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Scott TaylorView profile

Professor

• Feminism in Organizations
• Gender and Leadership
• Organizational Studies
• +7 more

Scott Taylor is a Professor of Leadership & Organization Studies in the Department of Management at the University of Birmingham’s Birmingham Business School. He also serves as the Business School Director of External Engagement and Responsible Business and as Academic Lead for Accreditations. He has held academic positions at Open, Essex, Exeter, and Loughborough Universities, and has been affiliated with the University of Birmingham in two periods: 2002–2007 and since 2013, confirming his current active status. Education: PhD in Management, Manchester Metropolitan University MA in Human Resource Management, University of Bolton MA in Arts, University of Glasgow His research centers on feminism in organizations, with current projects exploring gender quotas in political parties and women in the craft brewing sector. His work critically examines misogyny, gendered workloads, and the roles of men in feminist organizational change. He has published extensively in top journals such as Gender, Work & Organization , Human Relations , and Organization . His recent publications reveal a consistent focus on gender, power, and organizational ethics, often from a feminist and critical perspective. Scott Taylor is currently Development Editor for the Journal of Business Ethics and serves on the editorial boards of Academy of Management Learning & Education , Leadership , and Organization . He supervises several postgraduate students including Jennifer Davies, Isbahna Naz, and Dannielle Dorn. He is also a member of the Chartered Association of Business Schools’ Equality, Diversity & Inclusion committee, reflecting his institutional leadership in equity issues. Scientific Awards and Recognitions: Member, Chartered Association of Business Schools EDI Committee He has taught and visited universities internationally, including Auckland, Delhi, São Paulo, Jeddah, Melbourne, and Lapland. His academic leadership, editorial roles, and sustained research output confirm his active and influential position in the field of organizational studies.

Daniel LeunbachView profile

Senior Lecturer

• Entrepreneurship
• Decision-Making Under Uncertainty
• Philosophy
• +3 more

Daniel Leunbach is a Senior Lecturer at the School of Business, University of Oslo , specializing in Entrepreneurship . He teaches courses in strategy, innovation management, and entrepreneurship while integrating philosophy and psychology into his research to study decision-making under uncertainty. Ph.D. in Strategy and Entrepreneurship, University of Oslo MSc in Innovation and Entrepreneurship, University of Oslo BSc in Molecular Biology, University of Bergen His research explores the intersection of entrepreneurship theory , philosophy , and psychology , focusing on how entrepreneurs navigate situations where outcome probabilities are impossible to quantify. Recent publications investigate topics such as entrepreneurial imagination through poetry , resource acquisition negotiations , and Wittgensteinian approaches to defining entrepreneurship . Leunbach collaborates with scholars like Truls Erikson and Glenn Roger Kristiansen , publishing in journals such as Academy of Management Review , Scandinavian Journal of Management , and Journal of Business Venturing Design . He is affiliated with the Entrepreneurship (ENT) research group and consults on entrepreneurial decision-making frameworks.

Muhammad RashedView profile

Assistant Professor

• Electronic Design Automation (EDA)
• In-Memory Computing
• Artificial Intelligence Acceleration
• +5 more

Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015). Ph.D. : Computer Engineering, University of Central Florida, 2024 B.S. : Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015 His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing. The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals. Scientific Awards: UTA CARES Grant for OER Creation Research Experiences for Undergraduates (REU) Grant Alireza Seyedi Doctoral Research Innovation Endowed Scholarship David T. & Jane M. Donaldson Memorial Scholarship IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination Best Research Video Award, Design Automation Conference (DAC) Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college. His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.

Kenneth RossView profile

Professor

• Database Systems
• Query Processing
• Query Language Design
• +7 more

Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.

Melanie WeberView profile

Assistant Professor

• Geometric Machine Learning
• Optimization on Manifolds
• Non-Euclidean Machine Learning
• +3 more

Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.

• Computational Linguistics
• Computer Science
• Machine Learning
• +1 more

Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.

Kihana Miraya RossView profile

Assistant Professor

• Black Education Studies
• Antiblackness in Education
• Educational Fugitivity
• +5 more

Kihana Miraya Ross is an Assistant Professor in the Department of Black Studies at Northwestern University, affiliated with the Weinberg College of Arts & Sciences. She holds a Ph.D., M.A., and B.A. from UC Berkeley in Social and Cultural Studies of Education, African American Studies, and Sociology. Her research focuses on antiblackness in education, Black educational fugitivity, and educational reparations, employing critical ethnography and participatory design methods. Her book project examines the afterlife of school segregation and strategies for Black educational futurities. She co-directed the documentary Stolen School , exploring the closure of Evanston’s Foster School and its lasting impacts. Ross has received major grants from the Spencer Foundation and fellowships from the Ford Foundation. Education: Ph.D. in Social and Cultural Studies of Education (UC Berkeley, 2016) M.A. in Social and Cultural Studies of Education (UC Berkeley, 2011) B.A. in African American Studies, Sociology, and Education (UC Berkeley, 2002) Key Projects: Spencer Foundation-funded research on Evanston’s educational reparations Documentary Stolen School (2023) Awards: 2021 Spencer Foundation Large Grant 2017 Ford Foundation Postdoctoral Fellowship Ross’s work bridges historical analysis and contemporary activism, advocating for systemic change through frameworks like Black fugitivity. She collaborates with community organizations like Shorefront Legacy Center and mentors interdisciplinary projects on race, education, and spatial justice.

Clyde KruskalView profile

Associate Professor

• Parallel Computing
• Algorithms
• Computer Architecture
• +3 more

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.

Jing JiangView profile

Professor

• Natural Language Processing
• Machine Learning
• Data Mining
• +4 more

Roles and Affiliations: Full Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). Research Advisor to Xiaosen Zheng and Kankan Zhou. Serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) , Program Co-Chair of EMNLP 2019, and Editorial Board Member of Computational Linguistics (2015-2017). Education: PhD in Computer Science, University of Illinois at Urbana-Champaign (2008) B.S. and M.S. in Computer Science, Stanford University Research Focus: Specializes in natural language processing (NLP), text mining, machine learning, and data mining. Current interests include question answering, social media content analysis, and combating misinformation. Explores topics like counterfactual syntax for cross-lingual understanding, interventional training for robust NLU, and bias detection in vision-language models. Publications: Over 100+ peer-reviewed papers across top conferences (ACL, EMNLP, NAACL) and journals. Recent work emphasizes multimodal analysis, hate speech detection in memes, and robustness improvements for large language models. Key themes include cross-lingual systems, knowledge base question answering, and misinformation mitigation. Grants & Advising: Supervises PhD/Master’s students in cutting-edge NLP research. Leads projects on model memorization studies, hate meme classification, and interventional training frameworks. Active in organizing conferences and editorial roles. Teaching: Teaches courses in software foundations and programming fundamentals, bridging theory and practical NLP applications.

Ahmed El AlaouiView profile

Assistant Professor

• High-Dimensional Statistics
• Probability Theory
• Algorithms on Random Structures
• +4 more

Ahmed El Alaoui is an Assistant Professor in the Department of Statistics and Data Science at Cornell University, with a secondary affiliation in the Department of Computer Science. He joined Cornell in 2021 after completing a postdoctoral fellowship at Stanford University under Andrea Montanari. His research focuses on high-dimensional statistics, probability theory, algorithms on random structures, and statistical physics, with particular emphasis on spin glasses and algorithmic thresholds. El Alaoui holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2018), advised by Michael I. Jordan, and a Master's from Ecole Normale Supérieure/Ecole des Ponts Paristech. His work bridges theoretical and applied domains, addressing challenges in high-dimensional inference, computational trade-offs, and probabilistic models. He has contributed to foundational results in random matrix theory, detection limits in spiked models, and algorithmic approaches to complex systems. His teaching includes courses on high-dimensional statistics, probability models, and theoretical computer science. He has been recognized for his innovative approaches to sampling and optimization in disordered systems, with publications in top journals like Annals of Probability and venues such as NeurIPS and FOCS. El Alaoui's research explores the interplay between statistical physics principles and algorithm design, aiming to uncover computational barriers in high-dimensional problems. His lab develops methodologies for analyzing complex systems and improving the efficiency of statistical estimation in challenging scenarios.

Giancarlo Ferrari TrecateView profile

Adjunct Professor

• Automatic control
• State estimation
• System identification
• +7 more

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

H. Jerry QiView profile

Professor

• Soft Active Materials
• 4D Printing
• 3D Printing Technologies
• +5 more

H. Jerry Qi is a Professor in the Department of Mechanical Engineering at the Georgia Institute of Technology. He specializes in finite deformation multiphysics modeling of soft active materials, with a focus on shape memory polymers, 4D printing, and material recycling. His research integrates experimental and computational approaches to advance additive manufacturing technologies. Education: Sc.D., Massachusetts Institute of Technology, 2003 Ph.D., Tsinghua University, China, 1999 B.S., Tsinghua University, China, 1994 Research Interests: Dr. Qi's work spans 4D printing of active materials, mechanics in 3D printing, and sustainable polymer processing. His group develops hybrid printing methods and recyclable thermosetting polymers, collaborating with institutions like SUTD and AFRL. Key areas include smart material design, photomechanical experiments, and finite element modeling. Scientific Awards: ASME Fellow (2015) Woodruff Faculty Fellow (2015) J. T. Oden Faculty Fellowship (2012) NSF Career Award (2007) Advising & Grants: Dr. Qi actively seeks undergraduate, PhD, and postdoc researchers. His projects are funded by NSF, AFOSR, and industry partnerships. He leads a research group focused on advancing active materials and sustainable manufacturing. Labs & Teams: His lab integrates computational modeling, experimental mechanics, and additive manufacturing to create innovative materials and structures for applications in aerospace, biomedical, and environmental engineering.

Monica LamView profile

Professor

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +4 more

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Jerelle A. JosephView profile

Assistant Professor

• Biomolecular Condensates
• Phase Separation
• Computational Biology
• +4 more

Jerelle A. Joseph is an Assistant Professor at Princeton University , affiliated with the Department of Chemical and Biological Engineering and the Omenn-Darling Bioengineering Institute . They also hold associated faculty roles in the Department of Chemistry , Andlinger Center for Energy and the Environment , Princeton Institute for Computational Science and Engineering , and the Biophysics Graduate Program . Research Interests : The Joseph Group investigates the physicochemical principles governing biomolecular condensate formation, dissolution, and misregulation . Their work focuses on phase separation mechanisms , computational modeling of protein-RNA interactions , and engineering condensates for biomedical and sustainability applications , including therapeutic targeting of neurodegenerative diseases and design of synthetic microreactors . Scientific Awards : NIGMS MIRA (R35) Award (2024) Biophysical Society Award Lecture (2024) Chan Zuckerberg Initiative Investigator (2023) Postdoctoral Award, Biophysical Society IDP Subgroup (2022) Rising Star in Soft and Biological Matter (University of Chicago, 2020) Advising : Dr. Joseph advises graduate students including Ananya Chakravarti , Dominic Curtis , and Pablo Garcia . The group develops chemically-specific coarse-grained models using molecular dynamics , Monte Carlo sampling , and machine learning to study condensate microstructure , aging dynamics , and surface electrostatics .

Ellie PavlickView profile

Assistant Professor

• Language Acquisition
• Grounded Language Learning
• Neural Language Models
• +7 more

Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University, where she leads the Language Understanding and Representation (LUNAR) Lab . Her work bridges computational linguistics and cognitive science, focusing on grounded language learning and the structural dynamics of neural language models. PhD from University of Pennsylvania (2017), specializing in paraphrasing and lexical semantics Collaborates with Brown's Robotics, Visual Computing labs, and Cognitive, Linguistic, and Psychological Sciences department Research interests center on cognitively-inspired approaches to language acquisition, analyzing how LLMs and humans encode abstract reasoning and compositional structures. Her lab explores: Grounded language learning in multimodal systems Emergence of compositional behavior in neural networks Interpretability frameworks for black-box AI Key publication areas include: Transformer mechanisms and cognitive modeling Formality and complexity in language systems Relational reasoning and multimodal integration Ellie teaches courses on computational linguistics and human-machine language processing, with a focus on synergies between AI and psychological science.