Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Steve Margulis is a Professor in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (UCLA). His research focuses on surface hydrology and hydrometeorology, particularly in snow-dominated mountainous regions. University: University of California, Los Angeles Department: Civil and Environmental Engineering Research Interests: His work aims to improve characterization of hydrologic states and fluxes through remote sensing and modeling, with applications to water resource management and environmental hazard mitigation. Key areas include: Land surface and atmospheric boundary layer modeling Remote sensing of snow and soil moisture Data assimilation techniques Hydroclimatology and climate change impacts Publications Trends show consistent focus on snow hydrology, remote sensing applications, and data assimilation frameworks across diverse mountain regions including the Sierra Nevada, Andes, and High Mountain Asia. Recent work emphasizes model improvements through spatially distributed precipitation bias correction and satellite data integration. Students: Mentoring includes Ph.D. candidates Yiwen Fang, Yufei Liu, Jacob Schaperow, and Manon von Kaenel. Group alumni include notable researchers at institutions like NASA, Ohio State University, and University of Colorado. Research Projects are funded by NSF, NASA, and DOE/CERC-WET. Key initiatives include: Snow reanalysis frameworks for Sierra Nevada and Andes Investigations into land-atmosphere interactions Global frameworks for SWOT data products
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Houtan Jebelli is an Assistant Professor in Civil and Environmental Engineering at the University of Illinois. His research focuses on construction robotics, human-robot collaboration, and wearable sensing technologies for worker health and safety monitoring. He directs research on exoskeleton applications, fall risk detection, and AI-enabled monitoring systems for construction environments. Research interests include: Human-robot collaboration in construction sites Physiological monitoring using wearable sensors Exoskeleton technology and ergonomic assessment AI-enabled safety management systems Robotic inspection and defect detection Jebelli's recent work demonstrates strong interest in bridging robotics with occupational health, particularly studying cognitive and physiological impacts of wearable robotics. His publications frequently address real-time monitoring systems and human factors in construction technology adoption.
Babak Taati is an Associate Professor at the University of Toronto (UofT), affiliated with the Department of Computer Science, Institute of Biomedical Engineering (BME), and Rehabilitation Sciences Institute (RSI). He holds the Barbara G. Stymiest Chair in Rehabilitation Technology Research at UHN and is a Senior Scientist at KITE, UHN's research arm. He is also a Vector Institute Faculty Affiliate. His work focuses on applying computer vision and machine learning to healthcare challenges, particularly in rehabilitation technologies for aging populations, gait analysis, fall prevention, and dementia care. Taati leads the Aging team at KITE and is affiliated with the Intelligent Assistive Technology and Systems Lab (IATSL) and the Computational Vision group. Research interests include noninvasive monitoring of health conditions such as Parkinsonism, sleep apnea, and pain management in older adults. His contributions span datasets like the Toronto NeuroFace Dataset and TOAGA archive, emphasizing clinical applications. Taati has taught CSC420 (Image Understanding) repeatedly and has organized workshops on topics like AI in dementia care and ambient intelligence in healthcare. His awards include the TRI-UHN Best Paper Award (2017) and the AMS Healthcare Fellow in Compassion and Artificial Intelligence (2021). He has advised students like Michael Li and collaborates on grants involving federal initiatives (e.g., FedDev Ontario). His research bridges theoretical computer science with practical healthcare solutions, addressing unmet clinical needs through vision-based systems.
David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.
Christopher Goyne is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia (UVA) and Director of the UVA Aerospace Research Laboratory. He holds a B.Eng. (1991) and Ph.D. (1999) in Mechanical Engineering from the University of Queensland, Australia. His research focuses on hypersonic propulsion, scramjet technology, instrumentation development, and advanced manufacturing. He leads the UVA Hypersonics Research Complex and is a key figure in the University Consortium for Applied Hypersonics. Goyne’s work includes contributions to NASA’s Hyper-X Program and the HyShot scramjet flight test program. He is an Associate Fellow of the AIAA and serves on editorial and advisory boards for journals and organizations such as the Shock Waves journal and Virginia’s Aerospace Advisory Council. Education: B.Eng. (Mechanical Engineering, University of Queensland, 1991); Ph.D. (Mechanical Engineering, University of Queensland, 1999). Research Interests: Hypersonics and scramjet propulsion Diagnostic techniques (e.g., laser-based methods, optical emission spectroscopy) Wind tunnel and flight testing Controls and adaptive systems for hypersonic flow paths Advanced manufacturing for aerospace components Awards: Recipient of the 2023 James C. McDaniel Fellow Award and 2022 Outstanding Researcher Award. Holds leadership roles in AIAA committees, including past Chair of the HyTASP Program Committee. Recognized with the Sigma Gamma Tau Outstanding Aerospace Professor Award (2006) and multiple research fellowships. Grants and Projects: Funded by NASA, the Air Force Office of Scientific Research, and industry partners. Leads UVA’s contributions to hypersonic ground and flight testing, including sensor development and combustion efficiency studies. Labs and Teams: Directs the UVA Aerospace Research Laboratory, collaborating on projects such as the UVA Hypersonics Research Complex and the University Consortium for Applied Hypersonics. Advises student chapters of AIAA and Sigma Gamma Tau.
Bärbel Finkenstädt Rand is a Senior Tutor at the Warwick Medical School , University of Warwick, with extensive research contributions at the intersection of statistics, machine learning, and biomedical sciences. Her work focuses on developing advanced methodologies for analyzing temporal and spatio-temporal data, particularly in circadian rhythms and disease dynamics. Research Themes : Bayesian inference, Hidden Markov Models, circadian rhythm stability, transcriptional bursting, and wearable sensor data analysis. Collaborations : Chronotherapy Group at Warwick, Université Paris-Saclay, and interdisciplinary teams across medicine, genetics, and computational biology. Publications reveal a strong emphasis on circadian health monitoring, gene expression dynamics, and epidemic modeling using stochastic frameworks. Her recent work prioritizes personalized medicine applications through telemonitored biomarkers and IoT platforms . Methodological Innovations include spline-based HMMs, distributed delay systems, and harmonic modeling for nonstationary time series. Applications span oncology, sleep medicine, and population ecology.
Professor Washington Yotto Ochieng serves as Head of the Department of Civil and Environmental Engineering and Chair Professor in Positioning and Navigation Systems at Imperial College London. He directs the Centre for Active Resilience and Security (CARS) and maintains key affiliations with the Centre for Systems Engineering and Innovation, Centre for Transport Engineering and Modelling, Institute for Molecular Science and Engineering, and Space Lab. His extensive advisory roles include the Science Museum Group Board of Trustees, Royal Institute of Navigation Presidency, and Royal Academy of Engineering Africa Steering Committee. His educational background includes a BSc (First Class) in Engineering from the University of Nairobi and MSc (Distinction) and PhD in Civil Engineering from the University of Nottingham. He received an honorary DSc from Technical University of Kenya in 2023. Ochieng's research pioneers critical infrastructure resilience, user-centric mobility, and positioning/navigation/timing (PNT) systems. He has designed satellite navigation systems (including Europe's EGNOS and GALILEO) for multi-domain applications and advanced Air Traffic Management and Intelligent Transport Systems. His work integrates geomatics, transportation engineering, and sustainable mobility to solve global urban infrastructure challenges, with recent emphasis on decarbonization and AI-driven solutions. His 2024-2025 publications reveal strong trends in sustainable transportation decarbonization, AI-optimized traffic management, and resilient urban positioning systems. Research focuses on hydrogen fuel cell trains, carbon-efficient aviation, and deep reinforcement learning applications for emission reduction, demonstrating interdisciplinary integration of engineering, environmental science, and artificial intelligence to address climate challenges. Fellow of the Royal Academy of Engineering (2013) Harold Spencer-Jones Gold Medal from Royal Institute of Navigation (2019) Doctor of Science (honoris causa) from Technical University of Kenya (2023) Elder of the Order of the Burning Spear (EBS) from Kenya (2023) Commander of the Order of the British Empire (CBE) (2024) Ochieng provides strategic guidance to UK Government bodies (Government Office for Science, Department for Transport, FCDO), European Parliament, and European Court of Auditors. His advisory work shaped the Blackett Review on Satellite-derived Time/Position, UK Space Strategy, and Future of Mobility report. He chairs the Science Museum London Advisory Board and leads FCDO's Sustainable Urban Economic Development program in Africa, with significant grant influence through UK National Physical Laboratory and Department for International Development. He directs the Centre for Active Resilience and Security (CARS) and leads Space Lab initiatives, focusing on mission-critical PNT systems and infrastructure resilience. His teams collaborate with international consortia including RTCM Special Committee 134 and US Institute of Navigation, developing next-generation navigation solutions for safety-critical applications across transport, aviation, and urban environments.
Jinsang Kim is a Professor in the Department of Materials Science and Engineering at the University of Michigan, with affiliations in Biomedical Engineering (BME). His research focuses on bio-micro/nanotechnology, bio-nanomaterials, and biomedical imaging technologies. He specializes in developing advanced materials for applications such as retinal hypoxia detection, organic light-emitting diodes, and sensor technologies. His work integrates polymer chemistry, organic electronics, and biomedical engineering to create innovative materials for medical diagnostics, optoelectronics, and nanotechnology. Key research areas include surface functionalization strategies, organic phosphorescent nanosensors, and stimuli-responsive materials. Kim’s publications emphasize cutting-edge advancements in organic phosphorescence, polymer design for high thermal conductivity, and biomedical imaging tools. His contributions span from fundamental material science to applied biomedical solutions, with a focus on translating discoveries into practical applications.
Jennifer Hicks is the Executive Director of the Wu Tsai Human Performance Alliance at Stanford University, focusing on collaborative research to advance understanding of human performance through biomechanical modeling and machine learning. She also serves as Director of Research for the NIH-funded Mobilize Center and Restore Center, integrating engineering tools into rehabilitation science. Her work emphasizes predictive modeling of surgical outcomes, mobile health data analysis, and exoskeleton design. Dr. Hicks leads software development for the OpenSim project, guiding its user-centric evolution and promoting open-source biomedical tools. Her research spans musculoskeletal dynamics, wearable technology, and clinical applications of AI. Key contributions include smartphone-based motion capture (OpenCap) and foundational datasets like AddBiomechanics. She co-develops training programs for interdisciplinary teams and advocates for large-scale health data utilization. Dr. Hicks' efforts bridge academia and industry, supporting translational research in neurorehabilitation, sports performance, and chronic disease management.
Prof. Harald Sternberg is a distinguished academic at HafenCity University Hamburg, holding the position of University Professor for Hydrography and Geodesy. His affiliations include the Department of Geodesy and Geoinformatics, where he leads research in hydrographic education and advanced geomatics technologies. He previously served as Vice President for Teaching and Studies (2009-2022) and Acting President (2010) of HCU. Education: Ph.D. in Geodesy from University of the Bundeswehr Munich (1999), specializing in trajectory determination of land vehicles using hybrid systems. Early career included roles as scientist at Bundeswehr University (1991-2001) and academic leadership at HAW Hamburg (2005-2009). Research focuses on underwater mapping, navigation systems, and sensor integration. Key projects include: Level 5 Indoor Navigation (5G-based positioning), hydrothermal vent exploration using deep-towed multibeam systems, and low-cost mobile mapping solutions. He also investigates smartphone-based inertial navigation and autonomous underwater vehicles for infrastructure monitoring. Publications span underwater vision systems, satellite-derived bathymetry, and 3D point cloud analysis. Over 200 peer-reviewed articles and book chapters reflect expertise in geomatics applications. Current research emphasizes 5G-enabled indoor navigation and environmental sensor networks. Grants include BMWK-funded autonomous deep-sea monitoring and BGR exploration projects in the Indian Ocean. His lab develops innovative tools like the HOMESIDE sled for seafloor surveys. Supervises Ph.D. research on hydrothermal vent analysis and data-driven inertial localization.
Dieter Uckelmann serves as Professor of Information Logistics and Scientific Director of the Institute for Applied Research at Stuttgart University of Applied Sciences (HFT Stuttgart). He holds multiple leadership positions including Spokesperson for the research focus 'Smart Technologies, Processes and Methods' at HFT Stuttgart since March 2023 and Scientific Director of the Institute for Applied Research since September 2023. His academic journey began with mechanical engineering studies in Braunschweig, followed by doctoral research at the University of Bremen focusing on 'Quantifying the Value of RFID and the EPCglobal Architecture Framework in Logistics.' Uckelmann's research spans Internet of Things applications across Industry 4.0, logistics, smart buildings, and smart cities, with significant contributions to educational technology including learning analytics and AI in teaching. His work bridges technical innovation with practical implementation, particularly in digital transformation of laboratories and smart city infrastructure. He has led numerous research projects including KNIGHT (AI for teaching), InDeckLe (earth composite ceiling systems), iCity initiatives, and DigiLab4U (online laboratories). His publication record shows a clear progression from foundational RFID and IoT research toward emerging technologies like the Industrial Metaverse, 5G applications, and AI-driven educational systems. Recent work demonstrates strong integration of physical and digital systems, particularly in urban environments and educational contexts, with increasing emphasis on sustainability and energy efficiency applications. Co-editor of International Journal of RF-Technologies: Research and Applications Member of PhD Association BW, Research Unit III Computer Science and Electrical Engineering Mentor in the HAWCareer mentoring program Program Committee Member for IEEE RFID, IEEE/ITMC, AIET, and other major conferences Associate Editor for Journal of Online and Biomedical Engineering Professor Uckelmann actively mentors students and researchers, with his team contributing to projects across smart city infrastructure, digital learning platforms, and industrial IoT applications. He leads the Industrie 4.0 Laboratory which focuses on industrial IoT applications, digital twins, and the industrial metaverse, with research spanning RFID, RTLS, wireless sensor networks, AR/VR, and IoT architectures. His work extends to international collaborations including visiting professorships at Auburn University and the University of Parma.
Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.
