Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Mikael Gidlund is a Full Professor of Computer Engineering at Mid Sweden University in Sundsvall and holds an adjunct professorship at Beijing Jiaotong University, China. He serves as head of the Computer Engineering subject and program manager for the international MSc program in Computer Engineering. His academic journey includes a Ph.D. in Electrical Engineering from Mid Sweden University (2005), followed by roles at ABB Corporate Research (2008-2014) where he led wireless technologies research. Dr. Gidlund's research spans Wireless Communication, Industrial IoT, 5G/6G Networks, and Network Security . His group focuses on AI/ML for beyond-5G wireless communication, time-critical industrial applications, and IoT security. Current research themes include Future Wireless Networks (5G/6G) using AI/ML, Time-and mission-critical wireless communication, Industrial IoT, and IoT Security. His work demonstrates strong interdisciplinary connections between wireless systems, industrial automation, and security. His publication portfolio includes over 200 scientific articles and 20+ patents. Recent publications show a clear trend toward AI/ML integration in wireless systems, NOMA techniques, RIS technologies, and security solutions for industrial applications. The research output demonstrates strong international collaboration across six continents. Best Paper Award at IEEE International Conference on Industrial IT (2014) Co-author of IEEE Sweden VT-COM-IT Joint Chapter Best Student Journal Paper Award (2022) Dr. Gidlund actively mentors 6 current PhD students and has supervised 16 former PhD students who now hold positions at institutions including Ericsson, Lund University, Aalborg University, and Mid Sweden University. His research is supported by multiple active projects including IRS TransTech, NIIT, ENSURE 6G, and TRUST. He collaborates with institutions worldwide including City University of Hong Kong, Iowa State University, Kyung Hee University, and KTH Royal Institute of Technology. His research group maintains strong industry connections through projects with ABB, Ericsson, and other industrial partners, focusing on practical implementations of wireless technologies for industrial automation and critical infrastructure.
Thomas DC Little is a Professor of Electrical and Computer Engineering in the College of Engineering at Boston University. He serves as the Associate Dean for Educational Initiatives, driving the growth of the engineering master’s program and enhancing pedagogy through mobile and cloud technologies. Additionally, he is the Associate Director and Principal Investigator of the National Science Foundation Smart Lighting Engineering Research Center (LESA), a multi-institutional effort advancing visible light communication and smart lighting systems. Professor Little's research centers on ubiquitous computing and communications, with a focus on using optical cells to expand wireless data capacity for mobile devices. He pioneers ambient intelligence that enables environments to anticipate human needs. His key areas include Visible Light Communications (VLC), Optical Wireless Communications, Indoor Positioning Systems, and Smart Lighting. By integrating lighting infrastructure with communication networks, his work addresses the growing demand for wireless data and enables energy-efficient, responsive smart buildings and urban environments. Analysis of his recent publications (2019-2024) shows a strong trend toward occupancy sensing, indoor positioning, and hybrid RF/VLC networks. His team develops innovative solutions for people counting, zone-based positioning, and interference mitigation in dense optical wireless environments. There is increasing integration of machine learning for security and optimization, with applications in energy-efficient buildings and user-centric smart spaces. Scientific awards received by Professor Little include: Janetos Award for Continuous Indoor Air Quality Assessment for BU Buildings (2025) Professor Little actively mentors graduate students and postdocs, with notable advisees including Iman Abdalla (awarded Best Computer Engineering Dissertation, 2020-2021) and the MenuNav team (Societal Impact Award for a navigation app for the blind). He has secured significant research funding, including a $1M Department of Energy/ARPA-E project for occupancy sensing to reduce energy costs in commercial buildings and grants for indoor air quality sensor development. He leads the NSF Smart Lighting ERC (LESA), which develops COSSY people counting technology, sensory lighting systems, and dynamic light control applications. His team collaborates with industry and has spun off Helux Technologies, Inc. to commercialize dynamic lighting control. Current projects focus on creating safe, energy-efficient buildings through advanced sensor integration and wireless communication.
Areg Danagoulian is an Associate Professor of Nuclear Science and Engineering at the Massachusetts Institute of Technology (MIT), where he conducts research at the intersection of nuclear physics and security applications. His work focuses on developing technological solutions for nuclear nonproliferation, arms control, and cargo security. Dr. Danagoulian earned his PhD in Experimental Nuclear Physics from the University of Illinois at Urbana-Champaign, where his thesis focused on real Compton scattering on the proton at 2-6 GeV to probe the proton's internal structure. Following his PhD, he worked as a postdoctoral researcher at Los Alamos National Laboratory and then as a senior scientist at Passport Systems, Inc. (PSI), where he developed the Prompt Neutron from Photofission (PNPF) technique for detecting shielded fissionable materials in cargo traffic. His research interests span multiple critical areas in nuclear security, including arms control verification technologies, nuclear nonproliferation methods, cargo security systems, and nuclear detection techniques. Dr. Danagoulian's work on nuclear resonance phenomena for warhead verification represents groundbreaking contributions to the field of nuclear disarmament verification. Dr. Danagoulian's research has earned him significant recognition, including: Fellow of the American Physical Society, Forum on Physics and Society (2025) - "For seminal technological contributions in the field of arms control and cargo security, which significantly benefit international security" Arms Control Association's Arms Control Person(s) of the Year award (2020) - "For developing an innovative new nuclear disarmament verification process using neutron beams" American Nuclear Society Radiation Science and Technology Award (2019) - "For technology-critical contributions exploiting nuclear resonance phenomena for warhead verification in nuclear disarmament and nuclear detection techniques in cargo security" In addition to his research, Dr. Danagoulian is actively involved in teaching and mentoring. He serves as faculty co-director for MIT's MISTI Eurasia program and teaches several graduate courses including Nuclear Detection Laboratory (22.09, 22.90), Advanced Nuclear Laboratory (22.s902), and Applied Nuclear Physics (22.101). His teaching approach emphasizes hands-on laboratory experience to prepare students for real-world nuclear detection challenges. Dr. Danagoulian leads the Laboratory for Applied Nuclear Physics (LANPh) at MIT, where his team develops innovative technologies for nuclear security applications. Current research directions include nuclear resonance transmission analysis for material identification, portable detection systems for cargo security, and cryptographic approaches to nuclear warhead verification that protect sensitive information while enabling verification.
Abraham D. Stroock is an Assistant Professor at the School of Chemical and Biomolecular Engineering, Cornell University, since 2003. He holds a B.A. in Physics (Cornell, 1995), M.S. in Solid State Physics (University of Paris, 1997), and Ph.D. in Chemical Engineering (Harvard, 2002). His research bridges microfluidics, biophysics, and sustainable energy. Education: B.A., Physics, Cornell University (1995) M.S., Solid State Physics, University of Paris VI/XI (1997) Ph.D., Chemical Engineering, Harvard University (2002) The Stroock Lab explores micrometer-scale chemical processes inspired by plant biology, focusing on liquid manipulation, negative-pressure water properties, vascular development in tissue engineering, and fluid mechanics in microsystems. Key technologies include microtensiometers and nanoporous membranes . His recent work (2025-2024) spans optical phenotyping using soft robotics, hydromechanical signaling in plants, tissue scaffolds for regenerative medicine, and advanced models for transpiration control. These studies integrate bioengineering, nanotechnology, and environmental science. Scientific Awards: Van Ness Lectureship (2010) Camille Dreyfus Teacher Scholar Award (2009) NSF CAREER Award (2008) MIT Technology Review TR35 (2007) ONR Young Investigator Award (2004) 3M Non-Tenured Faculty Award (2006) Beckman Young Investigator Award (2006) Dreyfus New Faculty Award (2003) He has led projects on superheated loop heat pipes , phosphorescent oxygen sensors , and synthetic tree-on-a-chip systems. His teaching includes advanced biomolecular engineering (ChemE 7770), and he contributes to policy through the Chemistry and Chemical Biology (CBE) Policy Committee.
Julian Knight is a Professor of Genomic Medicine at the University of Oxford, with affiliations including the Centre for Human Genetics , Merton College , and leadership roles in the NIHR Oxford Biomedical Research Centre and Central and South NHS Genomic Medicine Service . His work bridges clinical practice and research, focusing on translational genomics. Principal Investigator Deputy Director, Centre for Human Genetics Honorary Consultant Physician Tutor and Fellow, Merton College Director, Medical Sciences Division Graduate School Genomic Medicine Theme Lead, NIHR Oxford BRC Research interests include mechanisms of dysregulated immune responses in sepsis , autoimmune disease , and infection . Key contributions involve RNA signature stratification for sepsis outcomes and HLA allele associations in COVID-19 immunogenicity. Current work explores genetic/epigenetic modulators of innate immunity and causal relationships in multi-omic datasets. Recent publications highlight diverse applications of his group’s work: from pleural infection endotyping (2025) to TLR7 variants in severe COVID-19 (2024), with methodological advancements in single-cell demultiplexing (2024) and pathway analysis (2025). Keywords span genomic medicine , immunology , and multi-omic integration . Knight’s leadership extends to clinical implementation of genomics, education (DPhil/MSc programs), and public engagement. Collaborations span institutions including Imperial College , Wellcome Sanger Institute , and Queen Mary University of London .
Koray Tahiroglu is a University Lecturer at Aalto University's School of Arts, Design and Architecture, specializing in Sound and Music Computing. His work bridges artificial intelligence, digital musical instruments, and embodied interaction, with a focus on deep learning applications in audio synthesis and human-AI creative collaboration. His research explores New Interfaces for Musical Expression (NIME), sonic interaction, and physical computing. He collaborates with SOPI Research Group and Google Brain Team (Magenta) on AI-driven artistic innovation. Recent Publications : 2024 studies on dance-sound cross-correlation and intra-action frameworks; 2023 work on AI-terity and deep learning syllabi; 2022 explorations of GAN synthesis, musical expectations, and lifeworld sonification. Scientific Awards : Co-Creative Artificial Intelligence of Music (2022) 2010 grant for scientific publications and artistic activities 2017 Honorable Mention for mobile cultural heritage research Tahiroglu contributes to digital art education and leads projects at Media Lab Helsinki, advancing sonic interaction and generative audio systems.
Kwanghee Jeong is a Research Fellow at the University of Western Australia , affiliated with the Fluid Science and Resources research group within the School of Engineering and Chemical Engineering Department . His work focuses on energy transport, decarbonisation technologies, and flow assurance. Education: PhD in Chemical Engineering (UWA, 2020), BSc in Mechanical Engineering (Dongguk University, 2014) Research Themes include: Flow Assurance for hydrogen, CO2, and natural gas pipelines Carbon Capture & Emissions Management (MOFs, Raman spectroscopy) Cryogenic Hydrogen Process Engineering (liquefaction, boil-off gas) Cold Energy Utilisation and Waste Heat Recovery Hydrate Formation Kinetics via Acoustic Levitation Article Trends reflect expertise in: Using Raman spectroscopy for real-time adsorption and phase transition analysis Developing Joule-Thomson loops to simulate pipeline conditions Optimizing Metal-Organic Frameworks for GHG separation Advancing hydrogen liquefaction efficiency through catalysis Addressing microplastics and hydrate nucleation via spectroscopic methods Scientific Awards : Best Poster Award (2023) - Natural Gas UWA Travel Award (2017) ARC PhD Scholarship (2016) He contributes to UN Sustainable Development Goals via decarbonisation research and has collaborated with Chevron, Woodside Energy, and Curtin University. His technical skills include Aspen HYSYS, OLGA simulations, HAZOP studies, and Differential Scanning Calorimetry (DSC).
Brandon Schmandt is a Professor in the Department of Earth, Environmental and Planetary Sciences at Rice University, where he leads research using seismology to investigate Earth systems. His work integrates interdisciplinary approaches, data science, and numerical modeling to study tectonic processes, magmatic systems, and environmental interactions. His educational background includes a PhD in Geological Sciences from the University of Oregon (2011) and a BA in Environmental Studies from Warren Wilson College (2006). Dr. Schmandt's research focuses on seismology, tectonics, volcanology, and surface processes , with emphasis on seismic imaging of subsurface structures. His group employs innovative time-series analysis and field projects to resolve geologic history and contemporary Earth dynamics, particularly examining fault zones, magmatic reservoirs, and deep convective processes. Key methodologies include dense seismic arrays and machine learning applications. Analysis of his recent publications (2023-2025) reveals dominant trends in seismic event discrimination (earthquakes vs. explosions), magmatic system imaging (Yellowstone, Cascades), and global mantle structure studies. There is strong emphasis on induced seismicity, machine learning applications, and high-resolution imaging of Earth's discontinuities using dense arrays. His distinguished honors include: Aki Award of the AGU Seismology Section GSA Donath Medal AGU Macelwane Medal Body Dr. Schmandt directs an active research group conducting field projects across diverse settings including the Raton Basin, Yellowstone, Antarctica, and the Caribbean. While specific student advisees and grant details aren't provided in available materials, his group's work involves collaborative data collection, advanced computational modeling, and development of novel seismic analysis techniques applicable to both natural and anthropogenic seismic sources. The research program maintains focus on magmatic systems beneath volcanic regions, induced seismicity mechanisms, and global mantle structure using dense node arrays and interdisciplinary approaches to address fundamental questions in Earth dynamics.
Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.
Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Dr. Kibret Mequanint is a full Professor at Western University's Department of Chemical and Biochemical Engineering, with cross-appointments in Biomedical Engineering. Holding a PhD from University of Stellenbosch and postdoctoral experience at Technical University of Darmstadt and McMaster University, his research bridges polymer science, materials engineering, and life sciences with applications in Biomaterials , Tissue Engineering , and Regenerative Medicine . His work spans both fundamental and translational research in cell-material interactions , polymer biomaterial design , and therapeutic radiation dosimeters , with technologies transferred to commercial applications. Leading scholar and educator with awards from NSERC, CIHR, and Western University Fellow of: American Institute for Medical and Biological Engineering (AIMBE), Ethiopian Academy of Sciences, International Union of Societies for Biomaterials Science and Engineering, Canadian Academy of Engineering Extensive editorial and panel service for NSERC, CIHR, and international journals His research program has produced over 170 refereed publications, focusing on conductive hydrogels , bioadhesives , and vascular tissue engineering . Recent work on endoscopy-deliverable bioadhesives and snake venom-derived hemostatic gels has attracted global media attention. He has served in leadership roles at the Canadian Biomaterials Society and university governance bodies including Senate and Board of Governors.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Marco Pirola is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin, Italy. He is a member of the Interdepartmental Center 'CleanWaterCenter@PoliTo' and actively contributes to research in high-frequency electronics and microwave engineering. His work focuses on power amplifiers, device characterization, and advanced microwave circuit design. Research Interests: Microwave power devices, GaN technology, 5G/mm-Wave applications, space communications, and smart pipeline monitoring systems. Awards: IEEE Fellow (since 2019), IEEE Senior Member. Recent Publications address topics like Ka-band MMIC amplifiers for SAR systems, broadband Doherty amplifiers using GaN, and harmonic analysis of current-mode power stages. His projects include STARGATE (European GaAs power architectures) and Millimetre-Wave GaN Radar for UAV detection. Teaching: He leads courses on 'Radio Frequency Integrated Circuits' and 'Advanced Devices for High Frequency Applications' at the Polytechnic University of Turin. Supervised PhD students include Wenjun Zhang and Abbas Nasri, who worked on III-V HEMT circuits and GaN power amplifiers.
