جست‌وجوی استاد و پژوهشگر

Brendan E. Depueمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Neuroimaging
• Emotional Memory
• +۳ مورد دیگر

Brendan E. Depue, Ph.D., is an Associate Professor and Endowed Chair of Behavioral Brain Imaging and Neurobiology in the Department of Psychological and Brain Sciences at the University of Louisville. He also serves as an Affiliate Assistant Professor in the Department of Anatomical Sciences and Neurobiology. His research focuses on the neuroanatomical substrates of inhibitory and cognitive control, particularly within the prefrontal cortex (PFC), using neuroimaging techniques such as fMRI and structural MRI. Depue’s work explores emotional memory regulation, PTSD, anxiety, and decision-making processes. Depue earned his Ph.D. in 2009 from the University of Colorado at Boulder. His lab, the (N)euro(I)maging (L)aboratory of (C)ognitive, (A)ffective and (M)otoric Processes (NILCAMP), investigates how brain networks regulate cognitive, emotional, and motor functions. His research has identified key neural correlates of memory suppression in PTSD, structural covariance differences in depression, and gender-specific neural connectivity during emotion regulation. Recent publications highlight his exploration of fear learning mechanisms, neural networks underlying interoceptive awareness, and the translational potential of the bed nucleus of the stria terminalis in anxiety research. Depue’s interdisciplinary approach integrates clinical neuroscience with advanced imaging methodologies to address complex mental health challenges.

Justin Gottschlichمشاهده پروفایل

عضو هیئت علمی

• Machine Programming
• Artificial Intelligence
• Software Engineering
• +۳ مورد دیگر

Justin Gottschlich is an Adjunct Lecturer in the Computer Science Department at Stanford University, where he teaches the graduate course Machine Programming (CS 329M) . He also serves as Founder, CEO, and Chief Scientist of Merly Inc., a startup focused on machine programming systems to improve software development efficiency and quality. Previously, he led the Machine Programming Research group at Intel Labs, pioneering advancements in automating software development through a fusion of machine learning, programming languages, and systems research. His academic roles include prior positions as Adjunct Professor at University of Colorado-Boulder and Adjunct Assistant Professor at University of Pennsylvania. He has advised numerous graduate students across institutions, contributing to their research in machine programming and related fields. Gottschlich has authored dozens of research papers and holds multiple patents, with his work highlighted by prominent outlets like the Wall Street Journal and Communications of the ACM. He actively contributes to academic committees, including serving as Steering Committee Chair for the ACM SIGPLAN Machine Programming Symposium (MAPS). His research interests span machine programming, autonomous software development, formal methods, and the integration of AI into software engineering practices. Education: PhD in Computer Science from University of Colorado-Boulder Keynote Engagements: LADSIOS (2021), MIT DSAIL (2021), Penn PRECISE (2019) Labs/Teams: Intel Labs Machine Programming Research Group, Merly's R&D Team Grants & Funding: Extensive industry and academic research funding through Intel Labs and Merly

Marc HONمشاهده پروفایل

استادیار

• Asteroseismology
• Time-domain Astronomy
• Machine Learning
• +۳ مورد دیگر

Marc HON is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship, specializing in time-domain astronomy and machine learning applications for NASA missions including Kepler, TESS, and the Roman Space Telescope. His work focuses on characterizing stellar populations and discovering novel astrophysical phenomena through data-driven methodologies. His research spans asteroseismology for probing stellar interiors and Galactic archaeology to map the Milky Way's evolution using variable stars, alongside exoplanetary science investigations into planetary system evolution, habitable worlds, and James Webb Space Telescope atmospheric characterization. A core methodology involves developing machine learning frameworks like deep learning classifiers and generative models for large-scale astronomical datasets. HON's publication trends (2018-2024) reveal consistent innovation at the astrophysics-ML intersection, with emphases on red giant asteroseismology, exoplanet dynamics, and scalable analysis pipelines for space telescope data. Key contributions include flow-based stellar evolution emulators, deep learning oscillation detectors, and large-scale TESS Galactic archaeology studies. Scientific recognition includes: NASA Hubble Fellowship (2020) He actively contributes to major international collaborations as a member of both the TESS and Kepler Asteroseismic Science Consortia, with direct involvement in MIT's TESS mission operations and data pipelines.

Donya Hajializadehمشاهده پروفایل

دانشیار

• Structural Health Monitoring
• Machine Learning
• Asset Management Systems
• +۴ مورد دیگر

Dr Donya Hajializadeh is an Associate Professor of Structural Engineering at the University of Surrey's School of Sustainability, Civil and Environmental Engineering. She holds multiple professional qualifications including Chartered Engineer (CEng) and European Engineer (EUR ING), and is a Fellow of the Higher Education Academy (FHEA). Her roles include Director of Employability (since 2020), Deputy Coordinator of the Surrey/ICE Scholarship (since 2019), and IStructE Liaison Officer (since 2021). She is also affiliated with the Surrey Institute for People-Centred Artificial Intelligence (PAI). Her education includes a BEng (Hons), MEng, and PhD in relevant fields. Research focuses on structural health monitoring (SHM), machine learning applications in asset management, and deep learning for damage identification. Key areas include railway bridge dynamics, vibration analysis, and resilience assessment under seismic and environmental hazards. Current PhD students include Chia Sadik (Transport Infrastructure Failure Assessment) and Michael Millgate (Dynamic Characterisation of Tall RC Buildings). Teaching responsibilities include ENG1073 Fluid Mechanics and ENGM054 Earthquake Engineering. Research aligns with sustainable development goals, emphasizing infrastructure sustainability and carbon reduction strategies. Notable projects include rail bridge innovation recognized by the Chief Scientific Adviser Award and presentations on damage identification techniques to government officials. She contributes actively to interdisciplinary initiatives, integrating AI with civil engineering for smarter infrastructure solutions.

Hung Yew Munمشاهده پروفایل

دانشیار

• Heat Transfer
• Fluid Dynamics
• Micro-scale Heat Transfer
• +۴ مورد دیگر

Dr. Hung Yew Mun is an Associate Professor and Interim Head of the Mechanical Engineering program at Monash University Malaysia’s Malaysia School of Engineering. His expertise spans heat transfer, thermodynamics, and fluid dynamics, with a focus on micro-scale phenomena, phase-change heat transfer, and graphene-based materials. He teaches courses including MEC3454/MEC4408 (Thermodynamics and Heat Transfer) and MEC4416 (Momentum, Energy & Mass Transport). Dr. Hung’s research addresses advanced cooling solutions for electronics, energy storage, and sustainable materials. Education: PhD in Mechanical Engineering from University of Multimedia, Malaysia (2010). Research Interests: Micro-scale heat transfer, phase-change mechanisms, graphene nanostructures, and applications in thermal management. His work contributes to UN SDG goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent Projects: Includes studies on MXene-biochar composites for energy storage, graphene-enhanced devices for electronics cooling, and multiscale modeling of postharvest fruit water transport. He has led/co-investigated 12 projects since 2013, emphasizing interdisciplinary collaboration. Publications: Over 100 peer-reviewed articles, with recent focus on graphene-mediated heat transfer enhancement, plasma-activated cooling, and nanofluid applications. His work addresses both fundamental mechanisms and industrial applications. Labs/Teams: Active in thermal engineering and nanomaterials research groups, collaborating with institutions globally on sustainable energy and advanced materials.

Pankaj Mehtaمشاهده پروفایل

استاد

• Statistical Physics
• Systems Biology
• Ecological Dynamics
• +۳ مورد دیگر

Pankaj Mehta is a Professor in the Department of Physics at Boston University, with additional affiliations in the Department of Biomedical Engineering. His research bridges statistical physics, theoretical biology, and interdisciplinary systems approaches. Key areas include ecological dynamics, synthetic biology, and the application of machine learning principles to biological systems. His work focuses on understanding emergent phenomena in biological systems, such as cell fate decisions, ecosystem stability, and signal processing in cellular networks. He has pioneered methods combining physics-based modeling with computational tools to study complex systems, including gene circuits, microbial communities, and cancer dynamics. Recent contributions highlight the use of order parameters for interpreting cellular states, geometric frameworks for ecological niches, and machine learning analogies to ecological principles. His interdisciplinary approach integrates experimental data with theoretical models to address questions in biomedicine, environmental science, and fundamental physics.

Yuko Munakataمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Neuroscience
• Executive Functions
• +۵ مورد دیگر

Yuko Munakata is a Professor in the Department of Psychology at the University of California, Davis, and Director of the Cognition in Context Lab. She holds affiliations with the Center for Mind and Brain. Her education includes a PhD in Psychology from Carnegie Mellon University and dual BS/BA degrees in Symbolic Systems and Psychology from Stanford University. Her research integrates behavioral experiments, computational modeling, neuroimaging (ERP/fMRI), and cross-cultural studies to investigate how thinking varies across developmental stages and environmental/social contexts. Core interests include executive functions, developmental trajectories, cognitive control mechanisms, and interventions to enhance adaptive thinking. Her work highlights the role of cultural habits, social influences, and proactive control in shaping decision-making and self-regulation. Recent work explores the impact of environmental predictability on cognitive control development, the link between procrastination in adults/children, and the efficacy of executive function training programs. She advocates for contextual frameworks in understanding individual differences and educational inequality. Awards: American Psychological Association Boyd McCandless Early Career Award Outstanding Mentor Award (UC Boulder) Fellowships in Association for Psychological Science and APA Grants: NIH funding since 1998, supporting studies on child development and executive functions. Labs: Cognition in Context Lab (UC Davis), Center for Mind and Brain collaborations.

Riyadh Baghdadiمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• Deep Learning
• +۹ مورد دیگر

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Aryeh Kontorovichمشاهده پروفایل

استاد

• Theoretical Machine Learning
• Probability
• Statistics
• +۴ مورد دیگر

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

Oskar Hallatschekمشاهده پروفایل

دانشیار

• Biological Physics
• Evolutionary Dynamics
• Population Genetics
• +۳ مورد دیگر

Oskar Hallatschek is an Associate Professor and McAdams Chair in the Department of Physics and Integrative Biology at the University of California, Berkeley. His research focuses on biological physics and evolutionary dynamics, particularly how collective patterns emerge from heterogeneous microbial populations. He leads the Hallatschek Lab, which maintains collaborations with institutions like the University of Leipzig. Research Emphasis: Microbial systems, population genetics, non-equilibrium statistical physics, evolutionary adaptation, and mechanical interactions in cellular populations. Publications span journals like Nature , PNAS , and Science , with recent work on allele surfing, jamming in microbial colonies, and long-range dispersal effects. Grants: Supported by NIH and NSF, as indicated by institutional logos in the lab web presence.

• Fluid Mechanics
• Aeroacoustics
• Computational Fluid Dynamics
• +۳ مورد دیگر

Antonio Alguacil Cabrerizo is an Assistant Professor (starting 2025) at Université de Sherbrooke, where he currently serves as a Postdoctoral Fellow (2023-2025). His academic trajectory includes dual doctoral degrees in Mechanical Engineering from Université de Sherbrooke and École Nationale Supérieure d'Aéronautique et de l'Espace, complemented by aerospace engineering degrees from ENSEEIHT and Universidad Politécnica de Madrid. His research integrates computational fluid dynamics with machine learning, focusing on: Aeroacoustic prediction and noise source identification Deep learning surrogates for fluid and acoustic systems Turbomachinery and airfoil aerodynamics Data-driven modeling of spatiotemporal physical systems This work advances computational efficiency in simulating complex wave propagation, turbulence effects, and fluid-structure interactions. Publication analysis reveals consistent focus on developing neural network-based computational methods for aeroacoustics and fluid dynamics. His 15 most recent works demonstrate progressive refinement in applying convolutional architectures to predict acoustic scattering, refraction phenomena, and turbomachinery noise with increasing physical accuracy and computational efficiency. Awards and recognition include: Top 5 in AIAA Best Student Paper in Aeroacoustics (2024) Graduate Scholarship Award from CFD Society of Canada (2022) Eureka Scholarship from Université de Sherbrooke (2021) Best Poster Prize at CRASH Day (2021) He secured a $70,000 CAD startup grant (2025-2028) from Université de Sherbrooke for establishing his research program. No student advising relationships or laboratory affiliations are currently documented.

Ismail Ben Ayedمشاهده پروفایل

دانشیار

• Computer vision
• Optimization
• Medical image analysis
• +۱ مورد دیگر

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

Nakul Gopalanمشاهده پروفایل

استادیار

• Language Grounding
• Robot Learning
• Human-Robot Interaction
• +۳ مورد دیگر

Nakul Gopalan serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) in Tempe, where he founded and leads the Logos Robotics Lab since joining in August 2022. His academic foundation was established through a PhD in Computer Science from Brown University completed in 2019. Education: PhD in Computer Science, Brown University (2019) Research Focus: Dr. Gopalan pioneers work at the critical intersection of language grounding and robot learning, developing algorithms that enable robots to interpret natural language instructions and learn from human demonstrations. His research directly addresses real-world usability challenges by focusing on hierarchical reinforcement learning, task planning, and human-robot collaboration frameworks that empower non-expert users to train robots for home and office environments. Key innovations include plannable representations for natural language instruction following and transfer learning techniques for robotic task execution. Publication Evolution: Recent publications (2023-2025) demonstrate accelerating specialization in language-conditioned robot learning, with 80% of his latest work exploring compositional instruction following, novice-user teaching interfaces, and explainable AI for robotics. His research trajectory shows a deliberate shift from foundational language grounding (2017-2020) toward practical human-robot collaboration systems, evidenced by increased focus on hardware-software co-design, cross-embodiment transfer, and clinical applications of explainable AI in neurology support systems. Scientific Recognition: Best Paper Award at RoboNLP workshop (Association for Computational Linguistics) 2017 RSS 2023 Best Student Paper Finalist Mentorship & Service: As lab director, Dr. Gopalan actively mentors graduate researchers while teaching core courses including Data Structures and Algorithms (CSE 310) and specialized seminars on robot learning. His significant service contributions include organizing the RSS 2021 "Robotics for People" workshop, serving as Action Editor for ICRA 2023/2024, and extensive reviewing for top-tier robotics conferences (RSS, ICRA, CORL) and AI venues (NeurIPS, AAAI). Research Infrastructure: The Logos Robotics Lab operates as his primary research vehicle, focusing on natural language interfaces for robot training, hierarchical task decomposition, and real-world deployment of language-grounded learning systems. Current projects integrate large language models with robotic control frameworks to enable zero-shot task generalization across different robot embodiments.

Ziming Zhangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.

• Epidemiologic Factors
• Epidemiologic Methods
• Glioblastoma
• +۷ مورد دیگر

Katsuhito Yasuno is a Research Scientist in the Department of Neurosurgery at Yale School of Medicine. He works within the Gunel Lab, focusing on neurogenetic research related to brain disorders, vascular conditions, and tumors. His work involves genomic analyses and molecular studies to understand the genetic basis of various neurological conditions. Education: Postdoctoral Fellow, Japan Science and Technology Corporation (2008) Postdoctoral Fellow, Japan Biological Informatics Consortium (2006) Postdoctoral Fellow, Tokai University School of Medicine (2004) PhD in Theoretical Physics, Tokyo Institute of Technology (2002) MS in Theoretical Physics, Tokyo Institute of Technology (1999) BS in Physics, Tokyo Metropolitan University (1997) Dr. Yasuno's research spans multiple areas of neurogenetics and molecular neuroscience. His primary interests include epidemiologic factors and methods related to brain tumors (particularly glioblastoma and meningioma), intracranial aneurysms, nervous system diseases and malformations, and vascular diseases. His physics background contributes to sophisticated analytical approaches in genetic research. His publication record shows consistent contributions to understanding the genetic mechanisms underlying neurological disorders, with recent work focusing on meningioma genetics, brain malformations, and vascular disorders. The research demonstrates a progression from basic genetic discovery to translational applications, with particular emphasis on molecular pathways that could serve as therapeutic targets. Dr. Yasuno collaborates extensively with leading researchers in the field, most notably with Dr. Murat Günel (28 common publications) and Dr. Kaya Bilguvar (34 common publications). His work involves both computational analysis and laboratory validation of genetic findings. As part of the Gunel Lab, Dr. Yasuno contributes to the lab's three major research interests: developmental neurogenetic disorders, neurovascular disorders, and brain tumor biology. The lab utilizes advanced genomics techniques and bioinformatic analysis for gene discovery followed by functional studies.