Panagiotis Tsakalides is Professor of Computer Science at the University of Crete and Head of the Signal Processing Laboratory at FORTH-ICS. His research develops statistical signal processing and machine learning methods with applications in computational imaging, sensor networks, and remote sensing. He has secured over €15M in research funding through 22 projects, including: Horizon Europe TITAN Project (€2.5M): Frugal AI for astrophysics applications H2020 PHYSIS Project (€1M): Sparse processing for hyperspectral systems FP7 CS-ORION Project (€1.3M): Compressed sensing for aerial surveillance Research specialties include non-Gaussian signal modeling, compressed sensing architectures, and distributed processing algorithms. Applications span medical imaging, environmental monitoring, and space systems.
Ajai Singh is a Professor and Chair of the Department of Finance at the University of Central Florida's College of Business, where he also serves as Director of the Dr. P. Phillips School of Real Estate. Previously, he held the Bolton-Perella Endowed Professorship at Lehigh University and taught at Case Western Reserve University, where his undergraduate finance program was ranked #1 by Bloomberg BusinessWeek. His research examines investment banking and corporate finance, with focus on rational and behavioral market responses to capital acquisition events. Research interests span finance, investment banking, corporate finance, banking, and real estate, with publications in top-tier journals including the Journal of Finance and Journal of Financial Economics. His work integrates practical insights from his experience at State Bank of India's investment banking division. Recent research trends show publications across finance, agricultural science, materials engineering, geophysics, and statistics, demonstrating interdisciplinary applications of quantitative methods and market analysis. Awards and Honors: Undergraduate Teaching Excellence Award, Case Western Reserve University (2010-2011) As department chair and program director, he oversees academic initiatives and curriculum development while maintaining active research collaborations.
Annalisa Appice is an Assistant Professor at the Department of Computer Science, University of Bari, Italy. She holds a Ph.D. in Computer Science from the same institution, with her thesis focusing on 'Learning Relational Model Trees'. Her international research experience includes positions at the University of Bristol (UK) and Jozef Stefan Institute (Slovenia). Her research spans: Knowledge discovery and data mining Big data analytics and stream data mining Spatio-temporal data mining and remote sensing analysis Network data analysis and process mining Artificial Intelligence applications in cybersecurity and environmental monitoring She develops innovative machine learning approaches including deep neural networks, vision transformers, and ensemble methods for applications ranging from forest health monitoring to cybersecurity threat detection. Appice has significant leadership experience in the academic community, having served as Program co-Chair for major conferences including DS 2020, ECML-PKDD 2015, and ISMIS 2017. She regularly contributes to program committees of premier conferences such as AAAI, IEEE ICDM, KDD, ICML, and IJCAI. As an editorial board member for the Journal of Intelligent Information Systems, she oversees publications in intelligent information systems. She has also organized six international workshops and guest-edited three special journal issues.
Hongbin Li holds the Charles and Rosanna Batchelor Memorial Chair Professorship in the Department of Electrical and Computer Engineering at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. Education includes PhD in Electrical Engineering from University of Florida (1999). Research focuses on: Advanced signal processing algorithms Machine learning for RF systems Compressive sensing methodologies Wireless communications architectures Radar and sensor network technologies Recent publications concentrate on automotive radar systems, physical-layer authentication, sub-Nyquist sampling techniques, and intelligent reflecting surface applications. Honors include IEEE Fellow, Provost's Award for Research Excellence, IEEE Jack Neubauer Memorial Award, and fellowships in AAIA and AIIA. His research is funded by DARPA, NSF, ONR, AFOSR, ARO, and other agencies.
Arashdeep Kaur is a Senior Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). She holds a Ph.D. in Computer Science and Engineering from Amity University (2017), an M.Tech. from Punjab Technical University (2008), and a B.Tech. (2006) in the same field. Her research focuses on artificial intelligence, audio watermarking, deep learning applications, environmental science, and IoT-based healthcare solutions. She has contributed to crop freshness assessment using deep learning, ethanol production optimization from food waste, and heavy metal adsorption using nanotechnology. Dr. Kaur’s work spans over 20 years, with notable contributions in audio watermarking algorithms for security and robustness, including methods leveraging multi-resolution decomposition and neural networks. Her environmental projects address waste valorization and sustainable energy solutions. She actively teaches courses in AI and computer science fundamentals at NJIT. Her publications highlight interdisciplinary applications of CS in agriculture, cybersecurity, and environmental engineering. While no scientific awards are explicitly mentioned, her prolific research output indicates impactful contributions to multiple fields. She has advised no listed students, but her courses mentor future computer science professionals. Labs or collaborative teams are not detailed in the provided information, but her work intersects with NJIT’s strategic research areas in technology and sustainability.
Natalia Stepanova is a Professor in the School of Mathematics and Statistics at Carleton University. Her research focuses on high-dimensional statistical inference, nonparametric estimation, and hypothesis testing. She holds an office in Herzberg Laboratories (5229HP) and can be reached via email at nstep@math.carleton.ca . Her research interests emphasize modern challenges in statistical theory, particularly in developing adaptive methods for sparse data analysis, nonparametric models, and signal recovery. Recent work includes advancements in sup-functional analysis for empirical processes and efficient kernel-based density estimation. Stepanova’s publications (2010–2025) consistently address themes in nonparametric methods, high-dimensional data, and statistical efficiency. Key contributions include variable selection techniques, goodness-of-fit testing, and adaptive algorithms for sparse additive models. No scientific awards are explicitly listed in the provided materials. Her academic contributions span graduate advising and collaborative research within the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS). No specific grants or lab affiliations are detailed in the text.
Dr. Jiefu Chen is Associate Professor of Electrical and Computer Engineering at the University of Houston's Cullen College of Engineering. His research develops computational methods for electromagnetic logging, subsurface wireless communication, and seismic data analysis in oilfield applications. He serves as Associate Editor for IEEE Transactions on Geoscience and Remote Sensing and holds patents in logging-while-drilling technologies. Industry collaborations include Weatherford International where he received Technical Awards (2013-2015). Research focuses: Borehole electromagnetic telemetry systems Machine learning for geosteering inversion Real-time formation evaluation
Hyuk Park is an Assistant Professor at Universitat Politècnica de Catalunya (UPC-BarcelonaTech), affiliated with the Castelldefels School of Telecommunications and Aerospace Engineering. His research focuses on remote sensing, particularly passive microwave remote sensing, system design, modeling, and geophysical data analysis. He holds a B.S. in Mechanical Engineering from KAIST (2001) and M.S./Ph.D. in Information and Mechatronics from GIST (2003/2009). Notable grants include a Korean NRF Grant (2011) and a Juan de la Cierva Grant (Spain). He currently leads the RSLab and CommSensLab, exploring applications like GNSS-Reflectometry (GNSS-R) for soil moisture, ionospheric scintillation, and disaster monitoring. His work integrates satellite data from missions like CYGNSS, FY-3E, and FSSCat to advance environmental monitoring. Recent studies include correlating ionospheric anomalies with earthquakes, improving flood water level estimation, and developing AI-driven agriculture systems. Awards include the Ramon y Cajal Fellowship, highlighting his contributions to sensor technology and Earth observation. Key projects involve instrument design (e.g., FMPL-2 dual payload), algorithm optimization for hyperspectral imaging, and mitigating radio-frequency interference. His research bridges theoretical modeling with practical applications in climate science, geophysics, and disaster response.
Jihoon Ryoo is an Associate Professor in the Department of Computer Science at SUNY Korea, where he has been employed since 2017. He directs the AI2S Lab and co-founded the startup IDCITI. He holds a Ph.D. from Stony Brook University (2017) and M.S./B.S. degrees from Korea University. Research Interests: Dr. Ryoo's work focuses on practical implementations in wireless networking and mobile systems, including backscatter communication, IoT connectivity, saliency-based video streaming, and GNSS-independent localization. His projects span uGPS (metro localization), SALI360 (360° video optimization), and autonomous anti-drone systems, often leveraging deep learning and RF analytics. Awards & Grants: Incheon City Mayor Award (Entrepreneur, 2024 & S/W Hackathon, 2020) Prime Minister Awards (ICT Colloquium & Applied Data Competition, 2020) IITP Excellence Research Award (2019) Grants: National XR-Lab initiatives (MSIT), Incheon-RISE program (2025–2030), NRF streaming platform research, and multiple Incheon Techno Park projects. Teaching & Service: Courses include Computer Networks, Computer Vision, Algorithms, and Wireless Networks. Service includes TPC roles (MobiSys, ICCCN), Director of SUNY Korea's ICT CCP Program (2018–2020), and XR-Lab Director (2021–2023).
Professor Khan Wahid is a faculty member in the Department of Electrical and Computer Engineering at the University of Saskatchewan , affiliated with the Division of Biomedical Engineering . His research focuses on health informatics, IoT infrastructure, medical imaging, and wearable health monitoring , with notable contributions to wireless capsule endoscopy, smart-city applications, and sensor systems. He holds a GCC Stars in Global Health Award (2013) and has secured funding from multiple government and institutional grants. Education: BSc, MSc, PhD in relevant fields (details not specified in text). Research Interests : Health informatics & smart-health systems IoT design/deployment and drone infrastructure Medical imaging reconstruction (CT/MRI) Wireless sensor networks and body area networks Quantum-dot cellular automata (QCA) for nanoelectronics FPGA/ASIC-based embedded systems Funding & Recognition : Harper Government investment in medical imaging (2014) UofS GCC Stars in Global Health Award (2013) Advising & Grants : Supervises graduate students in interdisciplinary projects (specific names not listed). Active in securing grants for IoT-enabled healthcare and agricultural sensing systems. Collaborates on projects involving smart-pill devices, fluorometers for cancer detection, and LiDAR-based plant phenotyping. Labs & Teams : Leads projects integrating biomedical engineering, IoT, and nanotechnology within the College of Engineering. Collaborations span academic and industrial partners for medical device innovation.
Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.
Roel Snieder is the W.M. Keck Distinguished Professor of Professional Development Education at the Colorado School of Mines. His primary affiliation is with the university's academic affairs division, focusing on STEM education, geophysics, and professional ethics. He holds an office in Hill Hall 206A and can be contacted at rsnieder@mines.edu. Snieder's research spans geophysical methodologies, including seismic interferometry, Marchenko imaging, and computational geophysics. He is also deeply engaged in pedagogical innovation, emphasizing ethical practices and emotional intelligence in STEM education. His work integrates advanced mathematical approaches with practical applications in wavefield reconstruction and subsurface imaging. Notable contributions include the development of Marchenko-based imaging techniques and the exploration of pedagogical love in academia. His books, such as The Art of Being a Scientist and The Joy of Science , reflect his commitment to fostering scientific curiosity and ethical professionalism. His recent publications highlight advancements in compressive sensing for seismic data, energy partitioning in elastic media, and the ethical dimensions of engineering education. Snieder's interdisciplinary approach bridges geophysics with educational theory, influencing both technical and humanistic aspects of academic practice.
Ickjai Lee is an Associate Professor at the School of IT, James Cook University, specializing in geoinformatics and intelligence informatics. He leads the Information Technology department and has held roles including Senior Lecturer and Lecturer since 2003. He obtained his PhD in 2002 from the University of Newcastle, Australia. Research Interests: Geospatial data mining, trajectory analysis, Voronoi tessellations, mobile AR, and IoT applications. Projects: Includes VR healthcare trials, AI-driven vehicle damage assessment, and environmental monitoring systems. His work focuses on applying machine learning and geospatial techniques to solve real-world problems in health, environmental, and urban domains. Notable achievements include best paper awards at international conferences and faculty citations for teaching excellence. He collaborates on interdisciplinary projects, such as using AR for marine growth monitoring and developing tools for crime pattern analysis. His research also involves large-scale ecoacoustic data analysis and VR-based education platforms. Lee has supervised numerous PhD students exploring topics like differential privacy in biokinetics, spatio-temporal anomaly detection, and VR in cultural heritage preservation.
Pattichis Marios is an Associate Professor in the Department of Electrical and Computer Engineering and Radiology at the University of New Mexico (UNM). He directs the Image and Video Processing and Communications Lab (ivPCL) and serves on the board of the FPGA Mission Assurance Center (FMAC). His research focuses on biomedical image analysis, dynamically reconfigurable architectures, and educational technology for underrepresented student groups. Education: Ph.D. in Computer Engineering (UT Austin, 1998), dual bachelor's degrees in Mathematics and Computer Sciences (UT Austin, 1991). He has taught 15 courses across multiple universities and secured $15.9M in research funding from NSF, AFRL, and NIH. Research areas include CAD systems for medical imaging, explainable AI, and large-scale video analytics in clinical and educational settings. Developed AM-FM representations for image/video features and contributed to solar image analysis methodologies. Awards: EAMBES Fellow (2022), Harrison Faculty Excellence Award (2006), Best Paper Award (AIAI06), Teacher of the Year (UNM ECE). Current projects include the AOLME educational initiative and the DRASTIC adaptive video processing platform. He edits special issues in IEEE journals and Teachers College Record.
Jing Liu, PhD, is a Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco (UCSF). She holds adjunct faculty roles and leads research in advanced MRI techniques. Her primary focus is developing 3D dynamic MRI methods for cardiovascular imaging, including coronary imaging and cardiac-cine techniques. Dr. Liu's work emphasizes improving scan speed, spatial/temporal resolution, and clinical applicability of MRI. Education: BS (Electrical Engineering, University of Science and Technology of China, 2001), MS (Electrical Engineering, McMaster University, 2003), PhD (Electrical Engineering, University of Wisconsin-Madison, 2008). Postdoctoral Training: Radiology at Weill Cornell Medical College (2008–2010). Research interests include 4D MRI, fast imaging protocols, self-gating techniques, non-cartesian data acquisition, compressed sensing, and parallel imaging. She has pioneered methods for reducing scan time and motion artifacts in clinical settings. Publications reflect her expertise in MRI reconstruction algorithms, cardiac imaging, and automated segmentation. Key contributions include papers on respiratory-resolved 3D cine MRI and non-contrast black blood MRI for abdominal aneurysms. Awards: NIH K25 Award (2012–2017), multiple Distinguished Reviewer accolades, and American Heart Association grants. Professional Affiliations: International Society for Magnetic Resonance in Medicine, American Heart Association, Society of Cardiovascular Magnetic Resonance. Liu’s lab focuses on translational MRI technologies. Current projects include 3D multi-contrast MRI for glioma assessment and developing AI-driven segmentation tools for cardiac and vascular imaging.
