Find Academic Supervisors

Elad AlonView profile

Professor

• Integrated Circuits
• Energy-Efficient Systems
• Wireless Communication
• +3 more

Elad Alon is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and co-director of the Berkeley Wireless Research Center (BWRC). He joined UC Berkeley in 2007 and has held visiting/consulting roles at companies like Intel, AMD, and IBM Research. His research focuses on energy-efficient integrated systems, including circuit design, communication techniques, and optimization for computing and communications. Alon holds a Ph.D. (2006), M.S. (2002), and B.S. (2001) in Electrical Engineering from Stanford University. Education: Ph.D., Electrical Engineering, Stanford University, 2006 M.S., Electrical Engineering, Stanford University, 2002 B.S., Electrical Engineering, Stanford University, 2001 Research Interests: Alon's work spans energy-efficient integrated circuits, wireless communication systems, and neural engineering. His research emphasizes: Ultra-low-power digital logic using MEMS relays High-speed analog-to-digital converters (ADCs) Wireless implantable devices (e.g., neural dust) Phased-array transceivers and massive MIMO systems Power management and voltage regulation techniques Recent Article Trends: His recent publications focus on: Neural Dust: Wireless neural recording in peripheral nervous systems via ultrasonic communication RF Transceivers: Low-power 60 GHz phased arrays and D-band MIMO systems MEMS Circuits: Capacitive-gap oscillators and relay-based logic Processor Design: RISC-V vector processors and energy-efficient SoCs Awards: IEEE Fellow (2019) ISSCC Jack Raper Award (2010) Multiple Best Paper Awards (ISSCC, CICC, VLSI Circuits) UC Berkeley Outstanding Teaching Award (2010, 2017) Advising & Labs: Alon has advised over 20 Ph.D. students, many focusing on: Neural engineering interfaces RF/mm-wave transceivers Energy-efficient ADCs Massive MIMO architectures He leads the BWRC and collaborates with the Center for Neural Engineering & Prostheses (CNEP), advancing projects like the Human Intranet and OMNI neuromodulation systems.

• Antennas and Electromagnetics
• Wireless Communication Systems
• Reconfigurable Antenna Design
• +3 more

Jennifer Bernhard is the Donald Biggar Willett Professor of Engineering and Department Head of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC). She earned her Ph.D. in Electrical Engineering from Duke University (1994) and joined UIUC in 1999. Her research focuses on electromagnetics for wireless communication and reconfigurable antennas, emphasizing practical applications like IoT and energy efficiency. She leads the Applied Research Institute and has held roles such as Associate Dean for Research (2012–2019). Bernhard’s work is supported by advanced facilities like the Illinois Wireless Wind Tunnel and collaborations with the UIUC Center for Computational Electromagnetics. Education: Ph.D. in Electrical Engineering (Duke, 1994), BSEE (Cornell, 1988) Leadership Roles: Department Head (2024–present), Associate Dean for Research (2012–2019), Interim Director of Applied Research Institute Research Themes: Reconfigurable antennas, wireless communication, antenna array optimization, and IoT integration Her research group innovates in reconfigurable antenna systems using MEMS, ferroelectric materials, and mechanical actuation to enhance efficiency and reduce costs. Awards include the NSF CAREER Award (2000), IEEE Fellow (2010), and multiple teaching excellence recognitions. Bernhard is committed to diversity, mentoring over 70 students, with a focus on underrepresented groups. She serves on national boards like the Defense Science Board and the IEEE Antennas and Propagation Society. Recent articles highlight advancements in loop antennas, OAM generation, and secure polarization modulation. Her work bridges theoretical analysis with experimental validation, addressing challenges in high-data-rate wireless systems and compact antenna design.

• Traffic Engineering
• Transportation Systems
• Electric Mobility
• +13 more

Francesco Paolo Deflorio is an Associate Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin , where he has been active since 2018. He is affiliated with the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility and teaches courses such as Models and Technologies for Traffic and Transport , Simulation Tools for Transport Systems , and Traffic Observation, Simulation, and Regulation across various engineering programs including Civil, Mechanical, Automotive, and Environmental Engineering. He is a member of the PhD collegium in Civil and Environmental Engineering and supervises several doctoral candidates. Education: PhD in Automation and Computerization of Transport, Polytechnic University of Turin Master of Science in Civil Engineering, Polytechnic University of Bari His research focuses on transportation systems engineering , with emphasis on intelligent transport systems (ITS), electric mobility, sustainable logistics, traffic modeling, and simulation. He has made significant contributions to dynamic route guidance, wireless sensor networks for traffic safety, and in-motion charging for electric vehicles. His recent work explores autonomous vehicle integration, energy efficiency in freight transport, and smart road technologies. The analysis of his recent publications reveals a consistent trend toward sustainable and intelligent mobility systems , combining engineering modeling with real-world applications in urban and freight transport. His work bridges theoretical simulation with practical ITS deployment, particularly in energy-efficient and safety-enhancing technologies. Scientific Participations: President, Italian Association for Traffic and Transport Engineering – Piedmont and Aosta Valley Section (2012–2018) Full Member, Italian Society of Transport Teachers (SIDT) (2006–present) He leads numerous research initiatives, including the EPIGNOSIS project under the PNRR, and has served as scientific manager for over 20 funded research and commercial projects related to vehicle energy efficiency, traffic control, and smart mobility. He advises doctoral students on topics such as autonomous vehicle integration and electric mobility modeling. His research is supported by collaborations with regional authorities, industry partners, and foundations. Labs and Research Centers: Member, CARS@PoliTO – Center for Automotive Research and Sustainable Mobility Involved in the CLIK Laboratory (“Contamination Lab and Innovation Kitchen”) for innovative teaching challenges

Cynthia MossView profile

Professor

• Systems Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience
• +14 more

Cynthia Moss is a Professor of Psychological and Brain Sciences at The Johns Hopkins University, where she leads a research laboratory focused on systems, cognitive, and computational neuroscience. Her work is affiliated with the Neuroscience Training Program and the Psychological & Brain Sciences graduate program. She conducts pioneering research on echolocating bats to understand spatial perception, attention, and memory in natural behaviors. Research Interests: Dr. Moss investigates how the brain integrates sensory input with motor output during complex behaviors such as flight and navigation. Her primary model system is the echolocating big brown bat, which uses biological sonar to navigate and hunt. She studies neural mechanisms in key brain regions including the hippocampus, midbrain superior colliculus, and somatosensory cortex. Her research spans spatial perception , attention , memory formation , sensorimotor integration , and tactile feedback in flight control . She employs wireless neural recordings from free-flying bats to study brain activity during natural behaviors, offering insights into 3D spatial representation and adaptive sensing. Publication Trends: Her recent publications (2021–2024) reflect a strong focus on neural coding of 3D space, multisensory integration (especially tactile and visual cues in echolocation), adaptive vocal behavior, and social communication in bats. Her work increasingly bridges neuroscience with bio-inspired engineering, as seen in studies on wing mechanoreception and computational models of auditory processing. Collaborations with institutions like Columbia University expand the scope of her tactile sensing research. Scientific Contributions: Although specific awards are not listed in the provided text, her consistent publication record in high-impact journals such as Nature Neuroscience , PNAS , Current Biology , and eLife underscores her leadership in neuroethology and systems neuroscience. Advising and Grants: Dr. Moss mentors graduate students through the Neuroscience and Psychological & Brain Sciences programs at Johns Hopkins. While specific grant details are not mentioned, her extensive research output and long-term program suggest sustained funding from federal and private sources. She has trained numerous researchers and published over two decades of influential work on bat biosonar and brain function. Labs and Teams: The Moss Lab at Johns Hopkins uses advanced techniques including multi-channel wireless neural recording, high-speed motion tracking, and acoustic monitoring to study freely behaving bats. The lab collaborates with experts in biophysics, engineering, and sensory neuroscience, forming interdisciplinary teams to explore active sensing and neural dynamics in natural contexts.

Biju IssacView profile

Associate Professor

• Cyber Security
• Networks (Wired/Wireless)
• Artificial Intelligence/Machine Learning
• +4 more

Dr. Biju Issac is an Associate Professor and Head of Subject (Networks and Cyber Security) at Northumbria University's Computer and Information Sciences Department. He directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR) and leads the CyberNets Research Group. With a PhD in Networking and Mobile Communication, he holds a Chartered Engineer (CEng) certification and is a Senior Fellow of the Higher Education Academy (HEA). Affiliations: Editor-in-Chief of the International Journal of Information and Computer Security, Alan Turing Institute collaborator, and former Programme Leader for BSc (Computer Networks and Cyber Security) and BSc (Computer and Digital Forensics). Education: PhD (Networking & Mobile Communication), MCA (First Class), BE in Electronics and Communication Engineering. Research Interests: Cybersecurity (malware/botnets), AI/ML applications, IoT security, satellite/drone networks, secure routing protocols, Android security, and cloud computing optimizations. His Northumbria Cyber Clinic partners with law enforcement (NEBRC) to train students as ethical hackers and protect local businesses. Notable awards include the 'Innovator of the Year' (Dynamites 2020) and finalist for Excellence in Cybersecurity (IET 2020). Key Projects: Funded projects with Lockheed Martin Space (vulnerability detection tool) and the Alan Turing Institute (AI security for defense). Supervises PhD students in AI-driven cybersecurity, ransomware detection, and federated learning applications. Professional Activities: Editorial roles in journals like IEEE Access, peer-reviewer for multiple publications, and advisor to the EPSRC Peer Review College.

• Wireless Communication
• Antenna Design
• Microwave Engineering
• +6 more

Dr. Sam Lemey is an Associate Professor at the Faculty of Engineering and Architecture , Ghent University , and a researcher at IMEC . His work focuses on advanced antenna systems, microwave engineering, and wireless communication technologies. Primary affiliations: Ghent University, IMEC Key research areas: Millimeter-wave systems, UWB antennas, smart surfaces, RF-energy harvesting His research explores millimeter-wave distributed antenna systems for virtual reality and robotics, air-filled substrate-integrated waveguide (AFSIW) components , and machine learning applications in RF signal optimization. He has developed low-cost antenna arrays for 5G/6G, terahertz communication systems , and textile-integrated antennas for wearable IoT devices. Publications show trends in beamforming networks , radio-over-fiber integration, and human exposure reduction strategies. His work frequently involves collaborations with engineers and physicists on heterogeneous integration projects.

Hannes RaddatzView profile

Researcher

• Embedded Systems
• Industrial Internet of Things (IIoT)
• OPC UA Protocols
• +4 more

Dr. Hannes Raddatz is a researcher at the University of Rostock, focusing on embedded systems and industrial communication protocols. He works within the Department of Embedded Systems, contributing to projects involving OPC UA, MQTT-SN, and Time-Sensitive Networking (TSN) for Smart Manufacturing and IIoT applications. His expertise spans protocol evaluation, secure communication frameworks, and virtual prototyping of distributed systems. PhD in Embedded Systems Research Interests: Industrial IoT, Smart Factories, Cybersecurity His recent work includes protocol integration for sensor networks and optimizing real-time communication in industrial environments. He collaborates with institutions like the University of Rostock and researchers such as Prof. Christian Haubelt and Dr. Frank Golatowski. Key publications address OPC UA server evaluation, MQTT-SN binding for IIoT, and secure frameworks for Smart Home systems. He has presented at conferences like INDIN, ETFA, and WF-IoT. Scientific Contributions: Best Paper Award Nominee at PATMOS 2019 Key roles in projects: GenerIoT, SUSTAIN Dr. Raddatz contributes to advancing secure and efficient communication protocols in industrial and embedded contexts, with applications in logistics, crane systems, and smart manufacturing environments.

Hong YuView profile

Associate Professor

• EEG-Based BCI Systems
• Big Data Analysis
• Internet of Things (IoT)
• +5 more

Dr. Hong Yu is an Associate Professor in the Engineering Technology department at Fitchburg State University's School of Business and Technology. Specializing in wireless communication protocols, neural networks, and embedded systems, their research spans earthquake nowcasting, IoT applications, and industrial sensor development. Current projects include 5G network optimization and FPGA-based measurement systems. PhD in Electrical & Electronics Engineering from The Catholic University of America Active in NSF-funded initiatives like the Microcontroller Training System project Professional affiliations include IEEE Senior Membership and leadership roles in multiple IEEE chapters Teaches courses in microprocessor systems, digital electronics, and mobile application development Research focuses on merging biological neural network principles with technological applications, while maintaining expertise in analog electronics and environmental monitoring. Recent publications emphasize wireless protocol design (2021), color sensing automation (2021), and cross-platform smart home development (2020), showing consistent innovation across 5G networking, semiconductor design, and seismic analysis. Professional contributions include serving as Vice Chairperson in IEEE ROBOT Chapter (Worcester County) and IEEE AESS Chapter (Baltimore), plus judging robotics competitions like FIRST LEGO League.

Jun LuoView profile

Associate Professor

• Mobile and Pervasive Computing
• Wireless Networking
• Machine Learning
• +12 more

Jun Luo is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science from EPFL under the supervision of Prof. Jean-Pierre Hubaux and completed postdoctoral research at the University of Waterloo. He joined NTU in 2008 as an Assistant Professor and was promoted to Associate Professor in 2014. He served as Deputy Director of the Centre for Multimedia and Network Technology from 2010 to 2013. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), 2006 MS in Electrical Engineering, Tsinghua University, 2000 BS in Electrical Engineering, Tsinghua University, 1997 Research Interests: Jun Luo's research focuses on mobile and pervasive computing, wireless networking, machine learning, and applied operations research. His primary research thrusts include: Contact-free Sensing Driven by Deep Learning : Leveraging RF, acoustic, and visible light signals for human activity recognition, respiration monitoring, and localization without wearable devices. Visible Light Communication and Sensing : Exploring LED-camera systems for data transmission, occupancy inference, and indoor broadcasting. Indoor and Outdoor Localization and Tracking : Developing systems using WiFi, geomagnetism, and crowdsourced data for precise positioning. Machine Learning for Mobile Networking : Applying deep learning and optimization to improve wireless network performance, mobile crowdsensing, and resource allocation. Publication Trends: His recent publications (2021–2023) demonstrate a strong focus on deep learning-enhanced sensing using RF and acoustic signals, particularly for health monitoring (e.g., respiration, heartbeat), multi-person tracking, and privacy-preserving techniques. He frequently collaborates with researchers in signal processing, computer vision, and networking, publishing in top venues like IEEE Transactions on Mobile Computing, MobiCom, and INFOCOM. His work emphasizes practical deployment on commodity devices and integration of sensing with communication systems. Scientific Recognition: IEEE Fellow Advising and Grants: Dr. Luo has advised numerous PhD and Master's students, as evidenced by the extensive list of student co-authors across his publications. He has led significant research projects in wireless sensor networks, mobile computing, and IoT systems, likely supported by competitive grants from Singaporean and international funding agencies. His role as Deputy Director of a research center indicates leadership in managing research teams and collaborative efforts. Labs and Teams: He leads a research group focused on mobile and distributed computing, deep learning, and computer vision. His team actively publishes in top-tier conferences and journals, working on projects involving RF sensing, acoustic platforms, visible light communication, and privacy-aware systems. The group collaborates with researchers both within NTU and internationally, particularly in Canada and China.

• GNSS (Global Navigation Satellite Systems)
• Indoor Positioning Systems
• Pseudolite Technology
• +7 more

Baoguo Yu is a Professor at the Department of Navigation Science and Technology within the School of Geodesy and Geomatics at Wuhan University. He has established himself as a leading researcher in the field of navigation and positioning systems, with over 70 publications spanning from 2016 to 2025 across prestigious journals including IEEE Transactions on Instrumentation and Measurement, Sensors, and IEEE Access. His research interests primarily focus on GNSS (Global Navigation Satellite Systems), indoor positioning technologies, pseudolite systems, and signal processing for navigation applications. Professor Yu's work demonstrates a strong emphasis on practical implementations, particularly in challenging environments where traditional GNSS signals are degraded or unavailable. His recent publications reveal a growing integration of machine learning techniques with traditional navigation approaches, reflecting the evolving nature of the field. Analysis of his 15 most recent publications shows a clear research trajectory toward high-precision positioning solutions, with particular attention to indoor navigation challenges, LEO satellite navigation augmentation, and robust positioning under interference conditions. His work spans theoretical algorithm development to practical implementations for smartphones, unmanned vehicles, and specialized navigation equipment. Professor Yu maintains extensive collaborations with researchers across China, particularly with scholars from Wuhan University and other prominent Chinese institutions. His frequent co-authors include Lu Huang, Xingli Gan, Heng Zhang, and Chuanzhen Sheng, suggesting well-established research teams working on complementary aspects of navigation technology. His research has significant applications in autonomous vehicles, pedestrian navigation, emergency response systems, and any domain requiring precise positioning where GPS signals are unreliable. The consistent publication output in high-impact journals demonstrates his active research program and continued contributions to advancing navigation science.

Claudio TalaricoView profile

Professor

• VLSI Integrated Circuits
• Embedded Systems
• Hardware/Software Co-design
• +3 more

Claudio Talarico is a Professor in the Department of Electrical and Computer Engineering at Gonzaga University's School of Engineering & Applied Science. He holds a Ph.D. in Electrical Engineering from the University of Hawaii and an M.S. from the University of Genoa, Italy. With industry experience at Infineon Technologies, IKOS Systems, and Marconi Communications, his expertise spans VLSI design, embedded systems, and hardware/software co-design. His research focuses on: Low-power/high-performance VLSI circuits Embedded system-on-chip design Computer-aided design methodologies Wireless communication systems Hardware/software co-design Recent publications (2020-2024) demonstrate strong emphasis on: Beam steering architectures for 5G/wireless systems Angle-of-arrival estimation techniques Time synchronization in body area networks Health monitoring platforms FPGA/digital implementations He teaches core courses including VLSI Circuits & Systems, Computer Hardware Design, and Digital Systems. No awards or current students are documented in available materials.

Mitchell ThorntonView profile

Professor

• Cyber Security
• Quantum Informatics
• Quantum Photonics
• +5 more

Mitchell Thornton is the Cecil H. Green Chair of Engineering and Professor at Southern Methodist University (SMU) , with dual appointments in the Lyle School of Engineering ’s Department of Electrical and Computer Engineering and Computer Science (by Courtesy). He serves as Executive Director of the Darwin Deason Institute for Cyber Security and Program Director for SMU’s interdisciplinary M.S. in Data Engineering. Education Ph.D. in Computer Engineering, SMU (1995) MS in Computer Science, SMU (1993) MS in Electrical Engineering, University of Texas at Arlington (1990) BS in Electrical Engineering, Oklahoma State University (1985) Thornton’s research spans cybersecurity , quantum informatics , silicon photonics , and data processing algorithms . His recent publications focus on quantum circuit optimization, CMOS transceiver design, and silicon photonic components, reflecting his expertise in both classical and quantum computing systems. His work demonstrates interdisciplinary trends merging quantum computing with semiconductor design , cybersecurity applications in unmanned aerial systems , and innovations in high-speed communication hardware . Keywords include Quantum Algorithms , CMOS Technology , and Photonic Circuits . Scientific Awards Inducted into IEEE TCMVL Multiple-Valued Logic Researcher Hall of Fame (2022) Rotunda Outstanding Professor Award (2022) Gerald J. Ford Senior Research Fellow (2005) Texas Instruments Outstanding Researcher (1996-1998) IEEE-USA Citation of Honor (2009) Thornton has secured over $10M in sponsored research funding from federal agencies and industry partners. He holds P.E. licenses in Texas, Mississippi, and Arkansas, along with a General Radiotelephone Operator License from the FCC. His lab, the Darwin Deason Institute for Cyber Security, drives cutting-edge research in secure systems and quantum technologies.

• Wireless Sensor Networks
• Target Localization
• Target Tracking
• +12 more

Slavisa Tomic is an Associate Professor at Universidade Lusófona, Centro Universitário de Lisboa (UL-CUL), and a senior researcher at COPELABS (Communication Technologies and Systems Laboratory). His work focuses on wireless sensor networks, target localization, tracking, and UAV navigation in GPS-denied environments. His research interests include: Target localization and tracking using hybrid measurements (RSS, AoA, TOA) Robust estimation techniques (ML, MAP, Kalman filters, particle filters) Optimization methods (SOCP, SDP, GTRS) for localization UAV navigation in satellite-less environments Machine learning applications in signal processing and localization Security in wireless networks (malicious node detection, spoofing) His recent publications show a strong trend toward integrating deep learning (e.g., LSTM, CNN) with classical signal processing for autonomous navigation and localization. He has developed tools like AutoNAV for UAV simulation and contributed to embedded implementations of optimization algorithms. His work spans both theoretical algorithm development and practical implementation. Scientific Recognition: Ranked among the top 2% most influential scientists globally in Information and Communication Technologies (sub-area: Networks and Telecommunications) from 2019 to 2024, based on Stanford University methodology. He has advised or collaborated on research involving energy-based acoustic localization, test-bench development for embedded systems, and drone-based environmental monitoring. He has also contributed to special issues and book chapters in his domain. His research is supported by active publication in IEEE, Sensors, and other high-impact journals. He is affiliated with COPELABS, a research lab focused on communication technologies, where he contributes to projects involving secure, robust, and intelligent wireless systems.

• Networked Systems
• Software-Defined Networking
• Data Center Architectures
• +7 more

Professor Brighten Godfrey is a faculty member in the Department of Computer Science and an affiliate of the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He earned a Ph.D. in Computer Science from UC Berkeley (2009) and a B.S. from Carnegie Mellon University (2002). His research spans networked systems with a focus on low-latency networking , software-defined networks , microservices , and machine learning for networks . Ph.D. (2009) and B.S. (2002) in Computer Science Professor at UIUC since 2021 Technical Director at VMware (acquired Veriflow in 2019) His recent publications address microservice tracing , cluster verification , and mobile acceleration , with high-impact applications in XR systems and low-latency networks . Awards include the ACM SIGCOMM Rising Star Award , Sloan Research Fellowship , and multiple best paper and dataset awards . He has chaired program committees for SIGCOMM and HotNets . Teaching honors include Excellent Teacher and Outstanding Advising Awards . His research group has produced alumni now at institutions like Meta, Google, and ETH Zurich. Current projects include Service Layer Traffic Engineering (SLATE) and Learning-Based Congestion Control (Aurora, PCC).

• Wireless Networking
• WiFi Technology
• Indoor Localization Systems
• +5 more

Adam Wolisz is a Professor at Berlin University of Technology (Technische Universität Berlin) in the Faculty of Electrical Engineering and Computer Science, specifically within the Department of Telecommunication Systems. With a prolific publication record spanning from 1987 to the present day, including publications as recent as 2024, he has established himself as a leading researcher in wireless networking and communication systems. His work demonstrates sustained academic activity and significant contributions to the field of telecommunications. Professor Wolisz's research primarily focuses on wireless networking technologies, with particular emphasis on WiFi systems, vehicular communications, indoor localization, and cross-technology communication. His work often integrates machine learning techniques with traditional networking approaches, as evidenced by numerous publications on reinforcement learning applications for resource allocation in wireless networks. He has made notable contributions to the understanding of WiFi fingerprinting for indoor positioning, vehicular communication scheduling, and spectrum management in coexisting wireless technologies. His research shows a clear evolution from fundamental networking principles to more sophisticated AI-integrated approaches in recent years. The analysis of his publication trends reveals a consistent focus on practical wireless communication challenges, with increasing sophistication in methodology. From 2019-2024, his work shows a strong emphasis on applying AI and machine learning techniques to solve traditional networking problems, particularly in vehicular communications and spectrum management. His research group has produced significant work on cross-technology communication between LTE and WiFi, vehicular networking solutions using reinforcement learning, and privacy-preserving approaches for contact tracing during the pandemic. The breadth of his publications across top-tier conferences (INFOCOM, VTC, WoWMoM) and journals (IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing) demonstrates the high impact of his research. Professor Wolisz has mentored numerous researchers who have become frequent collaborators, including Filip Lemic, Anatolij Zubow, Vlado Handziski, and Taylan Sahin. His research group appears to be part of the larger networking research community at TU Berlin, with connections to various European research initiatives. His work often addresses real-world networking challenges with practical implementations, suggesting strong industry relevance and potential technology transfer.