جستجوی استاد راهنمای دانشگاهی

Guo-Cheng Yuanمشاهده پروفایل

استاد

• Genomics
• Epigenetics
• Gene Expression
• +۴ مورد دیگر

Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology

Xingye Qiaoمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Xingye Qiao is a Professor and Chair in the Department of Mathematics and Statistics at Binghamton University, State University of New York . He serves as Chair of the Data Science Transdisciplinary Area of Excellence steering committee and has been affiliated with Binghamton since 2010. Education : Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill M.S. in Statistics (2007), University of North Carolina at Chapel Hill B.S. in Mathematics and Applied Mathematics (2005), Fudan University His research focuses on Statistics, Machine Learning, and Causal Inference , with recent work exploring conformal prediction methods, treatment effect estimation, and set-valued classification techniques. His publications span journals like Transactions on Machine Learning Research , NeurIPS , and AAAI . His 15 most recent articles (2025-2020) demonstrate expertise in areas including: bandit feedback systems, treatment effect heterogeneity analysis, spectral clustering for neuroscience, and conformal prediction under distribution shifts. These works blend statistical theory with real-world applications in healthcare, ecology, and data science. He mentors Ph.D. students in mathematical sciences and has supervised research topics such as: machine learning in precision medicine, goodness-of-fit tests for spatial processes, and high-dimensional data analysis. Courses he teaches include Math 605: Theory of Machine Learning and Data 501: Predictive & Inferential Analytics .

• Optimization under uncertainty
• Machine Learning
• Healthcare Operations
• +۶ مورد دیگر

Anh T. Ninh is an Associate Professor in the Department of Mathematics at William & Mary, where he also contributes to the M.S. program in Computational Operations Research within the Department of Computer Science. His academic work bridges mathematics, computer science, and healthcare applications, with a strong focus on optimization and machine learning. Research Interests: His primary research areas include optimization under uncertainty, machine learning, and their applications in healthcare systems, particularly in clinical trial design and pharmaceutical supply chain management. He develops advanced mathematical models to improve decision-making under uncertainty in complex operational environments. Publication Trends: His recent publications reflect a consistent focus on integrating stochastic and robust optimization with real-world healthcare logistics, clinical operations, and pharmaceutical planning. The works demonstrate a strong interdisciplinary approach, combining operations research, data science, and domain-specific knowledge to solve critical problems in health systems. Scientific Awards & Recognition: While no specific awards are listed in the provided text, his research is supported by the Bill & Melinda Gates Foundation, indicating significant recognition and impact in the field. He has also collaborated with major pharmaceutical companies such as Lifecell (now Abbvie), Sandoz, and IntegriChain, highlighting the practical relevance of his work. Advising and Grants: Dr. Ninh advises students through the Computational Operations Research program and leads externally funded research, including an active project on site selection funded by the Bill & Melinda Gates Foundation. His work bridges academia and industry, contributing to both theoretical advances and practical implementations in healthcare operations. Labs and Research Teams: While no formal lab name is mentioned, Dr. Ninh leads a research group focused on computational optimization and machine learning applications in healthcare. His team likely includes graduate students and collaborators from both mathematics and computer science, working on projects related to supply chain resilience, clinical trial efficiency, and data-driven healthcare decision-making.

• posttraumatic stress disorder
• prolonged grief disorder
• global mental health
• +۵ مورد دیگر

Scientia Professor Richard Bryant is a leading researcher in trauma psychology at the University of New South Wales, where he serves in the School of Psychology. With over three decades of research experience, he has established himself as a world authority on posttraumatic stress disorder (PTSD), prolonged grief disorder, and trauma-related mental health conditions. Professor Bryant received his B.A. (Hons.) from the University of Sydney in 1983, followed by his M.Clin. Psych. and Ph.D. from Macquarie University in 1986 and 1989 respectively. He was awarded a D.Sc. from UNSW in 2016, recognizing his substantial contributions to the field. His clinical training and academic background have positioned him at the forefront of trauma research and treatment development. Dr. Bryant's research primarily focuses on posttraumatic stress disorder, prolonged grief disorder, global mental health initiatives, cognitive behavior therapy applications, and clinical trials methodology. His work spans diverse populations including trauma survivors, refugees, first responders, and individuals affected by disasters. He has pioneered early intervention approaches following traumatic events and developed innovative treatments for persistent trauma reactions across various cultural contexts. An analysis of his recent publications reveals a strong emphasis on global mental health applications, particularly in conflict zones and with displaced populations. His research increasingly incorporates digital health solutions, examines cultural variations in trauma response, and investigates novel treatment approaches including memory specificity training and integration of technology in therapy delivery. His work consistently bridges clinical practice with research evidence to improve mental health outcomes worldwide. Professor Bryant maintains an active research program with numerous ongoing clinical trials and international collaborations. His work has significantly influenced clinical guidelines for trauma treatment globally, particularly through his contributions to World Health Organization interventions and evidence-based practice recommendations. Based at the University of New South Wales in Sydney, Professor Bryant continues to mentor emerging researchers while maintaining a robust publication record that demonstrates both depth and breadth across trauma psychology domains. His Mathews Building office serves as the hub for a productive research team investigating critical questions in trauma response and recovery.

• Signal Processing
• Electronics
• Biomedical Engineering
• +۵ مورد دیگر

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

Wang Chunمشاهده پروفایل

دانشیار

• Operations Management
• Stochastic Control
• Decision-Making under Uncertainty
• +۹ مورد دیگر

Wang Chun is a tenured Associate Professor in the Department of Management Science and Engineering at the School of Economics and Management, Tsinghua University. He currently serves as Deputy Director of the department and has held academic positions at Tsinghua since 2017. His work bridges stochastic control, reinforcement learning, and machine learning applications in operations management and financial technology. Education: Bachelor's Degree from Tsinghua University (2005) Master's Degree from the University of Wisconsin-Madison (2008) Doctorate in Operations Research from Columbia University (2014) Research Interests: His research focuses on stochastic control and decision-making under uncertainty, particularly in inventory management, revenue management, financial engineering, and sports analysis. He also investigates reinforcement learning algorithms for dynamic optimization and applies machine learning to business data analytics and financial technology. Published Work Trends: Wang’s publications span operations research, financial mathematics, and computational management science. Recent studies address seasonal optimization patterns, robust inventory models, and game-theoretic approaches to sports analytics. His work integrates theoretical advancements in dynamic programming with practical applications in financial engineering and market microstructure. Career History: 2023–Present: Associate Professor, Tsinghua University 2020–2023: Associate Professor, Tsinghua University 2017–2020: Assistant Professor, Tsinghua University Researcher at KCG (New York), leading studies on quantitative investment and asset optimization

• Fluid Power Engineering
• Hydraulic Systems
• Tribology
• +۴ مورد دیگر

Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.

• Bio-inspired Sensors
• MEMS
• Neuromorphic Engineering
• +۳ مورد دیگر

Claudia Lenk serves as Full Professor at Ulm University since 2024, leading the Biomedical Sensor Systems and Microsystems research group. Her work focuses on developing bio-inspired acoustic sensors to enhance human and machine hearing capabilities, particularly for speech processing in noisy environments through MEMS-based adaptive technologies. Education: Technical Physics, TU Ilmenau PhD in Biophysics (specializing in numerical/chemical modeling of atrial fibrillation mechanisms) Postdoctoral research developing MEMS-based artificial hair cells for hearing enhancement Her research integrates bio-inspired engineering with acoustic sensor design to create noise-robust systems that mimic biological hearing mechanisms. Key innovations include tunable MEMS resonators, neuromorphic auditory processing, and integrated signal pre-processing for hearing aids and robotics. This interdisciplinary approach bridges microsystems engineering, neuroscience, and auditory perception to address limitations in current speech processing technologies. Analysis of her 15 most recent publications (2020-2025) reveals a consistent trajectory toward adaptive neuromorphic acoustic systems. Dominant themes include resonance frequency control, dynamic range expansion through nonlinear dynamics, and bio-inspired feature extraction for low signal-to-noise ratio environments. These advancements target practical implementations in energy-constrained devices like hearing aids and autonomous systems. Scientific Awards: No awards documented in source materials Advising and Grants: Source materials contain no information regarding student supervision, research grants, or collaborative funding initiatives. Labs and Teams: She directs Ulm University's Biomedical Sensor Systems and Microsystems group, which develops cutting-edge sensor technologies for hearing applications, robotics, and speech processing systems through MEMS fabrication and neuromorphic computing approaches.

Arie Levantمشاهده پروفایل

استاد

• Nonlinear Control Theory
• Sliding Mode Control
• Homogeneous Discontinuous Control
• +۲ مورد دیگر

Prof. Arie Levant is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, actively teaching Spring 2025 courses with Monday reception hours via Zoom (17:10-18:00). His foundational work established High-Order and Homogeneous Sliding Mode Control theories alongside Robust Exact Differentiation. His educational background includes: Ph.D. (1987) from USSR Academy of Sciences, Moscow: Thesis "Higher-order sliding modes and their application in control of uncertain processes" supervised by Prof. S.V. Emelyanov Postgraduate studies (1983-1987) in mathematical control theory at same institute B.Sc./M.Sc. (1980) from Moscow State University under Prof. V.I. Arnold in Theory of Differential Equations Levant's research centers on Nonlinear Control Theory with specialization in Sliding Mode Control , Homogeneous Discontinuous Control , and Robust Exact High-Order Differentiation . His methodologies enable finite-time-exact tracking in uncertain systems and real-time noise-robust signal differentiation, addressing fundamental challenges in control system resilience. Analysis of his 15 most recent publications (2016-2023) reveals concentrated advancements in chattering reduction, discretization for digital implementation, and noise filtering within sliding mode frameworks. These works demonstrate consistent focus on bridging theoretical control principles with practical engineering applications, particularly in real-time signal processing and robust controller design.

Alain Oustaloupمشاهده پروفایل

استاد

• Fractional Calculus
• System Identification
• Control Theory
• +۴ مورد دیگر

Alain Oustaloup is a Professor in the AUTOMATIC CONTROL research group at Université de Bordeaux , leading the CRONE team. His work focuses on fractional calculus , system identification , and control theory , with applications spanning thermal systems , epidemiology , and automotive engineering . Expertise : Fractional Order Modeling, CRONE Control, Thermal Diffusion Analysis Key Collaborations : Université de Lorraine, CNRS, STMicroelectronics His research includes fractional differentiation models for continuous-time system identification, non-integer power models for viral spread (e.g., COVID-19 ), and infinite state approaches for complex system representation. Recent publications emphasize thermal modeling and fractional prefilters for MIMO systems. Applications of his work extend to automotive suspensions (CRONE method), battery diagnostics , and medical device modeling . Collaborations with institutions like CRAN (Nancy) and IMS-Bordeaux highlight his interdisciplinary impact.

• Transportation Engineering
• Intelligent Transportation Systems
• Machine Learning
• +۲ مورد دیگر

Pranamesh Chakraborty serves as an Assistant Professor in the Department of Civil Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), where he specializes in Transportation Engineering with expertise spanning Intelligent Transportation Systems, Machine Learning, Big Data Analytics, and Naturalistic Driving Studies. Faculty position: Assistant Professor, Department of Civil Engineering Academic qualifications: PhD from Iowa State University (2019), M.Tech from IIT Kanpur (2014), B.E. from IIEST Shibpur (2012) Previous appointments: Assistant Professor at Techno India University and KIIT University Dr. Chakraborty's research program focuses on developing innovative computational approaches to transportation challenges, with particular emphasis on applying machine learning techniques to traffic management, incident detection, and policy analysis. His work bridges civil engineering principles with cutting-edge data science methodologies. Analysis of his publication record reveals a consistent research trajectory centered on data-driven transportation solutions, with increasing emphasis on deep learning applications and real-world policy implications. His work demonstrates strong interdisciplinary integration across engineering, computer science, and urban planning domains. Research Excellence Award, Iowa State University, 2019 Best Student Paper, TRB Managing Roadways and Transit Together Conference, Seattle, 2018 Student Essay Competition Winner, ITS America, San Jose, 2016 Academic Excellence Award, Indian Institute of Technology Kanpur, 2013 Dr. Chakraborty maintains an active research program with multiple publications in high-impact transportation journals. His previous experience includes serving as a Graduate Research Assistant at Iowa State University and teaching positions at multiple Indian institutions before joining IIT Kanpur.

Carrie Weidnerمشاهده پروفایل

مدرس ارشد

• Quantum Physics
• Quantum Engineering
• Atom Interferometry
• +۳ مورد دیگر

Dr. Carrie Weidner is a Senior Lecturer at the University of Bristol, affiliated with both the School of Physics and the School of Electrical, Electronic and Mechanical Engineering. Her research spans quantum control, atom interferometry, and quantum technology education, with a focus on robust control techniques in optical lattices and spin networks. Principal Investigator for Quantum Positioning, Navigation, and Timing Hub (2024-2029) Lead on EPSRC-funded project EP/Y004728/1 for trapped ultracold atom interferometry (2023-2025) Her recent work includes energy landscape shaping for quantum systems, deterministic generation of squeezed states, and innovative educational tools like the Quantum Composer. Publications analyze robustness metrics, control algorithms, and quantum-classical system comparisons. Collaborations span international institutions in quantum physics and engineering domains. She contributes to quantum outreach through gamification and interactive platforms, targeting improved education and community inclusivity. Current research trends emphasize precision measurement, error mitigation, and AI integration in quantum control systems.

Mathukumalli Vidyasagarمشاهده پروفایل

استاد

• Machine Learning
• Systems and Control Theory
• Computational Biology
• +۴ مورد دیگر

Mathukumalli Vidyasagar is a Distinguished Professor at the Indian Institute of Technology Hyderabad and previously held the SERB National Science Chair and Cecil & Ida Green Chair in Systems Biology Science at the University of Texas at Dallas. He earned his Ph.D. from the University of Wisconsin, Madison, and has authored 13 books and over 160 peer-reviewed papers.

Vinh Nguyenمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +۵ مورد دیگر

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

• Machine Learning
• Statistical Learning Theory
• Algorithmic Fairness
• +۱۲ مورد دیگر

Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.