Find Academic Supervisors

Sung Kyung HongView profile

Professor

• Autonomous Flight Control Systems
• Unmanned Aerial Vehicles (UAVs)
• Sensor Applications
• +5 more

Sung Kyung Hong is a Professor in the Department of Intelligent Drone Convergence at Sejong University , specializing in autonomous flight control systems, UAV technology, and sensor applications. He has held leadership roles including Director of the Autonomous Unmanned Vehicle Research Center since 2017 and served as an Advisory Member of South Korea's Presidential Advisory Council on Science and Technology (2019-2021). Education : Ph.D. (1998) from Texas A&M University, M.S. (1989) and B.S. (1987) from Yonsei University. His research focuses on autonomous flight control , inertial sensor integration , and HILS testing for UAVs, with notable achievements in robust fault diagnosis, collision avoidance algorithms, and adaptive filtering in high-vibration environments. Recent publications highlight applications of deep reinforcement learning, fixed-time attitude control, and drone-view image dehazing techniques. The scientific awards section includes: Advisory Member, Presidential Advisory Council on Science and Technology (2019-2021) As advisor, he has mentored researchers in UAV dynamics and simulation, while leading the Autonomous Unmanned Vehicle Research Center to develop advanced drone technologies and hardware-in-the-loop testing frameworks.

Prabhakara AkarshView profile

Assistant Professor

• Wireless Systems
• Cyber-Physical Systems
• Machine Learning for RF
• +6 more

Akarsh Prabhakara is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, with an additional affiliation in the Department of Electrical and Computer Engineering. He earned his Ph.D. from Carnegie Mellon University in 2024, where he worked under Professors Anthony Rowe and Swarun Kumar. Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2024 B.Tech, Electronics and Communication Engineering, National Institute of Technology Karnataka, 2018 His research focuses on building high-fidelity wireless systems for perception and communication, particularly in cyber-physical and robotic applications. He explores machine learning-driven RF systems, novel communication paradigms, wireless-robotics integration, and embedded wireless sensing. His work aims to enable robust perception in challenging environments such as smoke or fog using millimeter wave radar and deep learning. His recent publications in CVPR, ICRA, MobiCom, and ICCV demonstrate a strong trend in using neural methods for radar simulation, super-resolution, and wireless intelligence. Key themes include implicit neural rendering for radar, end-to-end learning for perception, and high-resolution point cloud generation from low-cost sensors. His scientific contributions have been recognized through publications in top-tier venues, though specific awards are not mentioned in the provided text. He is actively involved in mentoring and recruiting students for research in wireless and robotics. He teaches courses such as Intro to Computer Networks and Big Ideas in Wireless: Perception and Communication . He leads research projects like RadarHD, which enables lidar-like perception from mmWave radar, and is developing tools and datasets for community use. His lab emphasizes practical, real-world applications of wireless systems in robotics and autonomous systems.

Alan MantoothView profile

Professor

• Analog and Mixed-Signal IC Design
• Power Electronics
• Semiconductor Device Modeling
• +3 more

Alan Mantooth is a Distinguished Professor holding the Twenty-First Century Research Leadership Chair in Engineering within the Department of Electrical Engineering at the University of Arkansas, Fayetteville. He serves as Director of the National Center for Reliable Electric Power Transmission (NCREPT), Executive Director for GRAPES (NSF I/UCRC) and SEEDS (DoE Center), and Deputy Director of the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). His educational background includes: B.S. in Electrical Engineering, University of Arkansas M.S. in Electrical Engineering, University of Arkansas Ph.D. in Electrical Engineering, Georgia Institute of Technology Dr. Mantooth's research centers on analog/mixed-signal IC design, power electronics CAD, and semiconductor device modeling with emphasis on harsh-environment applications. His pioneering work in silicon carbide (SiC) and gallium nitride (GaN) power systems has enabled high-temperature operation for electric vehicles and renewable energy infrastructure, significantly advancing reliability in extreme conditions. His 2025 publications reveal strong trends toward AI-driven power electronics (e.g., SolarFormer++ for PV profiling), wide-bandgap device modeling (β-Ga2O3, SiC), and innovative packaging solutions. Key themes include reliability engineering for extreme environments, multi-physics optimization, and explainable AI for safety-critical power systems. Major scientific recognition includes: IEEE Fellow (2009) for power electronic device modeling Three R&D 100 Awards (2009, 2014, 2016) for SiC power modules IEEE Power Electronics Society Technical Achievement Award (2019) Multiple university teaching/research awards including SEC Faculty Achievement Award (2015) As an exceptional mentor (UA Outstanding Mentor 2006-2008), he co-founded Lynguent and Ozark Integrated Circuits. His centers NCREPT, GRAPES, and SEEDS have secured major funding from NSF, DoE, and industry partners, supporting over 350 refereed publications and numerous patents. Current research focuses on AI-enhanced power electronics, recyclable packaging, and next-generation wide-bandgap device characterization. He leads the NCREPT test facility and multi-institutional teams developing grid-connected power electronic systems, secure energy delivery architectures, and thermal management solutions for high-power-density applications, with direct impact on electric transportation and renewable energy integration.

Anthony ClarkView profile

Assistant Professor

• Autonomous Robotics
• Evolutionary Robotics
• Computer Vision
• +7 more

Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.

• Food Science
• Marine Biotechnology
• Protein Chemistry
• +4 more

Ida-Johanne Jensen is Associate Professor at the Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). Her work integrates food science, marine biotechnology and sustainability to valorise marine resources, improve food quality and develop novel functional products. Education & Doctoral Training Dr Jensen obtained her PhD from UiT The Arctic University of Norway (UiT Norges arktiske universitet) in 2014 with the dissertation "Health benefits of seafood consumption – with special focus on household preparations and bioactivity in animal models" , laying the foundation for her expertise in seafood bioactivity and nutrition. Research Interests Her research agenda centres on: Protein chemistry and functionality of marine raw materials and side-streams (brewer’s spent grain, sea cucumber, fish proteins). Development of functional foods and nutraceuticals with antihypertensive, antioxidative and cardioprotective properties. Sensory and technological quality optimisation of foods for extreme environments (military rations, Arctic field meals). Smart packaging and intelligent freshness indicators using natural colorants (anthocyanin-based hydrogel beads). Life-cycle assessment and environmental sustainability of aquaculture and fisheries. Publication Trends Jensen has an extensive publication record (>60 papers since 2009). Recent work (2023–2025) demonstrates a clear pivot towards valorisation of underutilised marine resources (sea cucumbers, brewer’s spent grain) and application-oriented food engineering (freeze-drying, microencapsulation, jerky processing). Reviews on military rations and marine antihypertensive peptides highlight her translational focus on human health and performance. Scientific Awards & Recognition No specific awards are listed in the provided material; however, sustained high-output publication in top-tier journals ( Food Chemistry , npj Science of Food , Marine Drugs ) attests to strong peer recognition. Student Supervision & Grants She has supervised numerous master’s students at NTNU on topics ranging from Arctic field-ration development to protein extraction from brewer’s spent grain, indicating robust external funding and industry collaboration. Laboratories & Collaborations Research is conducted within NTNU’s Department of Biotechnology and Food Science laboratories, with active collaborations across Europe and the Indian Ocean region for marine resource assessments.

Jia DiView profile

Professor

• Asynchronous Integrated Circuit Design
• Hardware Security
• Extreme Environment Electronics
• +3 more

Jia Di serves as Professor and Department Head of the Department of Electrical Engineering and Computer Science at the University of Arkansas, holding the Rodger S. Kline Endowed Leadership Chair. He has been with the institution since 2004, progressing from Assistant Professor to his current leadership position within the College of Engineering. Education: B.S. in Automatic Control, Tsinghua University (1997) M.S. in Automatic Control, Tsinghua University (2000) Ph.D. in Electrical and Computer Engineering, University of Central Florida (2004) Research Focus: Dr. Di's work centers on asynchronous integrated circuit design and hardware security , with emphasis on Multi-threshold Null Convention Logic (MTNCL) for ultra-low-power secure systems. His research spans hardware Trojan detection, polymorphic logic gates, extreme environment electronics, and security solutions for IoT infrastructure. His Trustable Logic Circuit Design Lab has pioneered techniques for side-channel attack mitigation and cold boot attack prevention through self-destructive memory mechanisms. Publication Trends: Recent publications reveal a strategic shift toward hardware security applications for renewable energy systems and IoT edge devices, while maintaining core expertise in asynchronous circuit design. His work increasingly integrates machine learning (e.g., graph neural networks for hardware Trojan detection) and cross-platform verification frameworks, demonstrating evolution from pure circuit design to holistic cybersecurity solutions for critical infrastructure. Scientific Recognition: Senior Member of IEEE Eminent Member of Tau Beta Pi Elected Member of the National Academy of Inventors Research Leadership: Dr. Di has secured over $23 million in research funding for his Trustable Logic Circuit Design Lab, supporting development of 6 U.S. patents and two authoritative books. His lab collaborates with federal agencies and industry partners on hardware security challenges, with recent grants focusing on photovoltaic system protection and extreme-environment electronics. While specific student names aren't documented here, his extensive publication record indicates significant graduate mentorship in hardware security and asynchronous design. Lab Infrastructure: The Trustable Logic Circuit Design Lab maintains specialized capabilities for testing circuits in extreme environments (high temperature/radiation) and developing polymorphic security mechanisms. Current projects include RF aperture security, hardware-based IoT verification systems, and digital twin implementations for power electronics with integrated trust verification.

Konrad KowalczykView profile

Associate Professor

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +6 more

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

Tanvir ArafinView profile

Assistant Professor

• Hardware Security
• Trusted Computing
• IoT Security
• +4 more

Tanvir Arafin serves as an Assistant Professor in the Department of Cyber Security Engineering at George Mason University, where his research focuses on hardware security and trust mechanisms for emerging computing platforms. With publications in premier venues including IEEE Transactions on Very Large Scale Integration Systems, IEEE Transactions on Computers, and ACM International Conference on Computer-Aided Design, he addresses critical security challenges in next-generation systems through rigorous hardware-software co-design approaches. His research portfolio spans Hardware Security, Trusted Computing, and IoT Security, with specialized expertise in Side-Channel Attacks and Secure Hardware Design. Dr. Arafin investigates electromagnetic side-channel vulnerabilities in O-RAN networks, develops countermeasures for autonomous vehicle cybersecurity, and pioneers RRAM-based security solutions for memory-constrained devices. His work bridges theoretical security models with practical implementations, emphasizing real-world applicability in edge computing environments and autonomous navigation systems. Current projects explore machine learning integration for anomaly detection in connected vehicles and secure acceleration of cryptographic operations. Analysis of Dr. Arafin's 2022-2025 publications reveals strategic focus areas: electromagnetic fingerprinting for radio units in O-RAN (2025), spatial acceleration of Kolmogorov-Arnold Networks (2025), and NTT-based cryptography accelerators (2024). His research demonstrates consistent innovation in securing autonomous navigation systems and edge devices, with emerging work on in-memory computing architectures using resistive memory technologies. Key trends include hardware-centric defense against model inversion attacks, voltage overscaling for lightweight authentication, and robust multi-robot coordination in dynamic environments. Scientific Awards: No scientific awards, fellowships, or medals were documented in the source materials. Dr. Arafin leads significant collaborative research, including the NSF CISE-MSI grant (DP: CNS) for edge-based robust multi-robot systems. His educational initiatives feature Capture-the-Flag competitions targeting underrepresented students in cybersecurity. Current grant activities emphasize practical security solutions for autonomous navigation, multi-robot coordination, and IoT edge devices, with demonstrated focus on translating research into deployable countermeasures for real-world threats in dynamic operational environments.

Yoann AltmannView profile

Professor

• Statistical signal processing
• Image processing
• Bayesian inverse problems
• +5 more

Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Kiju LeeView profile

Associate Professor

• Robotics
• Swarm Intelligence
• Human-Robot Interaction
• +3 more

Kiju Lee is an Associate Professor in the Department of Engineering Technology & Industrial Distribution and Mechanical Engineering at Texas A&M University, with a joint appointment in Mechanical Engineering. His affiliations include the Adaptive Robotics and Technology Lab and the College of Engineering. He holds a Ph.D. in Mechanical Engineering from Johns Hopkins University (2008), an M.S.E. from the same institution (2006), and a B.S.E. in Electronics and Electrical Engineering from Chung-Ang University (2002). Education: Ph.D., Mechanical Engineering, Johns Hopkins University – 2008 M.S.E., Mechanical Engineering, Johns Hopkins University – 2006 B.S.E., Electronics and Electrical Engineering, Chung-Ang University – 2002 His research focuses on robotics , swarm intelligence , human-robot interaction , and tangible serious games . Recent work includes adaptive robotics systems for multi-terrain navigation, cognitive assessment tools using block games, and swarm-based agricultural automation. His projects bridge robotics with healthcare, education, and environmental monitoring. His publications highlight advancements in reconfigurable mechanisms (e.g., CLAW, Wheeler robots), swarm algorithms for non-convex coverage, and mixed-reality teleoperation systems. He has contributed to both theoretical frameworks (e.g., entropy-based consensus decision-making) and practical applications like amphibious robotics and crop monitoring. Awards: 2022 Engineering Genesis Award 2021 Charlotte & Walter Buchanan Faculty Fellow Dr. Lee’s advising and grants emphasize interdisciplinary collaboration, though specific grant details are not explicitly listed. His lab, the Adaptive Robotics and Technology Lab, drives innovation in robotic mobility, human-swarm teaming, and tangible interfaces for cognitive assessment. He maintains an active presence in both academia and industry, with work spanning robotics hardware design, algorithm development, and socio-technical applications of autonomous systems.

• Computer Vision
• Autonomous Systems
• Robotics
• +12 more

Dr Lounis Chermak is a Lecturer in Computer Vision and Autonomous Systems at the Centre for Electronic Warfare, Information and Cyber, part of Cranfield Defence and Security at Cranfield University, UK. He leads the Joint Autonomy Lab and is actively involved in research and education in autonomous systems with applications in defence and space. Research Interests: His work focuses on situational awareness in autonomous platforms, with core expertise in computer vision, sensor fusion, artificial intelligence, robotics, and navigation. He investigates perception, decision-making, and mobility across aerial, ground, maritime, and space systems, developing robust solutions for challenging environments including low visibility and extreme illumination. The recent publications reflect a strong trend in autonomous navigation, particularly for space and defence applications, using advanced computer vision techniques such as thermal stereo odometry, HDR imaging, stixel-based scene understanding, and lightweight 3D descriptors. Research also extends to cybersecurity of autonomous systems, including impersonation attack detection and optical countermeasures. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Dr Chermak leads research activities supported by postdoctoral researchers, PhD, and MSc students. His work is funded and applied in collaboration with major clients including aerospace organizations (ESA, UK Space Agency, Thales Alenia Space), defence agencies (MoD, DSTL, BAE Systems, MBDA), and technology companies (Samsung, Astroscale). He supervises research students in robotics and autonomous systems across civilian and defence domains. Labs and Teams: He leads the Joint Autonomy Laboratory, a 200 m² indoor facility equipped with drone netting, motion capture systems, virtual reality test benches, UAV and ground robot fleets, electric vehicles, and multiple sensors for vision, ranging, and motion. This lab supports both educational and cutting-edge research in autonomous systems.

Sandra M. ĐosićView profile

Associate Professor

• Electronics
• Real-Time Systems
• Fault-Tolerant Systems
• +7 more

Sandra M. Đosić is an Associate Professor at the Faculty of Electronics in Niš, University of Niš, Serbia, specializing in Electronics and Embedded Systems. She has been actively contributing to research in real-time systems, fault tolerance, wireless sensor networks, and indoor localization technologies. Research Interests: Her work spans fault-tolerant real-time systems, energy-efficient computing, UWB-based indoor localization, and communication protocols for wireless sensor networks. She explores techniques such as dynamic voltage and frequency scaling (DVFS), tone-based contention resolution, and deflection routing in networks-on-chip to enhance system reliability and efficiency. Publication Trends: Her recent publications (2009–2022) demonstrate a strong focus on improving robustness and performance in embedded and distributed systems. The research integrates signal processing, network optimization, and energy-aware design, primarily applied in industrial and indoor environments. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: While no specific students are listed, she is currently participating in two national research projects, indicating active involvement in funded research. Her collaborations with researchers such as Igor Stojanovic and Milica Jovanovic suggest a strong team-based research approach. Labs and Research Teams: Although no formal lab or team name is specified, her repeated co-authorship with colleagues from the Faculty of Electronics implies active participation in a research group focused on electronics, communications, and real-time systems.

Dipanwita ThakurView profile

Assistant Professor

• Green Artificial Intelligence
• Federated Learning
• Smart Healthcare
• +4 more

Dr. Dipanwita Thakur serves as Assistant Professor at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) at the University of Calabria, Italy since July 2023. She is an active member of the European Cooperation in Science & Technology (COST Action CA22104) focusing on cybersecurity and serves in the IEEE Future Networks Working Group for Artificial Intelligence/Machine Learning. Previously, she held a 15-year Assistant Professor position at Banasthali University, Rajasthan, and has industry experience at TechMahindra and C-DAC. Education: Ph.D. in Smart Healthcare from West Bengal University of Technology, Kolkata M.Tech. in Software Engineering from Banasthali Vidyapith MCA from NIELIT, Government of India B.Sc. from University of Calcutta Her research pioneers Green Artificial Intelligence with emphasis on energy-efficient federated learning and smart healthcare applications. She develops privacy-preserving human activity recognition systems using multimodal data fusion, focusing on performance evaluation and environmental sustainability. Her work bridges theoretical machine learning with practical healthcare solutions, optimizing AI systems for reduced carbon footprint while maintaining clinical efficacy through hardware-algorithm co-design and quantization techniques. Recent publications reveal a strong trajectory toward sustainable AI, with increasing focus on energy-aware federated learning frameworks, multimodal medical segmentation, and non-IID data handling. Her work consistently addresses the critical balance between model accuracy, convergence speed, and energy consumption across edge devices, with growing emphasis on quantization techniques and hardware-algorithm co-design for real-world deployment. Scientific Awards: Elevated to IEEE Senior Member (2024) Dr. B.C. Roy Memorial Scholarship for outstanding 10th Board results (1992) Student Science Seminar Award by West Bengal Government (1990) Dr. Thakur actively mentors students as evidenced by her congratulations to advisee Farwa for paper acceptances. She serves as Associate Editor for Information Fusion (Elsevier) and IEEE Sensors Journal, and holds editorial roles at Scientific Reports. Her research is advanced through COST Action CA22104 and IEEE working groups, though specific grant details aren't listed in the source material. She has organized key workshops including Green-Aware AI 2024 and Green Federated Learning at IJCNN 2025. She leads research within the MONAI community on data quality and federated learning, and contributes to IEEE IoT and Future Networks initiatives. Her work with the COST Action CA22104 Behavioral Next Generation in Wireless Networks connects cybersecurity with sustainable AI development, while her Missouri S&T visiting scholar position focuses on energy optimization for federated learning systems.

Kirill DjebkoView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +4 more

Kirill Djebko is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science, Faculty of Mathematics and Computer Science at the University of Würzburg . His work spans AI-based optimization systems , autonomous spacecraft operations , and deep reinforcement learning applications. Doctorate completed in 2020 Active in research since 2015 Key projects: KINERGY (heating system optimization), VeriKI (AI verification for space), LeLaR (AI attitude control for nanosatellites) Research focuses on metaheuristics , genetic algorithms , regression algorithms , and deep reinforcement learning for practical applications. His work integrates digital twin modeling , decision support systems , and AI robustness in industrial and space contexts. Recent publications (2024-2016) analyze heating system optimization , spacecraft fault detection , and autonomous nanosatellite operations . These studies emphasize sim-to-real-world transfer , data-driven calibration , and AI quality assurance in complex environments. Current affiliations include the Center for Artificial Intelligence and Data Science (CAIDAS) . Contact details: Email - kirill.djebko@uni-wuerzburg.de | Phone +49 931 31-86405 | Room 03.010, Emil-Fischer-Straße 50, Würzburg.