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Qi GuoView profile

Assistant Professor

• Computational Imaging
• Computer Vision
• Machine Learning
• +5 more

Qi Guo is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on computational imaging, computer vision, and applied optics, with an emphasis on developing next-generation visual sensors through the integration of optics, electronics, and signal processing. He leads a lab creating prototypes like Focal Split (a handheld depth camera inspired by spider vision) and MetaHDR (a metasurface-based HDR imaging system). Education: B.E. in Automation (Tsinghua University, 2015), M.S. and Ph.D. in Electrical Engineering (Harvard University, 2018 and 2021). His work bridges theory and practice, with recent breakthroughs in photon-limited depth estimation (Blurry-Edges) and robust boundary detection (CT-Bound). Research Interests: Computational Imaging Optical System Design Machine Learning for Vision 3D Sensing Metasurface Engineering Embedded Sensor Systems Notable Achievements: Best Paper Award (ECCV 2016) Best Demo Award (ICCP 2018) IEEE Technical Committee on Computational Imaging Editor of Open Journal of Signal Processing Lab Philosophy: Prioritizes 'maker mindset' for hands-on prototyping, requiring 10+ weekly hours from undergrad researchers. Offers SURF internships and collaborates on projects like satellite stereo imaging (Stellar dataset) and birefringent metasurface applications.

Fred Fialho Leandro Alves TeixeiraView profile

Senior Lecturer

• Computational Architecture
• Immersive Environments
• Bio-augmented Spaces
• +5 more

Dr. Fred Fialho Leandro Alves Teixeira is a Senior Lecturer and Deputy Director (Engagement) at the University of Queensland's School of Architecture, Design and Planning. His research focuses on computational architecture, immersive environments, and bio-augmented spaces. He co-founded the UQ Visualisation Lab, specializing in extended realities and spatial computing. With over 50 publications, his work bridges biology, art, and architecture. He holds a PhD from Istanbul Technical University and is accredited by RIBA and Portuguese architectural bodies. Awards include Dean’s Fellowship (UC) and Media Arts and Technology Fellowships. His projects, such as RoboBlox and digital twin frameworks, emphasize innovation in design and fabrication. Education: PhD in Architecture (Istanbul Technical University, 2014), Architectural Association (AA), and RIBA accreditation. Research: Bio-inspired design, mixed reality, robotic fabrication, and urban sustainability. Key Projects: UQ Visualisation Lab, Perception of Space studies, and SHErobots initiative. His teaching emphasizes transdisciplinary approaches, as seen in co-authored works on global design education. Collaborations include Zaha Hadid Architects and interdisciplinary teams in robotics and art.

Emma AlexanderView profile

Assistant Professor

• Computational Imaging
• Bio-Inspired Vision
• Physics-Based Computer Vision
• +3 more

Emma Alexander is an Assistant Professor of Computer Science at Northwestern University's McCormick School of Engineering, with a courtesy appointment in Electrical and Computer Engineering. Her work bridges computational imaging, biological vision principles, and sustainable AI systems. She leads the Bio Inspired Vision (BIV) Lab, focusing on developing cameras and algorithms inspired by natural vision systems. Key research areas include bio-inspired color sensing, depth estimation, and physics-informed machine learning for scientific imaging. Education: PhD and MS in Computer Science from Harvard University, BS in Physics and Computer Science from Yale University. Research emphasizes reverse-engineering biological vision to advance imaging technologies. Notable projects include hyperspectral colorization, zebrafish optic flow analysis, and metalens-based depth sensors. Awards include Best Student Paper (ECCV 2016) and Best Demo (ICCP 2018). Her lab collaborates with astrophysicists through the SkAI Institute and engages in outreach through courses like Natural and Artificial Vision. Teaching includes graduate Computational Optics and an advanced undergraduate course on vision science. She mentors over 10 students annually, with undergraduates contributing to publications at CVPR and MNRAS.

Jianguo Jack WangView profile

Lecturer

• Sensor Fusion
• Robotics
• Intelligent Transportation Systems
• +4 more

Dr. Jianguo Jack Wang is a Lecturer at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS). He holds a B.Sc. in Physics from Nanjing University, M.Sc. in Electrical and Information Engineering from The University of Sydney, and a PhD in Geomatics from UNSW. His research focuses on sensor fusion, robotics, intelligent transportation systems, and environmental engineering. He has published over 70 papers and supervised around ten PhD/Master students. Notable achievements include the Michael Richey Medal for his navigation method innovation. Dr. Wang’s research interests span robotics path planning, UWB-based positioning, and multi-sensor integration. His work emphasizes practical applications in autonomous systems and environmental sustainability. He actively reviews for top journals like IEEE Transactions on Intelligent Transportation Systems and conferences such as ICRA and IROS. He has led funded projects on sensor fusion for road assessment and participated in initiatives like the Centre for Built Infrastructure Research (CBIR). His teaching includes courses on Geographic Information Systems and Civil Engineering. Dr. Wang is affiliated with IEEE Robotics and Automation Society and the Institute of Navigation (ION).

Barani RamanView profile

Professor

• Computational Neuroscience
• Systems Neuroscience
• Pattern Recognition
• +7 more

Barani Raman is a Professor of Biomedical Engineering at the McKelvey School of Engineering, Washington University in St. Louis. His research integrates computational neuroscience, neuromorphic engineering, and sensor technologies to develop bio-inspired systems with applications in biomedical devices, environmental monitoring, and security. He holds a PhD from Texas A&M University and joined Washington University in 2010 after postdoctoral training at NIH and NIST. Education: PhD, Texas A&M University, 2005 MS, Texas A&M University, 2003 B.Eng., University of Madras, 2000 Research Interests: His lab explores how the brain processes sensory signals (e.g., olfactory cues) and translates these insights into technologies like electronic noses and neuromorphic devices. Recent work includes serotonin’s role in locust olfaction and nanoparticle interactions with neurons for treating neurological disorders. Grants & Collaborations: $650,000 NSF Convergence Accelerator grant (2024) for water quality monitoring and electronic nose development Collaborations with Srikanth Singamaneni on neuroengineering He leads the Raman Lab and is affiliated with the Center for Science and Engineering of Living Systems and the Imaging Science PhD Program.

Alexander AminiView profile

Researcher

• Autonomous Systems
• Machine Learning
• Robotics
• +9 more

Alexander Amini is a Postdoctoral Researcher at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) , working under the guidance of Prof. Daniela Rus. He earned his PhD (2022), Master of Science (2018), and Bachelor of Science (2017) in Computer Science from MIT, with a minor in Mathematics. Amini co-founded Liquid AI and Themis AI , and serves as a lead organizer and lecturer for MIT's deep learning course 6.S191 . His research focuses on the science and engineering of autonomy , particularly for safe decision-making in uncertain environments. Key contributions include Developing end-to-end control systems for autonomous agents Formulating confidence metrics in neural networks Creating mathematical models for human mobility analysis Innovating inertial refinement systems Amini's publications reveal trends across autonomous vehicle control , robust machine learning , and uncertainty quantification . His work spans continuous-time neural models, sensor optimization for soft robotics, and applications in molecular discovery and financial modeling. Notable collaborations include projects with NVIDIA and Harvard Medical School. Scientific recognition includes 2011 EU Contest for Young Scientists Grand Prize 2011 BT Young Scientist overall winner 2017 NSF Graduate Fellowship 2020 MIT Outstanding Mentor Award 2021 JP Morgan Fellowship As part of MIT's Distributed Robotics Laboratory , Amini works on systems that combine robotics with everyday life applications, supported by grants from FinTech@CSAIL and MachineLearningApplications@CSAIL for bias mitigation in financial and clinical domains.

Lei CuiView profile

Senior Lecturer

• Robotics
• Mechatronics
• Biorobotics
• +2 more

Lei Cui is a Senior Lecturer in Mechatronics at Curtin University's School of Civil and Mechanical Engineering, where he heads the Biorobotics Research Lab. He holds a PhD in Engineering (Robotics) from King's College London and was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. Research focuses on robotics and mechatronics with applications in biomedical engineering and environmental monitoring. Key areas include: rehabilitation robotics (exoskeletons for hand/knee function), underwater bio-inspired sensing systems, robotic manipulation via rolling/sliding contact, and neural-adaptive control systems. Recent projects integrate AI for soil carbon analysis and attention-based rehabilitation. Publications emphasize interdisciplinary innovation, with trends showing progression from fundamental kinematics (2015-2017) to AI-enhanced biomedical applications (2018-2022). Notable methodological advances include neural control frameworks, biomimetic sensor designs, and optimization techniques for assistive devices. Awards include: Editors' Choice Award, Journal of Mechanical Design (ASME 2018) IEEE/RSJ IROS Best Paper Finalist (2016) Curtin University Commercial Innovation Award (2015) Laboratory work centers on the Biorobotics Research Lab, focusing on human-robot interaction and neuromotor devices. No students or grants are detailed in provided materials.

Sanjoy DasView profile

Associate Professor

• Machine Learning
• Quantum Computing
• Multi-agent Systems
• +3 more

Sanjoy Das is Associate Professor in Electrical and Computer Engineering at Kansas State University's College of Engineering, with research expertise in bio-inspired computing and complex systems. His laboratory investigates machine learning, quantum algorithms, and optimization techniques applied to energy grids and natural resource management. Key research areas: Evolutionary algorithms and swarm intelligence Neural networks for pattern recognition Quantum computing architectures Sustainable resource allocation models Teaching encompasses bio-inspired computing, neural networks, scientific computing, electromagnetics, and computer architecture. His interdisciplinary work bridges electrical engineering with computational sustainability, supported by funding from NSF, ONR, and USDA.

Yuksel CelikView profile

Part-Time Lecturer

• Artificial Intelligence
• Cyber Security
• Optimization Algorithms
• +3 more

Dr. Yuksel Celik is an Adjunct Lecturer at the University at Albany's Massry School of Business and an Associate Professor at Karabuk University (Turkey). He holds a PhD in Computer Engineering from Selcuk University (2013) and completed postdoctoral research in Information Security and Digital Forensics at SUNY Albany. Research Focus: His work spans artificial intelligence, cybersecurity, and optimization algorithms, with applications in healthcare, genomics, and agriculture. Key methodologies include deep learning, bio-inspired computing, and computer vision for solving complex real-world problems. Publication Trends: Recent articles (2023-2025) demonstrate strong emphasis on hybrid AI models, security applications, agricultural technology, and medical diagnostics. Common themes include optimization techniques, anomaly detection, and multimodal learning approaches. Awards & Honors: Senior Member of IEEE Academic Contributions: Actively mentors graduate students and leads research projects funded by grants including the EU Erasmus+ program. Teaches courses in cybersecurity, data structures, and mobile application development.

• Cybersecurity
• AI/Machine Learning
• Wireless Networking
• +9 more

Dr. Danda B. Rawat is an Associate Dean for Research & Graduate Studies and Full Professor in the Department of Electrical Engineering & Computer Science at Howard University's College of Engineering and Architecture (CEA). He leads several initiatives, including the Howard University Data Science & Cybersecurity Center and the DoD Center of Excellence in AI/ML. His research focuses on cybersecurity, machine learning, and wireless networking for emerging systems like IoT, smart cities, and tactical autonomy. Dr. Rawat has secured over $110M in research funding and graduated 39 PhD students, many from underrepresented groups. He holds numerous awards, including the NSF CAREER Award and DHS Scientific Leadership Award. Education : PhD in Computer Science from Old Dominion University. Research Interests : Cybersecurity, AI/ML, wireless networking, tactical autonomy, federated learning, and quantum computing. His work addresses challenges in secure autonomous systems, edge intelligence, and ethical AI. Awards : Includes NSF CAREER, DHS Leadership Award, Presidents’ Medal (Howard University), and IEEE recognitions. He is a Fellow of the IET and ACM/IEEE Distinguished Lecturer. Labs & Teams : Directs the CWiNs Lab, DoD CoE-AIML, and RITA (UARC). Active in federal consortia linking HBCUs to defense innovation. Grants : Over $110M as PI, including a historic $90M USAF contract for RITA, the first HBCU-led UARC.

Giancarlo FortinoView profile

Professor

• Internet of Things
• Wearable Computing
• Distributed Systems
• +2 more

Giancarlo Fortino is a Full Professor of Computer Engineering at the University of Calabria, where he directs the SPEME lab and serves as Rector's delegate to International Relations. He holds a PhD in Computer Engineering from the University of Calabria and has held distinguished positions at Wuhan University of Technology and Huazhong Agricultural University. His research spans wearable computing systems, Internet of Things, e-Health, and distributed systems. He leads development of open-source frameworks including SPINE for body sensor networks and BodyCloud for cloud-assisted healthcare systems. His work consistently addresses real-world challenges in IoT and edge computing. Professor Fortino has received numerous honors including IEEE Fellow (2022), Highly Cited Researcher recognition (2020-2022), and best paper awards at premier conferences. He has authored over 600 publications and leads multiple EU-funded projects on IoT systems. He co-founded SenSysCal and BigTech spin-offs, serves on editorial boards of premier journals, and has graduated 10 PhD students who now hold academic and industry positions worldwide.

Riccardo CantiniView profile

Assistant Professor

• Deep Learning
• Large Language Models
• Sustainable AI
• +3 more

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).

• BioHealth Informatics
• Innovation
• Protein Electrodynamics
• +3 more

Dr. Ogan Gurel is an interdisciplinary academic holding adjunct professorships in Bioengineering (College of Engineering), Kinesiology (College of Nursing & Health Innovation), Management (College of Business), and Philosophy & Humanities (College of Liberal Arts) at the University of Texas at Arlington. His career spans medicine, industry, and academia, with prior roles at institutions like Columbia University, Harvard, and Samsung. He earned an MD from Columbia University, an M.Phil in Structural Biology, and a Harvard AB in Biochemical Sciences. His research focuses on innovation, protein electrodynamics, medical literacy, and information design, emphasizing interdisciplinary approaches. He has authored 14 peer-reviewed papers, a novel, and holds patents in biomedical engineering. Dr. Gurel’s teaching includes courses on innovation, bioelectromagnetics, and medical literacy, and he advises students on projects like 'Measuring Innovation' and 'The Logical Structure of Psychiatry'. He has also contributed to humanitarian efforts in medical relief following disasters. Education: MD, Columbia University College of Physicians & Surgeons (1996) M.Phil in Structural Biology, Columbia University (1991) AB in Biochemical Sciences, Harvard University (1986) Research Focus: Combining sciences and humanities to drive innovation. Key areas include THz medicine, protein dynamics, medical literacy enhancement, and information design. His work bridges fields like cognitive science, philosophy, and electrical engineering. Teaching & Service: Recent courses include 'Fundamentals & Practice of Innovation' and 'Principles of Bioelectromagnetics'. He serves as a judge in university research showcases and has organized interdisciplinary conferences. Inventions & Recognition: Co-inventor on 10 patent applications (4 granted), including microfluidics and THz imaging technologies. His novel Waves explores intersections of science and art.

Fuxin HuangView profile

Academic

• Ship Design and Optimization
• Hydrodynamics
• Computational Fluid Dynamics
• +3 more

Fuxin Huang holds the position of Research Assistant Professor in the Physics & Astronomy Department at George Mason University. His work focuses on computational methods in naval architecture and hydrodynamics, particularly ship hull design optimization using advanced algorithms and numerical techniques. He is affiliated with the university's research programs in marine engineering and fluid dynamics. Research Interests: Dr. Huang specializes in hydrodynamic optimization of ship hull forms, computational fluid dynamics (CFD), and the application of bio-inspired algorithms like the Artificial Bee Colony (ABC) for solving complex engineering problems. His work bridges theoretical fluid mechanics with practical ship design challenges, emphasizing drag reduction and seakeeping performance. Key Research Themes: His recent publications highlight contributions in NURBS-based hull modification techniques, validation of Neumann-Michell wave theories, and error analysis in Green function applications for ship wave simulations. The studies often combine numerical modeling with experimental validation to ensure practical relevance. Advising & Grants: No specific grants or advisees are listed in the provided materials. Dr. Huang's work primarily centers on collaborative computational projects within the naval engineering domain.

Alessandro Di StefanoView profile

Senior Lecturer

• Game Theory
• Network Science
• Machine Learning
• +4 more

Dr. Alessandro Di Stefano is a Senior Lecturer in Computer Science at Teesside University's Department of Computing & Games within the School of Computing, Engineering & Digital Technologies. He holds a PhD in Systems Engineering from the University of Catania (2015) and has held academic and research positions at King’s College London, the University of Cambridge, and the University of Catania. His research focuses on interdisciplinary methodologies combining game theory, network science, and machine learning to study socio-technological systems, human cooperation, and complex dynamics. Education: BSc (2009) and MSc (2012) in Telecommunications Engineering from the University of Catania; PhD in Systems Engineering (2015). Professional Qualifications: Postgraduate Certificates in Learning & Teaching in Higher Education (FHEA, 2022). Research Interests: Game Theory, Network Science, Machine Learning, Human Cooperation, Social Dynamics, Homophily, Epidemic Spreading. He collaborates globally, including with the University of Cambridge, Missouri S&T, and King’s College London. Grants/Projects: Lead PI on projects such as KTP - TaperedPlus (AI-driven design systems) and KTP - CSX Carbon (peatland restoration via deep learning). Active in conferences like CCS, ALIFE, and IJCAI, and serves as a reviewer for top journals (IEEE Transactions, Nature Scientific Reports). Labs/Teams: Part of the Interpretable & Beneficial AI group at Teesside, focusing on AI ethics, regulatory frameworks, and complex network modeling. Collaborates with industry partners on AI applications in construction, healthcare, and environmental monitoring.