جست‌وجوی استاد و پژوهشگر

Chris Irwin Davisمشاهده پروفایل

دانشیار

• Computational Linguistics
• Computational Semantics
• Natural Language Processing (NLP)
• +۷ مورد دیگر

Chris Irwin Davis is an Associate Professor of Instruction in the Department of Computer Science at the University of Texas at Dallas (UTD), where he earned his Ph.D. under Dr. Dan Moldovan. He serves on the UTD Academic Senate (2025-2026). His expertise spans computational linguistics, computational semantics, and NLP for under-resourced languages, particularly Indo-Iranian languages like Farsi and Urdu. His research also explores machine ethics, computational creativity, and artificial general intelligence. Education: Ph.D. in Computer Science from UT Dallas (advisor: Dr. Dan Moldovan). Graduate research at the Human Language Technology Research Institute. Research Interests: Computational semantics and event representation NLP for languages with sparse resources Machine translation and transliteration systems Machine ethics and AGI Assistive technologies and personal robotics Publications focus on NLP applications for under-resourced languages, transliteration systems (e.g., Tajik-Farsi), and machine learning models for grammatical error detection. Recent work explores curriculum learning for infant-inspired AI models and multimodal semantic processing. Teaching includes foundational computer science courses (Discrete Math, Programming Paradigms) and advanced topics like NLP and AI. No formal advisees listed, but extensive teaching and curriculum development experience. Active in developing open-source tools like Phramer (statistical phrase-based translator) and frameworks for event semantics in Persian.

• Wireless Communications
• Millimeter-wave Technology
• Computer Architecture
• +۱ مورد دیگر

Masoud Zarifneshat serves as a Lecturer in the Department of Computer Science at Renssela Polytechnic Institute (RPI), teaching computer architecture and hardware courses since 2022. He holds a PhD in Computer Science from Michigan State University and contributes to RCOS (Rensselaer Center for Open Source) as a supervisor for Fall 2025. His educational background includes: PhD in Computer Science, Michigan State University (2022) Dr. Zarifneshat's research centers on wireless communications and millimeter-wave technology, with emphasis on algorithmic solutions for reliable links. He investigates beam adaptation via out-of-band signals, multi-objective user association for load balancing, and machine learning-driven blockage prediction. His teaching expertise spans computer architecture and hardware, prioritizing student communication and tailored support. Publication trends show concentrated work on millimeter-wave communications (2021-2023) in top journals like IEEE Transactions on Mobile Computing, addressing blockage and reliability challenges. Earlier research (2013-2015) explored wireless sensor networks and mobility models, demonstrating consistent focus on network optimization. He actively mentors through RCOS supervision and teaches core courses including CSCI 4963 (Computer Architecture) and CSCI 2800 (CAOS). While specific grant funding isn't detailed, his publication record indicates ongoing research activity. His collaborative work with institutions like IEEE-affiliated journals highlights engagement with the broader academic community. Dr. Zarifneshat operates within RPI's Department of Computer Science without a named research lab, but his RCOS involvement provides student project leadership opportunities. His work bridges theoretical algorithm development and practical wireless network applications.

• Machine Learning
• Communication Theory
• Data Storage
• +۳ مورد دیگر

Elnaz Banan Sadeghian is an Assistant Professor at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2017), alongside MS degrees from Georgia Tech (2015) and AmirKabir University (2008), and a BS from Shahid Beheshti University (2005). Her research focuses on Communication Theory, Machine Learning, Data Storage (particularly Magnetic Recording), Assistive Technology, and Brain-Computer Interfaces (BCI). Recent work includes neural network-based approaches for multitrack detection in magnetic recording and low-latency BCI systems leveraging time-series prediction and self-attention mechanisms. She also explores subject-independent BCI frameworks using supervised autoencoders. Dr. Sadeghian has received notable grants including an NSF CAREER Award and NSF CRII funding for machine learning applications in magnetic storage. Her contributions span over 20 peer-reviewed publications since 2007, addressing topics like asynchronous track detection algorithms, timing error correction in read channels, and fractal analysis of EEG signals for BCI. She is an ASTC Fellow and active member of IEEE and EMBS societies. Currently, she advises PhD candidates with expertise in machine learning and communication theory.

Antonietta Lanzaمشاهده پروفایل

استادیار

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +۴ مورد دیگر

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.

Timothy Havensمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Timothy Havens is a Professor at Michigan Technological University in the Department of Computer Science within the College of Computing. He holds the William and Gloria Jackson Professorship and serves as Executive Director of both the Great Lakes Research Center and the Institute of Computing and Cybersystems. Dr. Havens also directs the PRIME Lab and has been recognized for both teaching and research excellence, including the 2014-15 Professor of the Year award from IEEE Eta Kappa Nu and Best Paper Awards at FUZZ-IEEE 2012 and IEEE SMC 2011. Dr. Havens received his Ph.D. in Electrical and Computer Engineering from the University of Missouri, Columbia in 2010. Prior to joining Michigan Tech, he was an NSF/CRA Computing Innovation Postdoctoral Fellow at Michigan State University under Dr. Anil Jain. Before his Ph.D. work, he was an Associate Technical Staff member at MIT Lincoln Laboratory. His educational background includes an M.S. in Electrical Engineering (2000) and a B.S. in Electrical Engineering (1999), both from Michigan Technological University. His research focuses on pattern recognition and machine learning, signal processing, and sensor fusion, with specific expertise in fuzzy integrals, Choquet integration, community detection in networks, and heterogeneous data mining. Dr. Havens has made significant contributions to explainable AI, particularly through the application of fuzzy integrals to deep learning systems. His recent publications demonstrate strong activity in developing novel regularization techniques for fuzzy Choquet integrals, efficient algorithms for community detection, and similarity measures for interval data. Dr. Havens' research has been consistently funded by prestigious organizations including DARPA, NSF, US Navy, Office of Naval Research, National Geospatial-Intelligence Agency, Ford Motor Company, MIT Lincoln Laboratory, and numerous other government and industry partners. His current projects include significant grants for radar systems in the Great Lakes, generative modeling of satellite imagery, and algorithms for autonomous robot systems. Scientific Awards: 2014-15 Professor of the Year by IEEE Eta Kappa Nu, Beta Gamma Chapter Best Paper Award at FUZZ-IEEE 2012 Best Paper Award at IEEE SMC 2011 Best Student Paper Award Finalist (2018) IEEE Franklin V. Taylor Memorial Best Paper Award (2011) Dr. Havens has successfully mentored numerous graduate students, many of whom appear as co-authors on his publications. His research grants demonstrate strong leadership in coordinating multi-institutional collaborations with substantial funding. Beyond his academic work, Dr. Havens is also an active musician, playing bass in several bands including MUFJAC, FLOTUS, Defenestra, Skills of Ortega, Psylocubik, Triptych, Wheels of Fire, and Odibil libidO.

Shuai Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Edge Computing
• Reinforcement Learning
• +۳ مورد دیگر

Shuai Zhang is Assistant Professor in the Department of Data Science at New Jersey Institute of Technology (NJIT). He earned his Ph.D. in Electrical and Computer Engineering from Rensselaer Polytechnic Institute under Dr. Meng Wang, with industry experience at IBM Thomas J. Watson Research Center and MIT-IBM Watson AI Lab. Research focuses on theoretical foundations and practical applications of machine learning, including reinforcement learning algorithms, transformer architectures, edge computing optimization, and underwater robotics. Key contributions address model efficiency, theoretical guarantees in deep learning, and multi-agent systems for challenging environments. Recent publications demonstrate expertise in: provable learning guarantees for transformers and neural networks, federated learning frameworks for non-IID data, reinforcement learning for marine robotics, and efficient model architectures for edge deployment. Emerging themes include real-time underwater systems, federated self-supervised learning, and algorithmic fairness in imbalanced data scenarios. Practical implementations include simulator development for underwater pursuit-evasion games (UPEGSim) and collaborative frameworks for unmanned marine vehicles operating in extreme conditions.

Ted Lakowskiمشاهده پروفایل

استاد

• Epigenetics
• DNA Methylation
• LC-MS/MS
• +۳ مورد دیگر

Dr. Ted Lakowski is Professor and Associate Dean (Research) at the College of Pharmacy, University of Manitoba, where he directs the pharmaceutical analysis laboratory. His research examines epigenetic modifications in disease states using advanced LC-MS/MS metabolomic approaches. Research focuses on quantifying histone/DNA modifications and developing targeted epigenetic therapies. Key discoveries include: identifying HDAC inhibitor effects on histone demethylase expression, characterizing DOT1L inhibitor impacts beyond primary targets, and discovering acetylated lysine residues in myelin basic protein relevant to multiple sclerosis. Current projects investigate insemination fraud biomarkers and epigenetic enzyme inhibition strategies. Research employs in vitro, cellular, and animal models across cancer, neurological, and metabolic disorders. The laboratory develops analytical methods for epigenetic modification quantification and drug metabolite tracking, including amantadine metabolite monitoring in glioblastoma. Dr. Lakowski mentors graduate students and postdoctoral fellows in pharmaceutical chemistry, with research supported by national health grants and cancer research organizations.

Evangelos Kranakisمشاهده پروفایل

استاد

• Algorithmics
• Distributed Computing
• Mobile Agent Computing
• +۳ مورد دیگر

Evangelos Kranakis is the Chancellor's Professor at Carleton University's School of Computer Science (SCS), where he has been since 1991 and served as director from 1994 to 2000. He holds a Ph.D. in Mathematics from the University of Minnesota (1980) and a B.Sc. from the National Technical University of Athens (1973). His research focuses on Algorithmics, Distributed and Mobile Agent Computing, Networks (Ad Hoc/Wireless/Sensor), Cryptographic Security, and Computational Biology . He has held visiting positions at institutions worldwide, including ETH Zurich, Université de Paris Sud, and Simon Fraser University. Research interests emphasize algorithmic foundations for distributed systems, mobile agents, and network security. His work bridges theoretical models with practical applications in wireless networks, robotic search strategies, and underwater communication. He leads the Distributed and Network Computing research group and maintains an academic homepage with publications and ORCID profile ( orcid.org/0000-0002-8959-4428 ). Key articles explore topics like optimal task offloading in edge computing, robot coordination for search/evacuation, and UAV trajectory design using reinforcement learning. His contributions span interdisciplinary fields, with a focus on fault-tolerant systems and network optimization. Notable technical achievements include pioneering work on distributed algorithms for mobile agents and underwater acoustic networks. His research also addresses quantum network protocols and multi-agent systems in dynamic environments.

Julien Leclercمشاهده پروفایل

استاد پژوهشی

• Robotic magnetic manipulation
• Underwater robots localization
• Motion planning for teams of robots

Dr. Julien Leclerc is a Research Assistant Professor in the Electrical and Computer Engineering department at the University of Houston's Cullen College of Engineering. His research focuses on robotic magnetic manipulation for medical applications, underwater robots localization and mapping, and motion planning for teams of robots. He holds a Ph.D. in Electrical Engineering from the University of Lorraine, France. Research interests include: Medical robotics for thrombus fragmentation Underwater pipe detection using magnetic imaging CFD simulation of magnetic swimmers 3D navigation systems for medical devices Network optimization for multi-robot systems Recent publications demonstrate strong focus on experimental validation of magnetic manipulation systems, with applications ranging from medical procedures to underwater infrastructure inspection.

Sreeraman Rajanمشاهده پروفایل

استاد

• Sensors and Sensor Systems
• Compressive Sensing
• Signal Processing
• +۸ مورد دیگر

Prof. Sreeraman Rajan is a Professor and Tier II Canada Research Chair in Sensor Systems at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. He holds a Ph.D. from the University of New Brunswick and has prior experience at Defence Research and Development Canada (DRDC), Biopeak Corporation, and JDS Uniphase. His research focuses on sensors, compressive sensing, signal processing, and machine learning with applications in biomedical, defense, and security domains. Education : Bachelor’s in Electronics and Communications Engineering (Bharathiyar University, 1987) M.Sc. Electrical Engineering (Tulane University) Ph.D. Electrical and Computer Engineering (University of New Brunswick) Research Interests : Sensors and sensor systems for biomedical and defense applications Compressive sensing and signal processing techniques Machine learning and pattern classification Doppler radar-based life sign monitoring and human activity recognition Biomedical signal processing for noninvasive diagnostics Awards : Tier II Canada Research Chair (2015) IEEE MGA Achievement Award (2012) Queen Elizabeth II Diamond Jubilee Medal (2012) Outstanding Contribution Award (Defence Research and Development Canada, 2013) Grants : Tier II Canada Research Chair: $500,000 (2015-2019) Correctional Services Canada: $98,850 (2014-2015) Royal Canadian Air Force: $50,000 (2013-2015) Advising & Collaborations : Supervised over 20 graduate and undergraduate students Co-founder of multiple patents in radar and biomedical systems Active in IEEE leadership roles, including chair of IEEE Ottawa Section Labs & Teams : Conducts research in Carleton’s Systems and Computer Engineering labs, focusing on sensor systems, radar technology, and biomedical signal processing. Collaborates with DRDC, industry partners, and international researchers on defense and healthcare projects.

Marcella Vaicikمشاهده پروفایل

استادیار

• Biomaterials
• Tissue Engineering
• Cell-Matrix Interactions
• +۴ مورد دیگر

Marcella Vaicik is an Assistant Professor of Biomedical Engineering at Illinois Institute of Technology (IIT), affiliated with the Armour College of Engineering. Her research focuses on biomaterials, tissue engineering, and regenerative medicine, particularly exploring hydrogel-based systems and extracellular matrix (ECM) interactions to address challenges in adipose tissue function, cardiac regeneration, and drug delivery. She holds a Ph.D. in Biomedical Engineering from IIT (2015), M.S. in Bioengineering from the University of Illinois at Chicago (2009), and B.S. in Chemical Engineering from Purdue University (2004). Research Interests : Her work bridges biomaterials design with cell behavior modulation, emphasizing hydrogel platforms for adipose tissue engineering, ECM-driven therapeutic delivery, and 3D bioprinting for cardiac repair. Recent projects include developing biocompatible nanocomposite emulsions for drug delivery, studying ECM composition effects on cancer treatment efficacy, and optimizing MRI protocols for cardiac imaging. Publications : Over 70 peer-reviewed articles, including studies on laminin α4 deficiency in adipose tissue expansion, dual-crosslinked alginate microbeads, and myocardial regeneration via engineered patches. Her work often intersects material science, cellular biology, and clinical applications. Awards : Recognized with awards from the Endocrine Society, Society of Women Engineers, and NIH, reflecting her contributions to biomedical innovation and leadership in engineering education. Collaborations : Active in multi-institutional projects, including studies with Northwestern University and the University of Chicago on ECM remodeling and cardiac tissue engineering.

John Crassidisمشاهده پروفایل

استاد

• Spacecraft attitude determination & control
• Formation flying guidance & navigation
• Robust nonlinear control
• +۳ مورد دیگر

John Crassidis is the Moog Professor of Innovation and SUNY Distinguished Professor at the University at Buffalo, Department of Mechanical and Aerospace Engineering. He serves as Executive Director of the Collaborative Institute for Multisource Information Fusion and Director of the Center for Space Cyber Strategy and Cyber Security. His research focuses on spacecraft attitude control, formation flying, nonlinear control, and vehicle dynamics. Crassidis holds a PhD (1993), MS (1991), and BS (1989) in Mechanical and Aerospace Engineering from the University at Buffalo. His work emphasizes advanced navigation systems, including inertial navigation, Kalman filtering, and multi-sensor fusion. Key contributions include optimizing spacecraft maneuvers, developing algorithms for attitude estimation using light curves, and analyzing space object characteristics via polarimetric data. He has pioneered applications of particle swarm optimization and total least squares methods in aerospace engineering. Crassidis has received numerous awards including the UB Meyerson Award for Teaching (2017), NASA iTech Semifinalist recognition (2017), and the AIAA Best Presentation Award (2017). His research bridges theoretical advancements with practical implementations in satellite systems and space situational awareness.

Shannon David Bluntمشاهده پروفایل

استاد

• Radar Signal Processing
• Waveform Design and Optimization
• Cognitive Radar Systems
• +۳ مورد دیگر

Shannon David Blunt is the Roy A. Roberts Distinguished Professor at the University of Kansas, affiliated with the School of Engineering's Department of Electrical Engineering and Computer Science. His research focuses on advanced radar signal processing, waveform design, cognitive radar systems, and radar-communications integration. He leads experimental projects such as the Waveform Diversity Experimentation System (WaDES) and the SMART Hub initiative addressing spectrum crisis challenges. His work emphasizes spectral efficiency, adaptive processing, and real-time cognitive radar solutions. Blunt holds editorial roles, including founding the IEEE Transactions on Radar Systems, and has contributed to over 100 peer-reviewed articles since 2017. Research Interests: Radar signal processing innovations include optimizing radar waveforms for multifunction digital arrays, developing random FM techniques, and advancing clutter suppression algorithms. His research bridges radar and communications via initiatives like Phase-Attached Radar-Communications (PARC) and Multi-User Radar/Communications (MURC). Key areas include waveform diversity, spectrum sharing, and real-time spectral notching for cognitive radar. He explores bio-inspired systems and medical signal processing applications, though his primary focus remains radar systems engineering. Recent Trends in Articles: Recent work (2023–2025) emphasizes hybrid FM waveforms, reiterative super-resolution algorithms, and physical waveform optimization. Publications address challenges in Doppler robustness, waveform recycling, and experimental validation of spectrum-sharing technologies. Collaborations with industry and government labs highlight applied research in spectrum management and mm-wave radar for automotive/urban mobility. Awards & Recognition: Roy A. Roberts Distinguished Professorship Leadership in the IEEE Radar Journal and Special Sections Grants & Labs: Directs the SMART Hub (Solving the Spectrum Crisis) and oversees WaDES. His lab's experimental systems demonstrate real-time cognitive radar and spectrum-sharing solutions. Advises on federal grants for radar spectrum engineering and vehicular RF convergence projects. Labs & Teams: Active in the University of Kansas's radar engineering group, collaborating with the SMART Hub consortium and industry partners like Raytheon and Boeing.

Jiang Luمشاهده پروفایل

دانشیار

• Intelligent Sensor Systems
• Biomedical Devices
• Tele-Healthcare
• +۶ مورد دیگر

Jiang Lu is an Associate Professor and Program Director of Computer Engineering at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Electrical and Computer Engineering from the University of Alabama, and prior degrees from the University of Florida and Shanghai Maritime University. His research focuses on intelligent sensor systems, biomedical devices, tele-healthcare, wireless communication, machine learning, and embedded systems. He is a member of IEEE and actively involved in conference organizing and journal reviewing. Dr. Lu's expertise revolves around integrating intelligent algorithms and wireless communication into biomedical applications. His work emphasizes sensor networks for healthcare monitoring, such as SmartSocks and SmartShoe systems for activity tracking and gait analysis. He has contributed to projects like robot-assisted emergency systems for elderly care and distributed sensor networks for multi-human tracking. His research spans areas like compressed sensing, neural network optimization, and IoT security, with notable publications in IEEE journals and conferences. He has received awards including the Best Student Paper Award at IEEE Sensors 2013 and a finalist position for the Best Paper Award at IEEE MFI 2012. Dr. Lu teaches graduate courses on wireless sensor networks and undergraduate courses in telecommunications and engineering design. His current projects include ubiquitous sensor systems for smart environments, tele-healthcare solutions leveraging computational intelligence, and low-cost biomedical monitoring devices. He explores applications of blockchain in hardware security and FPGA-based neural network acceleration.

Xiaojun "Gene" Shanمشاهده پروفایل

دانشیار

• Operations Research
• Healthcare Systems Engineering
• Game-Theoretic Modeling
• +۵ مورد دیگر

Dr. Xiaojun (Gene) Shan is an Associate Professor of Engineering Management at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Industrial and Systems Engineering from the University at Buffalo (SUNY-Buffalo) and an M.A. in Cognitive Psychology and Psycholinguistics from the same institution. His research focuses on applying operations research to healthcare systems, disaster risk management, and infrastructure resilience, with notable work in game-theoretic modeling of complex systems. Dr. Shan teaches courses in logistics management, engineering management planning, and business analytics. His research interests include healthcare delivery optimization, electric grid reliability, hurricane risk mitigation, and strategic defense against adversarial threats. He has been recognized with awards such as the Society for Risk Analysis Travel Award and finalist distinctions in INFORMS competitions. Dr. Shan’s work bridges theoretical models (e.g., multi-period defensive resource allocation) with practical applications like staffing optimization in healthcare and fraud detection frameworks. Education: Ph.D. in Industrial & Systems Engineering, SUNY-Buffalo (2008) M.A. in Cognitive Psychology & Psycholinguistics, SUNY-Buffalo (2008) His publications span journals like Reliability Engineering & System Safety and European Journal of Operational Research , with recent work addressing patient scheduling systems and green food recommendation algorithms. Dr. Shan collaborates on interdisciplinary projects, including disaster risk modeling and smart grid cybersecurity. He actively contributes to improving healthcare operations through data-driven approaches and lean methodologies. Awards: Society for Risk Analysis Travel Award (2011, 2012) INFORMS Decision Analysis Society Student Paper Award Finalist (2012, 2010) 11th Annual IIE Doctoral Colloquium Attendee (2012) Dr. Shan’s advising and grants focus on healthcare analytics and infrastructure resilience, with projects funded by organizations emphasizing operational excellence and risk mitigation. He leads efforts to standardize care management processes and optimize staffing models in clinical settings.