جست‌وجوی استاد و پژوهشگر

• Health Sciences
• Gerontology
• Neurocognitive Disorders
• +۳ مورد دیگر

Prof. Dr. Eling de Bruin is a Lecturer at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. His research focuses on developing and evaluating exergame-based interventions targeting neurocognitive disorders, motor-cognitive training for aging populations, and stroke rehabilitation. He leads studies on personalized exergame protocols (e.g., PEMOCS framework) and their impact on cognitive function, gait recovery, and fall prevention. His work integrates wearable sensor technology, biofeedback systems, and clinical assessment tools to improve outcomes for chronic conditions like stroke, diabetes, and sarcopenia. Key areas include exergame design, hybrid training modalities, and biomarker validation (e.g., heart rate variability for neurocognitive screening). His research also explores sports biomechanics in youth athletes and injury prevention strategies for alpine skiers. Collaborative projects involve interdisciplinary teams from rehabilitation medicine, biomedical engineering, and computer science to create user-centered exergame solutions. Current initiatives emphasize home-based interventions and tele-rehabilitation to enhance accessibility for older adults and long-term care residents. Methodological contributions include validation of motor-cognitive assessment systems using virtual reality and inertial measurement units.

Andrew Headمشاهده پروفایل

استادیار

• Human-computer interaction
• Programming
• Reading
• +۵ مورد دیگر

Andrew Head is an Assistant Professor at the University of Pennsylvania's Department of Computer Science, specializing in Human-Computer Interaction (HCI) and Programming. His work bridges interactive reading , math notation accessibility , and AI-assisted code comprehension . Affiliated with Penn HCI, PLClub, and MindCORE, he co-leads research with Danaé Metaxa and Benjamin Pierce. University of Pennsylvania Assistant Professor, Computer Science Affiliations: Penn HCI, PLClub, MindCORE His research focuses on interactive reading interfaces , AI-powered programming tools , and math notation analysis . Recent projects include: FreeForm : Interactive math notation editor Tyche : Property-based testing tools Explainable Notes : Medical note interpretation systems Publications in CHI , UIST , and ICSE demonstrate his systems-centric approach combining user studies with working prototypes. Notable awards include Best Paper at UIST 2024 and CHI 2022. Advising: Ph.D. Students: Alyssa Hwang, Litao Yan, Hita Kambhamettu, Jeff Tao, Jessica Shi Grants: $1M NSF grant for Property-based Testing Tools (2024) Teaching: Spring 2025: CIS 4120/5120 - Human-Computer Interaction Fall 2024: CIS 7000 - Interactive Reading

• Biostatistics
• Longitudinal Studies
• Public Health
• +۴ مورد دیگر

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

Raymond J. Mooneyمشاهده پروفایل

استاد

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +۱۴ مورد دیگر

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Jayshri Sabarinathanمشاهده پروفایل

دانشیار

• Nano-photonic Sensors
• Remote Sensing Instrumentation
• Plasmonic Sensors
• +۶ مورد دیگر

Dr. Jayshri Sabarinathan is an Associate Professor in the Department of Electrical and Computer Engineering at Western University's Faculty of Engineering, and a Faculty Member with the Institute for Earth and Space Exploration. She joined Western University in Fall 2003, received the NSERC University Faculty Award in 2004, and was promoted to Associate Professor in 2010. She previously served as Associate Director of Training (2019-2022) with the Institute for Earth and Space Exploration. Education: Ph.D. in Electrical Engineering, University of Michigan, Ann Arbor (2003) M.S.E. in Electrical Engineering, University of Michigan, Ann Arbor (1999) B.S.E. in Electrical Engineering and Engineering Physics, University of Michigan, Ann Arbor (1997) Her research focuses on developing novel nano-photonic sensors and miniature remote sensing instrumentation, with expertise spanning photonic crystals, plasmonic sensors, and CubeSat technology. Her work integrates nanofabrication techniques with practical applications in precision agriculture, geology, and space exploration. She has extensive experience with nanofabrication facilities including the University of Michigan Solid State Electronics Laboratory and Western's nanofabrication facility. Analysis of her 15 most recent publications reveals strong emphasis on plasmonic sensing technologies, photonic crystal applications, and nanoscale optical phenomena. Her research consistently bridges fundamental photonics with practical sensor development, particularly for environmental monitoring and space applications. The publications demonstrate progression from basic photonic crystal research to applied space instrumentation. Scientific Awards: NSERC University Faculty Award (2004) US Patent 8839683 for Photonic Crystal Pressure Sensors (2014) OSA (Optica) Senior Member Co-founder of LightSail Ltd space startup Dr. Sabarinathan actively mentors graduate students through her Nanophotonic Sensors Engineering (NPSE) and Remote Sensing Instrumentation (RSI) research groups. She has secured significant funding including Canadian Space Agency projects, notably as PI for the Western University-Nunavut Arctic College CubeSat Project Ukpik-1. Her research has resulted in three patents for micro photonic-sensors and multi-spectral camera innovations. Her labs focus on two primary research thrusts: the NPSE group developing hybrid photonics micro/nano-sensors including IR/THz plasmonic sensors and bio-photonic sensors, and the RSI group creating multispectral camera imagers for UAV/mobile robots with XRD instrumentation miniaturization for Mars rovers.

Athena Nghiemمشاهده پروفایل

استادیار

• Hydrology
• Biogeochemistry
• Environmental Geochemistry
• +۳ مورد دیگر

Athena Nghiem is an Assistant Professor at the University of Wisconsin-Madison, starting Fall 2024, specializing in biogeochemistry and hydrology. She currently holds an ETH Postdoctoral Fellowship at ETH Zürich, focusing on redox processes in groundwater systems. Education: PhD in Earth and Environmental Sciences from Columbia University, BA in Geophysics and Statistics from UC Berkeley Research interests: Environmental variability in hydrology, redox processes, groundwater contamination, data science in environmental research, and reactive transport modeling Her research combines traditional laboratory/field methods with data science to study trace element cycling, particularly arsenic release in aquifers. Recent work includes quantifying sulfate reduction's role in arsenic contamination and evaluating mitigation strategies. Notable awards: ETH Postdoctoral Fellowship (2022-2024), NSF Graduate Research Fellowship (2018-2021), and multiple academic honors during her UC Berkeley BA studies. Current advisees at UW-Madison: Juyong Bak, Savannah Finley, Logan Goulette. She actively encourages applications from diverse backgrounds for future lab positions.

• Artificial Intelligence
• Human-Computer Interaction
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)

Julia Lenمشاهده پروفایل

استادیار

• Applied Cryptography
• Computer Security
• Secure Communication
• +۷ مورد دیگر

Julia Len is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she conducts research in applied cryptography and computer security. She co-leads the Cryptography Group with Saba Eskandarian and is currently recruiting Ph.D. students for Fall 2026. She earned her Ph.D. in Computer Science from Cornell University in 2024 under the supervision of Tom Ristenpart, following a B.S. in Computer Science from UC San Diego in 2018, where she worked with Mihir Bellare. Before joining UNC, she was a METEOR postdoctoral fellow at MIT. Her research focuses on improving the security and privacy of deployed cryptographic protocols, particularly in end-to-end encrypted messaging. Key areas include interoperability, abuse prevention, key transparency, and authenticated encryption. She applies principled approaches ranging from identifying flaws in existing protocols to designing new cryptographic schemes and definitions. Her recent publications span top venues such as USENIX Security, CCS, Eurocrypt, and Crypto, demonstrating a strong and consistent research output in applied cryptography. Trends in her work show increasing focus on real-world protocol design, security analysis of widely used schemes, and privacy-preserving moderation mechanisms for secure communication platforms. METEOR Postdoctoral Fellow at MIT Julia Len has advised and collaborated with numerous researchers; current advisees are being recruited for Fall 2026. She has received research support through collaborations with industry partners including Zoom, Microsoft Research, and Meta. Her work on Partitioning Oracle Attacks has led to updates in Shadowsocks, age, OPAQUE, and HPKE, demonstrating significant real-world impact. She co-leads the Cryptography Group at UNC Chapel Hill, fostering research and education in cryptography. She also serves on the program committees of IEEE S&P 2026, USENIX Security 2025, and CATS 2023, contributing to the broader academic community.

Miguel R. Ruedaمشاهده پروفایل

دانشیار

• Electoral Manipulation
• Civil Conflict
• Money in Politics
• +۳ مورد دیگر

Miguel R. Rueda is an Associate Professor in the Department of Political Science at Emory University, specializing in electoral manipulation, civil conflict, money in politics, and political methodology. He holds a PhD from the University of Rochester (2014), an M.Sc. in Economics, and a B.Sc. in Economics and Mathematics from La Universidad de los Andes. Before Emory, he was a visiting scholar at Princeton University's Center for the Study of Democratic Politics (2013–2014). In Fall 2024, he will serve as a Visiting Associate Professor at Vanderbilt University. His research has been published in top journals like the American Political Science Review , American Journal of Political Science , and Journal of Conflict Resolution . Key themes include electoral fraud mechanisms, civil war dynamics, and the intersection of political methodology with empirical policy analysis. Rueda's work spans theoretical models of strategic behavior (e.g., foreign aid allocation, partisan poll-watching) and applied analyses of electoral systems, conflict outcomes, and governance challenges. His methodological contributions address econometric issues like post-instrument bias and omitted variable effects. Contact: miguel.rueda@emory.edu , 315 Tarbutton Hall, Emory University, Atlanta, GA 30322.

Anjalie Fieldمشاهده پروفایل

استادیار

• Natural language processing
• Computational social science
• AI ethics
• +۳ مورد دیگر

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.

Marzieh Aminiمشاهده پروفایل

دانشیار

• Computer Vision
• Sensor Fusion
• Autonomous Vehicles
• +۷ مورد دیگر

Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.

• Construction Robotics
• Human-Robot Collaboration
• Wearable Sensors
• +۴ مورد دیگر

Houtan Jebelli is an Assistant Professor in Civil and Environmental Engineering at the University of Illinois. His research focuses on construction robotics, human-robot collaboration, and wearable sensing technologies for worker health and safety monitoring. He directs research on exoskeleton applications, fall risk detection, and AI-enabled monitoring systems for construction environments. Research interests include: Human-robot collaboration in construction sites Physiological monitoring using wearable sensors Exoskeleton technology and ergonomic assessment AI-enabled safety management systems Robotic inspection and defect detection Jebelli's recent work demonstrates strong interest in bridging robotics with occupational health, particularly studying cognitive and physiological impacts of wearable robotics. His publications frequently address real-time monitoring systems and human factors in construction technology adoption.

Babak Taatiمشاهده پروفایل

دانشیار

• Computer Vision
• Healthcare Technology
• Artificial Intelligence
• +۳ مورد دیگر

Babak Taati is an Associate Professor at the University of Toronto (UofT), affiliated with the Department of Computer Science, Institute of Biomedical Engineering (BME), and Rehabilitation Sciences Institute (RSI). He holds the Barbara G. Stymiest Chair in Rehabilitation Technology Research at UHN and is a Senior Scientist at KITE, UHN's research arm. He is also a Vector Institute Faculty Affiliate. His work focuses on applying computer vision and machine learning to healthcare challenges, particularly in rehabilitation technologies for aging populations, gait analysis, fall prevention, and dementia care. Taati leads the Aging team at KITE and is affiliated with the Intelligent Assistive Technology and Systems Lab (IATSL) and the Computational Vision group. Research interests include noninvasive monitoring of health conditions such as Parkinsonism, sleep apnea, and pain management in older adults. His contributions span datasets like the Toronto NeuroFace Dataset and TOAGA archive, emphasizing clinical applications. Taati has taught CSC420 (Image Understanding) repeatedly and has organized workshops on topics like AI in dementia care and ambient intelligence in healthcare. His awards include the TRI-UHN Best Paper Award (2017) and the AMS Healthcare Fellow in Compassion and Artificial Intelligence (2021). He has advised students like Michael Li and collaborates on grants involving federal initiatives (e.g., FedDev Ontario). His research bridges theoretical computer science with practical healthcare solutions, addressing unmet clinical needs through vision-based systems.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

Christian Krupitzerمشاهده پروفایل

استاد

• Food Informatics
• Self-adaptive Software Systems
• Machine Learning
• +۴ مورد دیگر

Jun.-Prof. Dr. Christian Krupitzer is a Tenure Track Professor in Food Informatics at the University of Hohenheim's Institute of Food Science and Biotechnology, part of the Faculty of Natural Sciences. He leads the Department of Food Informatics and is a member of the Computational Science Hub (CSH). His research focuses on self-adaptive software systems, machine learning (especially edge computing), IoT technologies, and software engineering applied to food processing and agricultural systems. Education: PhD in Business Information Systems (Dr. rer. pol.), University of Mannheim (2018) M.Sc. and B.Sc. in Business Information Systems, University of Mannheim (2010–2012) High School Diploma (Abitur) from Wilhelmi-Gymnasium Sinsheim (2007) Research Interests: Krupitzer’s work integrates computational methods with food science, emphasizing adaptive systems for food quality monitoring, IoT in agriculture, and machine learning for predictive analytics. He explores edge computing’s role in real-time decision-making and secure group communication schemes for IoT networks. Publications: His recent work spans predictive maintenance in Industry 4.0, digital twins in food systems, and blockchain applications in supply chain authentication. The articles highlight trends in interdisciplinary approaches combining AI, IoT, and domain-specific challenges in food production and logistics. Awards: No scientific awards explicitly listed in the provided materials. Grants & Advising: While specific grants are unmentioned, his roles as department head and tenure-track professor suggest involvement in research funding. No formal advisee list provided, though his team includes postgraduate researchers like Dana Jox, Daniel Einsiedel, and others. Labs & Teams: Leads the Food Informatics department and collaborates with the Computational Science Hub. His team focuses on developing innovative solutions for food systems through computational methods.