Amel Dechemi is a Research Fellow in the Department of Computer Science, Electrical Engineering and Mathematical Sciences. Their work focuses on robotics applications in precision agriculture, assistive robotics, and computer vision for healthcare. Research spans autonomous systems for crop monitoring, infant action recognition, and data fusion techniques. Key research interests include robotic perception, agricultural automation, and lightweight neural networks for unconstrained environments. Their robotic systems address challenges like leaf retrieval, irrigation optimization, and infant motion analysis for pediatric rehabilitation. Publishing trends highlight contributions to IEEE Robotics & Automation Magazine and specialized journals. Ongoing projects aim to bridge robotics with sustainable agriculture and medical technology.
Mürvet Kırcı is an Associate Professor at the Department of Electronics and Communication Engineering, Istanbul Technical University. Her academic journey includes a PhD (1997), MSc (1987), and BSc (1983) in Electronics and Communication Engineering from ITU. She specializes in signal processing, biometrics, embedded systems, and acoustic wave propagation. Her research focuses on biometric authentication, fractional calculus applications, and agricultural monitoring systems. She has contributed to projects involving Petri nets modeling, ear recognition algorithms, and energy-efficient wireless protocols. Education: PhD in Electronics and Communication Engineering, Istanbul Technical University (1997) MSc in Electronics and Communication Engineering, Istanbul Technical University (1987) BSc in Electronics and Communication Engineering, Istanbul Technical University (1983) Research Interests: Dr. Kırcı’s work spans multiple domains including signal processing , embedded systems design , and biometric authentication . She has pioneered methods in fractional-order calculus for data augmentation and developed novel approaches for ear recognition and twin identification in unconstrained environments. Her embedded systems research includes hardware/software co-design for agricultural monitoring systems and energy-efficient sensor networks. Grants & Projects: Led projects on microwave imaging and portable hyperthermia devices for breast cancer treatment. Contributed to TÜBİTAK-funded research on compressed sensing for energy-efficient communication systems. Developed biometric authentication protocols for client-server and IoT networks. Labs & Teams: Active in the Electronics and Communication Engineering Department’s labs, focusing on embedded systems, signal processing, and agricultural robotics. Collaborates with industry partners like Procenne Company on integrated circuit design projects.
Emily M. Hand is an Associate Professor and Graduate Director in the Department of Computer Science & Engineering at the University of Nevada, Reno (UNR), where she directs the Machine Perception Laboratory (MPL). Her research bridges Machine Learning, Computer Vision, and Human Perception with a mission to develop wearable assistive technologies for individuals with visual impairments or on the Autism spectrum. Education Doctor of Philosophy, University of Maryland, College Park (2018) Master of Science, University of Maryland, College Park (2015) Bachelor of Science in Computer Science and Engineering, University of Nevada, Reno (2013) Bachelor of Science in Applied Mathematics, University of Nevada, Reno (2013) Research Focus Dr. Hand's work centers on explainable facial feature modeling , human-perception-inspired machine learning , and real-world assistive applications . Her MPL lab pioneers techniques for social interaction enhancement through visual and natural language processing, with emphasis on robustness in noisy environments. Key contributions include facial attribute recognition under unconstrained conditions, deep learning architectures for label noise resilience, and novel approaches to multi-task learning leveraging implicit feature relationships. Publication Trends Analysis of her 14 most recent publications (2012-2020) reveals a consistent trajectory toward socially impactful computer vision: early work focused on foundational techniques in facial recognition and neural network optimization, evolving toward assistive applications by 2017. Her research increasingly integrates temporal modeling (2018), noise-robust systems (2019), and real-world deployment challenges (2020), with 70% of recent work directly addressing accessibility needs through wearable technologies and social interaction aids. Scientific Recognition NSF CAREER-level grant for facial caricature research ($419,979) University of Maryland Future Faculty Fellow NSF Graduate Fellowship Honorable Mention Multiple conference paper acceptances at CVPR, AAAI, and ICRA Senior Scholar Mentor awards for undergraduate researchers Academic Leadership As Graduate Director and Faculty Advisor for UNR's Women in Computer Science and Engineering (WiCSE), Dr. Hand mentors students through the Senior Scholar program while securing significant external funding. Her $419,979 NSF grant develops facial verification systems using caricatures, and her SCO-funded CV-SIGHTT project advances synthetic image generation for defense applications. She actively shapes curriculum through courses in Machine Learning and Computational Linguistics. Laboratory & Outreach The Machine Perception Laboratory (MPL) operates from UNR's WPEB 415, developing wearable social interaction aids through interdisciplinary collaboration. Dr. Hand co-founded Reno's Girls Who Code chapter and participates in State Department speaker series, demonstrating commitment to broadening participation in computing through hands-on outreach and policy engagement.
Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry. Research Interests : Her work bridges numerical linear algebra and optimization, with emphasis on: Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks) Applications of Rayleigh quotients in optimization and eigenvalue problems Robust multigroup classification via trace ratio techniques Statistical analysis of Markov chains for farmland transitions and non-stationary processes Publications : Her recent work demonstrates contributions to: Subspace methods for large-scale trace ratio problems Advancements in unconstrained optimization via limited-memory gradient approaches Integration of Rayleigh quotients into gradient-based algorithms Harmonic frameworks for optimizing stepsizes in numerical methods Collaborations & Grants : Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding. Technical Expertise : Expertise in numerical analysis, statistical modeling, and linear algebra applications.
Theodoula N. Grapsa is an Associate Professor in the Department of Mathematics at the University of Patras, Greece. She is affiliated with the Division of Computational Mathematics and Informatics within the department. Her office is located at B/M 243, and she can be reached at grapsa@math.upatras.gr or theodoula.grapsa@gmail.com. Dr. Grapsa's research focuses on several key areas in computational mathematics: Numerical Analysis and Optimization Methods Systems of Nonlinear Equations Interval Analysis and Global Optimization Neural Networks and Machine Learning Algorithms Computational Mathematics and Numerical Algorithms Her recent work shows a strong emphasis on developing and improving optimization algorithms, particularly for unconstrained optimization problems. She has made significant contributions to Newton's method modifications, conjugate gradient approaches, and hybrid techniques that combine forecasting with optimization. Her research bridges theoretical mathematics with practical computational applications, particularly in the areas of neural network training and solving complex nonlinear systems. Dr. Grapsa has supervised numerous graduate students throughout her career, including PhD candidates and MSc students. Her past PhD students include Dimitris G. Sotiropoulos, John A. Nikas, and Aristotelis E. Kostopoulos. Current PhD students under her supervision are Eleftheria N. Malihoutsaki, Athanasia N. Papanikolaou, and Christina D. Nikolakakou. She has authored two books: "Εισαγωγή στην Ανάλυση Διαστημάτων - Interval Analysis" (2012) "Προγραμματίζοντας με Fortran'90" (2012) Dr. Grapsa is actively involved in teaching, including the course "Basic Principles of Programming," for which she organized an educational visit to the Patras Science Centre in Rio for first-year mathematics students in April 2013.
Prof. Panagiotis E. Pintelas is a Professor of Computer Science at the Department of Mathematics, University of Patras, Greece. He holds a B.Sc. in Mathematics from the University of Athens (1971), and M.Sc. and Ph.D. in Computer Science from the University of Bradford, UK (1973, 1976). His academic career includes roles as Head of the Division of Applied Mathematics and Informatics (1991–1993, 1998 onwards) and Professor in the Informatics Division of the Department of Mathematics since 1995. He is also involved with the Greek Open University as an academic leader for Informatics studies. Research Interests: His work focuses on Software Engineering, Educational Technology (including Virtual Reality and Intelligent Tutoring Systems), Machine Learning, and Optimization Algorithms. He has contributed extensively to semi-supervised learning, ensemble methods, and algorithm development for medical and educational applications. His research integrates computational techniques with real-world applications in healthcare, finance, and education. Awards and Grants: While no specific awards are listed, his contributions to educational software and machine learning are recognized through numerous research grants and projects, including coordination of national/EU-funded initiatives. He has authored/co-authored over 180 publications in top journals/conferences. Labs and Teams: He leads the Educational Software Development Laboratory (ESDLab) established in 1992, advancing research in educational tools and methodologies. His team collaborates on projects involving AI-driven educational systems, student performance prediction, and healthcare analytics.
Luis F. Zuluaga is an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he also serves as Co-Director of the Financial Engineering Program and is affiliated with the Healthcare Systems Engineering initiative. Previously, he held an Associate Professor position in the Faculty of Business Administration at the University of New Brunswick (Canada). His research focuses on polynomial optimization, large-scale optimization, quantum computing, financial engineering, and truss topology design. He has authored influential papers in top journals like Operations Research , SIAM Journal on Optimization , and Mathematical Programming . Education: Ph.D. in Operations Research, Carnegie Mellon University (2004) M.S. in Industrial Administration, Carnegie Mellon University (2000) M.S. in Industrial Engineering, University of Los Andes (Colombia, 1998) B.S. in Physics and Electrical Engineering, University of Los Andes (Colombia, 1996) Research Interests: Dr. Zuluaga's work spans polynomial optimization frameworks, quantum computing applications (e.g., QUBO formulation for combinatorial problems), and financial engineering. His recent projects include quantum interior point methods for semidefinite optimization and truss topology design for structural stability. His research bridges theoretical advances with real-world applications in energy systems, industrial networks, and risk management. Recent Article Trends: His 2023–2025 publications emphasize quantum computing (e.g., QUBO formulations, Hamiltonian-based algorithms) and optimization under uncertainty (e.g., semidefinite relaxations, nonnegativity reduction techniques). Earlier work includes contributions to truss design optimization and carbon pricing mechanisms. Awards: 2019 Journal of Global Optimization Best Paper Award (with student Xiaolong Kuang) Grants & Impact: His team secured a $2.1M DARPA grant for quantum computing research and a U.S. Army grant for optimization algorithms. Lehigh ISE's $500K+ impact on Pennsylvania’s critical sectors is partly attributed to his work. He advises students like Xiaolong Kuang and collaborates with the Quantum Computing and Optimization Laboratory (QCOL). Labs/Teams: Director of the Quantum Computing and Optimization Laboratory (QCOL), fostering interdisciplinary research in quantum optimization and its industrial applications.
Luca Lanzoni is an Associate Professor in the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. His expertise lies in structural mechanics, materials science, and sustainable construction. He teaches courses such as Construction Science, Structural Design of Dams and Reservoirs, and Theory of Structures, focusing on both theoretical and computational methods. His research emphasizes finite elasticity, nonlinear mechanics, and composite materials like Shot-Earth. Key areas include beam and plate mechanics, material characterization, and sustainable construction solutions. He has contributed to advancements in structural stability, energy forms in hyperelastic materials, and computational modeling of nanoscale systems. He maintains active collaborations in experimental and numerical analysis, with a focus on translating theoretical insights into practical applications. His work bridges micro and macro scales, addressing challenges in both traditional and cutting-edge materials.
Yuliang Xiu is a tenure-track Assistant Professor at Westlake University's AI department, leading the 远兮实验室 (endless.do) as PI. His research focuses on democratizing human-centric digitization through advancements in computer vision, graphics, and machine learning. Previously, he completed his Ph.D. at Max Planck Institute for Intelligent Systems under Prof. Michael J. Black, funded by the CLIPE Marie Sklodowska-Curie fellowship. His work bridges vision and graphics to achieve scalable, photorealistic 3D human digitization. Education: Ph.D., Max Planck Institute for Intelligent Systems (2025, advised by Michael J. Black & Dimitrios Tzionas) M.Sc., Shanghai Jiao Tong University (2019, advised by Cewu Lu) B.Eng., Shandong University (2016, advised by Lu Wang) Research Interests: His work emphasizes human-centric digitization , including 3D clothed human reconstruction, generative AI for garments, and training-free methods. His lab explores foundational models for scalable avatar creation, aiming to make human digitization accessible to all. Recent projects include Easi3R (dynamic motion estimation), ETCH (clothed body fitting), and ECON (explicit-implicit hybrid modeling). He advocates for generalizable, photorealistic systems that align with real-world constraints. Publications Trends: Over 15+ peer-reviewed papers, with 2025 highlights including ICCV and SIGGRAPH contributions. His work is characterized by innovations in: 3D reconstruction from single images/videos Implicit/explicit representation hybrids LLM-driven garment editing Efficient finetuning via butterfly factorization Awards & Recognition: 2025 China3DV Rising Star Award Best in Show (SIGGRAPH RTL 2020) CVPR Highlight 2023 Organized ECCV 2024 workshop on Foundation Models for 3D Humans Advising & Mentorship: Successfully mentored 3 master students into top PhD programs (TUM, MBZUAI, HKU). Actively hiring postdocs, PhDs, and researchers for lab expansion. Labs & Teams: Leads the 远兮实验室 (endless.do), focusing on democratizing human digitization through open-source tools and foundational research. Key contributions include the ECON, TADA, and TeCH frameworks.
Dr. Dimitri Androutsos is an Associate Dean (Undergraduate Studies and Student Affairs) and Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. He holds a BASc, MASc, and PhD in Electrical Engineering from the University of Toronto, and is a PEng-registered professional. His expertise spans Digital Signal Processing, Computer Vision, and Medical Imaging applications, with a focus on image manipulation, super-resolution, and AR/VR technologies. Education: PhD in Electrical Engineering, University of Toronto (1999) MASc in Electrical Engineering, University of Toronto (1994) BASc in Electrical Engineering, University of Toronto (1992) Research Interests: Dr. Androutsos explores advanced image and video processing techniques, including computational photography, stereoscopy, and AI-driven medical diagnostics. His work integrates machine learning and computer vision to address challenges in biomedical applications such as tumor cell detection, surgical navigation systems, and automated pathology analysis. Recent trends in his publications emphasize AI tools for improving diagnostic accuracy in breast cancer through techniques like Ki67 index quantification and domain adaptation for medical imaging. Professional Engagement: He is a Senior Member of IEEE and has held roles such as General Co-Chair for IEEE ICASSP 2021. His research bridges academic and applied domains, with contributions to augmented reality systems for surgical workflows and energy-efficient video processing algorithms. Labs & Teams: As a faculty member, he contributes to interdisciplinary projects within the Department of Electrical, Computer, and Biomedical Engineering, focusing on medical imaging, computer vision, and AI applications in healthcare.
Bacha Rehman is a Senior Lecturer in the Department of Computing (AI & Data Science) at Solent University, affiliated with the Science and Engineering Research Group. His research focuses on artificial intelligence, machine learning, medical imaging, and metamaterials design, with a strong emphasis on practical applications in healthcare technology, computer vision, and optoelectronics. He has contributed to over 16 peer-reviewed publications from 2011 to 2025, exploring topics such as brain tumor detection through CNNs, infrared ship tracking, and machine learning-driven metamaterial optimization. His work bridges theoretical advancements with real-world challenges, such as developing robust solar energy systems using nanostructured materials and enhancing facial expression recognition frameworks. Collaborations span international research networks, though specific partnerships remain unspecified in the provided data. No academic awards are documented, though his consistent output reflects sustained research engagement. Rehman’s email is bacha.rehman@solent.ac.uk , and his ORCID is 0000-0003-2081-5728 . His research trajectory demonstrates a commitment to interdisciplinary innovation within AI, materials science, and computational engineering.
Srirangaraj (Ranga) Setlur is a Principal Research Scientist and Co-Director of the Center for Unified Biometrics and Sensors (CUBS) at the University at Buffalo. He also serves as the Associate Director of Community Engagement at the Institute for Artificial Intelligence and Data Science. His research focuses on AI, Machine Learning, Pattern Recognition, Computer Vision, and Information Retrieval with applications in biometrics, document analysis, and healthcare. Education: MS in Industrial Engineering, University at Buffalo (1995) Research Interests: Development of AI-driven systems for biometric authentication (e.g., fingerprint, facial recognition) Design of datasets for chart analysis (CHART-Info), gait recognition (DIOR), and cross-domain fingerprint analysis Applications in healthcare diagnostics (e.g., dyslexia screening via handwriting analysis) Advancements in multimodal fusion (e.g., audio-visual, physiological signals) Recent Research Trends: His recent work emphasizes cross-domain learning (e.g., Ridgeformer), sparse feature aggregation (Proxyfusion), and AI applications in social robotics (AutoMisty). He consistently addresses challenges in unconstrained environments and under-represented data scenarios. Awards: Senior Member, IEEE 2019 ICDAR Best Student Paper Award (F. Xu) 2010 IBM Best Student Paper Award (X. Peng) UB Visionary Innovator Award Labs/Teams: Leads research teams in CUBS and the Institute for AI & Data Science, focusing on biometric systems, surveillance optimization, and multimodal AI applications.
Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics at the Leavey School of Business, Santa Clara University, and Program Director for the Master of Science in Information Systems (MSIS). He holds a PhD in Operations and Information Management from the University of Colorado Boulder and degrees in Computer Science from the University of Bologna. His research focuses on combining machine learning and optimization to improve business processes and healthcare systems. Key areas include reducing racial disparities in medical appointment scheduling, relational data mining, and metaheuristic algorithms. Collaborations include healthcare organizations dedicated to Black women, mental health nonprofits, and pharmaceutical companies. His work has been featured in MIS Quarterly , Manufacturing & Service Operations Management , and INFORMS Journal on Computing . Award highlights include the 2021 INFORMS Minority Issues Forum Paper Competition, AACSB's 2021 Innovations That Inspire, and inclusion in a United Nations report. Media features include Forbes and WIRED . He teaches courses such as Data Science with Python, Natural Language Processing, and Business Intelligence. His research emphasizes practical impact, addressing social equity through algorithmic fairness and operational efficiency in healthcare and retail. Ongoing projects explore ethical AI applications and decision support systems for vulnerable populations.
Stephen Wright is a Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, serving as Department Chair from 2023-2025. He previously held roles at Argonne National Laboratory (1990-2001) and the University of Chicago (2000-2001). His research focuses on computational optimization, with applications in data science, machine learning, and engineering. He co-authored seminal books such as Numerical Optimization (with J. Nocedal) and Optimization for Data Analysis (with B. Recht). Wright leads the Wisconsin Institute for Discovery's research initiatives and has developed widely-used optimization software like PCx and SpaRSA . Key awards include the 2024 George B. Dantzig Prize, 2020 Khachiyan Prize, and SIAM Fellow status since 2011. He has served as editor-in-chief of the SIAM Journal on Optimization and Mathematical Programming, Series B . His teaching includes courses on nonlinear optimization (CS726) and introductory optimization (CS524). Wright’s work bridges theory and practice, emphasizing scalable algorithms and interdisciplinary applications.
Heng Huang is the John A. Jurenko Endowed Professor at the University of Pittsburgh with dual appointments in the Department of Electrical and Computer Engineering and Department of Biomedical Informatics. His research spans machine learning, bioinformatics, and medical image analysis with significant contributions to Alzheimer's disease research, neuroinformatics, and precision medicine. His educational background includes a Ph.D. in Computer Science from Dartmouth College and M.S./B.S. degrees from Shanghai Jiao Tong University. Research focuses on Machine Learning , Big Data Computing , and Biomedical Informatics with emphasis on: Neurodegenerative disease prediction through transferable deep networks Medical image analysis for macular degeneration and Alzheimer's Watermarking techniques for LLM security and protein design Optimization methods for non-convex problems Recent publications show strong trends in multimodal learning (TV-LSTM, MIRROR), LLM security (CoTGuard, Web IP protection), and biomedical applications (Alzheimer's classification, single-cell analysis). He actively mentors students including Yanfu Zhang (recent tenure-track appointment at William & Mary) and seeks new PhD candidates/postdocs for research in big data, machine learning, and biomedical image analysis. His lab maintains active participation in top conferences including ICML, CVPR, NeurIPS, and MICCAI as evidenced by consistent paper acceptances (3 at ICML 2023, 5 at AAAI 2023).
