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Monica Martinez WilhelmusView profile

Assistant Professor

• Sea Ice Dynamics
• Remote Sensing Observations
• Fluid Transport
• +3 more

Dr. Monica Martinez Wilhelmus is the Thomas J. and Alice M. Tisch Assistant Professor of Engineering at Brown University's School of Engineering. She holds affiliations with NASA Jet Propulsion Laboratory (JPL) and is an adjunct professor at the University of California Riverside. Her research integrates experimental and numerical methods to study transport phenomena at the intersection of biology, oceanography, and fluid mechanics. Key interests include sea ice dynamics, remote sensing, and fluid transport by plankton aggregations. Education: B.Sc. in Mechanical Engineering from Universidad Nacional Autonoma de Mexico (2010), M.S. and Ph.D. in Mechanical Engineering from Caltech (2012, 2016). Her postdoctoral work at JPL/Caltech focused on collaborative ocean science projects. Research in the Wilhelmus Lab explores fluid mechanics in environmental and biological systems, with projects like Arctic sea ice tracking, robotic platforms, and plankton hydrodynamics. The lab's work bridges engineering and environmental science to address climate observation challenges and ocean turbulence. Her interdisciplinary approach combines satellite data analysis with field experiments, contributing to understanding Arctic Ocean eddies and submesoscale currents. Collaborative efforts include developing algorithms for ice floe tracking and advancing sediment diagenesis models.

Michele GermaniView profile

Professor

• Industrial Engineering
• Ergonomics
• Sustainability
• +7 more

Prof. Michele Germani is a Full Professor at the Polytechnic University of Marche, Department of Industrial Engineering and Mathematical Sciences. His research focuses on ergonomics, sustainability, and human-centered design, with particular emphasis on industrial automation, wearable technologies, and circular economy principles. He leads projects on ergonomic risk assessment, environmental impact modeling, and smart manufacturing systems. Academic Background: Holds a position in the Faculty of Engineering, contributing to both teaching and research. His work integrates machine learning, sensor-based systems, and virtual reality to address challenges in manufacturing, healthcare, and sustainable design. Research Interests: Prof. Germani explores topics such as design for disassembly, eco-design tools, and the application of advanced technologies (e.g., exoskeletons, cobots) to enhance workplace safety and efficiency. His studies often bridge engineering and environmental science, aiming to reduce carbon footprints through innovative design methodologies. Key Projects: Development of decision support systems for fall risk detection, sensor-based ergonomic evaluation tools, and frameworks for circular economy implementation. He also investigates the impact of Industry 4.0 technologies on workforce dynamics and operator training via mixed reality simulations. Grants & Collaborations: Engaged in interdisciplinary collaborations focusing on sustainable manufacturing, healthcare technology, and smart systems. His research has led to tools like Durabot for durability analysis and Greenbuild for green building design. Labs & Teams: Oversees labs developing eco-design strategies, human-robot collaboration systems, and ergonomic assessment protocols, with a focus on real-world industrial applications.

Fahad KhanView profile

Researcher

• Intelligent Automation
• Human-Robot Collaboration
• Machine Learning
• +3 more

Fahad Khan is a Researcher at Cranfield University's Centre for Robotics and Assembly, part of the Aerospace department. He holds an MSc in Robotics Engineering (Cranfield University) and a B.Tech in Mechatronics (NMIMS University). Currently employed full-time as a Research Assistant and part-time PhD candidate, his work focuses on intelligent robotics systems, human-robot collaboration (HRC), and adaptive automation technologies. Education: MSc Robotics Engineering, Cranfield University (UK) B.Tech Mechatronics Engineering, NMIMS University (India) Current Projects: Smart Cobotics (ISCF) project: Developing adaptive HRC systems using physiological and contextual data. EPSRC-funded research on facial emotion recognition for manufacturing robots. Research interests include IoT integration, motion control, and machine learning applications in robotics. He has published on HRC gesture design, emotion recognition systems, and ROS 2-based industrial frameworks. Awards: None explicitly listed. Grants: Supported by Engineering and Physical Sciences Research Council (EPSRC) through the Made Smarter Innovation project. Technical expertise spans ROS, OpenCV, MATLAB, and PLC programming. His LinkedIn and GitHub profiles showcase contributions to robotic systems and machine learning projects.

Robert JacobView profile

Professor

• Human-Computer Interaction
• Brain-Computer Interfaces
• Implicit Interaction Techniques
• +3 more

Robert Jacob is a Professor of Computer Science at Tufts University's School of Engineering, where he leads research in human-computer interaction with particular focus on implicit brain-computer interfaces. His work bridges computer science, cognitive science, and interface design to create adaptive systems that respond to users' cognitive states without explicit input. His educational background includes a Ph.D. from Johns Hopkins University. Professional milestones include: ACM CHI Academy membership (2007) ACM Fellow designation (2016) Leadership roles as ACM SIGCHI Vice President and conference chair for CHI, UIST, and TEI Professor Jacob's research centers on implicit interaction techniques, particularly using fNIRS brain sensing to create adaptive interfaces. His work has evolved from foundational studies in reality-based interaction and tangible programming to current neuroadaptive systems that measure cognitive workload in real-time. This research spans domains including music learning, museum education, and general user interface adaptation. His publications reveal consistent focus on brain-computer interfaces since 2012, with increasing sophistication in physiological measurement and machine learning techniques. Recent work integrates multiple physiological signals beyond brain data to create comprehensive user state models. Major recognitions include: CHI 2016 Best Paper Award for music learning research CHI 2014 and 2012 Best Paper Honorable Mentions Keynote addresses at major conferences including Neuroadaptive Technology Conference (2017) Extensive media coverage in New Scientist, IEEE Computer, and Boston Globe Professor Jacob has mentored 17 PhD students who now hold faculty positions at institutions including Worcester Polytechnic Institute, Northwestern University, and Carleton University. His HCI Lab, located in the Joyce Cummings Center, receives funding from NSF and other sources supporting neuroadaptive interface research. Current projects focus on broadening implicit interaction to include multiple physiological measurements while maintaining user privacy and system transparency.

Latifur KhanView profile

Professor

• Data Mining
• Cybersecurity
• Machine Learning
• +2 more

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Haynes HeatonView profile

Assistant Professor

• Computational Biology
• Genomics
• Bioinformatics
• +4 more

Haynes Heaton is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He holds a Ph.D. in Computational Biology from Cambridge University, an M.D. from Brown University, and a B.S. in Computer Science from Brown University. His interdisciplinary work bridges computational methods with biomedical applications, focusing on genomic technologies for malaria research and single-cell analysis. Research interests include genome sequencing of malaria vectors (e.g., Anopheles mosquitoes), development of bioinformatics tools (e.g., LCSKPOA, Souporcell), and structural variation analysis in human and pathogen genomes. His work has contributed to chromosomal reference genomes for multiple malaria mosquitoes, advancing understanding of vector biology and disease transmission. Publications highlight innovations in genome assembly, single-cell RNA-seq deconvolution, and computational methods for genomic data analysis. While no explicit awards are listed, his research has been featured in Auburn Engineering news for breakthroughs in leukemia diagnostics via software development. He collaborates with medical and computational teams to translate genomic insights into clinical applications. No advising records or grants are explicitly detailed in the provided text.

Sudhakar GantiView profile

Associate Professor

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +3 more

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

Seth PolsleyView profile

Assistant Professor

• Human-Computer Interaction
• Intelligent Systems
• Educational Technology
• +4 more

Seth Polsley is an Assistant Professor in the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska-Lincoln. His academic home resides in the School of Computing, where he bridges intelligent systems design with human-computer interaction to enhance educational and universal computing experiences. BS in Computer Engineering (2014) - University of Kansas MS (2017) & PhD (2023) in Computer Engineering - Texas A&M University His research explores: Intelligent tutoring systems Brain-computer interfaces Accessible educational technologies Machine learning for child development assessment Sketch recognition in STEM learning Recent publications demonstrate expertise in tactile learning interfaces, sketch-based developmental assessment, and equitable AI systems. Key disciplines span Human-Computer Interaction, Machine Learning, and Educational Technology. Scientific recognition includes: James Blackiston Memorial Graduate Fellowship Sigma Xi Research Award With professional experience at Lexmark International and MIT Lincoln Lab, Polsley combines practical engineering with educational innovation. His work on sketch-based tools and wearable systems addresses both technical and societal challenges in computing.

Guy BrownView profile

Professor

• Computational Auditory Scene Analysis
• Speech Recognition
• Auditory Modelling
• +3 more

Professor Guy Brown is Chair of Computer Science at the University of Sheffield's School of Computer Science. He holds a BSc in Applied Science (1984), PhD in Computer Science (1992), and MEd in Teaching and Learning (1997). His research focuses on Computational Auditory Scene Analysis (CASA), noise-robust speech recognition, auditory modeling, and binaural processing. Research interests include: Machine hearing systems for sound source separation Reverberation-robust speech processing Auditory scene analysis models for normal/impaired hearing Applications in robotics and healthcare technologies Publication trends show recent focus on deep learning approaches for biomedical applications including sleep apnea detection, respiratory sound analysis, and multimodal health monitoring systems using neural networks. Honors include: University Senate Award for Excellence in Teaching (2014) Microsoft Software Engineering Innovation Award (2013) He leads doctoral supervision for 15+ students and has secured research funding from EPSRC, Innovate UK, EU FP7, and AHRC. Manages the Speech and Hearing research group and has held visiting positions at international institutions including LIMSI-CNRS and ATR Japan.

Paul NuyujukianView profile

Assistant Professor

• Motor Systems Neuroscience
• Neuroengineering
• Brain-Machine Interfaces
• +4 more

Paul Nuyujukian serves as an Assistant Professor of Bioengineering and Neurosurgery, with courtesy appointment in Electrical Engineering at Stanford University. He is a Faculty Scholar of the Wu Tsai Neurosciences Institute, directing the Brain Interfacing Laboratory where his team develops neural interface technologies for clinical applications in stroke and epilepsy. Education: MD, Stanford University (2014) PhD in Bioengineering, Stanford University (2012) BS, UCLA (2006) Dr. Nuyujukian's research integrates motor systems neuroscience with neuroengineering to decode brain activity during movement and recovery from injury. His laboratory pioneers brain-machine interface (BMI) platforms that translate neural signals into communication and control systems, with particular emphasis on intracranial EEG recording and real-time neural decoding. Current work focuses on developing clinically viable BMI solutions for neurological conditions through both preclinical models and human trials, advancing our understanding of neural population dynamics in health and disease. Recent publications reveal strong trends in intracranial EEG acquisition systems, seizure detection algorithms using information theory, and closed-loop BMI applications for ambulatory neuroscience. His work bridges fundamental neuroscience with clinical translation, particularly in epilepsy monitoring, chronic pain management, and neural prosthetics for paralysis. A notable emphasis exists on creating scalable, minimally invasive recording platforms that reduce clinical burden while maintaining high-fidelity neural data. Scientific Awards: No specific awards listed in provided materials As director of the Brain Interfacing Laboratory, Dr. Nuyujukian mentors students and collaborators in neural engineering research while securing grant funding for BMI development. His group maintains active collaborations with Stanford's Department of Neurosurgery and Neurology for clinical translation, with current projects including real-time decision-state decoding and personalized network mapping for pain management. The laboratory operates advanced facilities for both animal and human neural recording, emphasizing seamless integration of engineering innovation with clinical neuroscience. The Brain Interfacing Laboratory comprises multidisciplinary scientists and engineers developing next-generation neural interfaces. Current initiatives include the LiCoRICE platform for ambulatory neuroscience, seizure detection systems using compression-enabled entropy estimation, and ketamine's effects on hippocampal connectivity. The team actively participates in clinical trials for BMI applications in stroke rehabilitation and epilepsy, with strong partnerships across Stanford's medical and engineering schools to accelerate technology translation.

Eileen ScanlonView profile

Academic

• Educational Technology
• Science Communication
• Public Engagement in Science
• +4 more

Eileen Scanlon is the Regius Professor of Open Education at the Open University, a title awarded for her groundbreaking contributions to educational technology and science communication. She holds a PhD in cognitive models of physics problem solving and has been affiliated with the University since 1975, working extensively in the Institute of Educational Technology (IET). As Associate Director of Research and Innovation in the IET, she leads strategic research initiatives in technology-enhanced learning (TEL), including directing the openTEL research theme. Her work focuses on bridging formal and informal learning contexts, advancing citizen science, and leveraging digital tools for inclusive education. She has pioneered platforms like nQuire for community-driven inquiry and contributed to influential reports like the Innovating Pedagogy series. Scanlon also holds visiting roles at the University of Edinburgh and previously served at Bletchley Park and the University of California, Berkeley. Her research interests span educational technology, science communication, and open educational resources (OERs), with an emphasis on democratizing access to science. Key achievements include developing frameworks for MOOCs, advancing virtual microscopy in distance learning, and promoting participatory citizen science initiatives. Education: PhD in physics education (1990), Open University Affiliations: Institute of Educational Technology (IET), Moray House School of Education (Edinburgh) Grants: Leadership in openTEL, EC-funded MAZI project, and MASELTOV incidental learning initiatives Labs: Computers and Learning Research Group (CALRG), Centre for Research in Education and Educational Technology (CREET)

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +3 more

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

Sundaresan JayaramanView profile

Professor

• Smart Textile-based Wearable Systems
• Computer-aided Manufacturing
• Engineering Design
• +8 more

Dr. Sundaresan Jayaraman is a Professor at the School of Materials Science and Engineering, Georgia Institute of Technology, and Founding Director of the Kolon Center for Lifestyle Innovation. His research focuses on converging textiles with computing, notably pioneering the concept of 'Fabric is the Computer.' Key contributions include the Smart Shirt (Wearable Motherboard™), featured in LIFE Magazine and archived at the Smithsonian. He has secured $16M+ in research funding from NSF, DARPA, and industry. His work spans smart textiles, respiratory protection systems, and computer-aided manufacturing. Awards include the 1989 Presidential Young Investigator Award and the 2018 Textile Institute Research Publication Award. He holds ten U.S. patents and serves on editorial and advisory boards for journals like the Journal of the Textile Institute. Professional roles include leadership in National Academies committees on manufacturing and personal protective equipment. Education & Early Career: Dr. Jayaraman’s career began at Software Arts, Inc. (developers of VisiCalc) and Lotus Development Corporation, where he contributed to early spreadsheet and equation-solving software. His PhD research led to TK!Solver, a pioneering equation-solving program. Research Interests: His work bridges engineering and healthcare through smart textiles, wearable biomedical systems, and advanced manufacturing. Current projects address respiratory protection systems, wearable sensor networks, and personalized healthcare technologies. He emphasizes interdisciplinary collaboration to address societal challenges in health, security, and quality of life. Publications & Impact: Over 100 refereed papers and book chapters highlight his contributions to textile informatics, healthcare wearables, and manufacturing automation. Recent articles focus on next-generation respiratory protection devices and continuous fit monitoring systems. Past innovations include the Wearable Motherboard™ and sensor-integrated garments for vital signs monitoring. Awards & Recognition: In addition to his NSF and Textile Institute honors, he received the Georgia Technology Research Leader Award (2000) and Distinguished Alumni Award from A.C. College of Technology (2019). He is a Fellow of the Textile Institute and founding member of IEEE Technical Committees on Biomedical Wearables. Labs & Teams: Leads the Kolon Center for Lifestyle Innovation and collaborates with industry partners on textile-based computing solutions. His lab’s work on 3D-printed respiratory devices and smart garments exemplifies cutting-edge translational research.

Ehud SharlinView profile

Professor

• Human-robot interaction
• Tangible user interfaces
• Virtual reality
• +4 more

Ehud Sharlin is a Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on Human-Computer Interaction (HCI) with specializations in human-robot interaction, tangible interfaces, virtual/augmented reality, and autonomous vehicle interactions. He holds a Ph.D. in Computing Science from the University of Alberta (2003), and M.Sc. and B.Sc. degrees in Electrical and Computer Engineering from Ben-Gurion University of the Negev (1997 and 1990). Teaches CPSC 481: Human-Computer Interaction I Recipient of the NSERC Discovery Accelerator Award (2019), ACM Creativity & Cognition Honourable Mention (2018), and multiple academic excellence awards Active in industry collaborations, particularly in medical simulation (e.g., VRSpineSim) and geosciences (e.g., PLANWELL) Research explores: Embodied interaction through robotics and wearables Autonomous vehicle-pedestrian communication systems Immersive tools for creative and professional domains Accessibility in human-technology interfaces Publications span over 100 peer-reviewed works, emphasizing design methodologies, user experience in XR systems, and ethical considerations in sociotechnical systems. His work bridges technical innovation with human-centered design principles.

Svetlana YanushkevichView profile

Professor

• Biometric Technologies
• Machine Learning
• Signal Processing
• +3 more

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.