جست‌وجوی استاد و پژوهشگر

• Algorithmic Fairness
• Machine Learning
• Computational Social Choice
• +۳ مورد دیگر

Benjamin Fish is a Professor in the Department of Computer Science and Engineering (CSE), focusing on fairness in algorithms, computational social choice, and theoretical computer science. His research addresses issues of equity in hiring markets, bias in machine learning, and ethical implications of technology. He is actively involved in mentoring PhD students, emphasizing personalized advising, regular one-on-one meetings, and fostering independence in research. Research Interests: Dr. Fish explores algorithmic fairness, graph neural networks, and the ethical design of computational systems. He investigates how social dynamics and institutional structures impact algorithmic outcomes, particularly in contexts like hiring and data-driven markets. His work often bridges theoretical analysis with practical applications in policy and technology governance. Education and Mentorship: While his formal education details are not explicitly listed, his mentorship philosophy prioritizes student autonomy and growth. Weekly meetings and flexible group sessions focus on research progress, writing, and professional development. He encourages internships, conference participation (with funding support), and teaching opportunities as part of a well-rounded academic experience. Advising and Grants: Students are expected to take ownership of their research projects, with authorship determined by contribution. Authorship order typically places student leads as first authors and the advisor as last. Regular feedback is provided through weekly meetings and annual reviews, balancing progress tracking with student well-being. Conferences and Funding: Conference attendance is supported for paper authors; additional travel funds are available for one annual conference per student. Funding depends on project grants and workshop submissions.

Christophe Duwigمشاهده پروفایل

استاد

• Process Simulation
• Fluid Mechanics
• Heat Transfer
• +۷ مورد دیگر

Christophe Duwig is a Professor at the Department of Chemical Engineering, KTH Royal Institute of Technology. He serves as Vice-Director of the KTH Energy Platform and coordinates strategic initiatives like the KTH Nuclear Next and Battery Initiative. His research focuses on process simulation, fluid mechanics, heat transfer, and clean energy technologies. He leads projects addressing carbon capture, renewable heat systems, and clean air solutions through collaborations with institutions like Stockholm University and RISE Research Institutes of Sweden. Research Interests: His work integrates high-fidelity computational fluid dynamics (CFD), including LES and CFD simulations of reacting flows, with advanced post-processing tools like machine learning and proper orthogonal decomposition. Key areas include CO2 capture, hydrogen combustion optimization, and waste heat recovery. He emphasizes translating research into practical solutions for decarbonization and clean air through initiatives like C3Air®. Publications: Recent articles highlight innovations in CO2 capture efficiency, hydrogen-fueled combustion dynamics, and heat transfer optimization in electronic cooling systems. His work often bridges fundamental fluid mechanics with industrial applications, addressing global challenges in energy sustainability and climate action. Grants & Leadership: Coordinates multi-disciplinary research efforts under SDG 7 (Clean Energy) and SDG 13 (Climate Action). Leads teams in projects such as C3Air® and the KTH Climate Action Centre, fostering international collaborations to tackle air pollution and carbon removal. Supervises PhD and MSc students in computational engineering and sustainable technologies. Labs & Teams: Active in the Division of Process Technology and KTH’s Energy and Environmental initiatives. His lab develops novel heat exchangers, combustion models, and smart energy management systems for buildings and industrial processes.

Isaac Skogمشاهده پروفایل

دانشیار

• Underwater Surveillance
• Signal Processing
• Navigation Systems
• +۵ مورد دیگر

Isaac Skog is an Associate Professor in Communication Systems at KTH Royal Institute of Technology, part of the Digital Futures Faculty. He also holds adjunct positions at Linköping University (Automatic Control) and the FOI Swedish Defence Research Agency. His research focuses on underwater surveillance, signal processing, navigation systems, and sensor fusion, with a particular emphasis on autonomous systems and acoustic environments. Education: BSc and MSc in Electrical Engineering, KTH Royal Institute of Technology (2003, 2005) PhD in Signal Processing, KTH Royal Institute of Technology (2010) Research Interests: Underwater acoustic surveillance and tracking Navigation systems using magnetic fields and inertial sensors Autonomous underwater vehicles and multi-agent systems Sensor calibration and fusion techniques Machine learning for sensor data analysis Recent Projects: COAST Project: Complex Acoustic Surveillance and Tracking Joint Sensing, Localization, and Communication for Next-Gen Autonomous Underwater Systems (WASP-funded) Tensor-Field Based Localization Electromagnetic Navigation for Smaller Unmanned Underwater Vehicles Grants & Collaborations: Funded by CENIIT, WASP Sweden, Swedish Research Council, Vinnova, and industry partners like Saab Dynamics. Active collaborations with FOI, Lund University, and Indian Institute of Science. Technical Demonstrators: Lyra platform for elevator condition monitoring OpenShoe experimental platform for foot-mounted inertial navigation Tactical Locator (TOR) for first-responder tracking Movelo AB spin-off for smartphone-based traffic monitoring

Fraida Fundمشاهده پروفایل

استادیار

• Networking
• Telecommunications
• Wireless Communication
• +۷ مورد دیگر

Fraida Fund is a Research Assistant Professor at the NYU Tandon School of Engineering, specializing in the Electrical and Computer Engineering Department. She is affiliated with the Center for Advanced Technology in Telecommunications (CATT) and NYU WIRELESS, which focus on 6G and mmWave technologies. Her research explores low-latency networking, congestion control, wireless systems, and reproducibility in computer science education. She teaches courses on machine learning systems, networking protocols, and computer engineering design. Her work emphasizes practical applications of wireless and edge computing, particularly in agriculture and connected vehicles. Recent studies include evaluating latency in mmWave networks and analyzing the adoption of BBR and L4S congestion control protocols. Fund has contributed to open-source platforms like PrairieLearn for algorithmic question generation in education. She was recognized by AFAR for her academic achievements. Fund’s research integrates economic analysis with engineering, such as modeling cellular infrastructure sharing and pricing strategies for video delivery. She actively uses public testbeds for reproducible experiments and advocates for improving reproducibility in computer science curricula. Her interdisciplinary approach bridges theoretical research with real-world deployment challenges in telecommunications.

Herschel Knappمشاهده پروفایل

دانشیار بالینی

• HIV Testing Implementation
• Telehealth Consultations
• Evidence-Based Nursing Practices
• +۵ مورد دیگر

Herschel Knapp is a Clinical Associate Professor in the Department of Social Work at Yeshiva University. He holds a Ph.D. in Social Welfare from UCLA (2001), M.S.S.W. from the University of Texas at Austin (1995), and B.A. in Psychology from California State University, Northridge (1988). His clinical work focuses on psychosocial services in ER, ICU, Oncology, and Code Blue Team settings. Research emphasizes HIV testing implementation, telehealth innovations, and evidence-based nursing practices. He teaches statistics, research methods, and clinical communication at both MSW and doctoral levels. Knapp has published extensively on HIV testing protocols, healthcare quality improvement, and educational interventions. He chairs the Student Appeals Committee and serves on the D.S.W. Committee. His work includes developing the ECHO Model for VA healthcare consultations and evaluating Meducation® for medication adherence. He has contributed to over 40 peer-reviewed articles and authored multiple textbooks on statistics and therapeutic communication. Key projects include nurse-initiated HIV testing in VA settings, human trafficking clinician training, and transgender veteran care curricula. His research spans healthcare technology evaluation, clinical assessment tools, and interdisciplinary education programs. Knapp’s work balances academic contributions with direct clinical impact, addressing critical gaps in healthcare access and quality.

James Longمشاهده پروفایل

استاد آموزشی

• Medical Imaging
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

James Long is an Assistant Teaching Professor of Bioengineering at Rice University. Previously, he served as a graduate instructor at Duke University while completing his Ph.D. in Biomedical Engineering. His work focuses on integrating practical, coding-based approaches into bioengineering education and advancing adaptive medical ultrasound imaging techniques through spatial coherence research. He has collaborated with Professors Gregg Trahey (Duke) and Nick Bottenus (University of Colorado Boulder), contributing to theoretical and applied advancements in ultrasound technology. James Long holds a Ph.D. in Biomedical Engineering from Duke University (2022), an M.S. in Biomedical Engineering from Duke University (2021), and a B.S. in Bioengineering from Rice University (2017). Long’s research interests are centered on medical ultrasound imaging and bioengineering education. He investigates spatial coherence to enhance adaptive imaging techniques, particularly in mitigating noise and optimizing clinical parameters. Additionally, he develops problem-based learning frameworks and coding modules to bridge conceptual and practical engineering skills, emphasizing team-based approaches for higher-order cognitive learning. His work includes collaborations on cellular modeling and process-oriented engineering solutions. Long’s recent publications highlight advancements in spatial coherence adaptive systems, educational pedagogy, and deep learning applications in ultrasound. His research trends emphasize interdisciplinary tools like 3D printing for phantom design and real-time parameter adjustments for clinical diagnostics. James Long has received several honors, including the TA of the Year in Biomedical Engineering (Duke University, 2020), NSF Graduate Research Fellowship (2019), and Stephen Smith Fellowship (2018). His advisory role at Rice University supports undergraduate bioengineering students, and he has pioneered team-based learning exercises to strengthen practical engineering competencies.

Laura Milneمشاهده پروفایل

استادیار

• Medical Education
• Clinical Skills Training
• Acute Care General Internal Medicine
• +۴ مورد دیگر

Dr. Laura Milne is an Assistant Professor at Queen’s University, affiliated with the Division of General Internal Medicine. She completed her undergraduate medical studies, residency, and fellowship at Queen’s University before joining the division in 2013. Her clinical practice focuses on acute care general internal medicine, stroke prevention, and refractory hypertension. Her academic interests center on medical education and clinical skills training. Her research spans public health policy, pandemic preparedness, and healthcare system analysis, with notable contributions to pandemic preparedness frameworks and energy policy evaluations. She has published widely on topics ranging from renewable energy incentives to geopolitical strategy. No formal awards are listed, but her work reflects active engagement in policy-oriented research. Education: Bachelor of Medicine, Queen’s University General Internal Medicine Residency, Queen’s University General Internal Medicine Fellowship, Queen’s University Research Interests: Dr. Milne’s work emphasizes practical applications of medical education and clinical excellence. Her recent research explores parallels between financial crisis management and pandemic preparedness, highlighting systemic risks in healthcare and economic systems. She also investigates renewable energy policies and their socio-economic impacts, particularly stakeholder dynamics in wind and solar energy projects. Her analyses of post-secondary education funding policies in Ontario reveal systemic challenges in public financing mechanisms. Publications Trends: Her 15 most recent articles (2018–2024) reflect interdisciplinary focus on public health crises, energy policy, and global governance. Key themes include pandemic preparedness stress-testing, renewable energy stakeholder analyses, and comparative studies of higher education challenges in Canada and Australia. Her work bridges clinical practice with policy implications, advocating for resilient systems in healthcare and energy sectors. Grants & Advising: No specific grants or advising roles listed in provided texts. However, her academic role suggests involvement in institutional teaching and research programs. Labs/Teams: Affiliation with Queen’s Division of General Internal Medicine implies collaboration with clinical and academic teams in medical education and acute care.

Yeonjong Shinمشاهده پروفایل

استادیار

• Applied and Computational Mathematics
• Artificial Intelligence
• Scientific Machine Learning
• +۶ مورد دیگر

Yeonjong Shin is an Assistant Professor of Mathematics at North Carolina State University (NC State), with prior tenure-track roles at KAIST (South Korea) and Brown University (USA). His research focuses on applied and computational mathematics, particularly in artificial intelligence, scientific machine learning, and numerical methods for partial differential equations. He holds a Ph.D. in Mathematics from The Ohio State University (2018) and dual B.S. in Mathematics and B.A. in Economics from Yonsei University (2013), with military service as a KATUSA (2009–2011). Shin’s expertise includes approximation theory, numerical optimization, and uncertainty quantification. He leads research in AI-driven scientific computing, reduced order modeling, and physics-informed neural networks. His work bridges computational mathematics and machine learning, addressing challenges in data-driven dynamical systems and high-dimensional function approximation. He has published extensively in top journals like SIAM Journals, Neural Networks, and Comput. Methods Appl. Mech. Eng. Notable contributions include thermodynamics-informed latent space dynamics (tLaSDI), S-OPT hyper-reduction algorithms, and error analysis for neural network-based PDE solvers. Shin has taught courses at NC State, KAIST, and Brown, including statistical inference, numerical optimization, and computational linear algebra. He actively participates in conferences such as SIAM CSE, ICIAM, and KSIAM, and has delivered invited talks at institutions worldwide.

Sergey Sorokinمشاهده پروفایل

استاد

• Solid Mechanics
• Computational Mechanics
• Structural Dynamics
• +۷ مورد دیگر

Sergey Sorokin is a Professor in the Department of Materials and Production at Aalborg University, Faculty of Engineering and Science, Denmark. His research centers on solid and computational mechanics, with a focus on structural dynamics, wave propagation, vibro-acoustics, and finite element modeling. His research interests include: Wave propagation in periodic and elastic structures Vibro-acoustics and noise control Finite element methods and reduced-order modeling Nonlinear dynamics of beams and shells Acoustic black hole effects and vibration suppression Structural optimization and industrial system analysis The recent publications (2020–2025) demonstrate sustained activity in mechanical wave analysis, nonlinear structural behavior, and acoustic optimization. Key themes include the suppression of bending waves, asymptotic modeling of cylindrical shells, and bi-orthogonality in waveguides. These works reflect an interdisciplinary blend of theoretical mechanics, numerical simulation, and engineering applications. Scientific contributions include: Active participation in long-term research projects on mechanical vibro-acoustics and wave transmission since 2010 Supervision of at least four PhD students Extensive publication record (188 research outputs) with consistent contributions to journals like the Journal of the Acoustical Society of America and European Journal of Mechanics A/Solids He has advised several researchers and contributed to collaborative projects involving structural optimization and industrial component analysis. His work is deeply integrated into both theoretical and applied mechanical engineering, with emphasis on modeling and enhancing structural performance under dynamic loads. Sergey Sorokin is actively involved in research through ongoing projects such as 'Mechanical Vibro-Acoustics and Noise' and 'Analysis and Optimization of Wave Propagation in Periodic Structures'. These efforts are supported by advanced computational modeling and collaboration with peers like N. Olhoff and E. Lund.

Andrés Mora Valenciaمشاهده پروفایل

دانشیار

• Quantitative Finance
• Financial Risk Quantification
• Machine Learning in Finance
• +۱۲ مورد دیگر

Andrés Mora Valencia is an Associate Professor in the Faculty of Management at the University of the Andes, Colombia, where he teaches in the Finance department. He holds a Doctor of Economics from the University of Salamanca and has a strong interdisciplinary background in industrial engineering, statistics, and quantitative finance. Education: Doctor of Economics, University of Salamanca, Spain (2014) Master's Degree in Banking and Quantitative Finance, University of Castilla-La Mancha, Spain (2013) Master in Industrial Engineering, University of the Andes, Colombia (2000) Statistics Specialist, National University of Colombia (2011) Advanced Mathematics Specialist, National University of Colombia (2003) Industrial Engineer, University of the Valley, Colombia (1997) His research focuses on quantitative finance , particularly financial risk quantification using advanced statistical and machine learning models. Key areas include Value-at-Risk (VaR), Expected Shortfall (ES), fat-tailed distributions, and volatility forecasting in cryptocurrency and energy markets. He actively applies Bayesian deep learning, semi-nonparametric methods, and real options theory to financial modeling. His recent publications (2021–2025) reveal a strong trend in applying machine learning to forecast financial indices like the VIX and Skew Index, analyzing higher-order moment spillovers in crypto-forex integration, and assessing risk in renewable energy and carbon markets. His work bridges econometrics, computational finance, and sustainability. Scientific Contributions: Developed models for skew index forecasting using machine learning Explored adaptive market hypothesis in Latin American and emission trading markets Advanced semi-nonparametric risk assessment for cryptocurrencies Applied real options to value renewable energy projects He advises several graduate and doctoral students in finance, including Andrés Raúl Cruz-Hernández, Esteban Nicolás Vanegas Herrera, and Leonardo Gerardo Santana Viloria. He teaches courses such as Derivatives, Financial Planning, Investment Decisions, and Computational Methods in Finance across undergraduate, master’s, and doctoral programs. He is affiliated with the Finance and Financial Economics research group at Uniandes.

Jos Maubachمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• Computational Science and Engineering
• +۳ مورد دیگر

Jos Maubach is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. He is part of the Centre for Analysis, Scientific Computing, and Applications (CASA). His research focuses on numerical methods, scientific computing, and computational science and engineering, particularly in iterative methods for partial differential equations and model order reduction. Education: MSc in Mathematics (cum laude, Radboud University Nijmegen, 1986); PhD in Mathematics (Radboud University Nijmegen, 1991). Joined TU/e in 1998. Research Interests: Numerical analysis, nonlinear dynamical systems, computational methods for engineering applications, and model reduction techniques. Publications Highlight Trends: Recent work emphasizes model order reduction for bilinear systems, H2 optimization in electronics reliability, and mechanics of materials (e.g., moisture-induced buckling in paper sheets). No scientific awards explicitly mentioned. Teaches courses such as Programming and Modelling, Scientific Programming, Analysis 1, and Advanced Calculus II. No ancillary activities reported. Collaborations: Works with groups like the Computational Quantum & Molecular Dynamics team and the High Tech Systems Center.

• Stochastic Analysis
• Numerical Methods for PDEs
• High-Dimensional Approximations
• +۳ مورد دیگر

Dr. Tuan Anh Nguyen is a researcher at the Faculty of Mathematics of Universität Bielefeld, Germany. He is affiliated with the Collaborative Research Centre (SFB) 1283, specifically sub-project B10, which focuses on high-dimensional Markov processes in cones. His research interests include stochastic analysis, numerical methods for partial differential equations (PDEs), and overcoming the curse of dimensionality through advanced techniques like multilevel Picard approximations and deep neural networks. Dr. Nguyen's work bridges theoretical mathematics and applied computational methods, addressing challenges in stochastic control, mean-field systems, and high-dimensional stochastic differential equations (SDEs). His contributions to numerical analysis emphasize scalable algorithms for semilinear PDEs and backward SDEs, leveraging machine learning frameworks to tackle dimensionality constraints. He collaborates on interdisciplinary projects within the university's strategic research area of the Mathematical World , focusing on stochastic processes, mathematical finance, and real-world system modeling. His research has been recognized through affiliations with leading projects like SFB 1283 and contributions to top-tier journals in computational mathematics.

Arne Sachtlerمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Differential Geometry
• +۳ مورد دیگر

Arne Sachtler is a researcher at the Technical University of Munich (TUM) and the German Aerospace Center (DLR), affiliated with the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems under Prof. Alin Albu-Schäffer. His research focuses on applying differential geometry and nonlinear dynamics to robotics, particularly in areas like elastic robots, energy-efficient locomotion principles, and intrinsic dynamics of mechanical systems. He specializes in robot control strategies, including impedance control and eigenmanifold-based model predictive control (EigenMPC). Education details are not explicitly listed, but his work indicates expertise in robotics and mathematical physics. Key research interests include robot kinematics, multi-body dynamics, and the integration of geometric principles into control frameworks. His publications span topics such as nonlinear normal modes, redundant manipulator control, and autonomous robotic assembly with visual-tactile sensing. Dr. Sachtler collaborates with institutions like DLR, contributing to both academic and industrial robotics research. His work emphasizes theoretical foundations with practical applications in energy-efficient robotics and human-robot interaction. Contact him via arne.sachtler@tum.de or arne.sachtler@dlr.de for inquiries.

• VLSI Design
• Digital Circuit Testing
• Test Data Compression
• +۴ مورد دیگر

Χρ. Καβουσιανός is a Professor at the Department of Computer Engineering & Informatics, University of Patras (Polytechnic School). His research focuses on VLSI design, digital circuit testing, and test data compression. He holds a Diploma (1996) and PhD (2000) from the same department, with notable scholarships including the Maritsas Doctoral Scholarship (1999). Key roles include leading the Heraclitus II and Pythagoras II research programs as principal investigator. Collaborations include Duke University (USA) with Prof. Krishnendu Chakrabarty. Research highlights: Pioneered techniques in scan-based testing, test set embedding, and low-power testing. Over 45 publications in top journals/conferences (e.g., IEEE TCAD, DAC). Notable work includes defect-oriented testing methods and Huffman-based compression schemes. Has supervised 9 advisees across doctoral/postgraduate levels. Holds a 2010 US patent application on power switch design.

Maria Hybinetteمشاهده پروفایل

دانشیار

• High-Performance Simulation Systems
• Multi-Agent Behavior Modeling
• Financial Market Simulation
• +۳ مورد دیگر

Maria Hybinette is an Associate Professor of Computer Science at the University of Georgia's School of Computing. Her research focuses on high-performance simulation systems, particularly in multi-agent behavior modeling and financial market simulation. She emphasizes hybrid (micro/macro) simulations, optimization for parallel processing, and usability improvements in simulation frameworks. Her work spans over two decades, with notable contributions to agent-based simulation frameworks like ABIDES, Pickle, and SASSY. She has also pioneered methods for parallelization in discrete event simulations, including latency reduction techniques and adaptive caching strategies. Key research areas include stock market dynamics modeling, autonomous agent coordination, and real-time multi-target tracking systems. Her publications consistently explore scalability, computational efficiency, and validation methodologies in simulation systems. Awards and recognition are not explicitly listed in the provided materials. She is affiliated with the Boyd Research and Education Center in Athens, GA, and maintains active collaborations in computational finance and robotics.