focused on smart infrastructure solutions, accessible via http://smart.engr.uconn.edu .

Ramesh B. Mallaمشاهده پروفایل

استاد

• Structural Mechanics
• Structural Dynamics
• Finite Element Analysis
• +۵ مورد دیگر

Ramesh B. Malla is a Professor in the School of Civil and Environmental Engineering at the University of Connecticut (UConn). He holds a Ph.D. from the University of Massachusetts Amherst (1986). His research focuses on theoretical and applied structural mechanics, with specialties in structural dynamics, finite element analysis, lunar and orbital structures, and planetary exploration systems. His work extends to railroad and highway bridge engineering, geomechanics, and renewable energy harvesting. Key research areas include: Lunar habitat design under extreme thermal and impact conditions Dynamic analysis of truss and frame structures Health monitoring of civil infrastructure using sensor systems Planetary drilling mechanisms for extraterrestrial exploration Railroad bridge vibration and failure analysis He leads projects funded by NASA and federal agencies, with a focus on resilient space habitats and advanced sensor technologies. His lab develops innovative solutions for extreme environment engineering challenges, including meteoroid impact shielding and regolith utilization. Recent work highlights include: Development of AI-driven wireless sensing systems for bridge integrity assessment Finite element modeling of lunar surface structures Experimental studies on granular material behavior for life-support systems Collaborative efforts involve academic-industry partnerships for space technology and sustainable infrastructure. His research has been published in top journals and presented at international conferences.

Miroslav Bјekićمشاهده پروفایل

استاد

• Electrical Power Engineering
• Electric Micromachines
• Simulation and Modeling of Electromechanical Processes
• +۴ مورد دیگر

Professor Miroslav Bјekić is a Full Professor at the Department of Power Engineering within the Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. With a career spanning over three decades, he has established himself as a leading expert in electrical power engineering, particularly in the areas of electric machines, drives, and energy efficiency. Education background: Graduated in Electrical Engineering (Industrial Power Engineering) from Technical Faculty Čačak in 1991 Completed Magister studies at Faculty of Electrical Engineering, University of Belgrade in 1995 Obtained PhD in 2006 from Technical Faculty Čačak with dissertation on step motors Professor Bјekić's research focuses on electric micromachines, simulation and modeling of electromechanical processes, and laboratory investigation of electric machines and drives . His work bridges theoretical electrical engineering with practical applications, particularly in energy efficiency. He has developed numerous educational tools and software applications that enhance the teaching of complex electrical engineering concepts through visualization and simulation. His publication record demonstrates a consistent research trajectory with recent work increasingly focusing on educational technology applications (particularly GeoGebra tools for electrical machine instruction), advanced motor control algorithms, and energy efficiency optimization. The interdisciplinary nature of his work connects electrical engineering with building science, robotics, and educational methodology. Scientific recognition: Recipient of 2nd award at international PhD seminar in Ohrid, 2005 Coordinator of multiple national and international research projects Active participant in standardization committees for electrical machinery Professor Bјekić has secured significant research funding through national projects including TR33016 'Research, Development and Implementation of Programs and Measures for Energy Efficiency of Electric Drives' and has participated in international initiatives like TEMPUS projects. He has developed accredited teacher training programs and has modernized laboratory facilities for electric machines and drives through project funding and industry collaboration. He leads the Laboratory for Electrical Machines, Electromotor Drives and Automation (www.empa.tfc.kg.ac.rs), which has evolved into a center for both research and educational innovation, featuring remote laboratory experiments that connect university and secondary vocational school education.

• Communications
• Radio Waves Propagation
• Electromagnetic Compatibility
• +۳ مورد دیگر

Dr. Jarosław Janiszewski is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He is actively engaged in the Department of Telecommunications and Teleinformatics, focusing on wireless communication technologies and electromagnetic wave behavior. Research interests include: Radio wave propagation in urban environments Electromagnetic compatibility in mobile networks Frequency spectrum allocation and management Dynamic channel assignment in radio networks Broadband network planning Publication trends: His 15 most recent (up to 1996) works focus on telecommunications infrastructure, wireless system interoperability, and radio frequency analysis. Key themes include GSM/CDMA network coexistence, mobile satellite systems, and urban electromagnetic propagation modeling. Contact: Email jaroslaw.janiszewski@pwr.edu.pl • Room 907, Building C-5 • Office hours: Fridays 11:00-12:00

• Telecommunications Engineering
• Electromagnetic Field Theory
• Wireless Sensor Networks
• +۳ مورد دیگر

Dr. Jarosław Emilianowicz holds a PhD in Electrical Engineering and serves as a researcher at the Department of Telecommunications and Teleinformatics within Wrocław University of Science and Technology's Faculty of Information and Communication Technology. Doctoral degree in Electrical Engineering Specializes in electromagnetic field applications and wireless communication systems Active in LPWAN technologies and IoT implementations His research spans electromagnetic field shaping, wireless sensor networks, and real-time location systems. Recent work focuses on LoRaWAN-based environmental monitoring in large buildings, demonstrating practical IoT applications for smart infrastructure. Publications show expertise in microwave techniques, WLAN security analysis, and embedded communication systems. Key technical interests include signal processing algorithms, secure wireless transmission mechanisms, and microprocessor-based measurement devices. Active in conference proceedings and book chapters related to telecommunications applications, with contributions appearing in IEEE publications and Polish technical monographs.

• Optoelectronics
• Laser Spectroscopy
• Photodetection Technology
• +۳ مورد دیگر

Dr. Janusz Mikołajczyk is a Lecturer at the Institute of Optoelectronics, Warsaw University of Technology. His research focuses on optoelectronic systems, photodetectors, and laser-based sensing technologies. He specializes in infrared communication systems, gas detection using quantum cascade lasers, and advanced noise measurement techniques for photodetectors. His work spans applications in free-space optics, environmental monitoring, and military communication systems. With 155 publications and 7 research projects, he has contributed significantly to fields like cavity-enhanced spectroscopy, optoelectronic sensor development, and high-speed data transmission. His h-index of 20 (WoS) reflects his impactful research in optoelectronics and materials science. Key areas of exploration include THz radiation detectors, ultraviolet photodetectors, and hybrid communication systems combining optical and radio technologies. He has designed innovative detection modules and noise analysis systems, enhancing the reliability of optoelectronic devices in harsh environments.

• Digital Signal Processing
• Spectral Analysis
• Pattern Recognition
• +۷ مورد دیگر

Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.