جستجوی استاد راهنمای دانشگاهی

Jesse Cougleمشاهده پروفایل

استاد

• Anxiety Disorders
• Obsessive-Compulsive and Related Disorders
• Problematic Anger
• +۷ مورد دیگر

Jesse Cougle is a Professor of Psychology at Florida State University, focusing on anxiety disorders , obsessive-compulsive and related disorders (OCRD), and problematic anger . He leads the Cougle Lab, which develops computerized treatments and investigates distress intolerance , courage , and transdiagnostic factors . Education: University of Texas at Austin (2008), Oxford University (postgraduate) Research: Cognitive-behavioral mechanisms in anxiety disorders, technology-based interventions for social anxiety and BDD, and biological processes in problematic anger Students: Mentoring Tapan Patel, James Zech, Victoria Swaine, and others His work spans interpretation bias modification , safety behavior reduction , and 'not just right' experiences in OCRD. Recent publications emphasize digital interventions for mental health and distress tolerance as a key transdiagnostic factor. Scientific Awards: President’s New Researcher Award (ABCT) Graduate Faculty Mentor Award (FSU) He serves as Editor-in-Chief of the Journal of Obsessive-Compulsive and Related Disorders and holds editorial roles in major journals. The Cougle Lab explores anxiety , perfectionism , and appearance-related psychopathology through multimodal research .

Alan Sussmanمشاهده پروفایل

استاد

• Databases and Big Data
• High Performance Computing
• Parallel and Distributed Systems
• +۴ مورد دیگر

Alan Sussman is a Professor and Associate Chair of Undergraduate Education in the Computer Science department at the University of Maryland. His research focuses on databases, high-performance computing, parallel systems, and educational curriculum development for computing disciplines. He holds a Ph.D. from Carnegie Mellon University (1991) and a B.S.E. from Princeton University (1982). His educational contributions include integrating parallel and distributed computing concepts into early undergraduate courses, supported by NSF-funded initiatives like the CyberTraining program. He has advised students such as Harshit Soora (Master's) and Xiaolong Tian (PhD). His research spans compiler optimizations for parallel programs, distributed data management systems, and scientific workflow frameworks like DYFLOW. He collaborates with UMIACS and contributes to interdisciplinary projects like the TASCS center. Key innovations include VeloxDFS for distributed dataset streaming, compiler techniques for irregular memory access in PGAS programs, and NetCDFaster for geospatial data optimization. His work emphasizes productivity improvements for high-performance applications and curriculum modernization to address emerging computational challenges. Awards: No individual awards explicitly listed; however, collaborator Jik-Soo Kim received a best paper award in 2006. Grants: NSF CyberTraining, TCPP Curriculum Initiative, and Center for Technology for Advanced Scientific Component Software (TASCS). Labs/Teams: Active in UMIACS and interdisciplinary collaborations, including the TASCS center and InterComm framework development.

Alvin J. K. Chuaمشاهده پروفایل

استادیار

• Gravitational-Wave Astronomy
• General Relativity
• Machine Learning for Science
• +۳ مورد دیگر

Dr. Alvin J. K. Chua is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship. He holds a joint appointment in the Department of Mathematics and a courtesy appointment in the Department of Statistics and Data Science. His research focuses on gravitational-wave astronomy , particularly extreme mass ratio inspirals (EMRIs) as key sources for the LISA mission. He develops computational and statistical methods for modeling GW sources and analyzing detector data, with recent work on machine learning and Bayesian inference techniques. His selected publications highlight advancements in modeling beyond-vacuum-GR effects in EMRIs non-local parameter degeneracies rapid relativistic waveform generation neural networks for GW inference statistical sampling on manifolds These works span gravitational-wave astrophysics , computational relativity , and applied statistics . Scientific recognition includes the NUS Presidential Young Professorship. He collaborates with the LISA Consortium and the North American Nanohertz Observatory for Gravitational Waves (NANOGrav).

Ben Bloem-Reddyمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Bayesian Methods
• +۳ مورد دیگر

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.

Christina Lee Yuمشاهده پروفایل

استادیار

• Algorithm Design and Analysis
• High Dimensional Statistics
• Inference over Networks
• +۵ مورد دیگر

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE). She holds a PhD and MS in Electrical Engineering and Computer Science from MIT (2017, 2013) and a BS in Computer Science from Caltech (2011). Her research focuses on algorithm design, high-dimensional statistics, causal inference in networks, and reinforcement learning. She is also an Amazon Scholar and has received prestigious awards, including the NSF CAREER Award and Intel Rising Stars Award. Her work is supported by grants from the NSF and Air Force Office of Scientific Research. Education: PhD in EECS, MIT (2017) MS in EECS, MIT (2013) BS in Computer Science, Caltech (2011) Research Interests: Algorithm design and analysis Inference over networks and causal inference Sequential decision making under uncertainty Online learning and reinforcement learning High-dimensional statistics Awards and Honors: NSF CAREER Award (2024) ACM SIGMETRICS Rising Stars Award (2024) Intel® Rising Stars Award (2021) JPMorgan Faculty Research Award (2021) Simons Institute Research Fellow (2020) INFORMS Dantzig Dissertation Award Honorable Mention (2018) Grants and Funding: National Science Foundation (NSF) CAREER Grant Air Force Office of Scientific Research Grant Advising: PhD Students: Sean Sinclair, Tyler Sam, Xumei Xi Collaborators: Mayleen Cortez, Matthew Eichhorn Labs and Collaborations: Member of ORIE, Statistics, CAM, and CS graduate fields at Cornell Amazon Visiting Academic in Fulfillment Optimization (2025)

Guy-Vincent Jourdanمشاهده پروفایل

استاد

• Software Security
• Cybersecurity
• Distributed Systems
• +۶ مورد دیگر

Professor Guy-Vincent Jourdan is affiliated with the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Ph.D. from Université de Rennes/INRIA (France, 1995) focusing on distributed systems analysis. Prior to academia, he served as CTO and CEO of Decision Academic Graphics, an Ottawa-based firm. His research interests span software security, cybersecurity (including cybercrime prevention), distributed systems modeling, formal methods, mobile applications, and rich internet applications. Specific technical emphases include phishing detection systems, blockchain fraud analysis, and adversarial machine learning. Professor Jourdan has pioneered tools like D-ForenRIA for reconstructing user interactions in Rich Internet Applications and contributed to cybersecurity frameworks such as HEART for log anomaly detection. His work integrates machine learning techniques with domain-specific challenges in network security and software verification. His publications (2023-2025) reflect advancements in AI-driven vulnerability analysis, blockchain fraud detection, and automated phishing detection systems. Notable projects include SV-TrustEval-C for source code vulnerability analysis and Intellitweet for social media threat detection. While no scientific awards are explicitly listed, his prolific publication record and industry-academia transition highlight sustained contributions to computer science and cybersecurity domains.

Gianvito Lanzollaمشاهده پروفایل

استاد

• Digital Transformation
• Technology Strategy
• Competitive Dynamics
• +۷ مورد دیگر

Gianvito Lanzolla is a Professor of Strategy at Bayes Business School (formerly Cass), City, University of London, where he has been employed since April 2006 and served as Dean of the Faculty of Management from 2016-2020. He founded the Bayes Digital Leadership Research Centre and is a Fellow of the Royal Society of Arts. Education: MSc in Mechanical Engineering, PhD in Strategic Management Visiting Appointments: London Business School, Indian School of Business, ESMT Berlin His research focuses on digital transformation, technology strategy, institutional change, and competitive dynamics. He examines how CEOs' digital orientation and board characteristics affect firm value, with publications in Academy of Management Journal , Harvard Business Review , and California Management Review . His work has won multiple academic prizes and been featured in major media outlets. Recent publications analyze digital ecosystems, agile transformation, and AI applications in finance. He teaches 'Strategic Leadership' and 'Digital Transformation' in MBA programs, having won teaching excellence awards in 2007, 2008, 2009, 2012, and 2015. Scientific Awards: City University Teaching Excellence Awards (2015, 2012, 2009, 2008) Strategic Management Society Best Paper Award (2012) SSRN Top Ten Downloaded Paper (2008) Spanish Academy of Management Best Paper Award (2009)

Jonathan Hauensteinمشاهده پروفایل

استاد

• Numerical Algebraic Geometry
• Scientific Computing
• Numerical Solution of Differential Equations
• +۵ مورد دیگر

Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.

Jesper Rindom Jensenمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Artificial Intelligence
• Speech Enhancement
• +۱۲ مورد دیگر

Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.

Christine Skubiszمشاهده پروفایل

استادیار

• Communication
• Persuasion and Social Influence
• Health Communication
• +۴ مورد دیگر

Christine Skubisz is an Assistant Professor in the Department of Natural and Applied Sciences at Bentley University, located in Waltham, Massachusetts. She is affiliated with the College of Arts and Sciences and contributes to interdisciplinary education through courses in health communication, nonverbal behavior, and applied research. Educational Background: B.A., Purdue University M.A., Michigan State University Ph.D., University of Maryland Post-Doctoral Fellowship, University of Pennsylvania Her research focuses on human communication with an emphasis on persuasion, social influence, and health-related messaging. She explores how individuals process risk information and how communication strategies can improve patient-centered outcomes. Her work intersects psychology, public health, and behavioral science, aiming to enhance health campaigns and interpersonal dynamics in medical settings. Christine is an active member of leading academic associations, including the Society for Health Communication (since 2015), the International Communication Association (since 2011), and the National Communication Association (since 2011), reflecting her sustained engagement with the scholarly community. She has not listed any publications or scientific awards in the provided text, but her teaching portfolio includes key courses such as Health Communication Campaigns (NAS 306), Nonverbal Behavior (PSY 330), Falcon Discovery Seminar (FDS 100), and Applied Research Seminar (PHD 1755), indicating a strong commitment to undergraduate and experiential learning. There is no information available about student advising, research grants, or leadership in labs or research teams within the current data.

Gabi Laskeمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Earth's Internal Structure
• +۸ مورد دیگر

Gabi Laske is a Professor at the Institute of Geophysics and Planetary Physics (IGPP), Scripps Institution of Oceanography (SIO), University of California, San Diego (UCSD). She is a leading researcher in seismology and geophysics, with a focus on Earth's internal structure, crustal and mantle modeling, and ocean-bottom seismology. Her work has significantly advanced global crustal models, including CRUST5.1, CRUST2.0, and CRUST1.0. Her research interests include seismology, geophysics, Earth's internal structure, surface wave tomography, normal mode analysis, crustal and lithospheric modeling, ocean bottom seismology, mantle plumes, inner core rotation, ambient noise seismology, and earthquake signal processing. She has led major projects such as the Hawaiian PLUME and SWELL experiments, utilizing ocean-bottom seismometers to study mantle dynamics and lithospheric rejuvenation. Her work on inner core differential rotation, particularly with Guy Masters, has been published in top journals like Nature and Science . The 15 most recent publications reflect a strong trend in ocean-bottom seismology, ambient noise analysis, instrument calibration, seismic signal quality, and imaging of crustal and mantle structure. Her work combines observational seismology with advanced signal processing and modeling techniques, often in collaboration with students and international teams. She has made significant contributions to understanding seismic anisotropy, normal modes, and the structure of volcanic and tectonic regions. Funded by NSF (OCE, EAR, CSEDI, MG&G) Collaborative projects with USGS, international institutions Advisor to PhD students, including Adrian Doran Lead developer of DLOPy for OBS orientation Contributor to global reference models (CRUST1.0, LITHO1.0) Gabi Laske has made enduring contributions to geophysics through her development of global crustal models, leadership in major seismic experiments, and mentorship of the next generation of seismologists. Her work continues to shape our understanding of Earth's deep interior and surface processes.

Jason J. Corsoمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Jason J. Corso is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan . His research focuses on high-level computer vision , video understanding , and the intersection with human language and robotics . His work emphasizes Bayesian approaches to segmentation and recognition, with applications spanning biomedicine and recreational video analysis . He is particularly known for contributions to video object segmentation , activity recognition , and vision-language frameworks . Scientific awards include: NSF CAREER award (2009) ARO Young Investigator award (2010) Google Faculty Research Award (2015) DARPA CSSG grant He also leads major projects like YouCook2 dataset , Video2Text.net , and LIBSVX framework.

Akihiko Nishimuraمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +۹ مورد دیگر

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

Aaditya Ranganمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Neuroscience
• +۴ مورد دیگر

Aaditya Rangan is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. from UC Berkeley (2003) and a B.A. from Dartmouth College (1999). His research focuses on applying numerical analysis and scientific computing to biological systems, including neuronal network dynamics in the insect olfactory system and mammalian visual cortex. He also develops computational tools for genomic data analysis, particularly biclustering methods for gene expression and SNP datasets. Rangan currently directs NYU's master's program in mathematics. Key contributions include models of synaptic depression in neural systems and algorithms for cryo-EM data processing. His work is published in journals like the Journal of Computational Neuroscience and PLoS Computational Biology , and his software tools are available on GitHub.

• Wireless Communications
• 6G Technology
• Signal Processing
• +۷ مورد دیگر

Erik G. Larsson is a Professor and Head of the Division for Communication Systems within the Department of Electrical Engineering (ISY) at Linköping University (LiU), Sweden. He joined LiU in September 2007 and has previously held academic and research positions at the Royal Institute of Technology (KTH), University of Florida, George Washington University, and Ericsson Research. Research Interests: Enabling technologies for 6G wireless communication Statistical inference and signal processing Network science and complex networks Decentralized and federated machine learning over networks Physical layer security and privacy Energy-efficient digital signal processing His research group, active in areas like RadioWeaves and massive MIMO, focuses on robust, efficient, and secure wireless connectivity. Recent publications highlight trends in decentralized learning, resource allocation in wireless networks, and the integration of AI into mobile networks, particularly through projects like 'Turning the Air into an AI Computer' funded by the Knut and Alice Wallenberg Foundation. Scientific Awards and Honors: IEEE Signal Processing Magazine Best Column Award (2012, 2014) IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) IEEE ComSoc Best Tutorial Paper Award (2018) IEEE ComSoc Fred W. Ellersick Prize (2019) IEEE SPS Donald G. Fink Overview Paper Award (2023) IEEE Fellow Member, Royal Swedish Academy of Sciences (KVA) Gyllene Moroten Best Teacher Award (2021) Advising and Grants: He has supervised numerous Ph.D. and Licentiate students, many of whom now hold positions at leading industry and academic institutions. His research is currently funded by major organizations including the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research (SSF), ELLIIT, Security-Link, Swedish Research Council (VR), and EU Horizon 2020 (H2020-SNS-6GTandem). Previous sponsors include VR, KVA, NSF, ORAU, and multiple EU FP7 and H2020 projects (e.g., MAMMOET, REINDEER, 5G-Wireless). Leadership and Service: He has served as Associate Editor for IEEE Transactions on Communications and IEEE Transactions on Signal Processing, chaired technical committees and steering committees in IEEE Signal Processing Society, and held leadership roles in major conferences such as the Asilomar Conference on Signals, Systems and Computers. He was a Visiting Fellow at Princeton University in 2015.