جست‌وجوی استاد و پژوهشگر

Manish Vermaمشاهده پروفایل

استاد

• Multimodal transportation
• Dangerous goods transportation
• Risk assessment
• +۵ مورد دیگر

Manish Verma is Professor of Operations Management and Associate Dean, Graduate Studies at the DeGroote School of Business, McMaster University. His academic journey began with an MBA and PhD in Business Administration with Operations Management/Management Science specialization from Desautels Faculty of Management at McGill University. Dr. Verma's research focuses on multimodal transportation of dangerous goods, risk assessment, network design and planning in transportation, humanitarian logistics, green supply chain management, and disruption/resilience in transportation systems. His current research engagements center on safety and security issues in freight transportation and humanitarian logistics, funded by NSERC and SSHRC grants. He has been frequently approached by media to comment on railroad accidents involving dangerous goods. An analysis of his recent publications reveals a strong emphasis on hazardous materials transportation risk management, with significant contributions to rail-truck intermodal systems, hazmat risk modeling using value-at-risk methodologies, and emergency response planning for transportation networks. His work bridges theoretical operations research with practical transportation safety applications. $245K research grant for rail safety research from Government of Canada As an educator, Dr. Verma has taught courses including Predictive Analytics for Managers, Network Design Issues in Freight Transportation, and Management Science Research Issues. His scholarly impact is evidenced by publications in leading journals such as Transportation Research Part E, European Journal of Operational Research, and Safety Science. He actively contributes to real-world transportation safety through media commentary and research that informs policy decisions regarding dangerous goods transportation.

Gerd Bruderمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۷ مورد دیگر

Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.

• Self-Driving Database Management Systems
• Machine Learning for Databases
• GPU-Accelerated Data Analytics
• +۴ مورد دیگر

Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.

Carlos Cinelliمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Methodology
• Machine Learning
• +۵ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.

• Robotics
• Micro/Nanorobotics
• Physical Intelligence
• +۳ مورد دیگر

Metin Sitti is a Professor and currently serves as a part-time adjunct professor at Koç University's Faculty of Medicine and Engineering. He is also the President of Koç University and Director of the Max Planck Institute for Intelligent Systems since 2014. His research spans robotics , micro/nanorobotics , physical intelligence , bio-inspired robotics , and materials science . Undergraduate: Boğaziçi University (Electrical and Electronics Engineering, 1992) Master's: Boğaziçi University (1994) PhD: University of Tokyo (Electrical Engineering, 1999) His research focuses on small-scale robotics for biomedical applications, including magnetic microrobot swarms , biohybrid systems , and functional materials . Recent publications emphasize 3D control , wireless actuation , and clinical translation of robotic systems. Scientific Awards : 2020 Scientific Breakthrough of the Year 2018 Koç University Rahmi M. Koç Science Medal 2014 IEEE/ASME Best Mechatronics Paper Award 2013/2012 World RoboCup Micro-Robotics First Prize He has advised numerous students and collaborates with institutions like Carnegie Mellon University and University of Stuttgart. His work integrates physical intelligence into miniature robots for medical imaging , drug delivery , and autonomous systems .

Paola Cascante-Bonillaمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Embodied AI
• +۵ مورد دیگر

Paola Cascante-Bonilla is an Assistant Professor in the Department of Computer Science at Stony Brook University, with expertise in computer vision, natural language processing, and embodied AI. Her research focuses on developing systems for compositional reasoning, common-sense inference, and trustworthy AI using vision-language models, while addressing cultural bias and explainability challenges.

Vijay Kumarمشاهده پروفایل

استاد

• Robotics
• Multi-Robot Systems
• Micro Aerial Vehicles
• +۶ مورد دیگر

Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering at the University of Pennsylvania, with faculty appointments in the Departments of Mechanical Engineering, Computer and Information Science, and Electrical and Systems Engineering. He is a leading figure in robotics and computer architecture research. Research Interests include robotics, particularly multi-robot systems and micro aerial vehicles (MAVs), as well as computer architecture innovations for machine learning, GPU acceleration, and datacenter efficiency. His work spans theoretical foundations and practical applications in autonomous systems and hardware optimization. Scientific Awards include: 1991 NSF Presidential Young Investigator Award 1996 Lindback Award for Distinguished Teaching 2012 ASME Mechanisms and Robotics Award 2014 Engelberger Robotics Award 2017 IEEE George Saridis Leadership Award Multiple best paper awards at DARS, ICRA, and RSS conferences Editorial Leadership includes serving as Editor of the ASME Journal of Mechanisms and Robotics and Advisory Board Member of AAAS Science Robotics Journal . His GRASP Lab team developed foundational frameworks for micro UAV testbeds and swarm robotics.

Michael U. Gutmannمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +۹ مورد دیگر

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

Faye Ginsburgمشاهده پروفایل

استاد

• Social anthropology
• Ethnographic film
• Ethnography of media
• +۳ مورد دیگر

Faye Ginsburg is the David B. Kriser Professor of Anthropology at New York University (NYU), affiliated with the College of Arts & Science. Her work focuses on social anthropology, ethnographic film, media studies, indigenous media, and disability. She holds a Ph.D. from CUNY (1986) and a B.A. from Barnard College (1976). Research interests include global media dynamics, disability studies, and indigenous sovereignty. She co-edited pivotal works like *Media Worlds: Anthropology on New Terrain* (2002) and *Disability Worlds* (2024). Recent publications address disability in the digital age, indigenous media sovereignty, and reproductive politics. Awards include the 2025 American Academy of Arts and Sciences membership and the 2025 CUNY President's Distinguished Alumni Award. She directs NYU’s Graduate Certificate Program in Culture and Media and co-founded the NYU Council for the Study of Disability. Her grants include NEH funding for disability research and leadership roles at the Center for Religion and Media. Key initiatives include SKILLS transition program for NYC youth with learning disabilities and collaborations with global media festivals (e.g., Margaret Mead Film Festival). Her interdisciplinary work bridges academia, activism, and media practice.

• Materials Science
• Corrosion Engineering
• Alloy Design
• +۳ مورد دیگر

Dr. KN Sasidhar is a Researcher in the Department of Microstructure Physics and Alloy Design at Heinrich Heine University Düsseldorf. His work focuses on advanced materials science, particularly corrosion mechanisms, alloy design, and nanoscale structural analysis. He employs cutting-edge techniques like in situ synchrotron investigations and deep learning frameworks to study material behavior under extreme conditions. Current research emphasizes corrosion resistance in stainless steels, phase transformations during nitriding, and radiation effects on coatings. Key achievements include pioneering studies on nanoscale amorphization in metallic systems, data-centric approaches for materials discovery, and the development of predictive models for alloy performance. His work bridges experimental materials characterization with computational methods, addressing challenges in energy and aerospace applications. Publications span corrosion analysis, microstructural evolution under irradiation, and phase separation phenomena. Collaborative projects involve synchrotron facilities and interdisciplinary teams focusing on materials informatics. No formal awards or grants are explicitly listed in the provided texts, though his prolific publication record indicates active academic engagement.

Kristin A. Perssonمشاهده پروفایل

استاد

• Lithium-ion Batteries
• Multivalent Batteries
• Organic Electrolytes
• +۴ مورد دیگر

Dr. Kristin A. Persson is a Professor and Daniel M. Tellep Distinguished Professor in Engineering at the University of California, Berkeley's Department of Materials Science and Engineering. She leads the Persson Group at Lawrence Berkeley National Laboratory (LBNL), focusing on atomistic computational methods for energy materials. As director of the Materials Project, she pioneers high-throughput computing and data-driven approaches to accelerate material discovery for clean energy applications, including batteries, electrolytes, and photocatalysts. Her research spans lithium-ion and multivalent batteries, with a focus on electrolyte design, interfacial chemistry, and sustainable materials. Persson has directed the Materials Project since its inception, a global initiative to computationally predict material properties and provide open-access data. She holds affiliations with LBNL’s Energy Sciences Area and collaborates with industry and academia on projects like the Electrolyte Genome and piezoelectric materials databases. Key achievements include election to the National Academy of Engineering (2025), Royal Swedish Academy of Sciences (2024), and Fellowships from the AAAS (2022) and APS (2021). Her group’s work has produced over 200 publications, with recent highlights on disordered cathodes, ML-driven material predictions, and circular polymers. Persson advises a dynamic team of ~50 graduate students, postdocs, and staff, fostering interdisciplinary innovation in energy storage and materials informatics. Awards include DOE’s Distinguished Scientist Fellowship (2024), Cyril Stanley Smith Award (2022), and Web of Science Highly Cited Researcher recognition (2020). Her lab’s infrastructure supports projects from computational workflows to experimental collaborations, with a focus on translating theory into real-world energy solutions.

Jennifer Katzمشاهده پروفایل

دانشیار

• Inclusive Education
• Social and Emotional Learning (SEL)
• Mental Health in K-12
• +۳ مورد دیگر

Dr. Jennifer Katz serves as an Associate Professor in the Department of Educational and Counselling Psychology, and Special Education (ECPS) at the University of British Columbia's Faculty of Education. She joined UBC in July 2016 after previously holding an Associate Professor position in Inclusive Education at the University of Manitoba. Renowned for creating the Three Block Model of Universal Design for Learning (UDL), her framework is now implemented across Canadian school divisions to advance inclusive educational practices. Dr. Katz's research centers on inclusive education K-12 , social and emotional learning (SEL) , and mental health promotion in schools . Her work integrates the Three Block Model of UDL to address diverse student needs while emphasizing indigenous perspectives through extensive collaboration with First Nation elders and communities in Manitoba, Alberta, and Quebec. This intersectional approach reimagines inclusive education through reconciliation frameworks and culturally responsive practices. Analysis of her recent publications reveals consistent focus on mental health literacy programs for students with developmental disabilities, UDL's impact on academic achievement and teacher efficacy, and the integration of Indigenous knowledge systems. Her research bridges theory and practice through classroom-based interventions, cluster-randomized trials, and transformative learning frameworks that prioritize student well-being and social inclusion. Scientific Awards: No scientific awards were documented in the provided sources. Advising and Grants: While specific graduate students and research grants aren't detailed in the text, Dr. Katz's extensive publication record and community partnerships indicate active research leadership. Her work with school divisions across Canada suggests significant program implementation funding, though grant specifics remain unreported. Labs and Teams: Dr. Katz maintains critical partnerships with First Nation communities, embedding Indigenous knowledge into educational frameworks. Her Three Block Model implementation involves multi-school division collaborations nationwide, creating practitioner-researcher networks focused on inclusive classroom transformation.

• Responsible Computing
• Data Protection & Governance
• Digital Well-Being
• +۳ مورد دیگر

Asia J. Biega is a tenure-track faculty member (W2) at the Max Planck Institute for Security and Privacy (MPI-SP), where she leads the interdisciplinary Responsible Computing group. She is also a principal investigator of the Cluster of Excellence CASA and the FINDHR consortium. Her work sits at the intersection of computing and society, focusing on responsible computing, data protection & governance, and digital well-being within data-driven and AI-based systems. Dr. Biega's research spans multiple disciplines including computer science, law, philosophy, and social sciences. Her work examines how principles of responsible computing can be computationally operationalized, with particular attention to data protection frameworks, privacy technology governance, and digital well-being. She actively collaborates across disciplinary boundaries to make technical contributions while supporting research in other fields. Her approach combines theoretical rigor with practical applications, often drawing from her industry experience at Microsoft and Google. Her publication record reveals a strong focus on the intersection of fairness, privacy, and transparency in information retrieval systems. She has pioneered work on data minimization compliance, fair ranking algorithms, and user perceptions of data collection practices. Her research consistently bridges technical and legal perspectives, particularly examining how GDPR principles can be computationally implemented. Recent work shows increasing attention to generative AI governance, algorithmic hiring systems, and the relational aspects of data in recommender systems. Dr. Biega has received several prestigious awards including the Council of Europe's Rodotà Award for innovative research in data protection, the SaTML Notable Reviewer Award, the GI-DBIS Dissertation Award of the German Informatics Society, and recognition as one of the '100 Brilliant Women in AI Ethics' in 2025. She has advised numerous PhD students and postdocs who have gone on to faculty positions at institutions including the University of Trieste, Penn State, and the University of Washington. Her research is funded by the Max Planck Society, Alexander von Humboldt Foundation, and the European Union (Horizon Europe FINDHR). She serves as General Co-Chair for ACM FAccT 2025 and has held leadership roles in multiple academic conferences.

Tatsunori Hashimotoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.