Russell Tessier is a Professor and Department Head of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on reconfigurable computing, FPGA architectures, and hardware security, with notable contributions in CAD algorithms for FPGAs, embedded systems, and multi-tenant FPGA vulnerability analysis. Education: B.S.C.S.E., Rensselaer Polytechnic Institute (1989) M.S. and Ph.D., Massachusetts Institute of Technology (1992 and 1999) Research Interests: Dr. Tessier's work spans FPGA security (e.g., side-channel attacks, power distribution vulnerabilities), reconfigurable cloud computing, and hardware acceleration for applications like SAR imaging and machine learning. His lab, the Reconfigurable Computing Group, develops open-source FPGA cores (e.g., FlexGrip GPGPU, DE4 NetFPGA) and explores cutting-edge security countermeasures. Awards and Honors: Chancellor's Leadership Fellow (2015-2016) NSF Information Technology Research Grant Lilly Teaching Fellow (2002-2003) Multiple College of Engineering Excellence Awards Grants and Projects: Active funding includes NSF SaTC grants on reconfigurable cloud security and NASA support for snowpack measurement systems. His research also addresses FPGA-based solutions for cybersecurity, such as intrusion detection and power-side channel mitigation. Labs and Teams: Leads the UMass Reconfigurable Computing Group, which collaborates on open-source FPGA tools, security frameworks, and embedded system designs. The group maintains platforms like the DE4 NetFPGA and FlexGrip architecture.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.
Majid Ghaderi is a Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His expertise spans network algorithms, secure communication, and machine learning applications in network control. He holds a Ph.D. in Computer Science from the University of Waterloo (2006), and M.Sc. and B.Sc. degrees in Software Engineering from Sharif University of Technology (2001 and 1999). Education: Ph.D. Computer Science, University of Waterloo, 2006 M.Sc. Software Engineering, Sharif University of Technology, 2001 B.Sc. Software Engineering, Sharif University of Technology, 1999 Research Interests: Dr. Ghaderi focuses on optimizing network algorithms, securing communication in distributed systems, and leveraging machine learning for network control. His work addresses challenges such as secure wireless protocols, SDN-based network management, and efficient resource allocation in data centers. He explores proactive traffic scheduling and anomaly detection in critical infrastructures like industrial control systems and vehicular networks. Publications Trends: His recent work emphasizes covert communication in heterogeneous networks, adaptive federated learning in edge environments, and low-overhead diagnostic systems for cloud networks. He also investigates cybersecurity defenses against hardware vulnerabilities and dynamic threat landscapes. Awards: Best in-session Presentation Award, IEEE INFOCOM 2018 Municipal Excellence Award, Government of Alberta 2018 Faculty of Science Excellence in Teaching Award 2012 Advising & Grants: While no specific advisees are listed, his research has been supported by grants focusing on network security, edge computing, and IoT applications. He teaches CPSC 441 (Computer Networks) and maintains an active lab focused on network systems and cybersecurity. Labs & Teams: His research group collaborates on projects involving software-defined networks, vehicular communication, and industrial IoT security. The team develops open-source tools for network monitoring and anomaly detection.
Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.
Dr. Yelda Turkan is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University, where she leads research in automation, computer vision, and machine learning for sustainable infrastructure. She holds a PhD from the University of Waterloo and dual BS degrees in Civil Engineering and Geomatics Engineering from Istanbul Technical University. Her work focuses on leveraging lidar, digital twins, and BIM to improve construction operations and decision-making in the built environment. She has secured over $4M in grants from NSF, FHWA, and other agencies, and currently leads the NSF Convergence Accelerator-funded 'Deep Reality' project for AI-driven infrastructure management. Education: Ph.D., Civil Engineering, University of Waterloo, 2012 M.S., Engineering Informatics & Remote Sensing, Istanbul Technical University, 2006 B.S., Civil Engineering (double major in Geomatics Engineering), Istanbul Technical University, 2005/2003 Professional Roles: Vice President, International Association for Automation and Robotics in Construction (IAARC) Chair, ASCE Computing Division Education Committee Associate Editor, ASCE OPEN Journal Her research emphasizes automation in construction quality control, infrastructure inspection via drones and lidar, and immersive education tools using VR/AR. Recent projects include automated curb ramp compliance analysis, wildfire impact modeling, and digital twin development for timber structures. She has published over 80 peer-reviewed articles and actively promotes computing integration in civil engineering education and professional practice.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Dr. Kezhi (Ken) Li is an Associate Professor of AI in Healthcare at the Institute of Health Informatics, University College London (UCL). He leads the AI for Health research group and has established himself as a leading expert in applying artificial intelligence to solve complex problems in healthcare, with over 130 publications in leading journals (total impact factor greater than 400). Dr. Li earned his Doctor of Philosophy from Imperial College of Science, Technology and Medicine in 2013, followed by research positions at the Medical Research Council (2015-2017), University of Cambridge (2014-2015), and Royal Institute of Technology (KTH) (2013-2014). His academic journey reflects a consistent trajectory from technical AI research toward increasingly healthcare-focused applications. Dr. Li's research focuses on solving physiological, medical, clinical, and operational problems in healthcare using AI techniques. His specific expertise includes AI in healthcare using electronic health records (EHR), biomedical time series analysis using monitors/wearables, diabetes management, large language models (LLM) in healthcare (especially mental health), patient flow optimization, and digital health with federated learning. His work bridges the gap between cutting-edge AI methodologies and practical healthcare applications, with a strong focus on improving patient outcomes and healthcare system efficiency. Analysis of Dr. Li's publication history reveals a strong emphasis on diabetes management technologies, particularly blood glucose prediction systems using advanced neural network architectures. More recently, his work has expanded into mental health applications of large language models, blockchain-based federated learning for healthcare data, and mortality prediction in critical care settings. His research demonstrates a clear evolution from purely technical AI development toward increasingly clinically impactful applications, with growing emphasis on explainability, privacy preservation, and real-world implementation challenges. Dr. Li has received numerous prestigious awards recognizing his contributions to healthcare AI: Best Application Award of IEEE Global Blockchain Conference (2025) Fellow of British Computer Society (2025) Fellow of the Royal Society for Public Health (2024) Healthcare Partnership of the Year category at the London Higher Awards (2024) ECR Promising Project Award (2023) Gallivan Award finalists (2022) Stylianos Kalaitzis PhD Award Winner (2022) HDR UK Team of the Year (COVID-19) Award (2021) As an educator, Dr. Li serves as the Director of MRes study (AI-enabled Healthcare Systems) at UCL. He leads multiple key modules including Healthcare Artificial Intelligence Journal Club, Dissertation in Artificial Intelligence Enabled Healthcare, and Advanced Machine Learning for Healthcare. His supervision extends across dissertation projects and junior researchers in his AI for Health group. His research has been supported by various grants, including those from HDR UK, focusing on translating AI innovations into practical healthcare solutions. Dr. Li leads the AI for Health research group (https://ai4hucl.github.io/ai4h_webs/), which comprises researchers with diverse expertise in machine learning, healthcare systems, and clinical domains. The group maintains strong collaborations with healthcare providers and industry partners to ensure their research addresses real-world healthcare challenges and can be effectively translated into clinical practice.
Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.
SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.
Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.
Scott McCabe is a Professor of Marketing at Birmingham Business School, University of Birmingham. He earned his PhD from the University of Derby in 2001, focusing on visitor motivations in the Peak District National Park, and holds an MA in Leisure and Tourism Studies from the University of North London (1993) and an HND in Leisure Studies from the University of Salford (1991). Current co-editor in Chief of the Annals of Tourism Research Editorial board member of Tourism Management Elected fellow of the International Academy for the Study of Tourism (2019+) His research spans social tourism , responsible tourism , tourist emotions , and socio-linguistic methodology , with significant work on wellbeing outcomes from supported holidays for disadvantaged families. He has contributed to Annals of Tourism Research , Journal of Travel Research , and other top-tier journals. Recent publications address topics like qualitative research sampling , tourism theory , dark tourism motivations , and smart destination engagement . His work combines tourism policy critique , consumer behavior analysis , and methodological innovation . Scientific awards include fellowships and leadership roles in international tourism research committees. He has served as VP for the International Sociological Association's Tourism Research Committee and co-chairs the Academy of Marketing's Tourism Marketing SIG.
Bing Zhou serves as an Assistant Teaching Professor in the Department of Geography and the John A. Dutton Institute for Teaching and Learning Excellence within the College of Earth & Mineral Sciences at The Pennsylvania State University, focusing on advancing geospatial education and research. His educational background includes: Ph.D. in Geography from Texas A&M University Dr. Zhou pioneers research in geospatial data science and GeoAI, developing advanced algorithms to extract insights from big geospatial data (crowd-sourced data, mobility data, social media) for climate resilience and disaster response. He founded the revolutionary field of Responsible GIScience, which integrates ethical principles, human values, and societal interests into geospatial research and education. His work specifically targets identifying location-based help information from social media to support disaster response in high-risk communities, aiming to build resilient, equitable, and healthy societies through responsible spatial thinking. He teaches core courses including GEOG 589: Emerging Trends in Remote Sensing and GEOG 480: Exploring Imagery and Elevation Data in GIS Applications. Information regarding scientific awards, student advising, grant funding, and laboratory teams is not documented in the provided materials.
Roel C.G.M. Loonen is an Associate Professor at the Unit Building Physics and Services within the Department of the Built Environment at Eindhoven University of Technology (TU/e), Netherlands. He holds joint appointments with EAISI High Tech Systems and EIRES Research groups, focusing on building performance simulation and energy systems. His work bridges academic research with practical applications through collaborations with SMEs in the building industry. Loonen received his BSc and MSc (cum laude) in Building Services from Eindhoven University of Technology, followed by a PhD in 2018 with a dissertation on 'Approaches for computational performance optimization of innovative adaptive facade concepts.' His educational background has positioned him as a leading expert in building performance simulation and sustainable building technologies. His research interests center on developing and applying modeling and simulation strategies to support decision-making for designing buildings that combine high indoor quality with minimal environmental impact. Key areas include adaptive facades, building-integrated renewable energy systems, and energy-efficient building envelopes. He specializes in creating and validating new building performance simulation models to advance innovative building technologies. His recent publications demonstrate a strong focus on practical applications of building performance simulation, with emphasis on residential energy efficiency, photovoltaic systems, and occupant-centered approaches to building design. The work shows increasing integration of machine learning techniques with traditional building simulation methods, particularly for sensitivity analysis and optimization of building performance. REHVA Young Scientist Award (2021) Best PhD supervisor award from Department of the Built Environment, TU/e (2018) First prize - REHVA International student competition (2011) Smart daylight control for optimal building performance (NWO Take-off award, 2018) Best paper award (2021) Loonen actively supervises PhD and Master's students, evidenced by his Best PhD Supervisor Award in 2018. He manages multiple research projects including Sustainable Summer Comfort (2024-2027), Modeling Innovative Use Scenarios for Future Domestic Comfort (2023-2026), and Just Prepare (2022-2026), with funding from sources including the Dutch Research Council (NWO). His professional service includes being a board member of the Dutch-Flemish IBPSA affiliate and co-chair of IBPSA World's website committee, plus reviewing for 35 academic journals. He leads research within the Building Performance group, focusing on creating practical tools and methodologies that bridge the gap between theoretical building performance models and real-world implementation in the construction industry. His work particularly emphasizes the integration of occupant behavior and practices into building performance models, recognizing that human factors are critical to achieving sustainable building performance in practice.
Ada Gavrilovska is a Professor at Georgia Tech's School of Computer Science under the College of Computing. Her work focuses on systems software for emerging technologies, including hybrid memory systems, edge computing, and cloud infrastructure. She leads projects in the PRISM Center and ADA Center , with funding from NSF, DoE, SRC, and industry leaders like Cisco and VMware. Education: PhD in Computer Science, Georgia Tech (2004) Research Interests: Designing systems for new hardware and applications, including edge computing, heterogeneous memory management, and LEO satellite platforms. Her work bridges low-level OS mechanisms with high-level distributed systems challenges. Recent Publications highlight trends in LEO satellite resource scheduling Edge-based ML preprocessing Hybrid memory OS abstractions Disaggregated graph analytics Compiler-assisted performance optimization Scientific Awards: Best paper, NFV World Congress (2016) Spotlight paper, IEEE Transactions on Cloud Computing (2014) ISCA-50 25-year retrospective (2023) Advising & Grants: Ada has mentored over 15 PhD students and 10 MS students, with research supported by NSF, DoE, SRC, and industry grants. She serves as PI in the SRC/DARPA PRISM Center.
