جست‌وجوی استاد و پژوهشگر

Jie Pengمشاهده پروفایل

استاد

• Graphical Models
• High Dimensional Inference
• Functional Data Analysis
• +۴ مورد دیگر

Jie Peng, Ph.D., is a Professor and Vice-Chair for Graduate Affairs in the Department of Statistics at the University of California, Davis. Her research focuses on statistical methodologies with applications in genomics, neuroimaging, and functional data analysis. She holds a Ph.D. from Stanford University and is actively involved in advancing statistical theory and its practical implementations in biomedical and computational sciences. Education Ph.D. in Statistics, Stanford University Research Interests Dr. Peng’s work spans several key areas including graphical models, high-dimensional inference, and neuroimaging analysis. She develops innovative statistical tools for analyzing complex biological data, such as tumor genomics and brain connectivity patterns derived from diffusion MRI. Her research emphasizes the interplay between theoretical statistics and real-world applications in healthcare and precision medicine. Professional Contributions She serves as an Associate Editor for the Journal of Computational and Graphical Statistics and has contributed to numerous high-impact publications. Her methods have been applied to identify biomarkers in ovarian cancer and to study brain lateralization using Human Connectome Project (HCP) data. Dr. Peng also leads efforts in developing computational frameworks for spatial transcriptomics and dynamic network modeling.

Dimitrios Koutsonikolasمشاهده پروفایل

دانشیار

• Wireless Networking
• Mobile Computing
• Millimeter Wave Networking
• +۵ مورد دیگر

Dr. Dimitrios Koutsonikolas is an Associate Professor in the Electrical and Computer Engineering Department at Northeastern University, leading the WiNS Lab. Previously, he held a tenured position at the University at Buffalo. His research focuses on experimental wireless networking and mobile computing, particularly millimeter-wave systems, 5G/6G networks, energy-efficient protocols, and high-bandwidth applications like VR/AR. He has published over 80 papers in top venues (e.g., MobiCom, INFOCOM), received NSF CAREER and IEEE awards, and led major grants including an NSF-funded $3M project for an open 5G/6G testbed. His lab explores cutting-edge technologies like O-RAN, beam management, and edge computing for latency-critical applications. Education: PhD in Electrical and Computer Engineering from Purdue University (2010). Research Interests: Experimental validation of wireless protocols, mmWave networking, latency-optimized edge computing, and cross-layer design. Current projects include TARGET (5G/6G latency solutions) and the X5G testbed for open spectrum utilization. Recent Trends in Articles: Focus on 5G deployment maturity, mmWave beam management, and 6G-ready technologies like autonomous space networks. Work bridges theoretical contributions with practical implementations, leveraging testbeds for real-world validation. Awards: Notable honors include IEEE Region 1 Innovation (2019), NSF CAREER (2016), and multiple best paper awards at MobiCom, WCNC, and Globecom. Recognized for both research and teaching excellence. Grants & Labs: Principal investigator on NSF grants ($3M+), leading collaborations with IMDEA Networks and industry partners. WiNS Lab develops open-source tools for 5G testing and explores sub-THz channels. Advises over 15 students, many advancing to top tech firms (e.g., Apple, HP Labs).

Max Fathiمشاهده پروفایل

استاد

• Optimal Transport
• Probability Theory
• Statistical Physics
• +۹ مورد دیگر

Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.

Benjamin Bloem-Reddyمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Probabilistic Modeling
• +۴ مورد دیگر

Benjamin Bloem-Reddy is an Assistant Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on probabilistic approaches in statistics and machine learning, emphasizing symmetry, causality, and model-driven scientific knowledge acquisition. Prior to UBC, he completed his PhD under Peter Orbanz at Columbia University and a postdoc with Yee Whye Teh at the University of Oxford. He holds a physics background from Stanford and Northwestern Universities. Education: PhD in Statistics, Columbia University Postdoctoral Research, CSML Group, University of Oxford Physics degrees from Stanford and Northwestern Universities Research Interests: Bloem-Reddy explores symmetry in modeling and inference, causal discovery, and integrating scientific models with statistical frameworks. His work includes developing hypothesis tests for symmetry, causal inference via cocycles, and leveraging invariance properties in neural networks. He collaborates with scientists to apply statistical methods to domain-specific problems. Awards: Best Student Poster Award at NeurIPS 2014 Workshop on Networks Teaching & Advising: He teaches courses like STAT 460/560 (Statistical Inference) and advises a vibrant research group. His students include Boyan Beronov, Kenny Chiu, and Johnny Xi. He emphasizes recruiting curious, mathematically skilled students aligned with his research themes. Grants & Funding: Supported by NSERC, CANSSI, and UBC, with computational resources from ARC at UBC.

Randa Herzallahمشاهده پروفایل

دانشیار

• Control Systems
• Energy Systems
• Machine Learning
• +۲ مورد دیگر

Dr. Randa Herzallah is an Associate Professor at the University of Warwick with interdisciplinary expertise spanning control systems, quantum engineering, and machine learning. Her research develops probabilistic frameworks for complex systems control. Research interests focus on probabilistic control methods applied to energy grids, quantum systems, and biomedical applications. Recent work integrates machine learning with control theory for smart grid optimization and quantum system management. Publication analysis shows consistent focus on probabilistic control frameworks, with recent expansion into quantum applications and deep learning for industrial applications. Research funding includes EPSRC and Leverhulme Trust grants supporting quantum control and energy systems projects. Leads research in probabilistic control methodologies with industrial applications.

Gerd Terördeمشاهده پروفایل

استاد

• Electrical Engineering
• Power Systems
• Control Engineering
• +۳ مورد دیگر

Prof. Dr.-Ing. habil. Gerd Terörde is a Professor of Electrical Drive Technology at Osnabrück University of Applied Sciences, affiliated with the Faculty of Management, Culture and Technology. He holds a Diplom in Electrical Engineering from RWTH Aachen and a Dr.-Ing. habil. from TU Berlin. His career includes research at RWTH Aachen, KU Leuven, and TU Berlin, as well as industry roles as a project leader at Atlas Copco Airpower in Belgium. Education: 1989–1996: Diploma in Electrical Engineering (RWTH Aachen) 2002: PhD in Electrical Engineering (KU Leuven) 2008: Habilitation in Electrical Drive Technology (TU Berlin) Research focuses on electrical drives, power electronics, and control systems, with emphasis on sensorless control, renewable energy integration, and high-performance drive systems. He teaches courses on electrical machines, power electronics, control theory, and regenerative energy systems. Publications include a textbook on electrical drives and numerous patents/contributions to international journals and conferences. His work addresses topics like inverter systems, motion control algorithms, and energy-efficient drive solutions. Labs/Teams: Active in university research groups focusing on electrical drives and energy systems within the Faculty of Management, Culture and Technology.

Klaus Moeltnerمشاهده پروفایل

استاد

• Environmental Economics
• Natural Resource Valuation
• Applied Econometrics
• +۳ مورد دیگر

Klaus Moeltner is a Professor in the Department of Agricultural and Applied Economics at Virginia Tech since 2015. He previously held positions at the University of Nevada, Reno, including Associate and Assistant Professor roles. His expertise lies in environmental economics, natural resource valuation, and applied econometrics. He has secured over $6 million in grants as PI or Co-PI and contributes to interdisciplinary research through the Global Change Center and Remote Sensing Program at Virginia Tech. Education: Ph.D., Economics, University of Washington, 2000 M.A., Economics, University of Washington, 1998 M.A., International Policy Studies, Monterey Institute of International Studies, 1994 M.S., Environmental Planning & Engineering, University of Agriculture and Forestry, Vienna, Austria, 1990 Research Interests: Dr. Moeltner’s work focuses on environmental and natural resource economics, including water quality valuation, wildfire health impacts, urban water use, and coastal flooding risks. His research employs advanced econometric methods, such as Bayesian modeling and meta-regression, to inform policy decisions. Current projects include analyzing the economic benefits of stream restoration, harmful algal bloom forecasts, and offshore wind farm co-location opportunities. Awards: 2023: Distinguished Graduate Teaching Award (AAEA) 2014: Best Paper in Environmental and Resource Economics (EAERE) 2004: CABNR Outstanding Instructor Award Advising & Grants: Advised 6 Ph.D. students and secured over $6 million in grants. Research collaborations include USDA-NIFA, EPA, and the European Union. Labs & Teams: Active in interdisciplinary groups like the Global Change Center and Remote Sensing Program at Virginia Tech.

• Applied Mathematics
• Mathematical Physics
• Computational Science
• +۳ مورد دیگر

Ben Goddard is a Professor in the School of Mathematics at the University of Edinburgh. His work bridges applied mathematics with real-world scientific challenges, emphasizing interdisciplinary collaboration across engineering, biology, chemistry, and physics. He earned his PhD at the University of Warwick, later completing his final year at TU Munich following his advisor. His research focuses on mathematical modeling, numerical methods, and asymptotic analysis applied to problems such as quantum chemistry, fluid dynamics, and biological systems. Education: Bachelor’s degree in Mathematics (undergraduate details unspecified) PhD in Mathematical Quantum Chemistry (University of Warwick/TU Munich) Research interests include: Dynamic density functional theory (DFT) for complex fluids and nanoparticles Interfacial phenomena and contact line dynamics Numerical optimization and pseudospectral methods Biological systems modeling (e.g., RNA transcription mechanics) Recent work explores applications like ouzo phase behavior, aerosol droplet stability, and opinion dynamics in social networks. His collaborations span diverse fields, including experimental biology at the Welcome Centre for Cell Biology. He advocates for mathematicians’ role in interdisciplinary problem-solving, emphasizing clear communication and adaptability. Advising and grants: While specific grant details are not listed, his projects reflect significant funding and team-based research. He actively promotes STEM engagement through activities like designing math-themed escape rooms with his spouse, a statistician. Labs/Teams: Collaborates extensively with Edinburgh’s Schools of Engineering, Biology, and Informatics, though no specific lab names are mentioned.

• Finance
• Portfolio Management
• Financial Econometrics
• +۳ مورد دیگر

Nathan Lassance is a Lecturer at the Louvain School of Management (LSM), Université catholique de Louvain (UCLouvain), and a member of the Louvain Finance (LFIN) research division. His work bridges financial theory, statistical modeling, and data-driven portfolio optimization, with a focus on addressing parameter uncertainty and improving risk-return tradeoffs in asset allocation. Research Interests: Portfolio management, covariance matrix estimation, financial econometrics, risk analysis, quantitative finance, and non-Gaussian return distributions. Publications: His recent work explores shrinkage methods for high-dimensional portfolio selection, sentiment-aligned covariance matrices, and the economic value of statistical metrics like mean squared error. He has also contributed to understanding the limitations of factor-based mispricing models and the statistical properties of mean-variance portfolios. Labs/Teams: Affiliated with the Louvain Institute of Data Analysis and Modeling (LIDAM) and the Louvain Finance (LFIN) group.

Shuangping Liمشاهده پروفایل

استادیار

• Probability Theory
• High-Dimensional Statistics
• Theoretical Machine Learning
• +۵ مورد دیگر

Shuangping Li is an Assistant Professor in the Department of Statistics and Data Science at Yale University. She was previously a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). Her research lies at the intersection of probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms. Ph.D. in Applied and Computational Mathematics, Princeton University (2022) B.Sc. in Mathematics, University of Hong Kong Her research interests include probability theory , high-dimensional statistics , theoretical machine learning , and theory of algorithms . She investigates foundational aspects of random constraint satisfaction problems, neural networks, spectral methods, and phase transitions in high-dimensional models. Her work often draws from statistical physics and combinatorics to explain algorithmic behavior. The recent articles highlight a strong focus on binary perceptrons , clustering in network models , and algorithmic phase transitions . Keywords across publications include probability, theoretical computer science, machine learning, and statistical inference. Subfields reveal deep engagement with spin glass theory, discrepancy minimization, spectral embedding, and information-computation gaps. Scientific awards include: Stein Fellow, Department of Statistics, Stanford University (2022–2025) She has advised and taught at both Stanford and Yale, including courses such as Advanced Probability , Theory of Probability , and Stochastic Processes . She has organized seminars at Stanford and has delivered invited talks at institutions including Cornell, Duke, UC Berkeley, and Princeton. Her collaborative research involves prominent scholars such as Allan Sly, Emmanuel Abbe, and Tselil Schramm. There is no mention of external grants, but her postdoctoral fellowship suggests research funding support. She is involved in academic service through organizing the Stanford Statistics and Probability Seminars. She maintains an active research presence with publications in top venues like STOC, FOCS, COLT, ICLR, and journals such as Annals of Probability and Annals of Statistics .

• Analysis
• Analytic Number Theory
• Functional Analysis
• +۳ مورد دیگر

Kristian Seip is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), since 1993. He has held leadership roles, including Department Chair (1997–2001) and Vice Dean for Education at the Faculty of Information Technology, Mathematics and Electrical Engineering (2006–2012). He holds an MSc and PhD from NTNU. His research focuses on analysis and analytic number theory, with notable contributions to Dirichlet series, Hardy spaces, and operator theory. Key awards include the Frontiers of Science Award (2024), Doctor Honoris Causa from Universitat de Barcelona (2024), and Inaugural Fellow of the American Mathematical Society (2012). He is a member of prestigious academies like the Norwegian Academy of Science and Letters and the Royal Norwegian Society of Sciences and Letters. Seip has led major research projects, including the 2012–2013 Centre for Advanced Study program on Operator-Related Function Theory (co-leader with Yurii Lyubarskii) and multiple grants from the Research Council of Norway. His work emphasizes Fourier methods, multiplicative analysis, and interpolation theory.

• Artificial Intelligence
• Machine Learning
• Medical Technology
• +۷ مورد دیگر

Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.

Mihran Tuceryanمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۳ مورد دیگر

Professor Mihran Tuceryan is a Professor of Computer Science at Purdue University Indianapolis, affiliated with the Department of Computer Science within the College of Science. He holds a PhD from the University of Illinois at Urbana-Champaign (1986) and a BS from MIT (1978). His expertise spans Computer Vision, Image Processing, Pattern Recognition, and Augmented Reality. Recent research focuses on crime prediction via video analysis, forensic imaging, and distributed tracking systems. He is a Senior Member of IEEE and ACM. Key research interests include augmented reality integration for industrial training, real-time illumination modeling, and monocular SLAM algorithms. His work addresses challenges in photorealistic AR, dynamic object labeling, and medical imaging applications such as hepatic fibrosis detection. He has contributed to projects like the e-DOTS indoor tracking system and forensic 3D impression acquisition. His publications span over three decades, emphasizing real-world applications in security, healthcare, and robotics. Education: Bachelor of Science in Computer Science and Engineering, MIT, 1978 PhD in Computer Science, University of Illinois at Urbana-Champaign, 1986 Awards: Senior Member, IEEE Senior Member, ACM Labs/Teams: Focus on AR, SLAM, and medical imaging applications Collaborative frameworks for distributed visual SLAM

Kevin Lynchمشاهده پروفایل

استاد

• Robotic Manipulation
• Robot Locomotion
• Physical Human-Robot Interaction
• +۴ مورد دیگر

Kevin Lynch is a Professor of Mechanical Engineering and Director of the Center for Robotics and Biosystems at Northwestern University. He holds a Ph.D. in Robotics from Carnegie Mellon University and a B.S.E. in Electrical Engineering (with honors) from Princeton University. His research focuses on robotic manipulation, robot locomotion, physical human-robot interaction, and distributed control of robot swarms. He has pioneered advancements in exoskeleton control, swarm formation algorithms, and haptic interaction frameworks. Professor Lynch has received significant recognition, including the IEEE Fellow distinction (2010), the Harashima Award (2017), and the Charles Deering McCormick Professor of Teaching Excellence award (2007–2010). He serves as Editor-in-Chief of the IEEE Transactions on Robotics and has authored over 150 peer-reviewed publications. Key contributions include the development of safety-aware human-robot collaboration systems and self-healing swarm control algorithms. He created the ME 333 Introduction to Mechatronics course and the Mechatronics Design Laboratory, fostering interdisciplinary robotics education. His lab, the Center for Robotics and Biosystems, integrates biomechanics with advanced robotics to address challenges in rehabilitation and autonomous systems.

Kaize Dingمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Large Foundation Models
• +۷ مورد دیگر

Kaize Ding is an Assistant Professor in Statistics and Data Science at Northwestern University, leading the REAL Lab and affiliated with the IDEAL Institute. He holds a Ph.D. in Computer Science from Arizona State University (2023) under Prof. Huan Liu, with prior degrees from Beijing University of Posts and Telecommunications. His research focuses on reliable AI systems for autonomous decision-making, knowledge-guided algorithms using GNNs/LLMs, and applications in healthcare, environmental science, and cybersecurity. Collaborations include Google Brain, Microsoft Research, and Amazon Alexa AI. Education: Ph.D. in Computer Science, Arizona State University (2023) M.S. and B.S., Beijing University of Posts and Telecommunications Research Interests: Developing robust AI for decision-making under uncertainty Graph-based machine learning for anomaly detection and network analysis Large language models (LLMs) integrated with domain-specific knowledge Cross-domain applications in healthcare diagnostics, environmental monitoring, and cybersecurity Recent Activities: Received Amazon Research Award and Google Research Grant NeurIPS 2025 Area Chair and ARR Area Chair Postdoc opening in AI4Health for swallowing disorder research Recent publications at AAAI, NeurIPS, EMNLP, and KDD Lab & Team: The REAL Lab focuses on advancing AI through interdisciplinary projects. Current students include Ruiyao Xu (PhD), Qingcheng Zeng (co-advised), and over 10 master's/undergraduate researchers. Alumni have moved to top PhD programs at UVa, UIC, and JHU.