Find Academic Supervisors

• Materials Science and Engineering
• Additive Manufacturing
• Materials Informatics
• +7 more

S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.

• Machine Learning
• Database Systems
• Artificial Intelligence
• +3 more

Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.

Yusuf AltintasView profile

Professor

• Machining Processes
• Computer Numerical Control (CNC)
• Machine Tool Dynamics
• +5 more

Yusuf Altintas is a Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, holding the NSERC–P&WC-Sandrik Coromant Industrial Research Chair and coordinating the Mechatronics Option. An internationally acclaimed scholar, he is a Fellow of 10 prestigious academies including the National Academy of Engineering (NAE), Royal Society of Canada (RSC), and ASME. His academic credentials include a Ph.D. from McMaster University, an Honorary Doctor of Engineering from the University of Stuttgart, and a Doctor of Technical Sciences from Budapest University of Technology and Economics. Professor Altintas's research pioneers the integration of physics-based modeling and data-driven approaches for machining systems. His work spans virtual high-performance machining simulation, machine tool dynamics, chatter stability prediction, and intelligent process control for CNC systems. Current projects focus on digital twin development for machining processes, spindle health diagnostics, ultrasonic vibration-assisted tooling, and adaptive damping systems for aerospace manufacturing applications. His methodologies bridge theoretical mechanics with industrial implementation in die/mold and aerospace sectors. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for spindle fault detection, topology-optimized tool design, and chatter avoidance in thin-walled component machining. Key thematic clusters include digital twin implementation (28% of recent work), dynamics modeling of multi-axis systems (35%), and intelligent monitoring algorithms (22%), with growing emphasis on anisotropic material machining and 3D printing process control. Georg Schlesinger Award (2016) NSERC Strategic Research Network in Virtual Machining Grant (2016) NSERC Synergy Award (2013) ASME Blackall Machine Tool and Gage Award (2013) Special Distinguished Scientist Award from Turkey's Scientific and Technical Research Council (2013) He directs the Manufacturing Automation Laboratory at UBC, leading an international research consortium on virtual machining systems supported by NSERC and industry partners including Sandvik Coromant and Pratt & Whitney Canada. His team develops real-time process monitoring frameworks and physics-based simulation tools that have been adopted in aerospace manufacturing for blade machining and die/mold production. The laboratory maintains advanced testbeds for five-axis machining dynamics, spindle health monitoring, and ultrasonic vibration-assisted tooling, serving as a hub for industry-academic collaboration in next-generation manufacturing technologies.

• Open Science
• Bibliometrics
• Scientific Communication
• +5 more

Dr. Vincent Larivière is a Full Professor (Professeur titulaire) at the École de bibliothéconomie et des sciences de l'information, Université de Montréal. He holds the UNESCO Chair on Open Science and the Canada Research Chair on Transformations of Scholarly Communication. His research focuses on the quantitative analysis of scholarly communication systems, open science, and the impact of digital technologies on knowledge production and dissemination. Education: PhD in Archivistics and Library Science (McGill University, 2010) MSc in History (UQAM, 2005) BSc in Science, Technology, and Society (UQAM, 2002) Research Interests: Scientific communication processes in the digital age Bibliometric methods and their applications Open science policies and infrastructure Gender and intersectional inequalities in research Recent Articles Trends: Focus on open access publishing challenges and opportunities Analysis of global scientific collaboration hierarchies Quantitative studies of authorship patterns and citation dynamics Scientific Awards: Member of the Royal Society of Canada's College of New Scholars (2017) Nominated for the Prix du Québec in Emerging Scientific Talent (2018) Grants & Labs: Director of the Consortium Érudit journal platform Co-lead of the Coalition Publica scholarly communication initiative Membre of the CIRST (Centre interuniversitaire de recherche sur la science et la technologie) Director of the Labo de transformations de la communication savante

• Computational Materials Science
• Electronic Structure Theory
• Solid-State Physics
• +5 more

Nicola Marzari is a Professor of Theory and Simulation of Materials at EPFL, where he also serves as Director of the National Centre for Computational Design and Discovery of Novel Materials (NCCD). He is Chairman of Psi-k, an international network for advanced materials' computational design. Previously, he held the Toyota Chair of Materials Engineering at MIT and leadership roles at the University of Oxford, including Director of the Materials Modeling Laboratory and a Statutory Chair in Materials Modeling. His education includes a Laurea in Physics (summa cum laude) from the University of Trieste, a PhD in Physics from the University of Cambridge under Prof. Michael C. Payne, and postdoctoral work at Rutgers University with Prof. David Vanderbilt. Marzari's research focuses on computational materials science, electronic structure theory, and high-throughput simulations. He develops methods for predicting material properties using first-principles approaches, machine learning, and quantum espresso software. Key areas include energy materials (batteries, thermoelectrics), magnetic materials, and optoelectronic systems. His work bridges fundamental physics and practical material design, emphasizing reproducible workflows and open-source tools like koopmans and AiiDA . His recent articles highlight advancements in machine learning for materials interfaces, dynamical Hubbard functionals, and thermal conductivity modeling. He actively contributes to EuroHPC initiatives for exascale materials design and OPTIMADE standards for materials data exchange. Marzari leads interdisciplinary teams at EPFL and collaborates globally on projects ranging from defect engineering in semiconductors to AI-driven materials discovery. His research aims to accelerate the development of sustainable energy and electronic technologies through computational innovation.

Abdol-Hossein EsfahanianView profile

Professor

• Graph Theory
• Computer Networks
• Algorithm Design
• +3 more

Abdol-Hossein Esfahanian is a Professor and Chairperson of the Computer Science and Engineering (CSE) Department at Michigan State University (MSU), part of the College of Engineering. He joined MSU in 1983 and has held leadership roles, including Graduate Director for 10 years and Associate Chair. His research focuses on applying graph theory to computer networks, algorithm design, and fault-tolerant computing. He has published extensively in journals like IEEE Transactions on Computers and Discrete Applied Mathematics, and serves as an editor for professional journals. Education: Ph.D. in Electrical Engineering and Computer Science from Northwestern University (1983), M.S. in Computer, Information, and Control Engineering from the University of Michigan (1977), and B.S. in Electrical Engineering from the University of Michigan (1975). Research interests include graph theory applications in network design, distributed systems, and fairness-aware algorithms. Notable awards include the Withrow Teaching Excellence Award (2005, 2015) and recognition as an IEEE Senior Lifetime Member. Teaching includes courses like CSE 835 (Algorithmic Graph Theory). He has contributed to curriculum development, emphasizing computational competencies for engineering students. His work integrates theoretical foundations with practical applications in networking and distributed systems.

Michael WeyrichView profile

Professor

• Industrial Automation
• Digital Twins
• Large Language Models (LLMs)
• +7 more

Prof. Michael Weyrich is a faculty member at the Institute of Industrial Automation and Software Engineering (IAS) within the University of Stuttgart , leading the Cluster of Excellence IntCDC . His academic rank is Professor, and he focuses on Industrial Automation , Digital Twins , and Large Language Models (LLMs) for manufacturing and automotive systems. His research explores integrating LLMs into industrial automation for adaptive control, cloud offloading of vehicle functions, and semantic interoperability via Asset Administration Shells . He investigates modular production architectures , connected vehicle systems , and synthetic data generation for autonomous machinery. Recent publications highlight LLM-driven production planning , dynamic sensor calibration , and machine learning for fault detection in electric vehicle powertrains. His work emphasizes real-time data modeling and flexible microservice orchestration .

• Photonics
• Laser Technology
• Machine Learning
• +5 more

Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).

• Embedded Systems
• Artificial Neural Networks
• Signal Processing
• +3 more

Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.

Emre SeferView profile

Associate Professor

• Bioinformatics
• Machine Learning
• Financial Networks
• +3 more

Dr. Emre Sefer is an Associate Professor at the Faculty of Engineering, Özyeğin University, specializing in machine learning and bioinformatics. He holds a Ph.D. in Computational Biology from Carnegie Mellon University (2015), an M.S. in Computer Science from University of Maryland College Park (2011), and a B.S. in Computer Engineering from Boğaziçi University (2008). His research bridges graph machine learning with financial networks, bioinformatics, and data engineering. Ph.D.: Computational Biology, Carnegie Mellon University M.S.: Computer Science, University of Maryland College Park B.S.: Computer Engineering, Boğaziçi University Research focuses on applying machine learning to financial and biological networks: Bioinformatics : 3D genome modeling, protein modifications, transcriptomic analysis Graph Machine Learning : GNNs for fraud detection, drug response prediction, and network evolution Financial Networks : Cryptocurrency investment strategies, asset price prediction His lab (OzU Machine Learning in Finance and Bioinformatics Lab) develops graph-based deep learning methods for cross-domain applications, including NFT market analysis and chromatin structure prediction. He received the Best research paper award at Recomb 2016 for work on 3D genome architecture. Former postdoc at CMU Machine Learning Department Industry experience as Quantitative Strategist at Goldman Sachs and JPMorgan

Brian UzziView profile

Professor

• Social Networks
• Complexity Theory
• Embeddedness
• +5 more

Brian Uzzi holds the Richard L. Thomas Professorship of Leadership and Organizational Change at Northwestern University's Kellogg School of Management. He serves as Co-Director of the Northwestern Institute on Complex Systems (NICO) and The Ryan Institute on Complexity (RIC), with additional appointments in Sociology at Weinberg College of Arts and Sciences and Industrial Engineering and Management Sciences at McCormick School of Engineering. His educational background includes a PhD in Sociology (1994) from State University of New York, Stony Brook, an MS in Organizational Psychology (1989) from Carnegie Mellon University, and a BA in Business Economics (1982) from Hofstra University. Prior to academia, he worked as a carpenter and musician. Research Focus: Dr. Uzzi's work centers on social networks, complexity theory, and the concept of embeddedness—the idea that individuals and organizations operate within social networks that significantly influence their achievements, economic activity, and creative output. His research examines how AI facilitates mind-machine partnerships and how network structures affect scientific collaboration, innovation, and leadership. His work spans sociology, management science, computer science, and ecology, with practical applications in business and government. His recent publications reveal a strong focus on the science of science, exploring topics like innovation abandonment, social media's role in political violence, promotional language in scientific grants, and gender diversity's impact on scientific creativity. The research consistently applies network science to understand patterns of human achievement and organizational performance. Euler Award recipient (2022) from the Network Science Society Member of the American Academy of Arts and Sciences Network Science Society Fellow Multiple 'Professor of the Year' awards at Kellogg World Wide Web Best Paper Prize (2016-2017) As an educator, Dr. Uzzi has developed innovative courses on network science for managers and executives. His consulting work extends to companies and governments worldwide, applying network science principles to real-world challenges in leadership, organizational design, and AI strategy. His research has been funded by DARPA, NSF, and other foundations, demonstrating its significance across multiple disciplines.

Gaël KermarrecView profile

Researcher

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +3 more

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

Sara MagliacaneView profile

Assistant Professor

• Causality
• Causal Representation Learning
• Causality-inspired Machine Learning
• +4 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +7 more

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.

• Public Finance
• Law and Economics
• Competition Policy
• +8 more

Angelo Castaldo is an Associate Professor in Public Finance at the Faculty of Law, Sapienza University of Rome, with a Ph.D. in Law and Economics from the University of Siena and an M.Sc. in Economics from the University of York. He serves as Chair of the Graduate Program in European Studies (LM-90) and the Master in Competition and Regulation of Markets (CORE), while also holding roles at international institutions like Zhongnan University of Economics and Law in China. Education: Ph.D. in Law and Economics, University of Siena M.Sc. in Economics, University of York, UK Master in Law and Economics, University of Siena Research Focus: Public Finance Law and Economics Environmental Crime Analysis Occupational Safety and Health Competition Policy Technological Innovation Impact His recent publications analyze workplace accident determinants, environmental crime drivers, and taxation policies for sin goods, employing empirical methods across European and Italian contexts. Current research projects focus on technology's impact on occupational safety and public investment incentives for workplace health improvements. He actively participates in academic conferences and serves as co-managing editor for public finance working papers at Sapienza University of Rome.