Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Computation Theory and Mathematics
• Computer Software
• Blockchain Technology
• +3 more

Ben Livshits is a Reader in Computing at Imperial College London's Department of Computing, part of the Faculty of Engineering. His research focuses on computation theory, computer software, and privacy-preserving technologies in blockchain systems. He holds a Ph.D. from Stanford University. Education: Ph.D. in Computer Science, Stanford University, 2007 Research Interests: Livshits specializes in cryptographic protocols for decentralized systems, ZK-Rollups optimization, MEV analysis, and privacy-enhancing technologies. His work bridges theoretical computer science with practical applications in blockchain scalability and security. Recent Research Trends: His 2024-2025 work emphasizes ZK-proof systems (e.g., zk-bench), MEV mitigation strategies, and formal foundations for rollups. He also explores community-driven verification (CrowdProve) and governance mechanisms for DeFi. Affiliations: He is affiliated with the Centre for Cryptocurrency Research and Engineering, contributing to interdisciplinary blockchain research. Grants & Labs: While specific grants are not listed, his work suggests involvement in Imperial's distributed systems and blockchain initiatives.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Developmental Psychology
• Educational Psychology
• Child Peer Relationships
• +4 more

Dr. Elian Fink is an Associate Professor in the School of Psychology at the University of Sussex, focusing on developmental psychology and child social development. His research explores socio-cognitive antecedents of peer relationships, bullying behavior across neurotypical and neurodivergent groups, and parental influences on children’s social competencies. He holds a PhD in Developmental Psychology from the University of Sydney and has held academic roles including Senior Research Associate at the University of Cambridge and Visiting Scholar at the Centre for Family Research. Dr. Fink’s work is funded by grants such as the ESRC’s 'School Readiness: Connecting Viewpoints on Child and Family Well-being' and the LEGO Foundation’s study on children’s relationships through play. His research emphasizes longitudinal studies, cross-cultural comparisons, and collaborations with institutions like the Centre for Family Research and PEDAL at the University of Cambridge. Teaching interests include the Psychology of Childhood, Developmental Psychology, and Children’s Peer Relationships. He advises undergraduate, postgraduate, and PhD students, with drop-in hours for academic support. Key research themes include: Socio-cognitive foundations of peer relationships Bullying behaviors and neurodiversity Parenting and language environments in early childhood School readiness and well-being across cultures Recent studies investigate links between spatial language and numeracy, mental health trends during the pandemic, and cross-cultural validation of child well-being measures.

Michael DeWeeseView profile

Associate Professor

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +3 more

Michael DeWeese is an Associate Professor of Physics and Neuroscience at the University of California, Berkeley. His research spans nonequilibrium statistical mechanics, machine learning theory, and systems neuroscience. He holds a BA in Physics from UC Santa Cruz (1988) and a PhD in Physics from Princeton (1995). Before joining UC Berkeley in 2007, he held postdoctoral positions at the Salk Institute and Cold Spring Harbor Laboratory. His work integrates principles from physics, neuroscience, and machine learning to address fundamental questions in theoretical and experimental biology, computation, and statistical mechanics. DeWeese Lab Website provides further details on ongoing projects and collaborations. Education: BA in Physics, UC Santa Cruz (1988) PhD in Physics, Princeton University (1995) Research Interests: Nonequilibrium Statistical Mechanics: Focuses on thermodynamic optimization, active matter, and non-equilibrium processes. Machine Learning Theory: Develops first-principles models to explain neural network performance and efficient algorithms for probabilistic models. Systems Neuroscience: Uses biologically inspired models to understand neural coding, sensory processing, and computational roles of neural networks. Advising & Grants: While no formal student advisees are listed, his lab actively collaborates across disciplines. Funding sources are not explicitly mentioned but likely involve NSF, NIH, or DOE grants based on research themes. His work on quantum control and neural networks suggests potential ties to interdisciplinary funding initiatives. Labs & Teams: Directs the DeWeese Lab, which bridges physics, neuroscience, and machine learning. Collaborations include institutions like the Helen Wills Neuroscience Institute (UC Berkeley).

Pouria AkbarighatarView profile

Research Fellow

• Responsible Data Science
• Data Studies in Information Systems
• Fuzzy Mathematics
• +3 more

Pouria Akbarighatar is a PhD Research Fellow in Responsible Data Science at the Department of Information Systems , University of Agder, Norway, and a visiting PhD researcher at the Centre for Information Resilience (CIRES) , University of Queensland Business School. He holds a BSc in Industrial Engineering and an MSc in Information Systems from the University of Tehran. His research focuses on Responsible AI , including maturity models , fairness frameworks , and ethical implementation in sociotechnical systems. His work combines Fuzzy mathematics , decision science , and data science to address uncertainty and complexity in AI systems. Recent publications explore responsible AI principles in practitioner contexts, operationalizing AI ethics , and credibility assessment in online reviews . He contributes to journals like AI and Ethics , IEEE Access , and Expert Systems with Applications , alongside conferences such as ECIS , AMCIS , and PACIS . He teaches courses in Data Science Applications , Algorithmic and Data Structures , and Data Science Applications II . He is affiliated with the Human-Centered AI (HCAI) research group.

Alan EdelmanView profile

Professor

• Julia Programming
• High Performance Computing
• Linear Algebra
• +3 more

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

Guillaume A SARTORETTIView profile

Assistant Professor

• Robotics
• Multi-Agent/Multi-Robot Systems
• Distributed/Decentralized Control
• +4 more

Guillaume A SARTORETTI is an Assistant Professor in the Mechanical Engineering Department at the National University of Singapore (NUS), part of the College of Design and Engineering. He specializes in distributed/decentralized coordination of multi-agent systems, with a focus on robotics, stochastic modeling, and reinforcement learning. His work spans applications in multi-robot systems, articulated robots, and swarm intelligence. Joined NUS in 2019 after a postdoctoral fellowship at Carnegie Mellon University (CMU) and a PhD from EPFL. Education: PhD in Robotics (EPFL, 2016); MSc and BSc in Mathematics/Computer Science (University of Geneva). Research interests include: Multi-agent pathfinding Decentralized control policies Swarm intelligence Reinforcement learning applications in robotics Recent publications emphasize scalable solutions for multi-agent systems, traffic signal control, and safe robotic exploration. His 2018 MFI postdoctoral fellowship recognized work on distributed reinforcement learning for pathfinding. Current projects involve bio-inspired locomotion and collaborative learning frameworks for heterogeneous robots.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

• Software Engineering
• Machine Learning Operations (MLOps)
• Cyber-Physical Systems
• +4 more

Prof. Dr. Klaus Schmid is a Professor in the Department of Software Systems Engineering at the University of Hildesheim, part of the Faculty of Mathematics, Natural Sciences, Economics, and Computer Science. His research focuses on Machine Learning Operations (MLOps), software product lines, adaptive systems, and variability modeling. He leads projects such as EXPLAIN and ReGaP, emphasizing explainable AI and industrial MLOps integration. His work addresses challenges in Cyber-Physical Production Systems (CPPS), including data management, model calibration, and domain knowledge integration. Key research interests include MLOps architecture design, variability modeling transformations (e.g., UVL to IVML), and incremental verification techniques for software product lines. He has published extensively in venues like IEEE ETFA, IEEE Software, and SPLC conferences. Notable achievements include a Best Paper Award for work on control patterns in self-adaptive systems. Collaborations with industry partners highlight his focus on bridging academic research and practical industrial applications. Prof. Schmid’s contributions extend to tool development, such as EASy-Producer for variability-aware software ecosystems, and frameworks for environment modeling in adaptive systems. His research addresses both foundational challenges (e.g., syntax-preserving slicing) and applied topics like MLOps platform comparisons and industrial case studies in Industry 4.0.

Shweta JainView profile

Professor

• Cybersecurity
• Blockchain Technology
• Wireless Networks
• +6 more

Shweta Jain is a Professor in the Department of Mathematics and Computer Science at John Jay College of Criminal Justice, part of the City University of New York (CUNY). She holds dual roles as Graduate Faculty in the Digital Forensics and Cyber Security program and Doctoral Faculty in Computer Science at CUNY's Graduate Center. With a Ph.D. in Computer Science from Stony Brook University (2007), her expertise spans Cybersecurity, Blockchain, Wireless Networks, and Software Development. Education Background: Ph.D. Computer Science, Stony Brook University, 2007 M.S. Computer Science, Stony Brook University, 2005 B.E. Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology (IIEST) Shibpur, 2005 Research Interests: Cybersecurity frameworks and digital forensics Blockchain applications in social systems Wireless network protocols and security Perceptual hashing for image authentication Network vulnerability analysis Notable Achievements: Recipient of 2014 IEEE Region-1 Award for Outstanding Teaching Senior Member of IEEE Over 30 peer-reviewed publications and patents in networks, forensics, and distributed systems Advising & Grants: Guided multiple student research projects in network security and forensics Developed innovative tools like E-Witness for digital evidence preservation Contributed to NSF-funded projects on wireless simulation realism Labs & Teams: Director of the Cybersecurity Research Lab at John Jay College Collaborates with WINLAB at Rutgers University on wireless protocols

Barbara S. Lancho BarrantesView profile

Senior Lecturer

• Bibliometrics
• Scientometrics
• Citation Analysis
• +7 more

Barbara S. Lancho Barrantes is a Senior Lecturer at the University of Brighton's School of Architecture, Technology and Engineering within the Computing and Mathematical Sciences department. She specializes in bibliometrics, scientometrics, and research analytics, with a focus on citation analysis, sustainable development goals, and research integrity. She holds a PhD in Bibliometrics from the University of Extremadura and has received prestigious awards including the Extraordinary PhD Award (2014) and the Responsible Research and Innovation Award (2022). Education includes a Bachelor's in Information Science (First Class Honours, 2006), a Master's in Digital Information Management (2007), and a PhD in Bibliometrics (2012). She is a Fellow of the Higher Education Academy and has held roles at institutions like the University of Leeds and Tecnologico de Monterrey. Research interests span citation dynamics, SDG mappings, and the impact of economic factors on scientific productivity. She leads the LIS Bibliometrics committee and organizes the annual international conference. Recent work includes exploratory factor analysis of SDG indicators and multilingual research dissemination practices. Key Awards: Extraordinary PhD Award, Responsible Research and Innovation Award Professional Affiliations: Chair of LIS Bibliometrics Committee, External Examiner at University of Wolverhampton Supervision Focus: Bibliometric methodologies, data science integration with research metrics Her articles analyze citation flows, SDG contributions, and questionable publishing threats. She advocates for responsible research metrics through workshops and peer-review activities in journals like Scientometrics and Education for Information .

Defang ChenView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +6 more

Defang Chen is a Researcher and Postdoctoral Associate in the Department of Computer Science and Engineering at the University at Buffalo, working under the supervision of Siwei Lyu. He is affiliated with the School of Engineering and Applied Sciences and is based at 301A Davis Hall in Buffalo, NY. His research focuses on advanced machine learning techniques, including knowledge distillation, diffusion models, graph neural networks, and domain generalization. Defang's work emphasizes improving model efficiency through distillation methods, exploring generative models like diffusion processes, and enhancing cross-domain adaptability. His contributions span both theoretical advancements and practical applications, such as accelerating diffusion sampling and optimizing neural network architectures. His publications between 2023–2025 highlight trends in model compression, generative AI, and graph-based learning. Notable themes include refining knowledge transfer between models, improving adversarial robustness, and developing scalable sampling techniques. Defang’s research has implications for computer vision, natural language processing, and semantic segmentation. No academic awards or grants are explicitly listed in the provided information. While no formal advisees are noted, his role as a postdoctoral researcher suggests collaborative involvement in academic projects and teams. Defang’s work is accessible via his Google Scholar profile .

Scott WeinsteinView profile

Professor

• Logic
• Philosophy of Mathematics
• Cognitive Science
• +3 more

Scott Weinstein is a Professor of Philosophy, Mathematics, and Computer Science at the University of Pennsylvania, where he also serves as Director of the Logic, Information, and Computation Program. His academic appointments span multiple departments within the College of Arts and Sciences, reflecting his interdisciplinary expertise. He holds a Ph.D. from Rockefeller University. Education: Ph.D. in Philosophy, Mathematics, and/or Computer Science from Rockefeller University Research Interests: Dr. Weinstein’s work focuses on foundational aspects of logic, philosophy of mathematics, cognitive science, and formal learning theory. His research bridges theoretical computer science, mathematical logic, and ancient philosophical paradoxes such as Zeno’s. He explores topics like truth detection models, inductive inference systems, complexity in finite structures, and the philosophical implications of theoretical terms in scientific inquiry. Articles Trends: His publications reflect a sustained engagement with formal methods in science and philosophy. Recent work (e.g., 2022) re-examines classical paradoxes through modern mathematical lenses, while earlier contributions (e.g., 1988, 1989) investigate inductive reasoning and computational models of scientific discovery. Key themes include the interplay between logic and empirical inquiry, the structure of scientific knowledge, and foundational questions in mathematics and computing. Awards: No scientific awards are explicitly mentioned in the provided text. Advising & Grants: While no formal advisees or grant details are listed, his role as Director of the Logic, Information, and Computation Program indicates leadership in interdisciplinary academic initiatives. His publications often involve collaborations with colleagues like Daniel Osherson and Michael Stob. Labs/Teams: As Director of the Logic, Information, and Computation Program, he oversees a collaborative research environment at the University of Pennsylvania, integrating philosophical, mathematical, and computational disciplines.

• Indigenous Methodologies
• Critical Race Theory in Education
• Higher Education Leadership
• +3 more

Dr. Sweeney Windchief is a Professor in the Department of Education at Montana State University, affiliated with the College of Education, Health & Human Development. His work focuses on Indigenous Methodologies, Critical Race Theory in Education, and leadership in tribal colleges and universities (TCUs). He holds a Ed.D. from the University of Utah (2011), M.A. from University of Montana (2004), and B.S. from University of Central Oklahoma (2001). Research interests include racial equity in higher education, Indigenous knowledge systems, and STEM education access for Native American students. He teaches courses like Indigenous Methodologies in Education Research, Critical Race Theory, and Law & Policy in Higher Education. Recent scholarship emphasizes integrating Indigenous knowledge into STEM fields, mentorship programs for Indigenous graduate students, and culturally responsive pedagogy. His work has been published in journals such as AlterNative: An International Journal of Indigenous Peoples and International Journal of Multicultural Education . Award-winning educator, Dr. Windchief has received accolades including the President’s Excellence in Teaching Award (2020) and Outstanding Faculty for Scholarship (2017). He actively contributes to academic service through roles like AERA Distinguished Mentor (2015-2018) and grant reviewer for National Science Foundation initiatives. Key engagement includes designing programs like the Indigenous Mentoring Program in STEM and collaborating on NSF-funded projects to improve diversity in engineering education. His research bridges traditional knowledge systems with contemporary academic practices, advocating for bicultural accountability in education.