جست‌وجوی استاد و پژوهشگر

Angelina Wangمشاهده پروفایل

استادیار

• Machine Learning Fairness
• Algorithmic Bias
• Responsible AI
• +۳ مورد دیگر

Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.

Gustav Eje Henterمشاهده پروفایل

استادیار

• Speech Synthesis
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Gustav Eje Henter is an Assistant Professor at KTH Royal Institute of Technology, holding roles as the Head of Research at Motorica AB and a Core Team Member of the Wallenberg Research Arena (WARA) for Media and Language. He is the Secretary of the ISCA SynSIG (Special Interest Group on Speech Synthesis) and a Co-Organiser of the GENEA Workshops on Embodied Agents' Non-Verbal Behavior. His research focuses on speech synthesis, gesture generation, and multimodal interaction, with contributions to TTS systems, neural networks, and embodied AI. He leads Digital Futures, a cross-disciplinary research center addressing societal challenges through digital technologies. This center is a collaboration between KTH, Stockholm University, and RISE. His work spans foundational research to industrial applications, emphasizing ethical AI, privacy in voice conversion, and human-robot interaction. Key research themes include causal reasoning in LLMs, adversarial privacy techniques, and benchmarking frameworks like the GENEA Leaderboard. He has organized international workshops (GENEA 2021-2024) and contributed to standards in TTS evaluation methodologies. His technical innovations include HiFi-Glot for formant synthesis and Matcha-TTS for fast waveform generation. His research integrates audio, gesture, and motion synthesis with deep learning, addressing challenges in spontaneous speech synthesis, multimodal coherence, and listener perception. He advocates for rigorous evaluation practices and open challenges to advance the field's reproducibility and real-world applicability.

• Catalysis Engineering
• Green Chemistry
• Biofuels
• +۳ مورد دیگر

Professor Jun Huang is a faculty member in the School of Chemical and Biomolecular Engineering at the University of Sydney, where he holds the rank of Professor and is Director of the Laboratory for Catalysis Engineering. He is also a Domain Leader for Materials at the nanoscale at Sydney Nano Institute and a member of several interdisciplinary institutes, including the China Studies Centre and Sydney Institute of Agriculture. His research focuses on catalysis engineering, with an emphasis on developing sustainable processes for renewable fuels, pollutant treatment, and greenhouse gas mitigation. Huang has held prestigious awards such as the Australia Research Council Future Fellowship (2022) and the Sydney Accelerator Fellowship (2018). Education: Huang earned his PhD from the University of Stuttgart (2008) and completed postdoctoral research at Georgia Institute of Technology and ETH Zurich. He joined the University of Sydney in 2010 as a Lecturer, advancing to Senior Lecturer, Associate Professor, and Professor. Research Interests: Huang's work centers on catalyst design for green chemical processes, including biomass conversion to biofuels, wastewater treatment, and CO2 utilization. He emphasizes sustainable manufacturing and environmental impact reduction through innovative catalytic systems. Current Projects: These include catalytic transformation of hydrocarbons/CO2/biomass, nano-catalysts for renewable energy, and advanced NMR spectroscopy for catalysis analysis. Collaborative projects involve anti-cancer therapies and drug pharmacology studies. Awards: Over 15 awards, including the 2021 ACS Sustainable Chemistry & Engineering Lectureship and 2017 Vice-Chancellor’s Research Excellence Award. Teaching: Huang instructs courses such as CHNG2801 (Conservation Processes), CHNG3802 (Industrial Systems), and advanced chemical engineering topics. He supervises PhD/Master students in catalysis and sustainable engineering. Labs/Teams: Leads the Catalysis Engineering Lab and collaborates with Sydney Nano Institute on nanomaterials research.

Eralp Demirمشاهده پروفایل

پژوهشگر

• Materials Science
• Mechanical Engineering
• Finite Element Analysis
• +۳ مورد دیگر

Eralp Demir is a Post-Doctoral Researcher at the Department of Engineering Science, University of Oxford. His research focuses on materials mechanics, crystal plasticity, and finite element methods. He holds a PhD from RWTH Aachen University and has conducted research at institutions including Carnegie Mellon University, Max Planck Institute, and Cornell University. His current work involves developing the OXFORD-UMAT framework for fusion energy materials in collaboration with UKAEA. He specializes in in-house finite element code development and commercial software integration (e.g., Abaqus, MSC Marc). His expertise spans computational materials modeling, microstructural analysis, and experimental validation using techniques like 3D XRD. Education: PhD in Engineering Science, RWTH Aachen University Advanced Studies at Carnegie Mellon University (Mechanical Engineering), Cornell University (MAE), and others Research Interests: Crystal plasticity modeling, fusion energy materials, finite element method development, microstructural mechanics, and additive manufacturing. His work bridges computational simulations with experimental techniques to understand material behavior under extreme conditions. Labs/Teams: Collaborates with the Tarleton Research Group at Oxford and UKAEA on fusion energy projects. Active in developing open-source tools for material modeling.

Shahram Rahimiمشاهده پروفایل

استاد

• Computational Intelligence
• Machine Learning
• Artificial Intelligence
• +۱۰ مورد دیگر

Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning

Daniel Innerarityمشاهده پروفایل

استاد

• Democracy
• Artificial intelligence (AI)
• Political and Social Philosophy
• +۲ مورد دیگر

Prof Daniel Innerarity serves as a Part-time Professor and Chair in AI & Democracy at the Florence School of Transnational Governance, European University Institute. He is simultaneously Professor of Political and Social Philosophy at the University of the Basque Country and the Ikerbasque Foundation for Science in Spain, and director of the Instituto de Gobernanza Democrática. His academic career spans multiple continents with previous positions including Robert Schuman Visiting Professor at EUI, Fellow of the Alexander von Humboldt Foundation at University of Munich, visiting professor at University of Paris 1-Sorbonne, professorship at Georgetown University, and visiting fellow at Max Planck Institut for International and Public Law at Heidelberg. Innerarity's research focuses on the critical intersection of democracy and emerging technologies, particularly artificial intelligence. His scholarship examines how complex democratic systems can be designed and maintained in the 21st century, with special attention to the epistemic challenges posed by AI systems. He investigates European integration through the lens of consent theory, arguing for democratic legitimacy in transnational governance structures. His publications demonstrate a consistent concern with how digital technologies reshape democratic processes, citizen participation, and political representation in contemporary societies. Scientific Awards: Miguel de Unamuno Essay Prize 2003 National Literature Prize in the Essay category Espasa Essay Prize Euskadi Essay Prize Prize for Humanities, Culture, Arts and Social Sciences from the Basque Studies Society/Eusko Ikaskuntza (2008) Príncipe de Viana Culture Prize (2013) Innerarity maintains an extensive international academic network across European and North American institutions. His work bridges theoretical political philosophy with practical governance challenges in the digital age, particularly focusing on how democratic institutions can maintain legitimacy and functionality amid rapid technological change. His leadership of the Instituto de Gobernanza Democrática demonstrates his commitment to translating theoretical insights into practical governance frameworks.

Shion Guhaمشاهده پروفایل

استادیار

• Human-Centered Data Science
• Artificial Intelligence Ethics
• Public Policy
• +۵ مورد دیگر

Shion Guha is an Assistant Professor at the University of Toronto's Faculty of Information, cross-appointed to the Department of Computer Science. He directs the Human-Centered Data Science Lab and is affiliated with the Schwartz Reisman Institute for Technology and Society and the Data Sciences Institute. His research focuses on integrating technical methodologies with critical social science approaches to address algorithmic biases in public sectors like child welfare, healthcare, and policing. Key roles include coordinating the Human-Centred Data Science concentration in the Master of Information program and advising national policies on AI ethics. Education: PhD in Information Science and Statistics, Cornell University (2016) MS in Information Science, Indian Statistical Institute (2010) Bachelor of Business Administration, Jadavpur University (India) Research Interests: Human-Centered Data Science, AI ethics, public policy, healthcare systems, algorithmic accountability, and marginalized communities' interaction with technology. His work emphasizes participatory design and real-world impact, addressing high-stakes decisions in public services. Recent Trends in Articles: Focus on participatory AI design in public sectors, algorithmic harms in child welfare, and cultural biases in NLP tools. His studies explore the intersection of technical systems and societal values, advocating for equity and transparency in algorithmic decision-making. Awards: Way-Klingler Early Career Award (2019) Connaught New Researcher Award (2021) Schwartz-Reisman Institute Faculty Fellowship (2023–2025) Grants & Advising: Secured funding from NSERC, CIFAR, and others for projects like the Responsible AI for Health Systems (RAIHS) framework. Advises PhD students through cross-departmental programs and collaborates with organizations like Parkview Health and the ACLU. Current supervisees include Ramaravind Kommiya Mothilal and Seh Young Moon. Labs & Initiatives: Leads the Human-Centered Data Science Lab, contributing to interdisciplinary research on AI ethics. Active in policy advocacy, including work with the Canadian government’s AI Policymakers Expert Group and the CIFAR AI Solutions Network.

Chenhao Chuمشاهده پروفایل

استاد

• RF/mm-Wave Circuits
• Power Amplification
• AI-Assisted Design Synthesis
• +۴ مورد دیگر

Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.

Thierry Langerمشاهده پروفایل

استاد

• Computer-Aided Molecular Design
• Pharmacophore Modeling
• Drug Discovery
• +۷ مورد دیگر

Professor Thierry Langer is a Full Professor of Pharmaceutical Chemistry at the University of Vienna’s Faculty of Life Sciences (Department of Pharmaceutical Sciences). He leads research in computational drug design, with a focus on pharmacophore modeling, 3D-QSAR analysis, and AI-driven molecular design. His work bridges theoretical and experimental chemistry, addressing targets like viral proteases (e.g., SARS-CoV-2), GABA receptors, and dopamine transporters. Research interests include: Pharmacophore-guided drug discovery for anti-viral and CNS therapies Development of next-generation computational tools (e.g., PharmacoMatch, QPhAR) Protein-ligand interaction modeling using neural networks and graph-based algorithms Recent studies focus on: Inhibitors for herpesvirus nuclear egress complexes, AI-optimized antivirals, and dopamine transporter inhibitors for cognitive enhancement. His lab collaborates on projects like the NeuroDeRisk initiative to de-risk neurotoxic compounds. Publications emphasize drug repurposing, metabolic pathway analysis, and scalable synthesis methods for promising drug candidates.

Elsa A. Olivettiمشاهده پروفایل

استاد

• Sustainable Materials
• Environmental Engineering
• Materials Science
• +۳ مورد دیگر

Elsa A. Olivetti is the Jerry McAfee (1940) Professor in Engineering and Professor of Materials Science and Engineering at MIT, and a MacVicar Faculty Fellow. She leads the Olivetti Group, focusing on sustainable materials design, recycling strategies, and computational models for environmental and economic impact assessment. Her work bridges materials science with sustainability, emphasizing circular economy principles and decarbonization. Education: B.S. in Engineering Science from University of Virginia (2000); Ph.D. in Materials Science and Engineering from MIT (2007). Her doctoral research centered on lithium-ion battery electrode materials. She joined MIT’s Department of Materials Science and Engineering (DMSE) in 2014 as an Assistant Professor, later advancing to full Professor. She co-directs the MIT Climate & Sustainability Consortium and chairs the MIT Climate Nucleus. Research interests include: sustainable materials systems, recycling-friendly material design, waste mining, and AI-driven materials discovery. She develops models for cost prediction, environmental impact analysis, and policy-relevant supply chain dynamics. Notable contributions include high-throughput zeolite design and battery recycling frameworks. Awards include the Bose Teaching Award (2021), NSF Early Career Award (2018), and Minerals, Metals & Materials Society Early Career Fellowship (2019). Her work emphasizes education and curriculum development, including courses for MIT’s Climate Scholars program. Labs/Teams: Olivetti Group (MIT), MIT Climate & Sustainability Consortium. Active in global sustainability initiatives, focusing on materials for energy transition and climate resilience.

• Theoretical Chemistry
• Electrochemistry
• Catalysis
• +۶ مورد دیگر

Christopher J. Stein is an Associate Professor of Theoretical Chemistry at the Technical University of Munich (TUM), part of the TUM School of Natural Sciences. His research focuses on theoretical (electro-)catalysis, developing electronic-structure models and solvation/embedding methods to understand and optimize catalytic processes. He leads the Stein Group, which integrates computational chemistry with high-throughput simulations to advance energy materials and battery technologies. His work emphasizes realistic modeling of catalyst behavior under operational conditions and has contributed to advancements in quantum embedding and automated reaction mechanism exploration. Education and Career: Earned his PhD in Theoretical Chemistry, with postdoctoral research at Caltech (2017-2020). Became an Associate Professor at TU Munich in 2023. He previously held roles at Karlsruhe Institute of Technology and contributed to projects like the BIG-MAP Materials Acceleration Platform. Research Interests: Theoretical chemistry, electrochemical interfaces, battery materials, high-throughput computational methods, and machine learning integration. His group explores topics like solid electrolyte interphases, charge transfer mechanisms, and automated workflows for materials discovery. Awards: While no explicit awards are listed, his contributions to materials acceleration platforms and theoretical catalysis have been widely recognized in the field. His work has been featured in journals like Journal of Chemical Physics , Chemical Science , and Angewandte Chemie . Labs/Teams: Leads the Stein Group at TUM, collaborating with institutions like the Munich Data Science Institute and MIRMI. His lab focuses on computational tools for accelerating energy material development, including quantum embedding and cloud-based simulations.

• Biomolecular Systems
• Synthetic Biology
• Stochastic Thermodynamics
• +۷ مورد دیگر

Thomas Ouldridge is a Royal Society University Research Fellow and Reader in Biomolecular Systems at the Department of Bioengineering, Faculty of Engineering, Imperial College London. He leads the 'Principles of Biomolecular Systems' group, which focuses on theoretical and computational modeling of complex biochemical systems, particularly exploring the interplay between molecular details and emergent behaviors like sensing, replication, and self-assembly. His work integrates natural systems analysis with synthetic biology applications, aiming to engineer artificial analogs of biological processes. His research spans interdisciplinary areas including stochastic thermodynamics, DNA-based computation, and molecular reaction networks. Key affiliations include the Physics of Life, Synthetic Biology Hub, and the Leverhulme Centre for Cellular Bionics. He has contributed to over 60 peer-reviewed articles since 2009, with recent work emphasizing energy-efficient molecular information processing and thermodynamic limits of biochemical systems. Awards: Royal Society University Research Fellowship (current). Labs/Teams: Principles of Biomolecular Systems Group, collaborating with multiple centers including the Centre for Synthetic Biology and Institute of Chemical Biology. Grants/Positions: Maintains research funding through the Royal Society and UKRI grants, focusing on non-equilibrium biomolecular systems and synthetic biology tools. Recent publications highlight advances in DNA templating networks, stochastic thermodynamic modeling of computation, and optimal protocols for molecular copying systems. His work bridges foundational physics with applied biotechnology, aiming to push the boundaries of synthetic biological engineering.

William Balchمشاهده پروفایل

استاد

• Proteostasis
• Genetic Variation
• Machine Learning in Biology
• +۶ مورد دیگر

William Balch, PhD, is a Professor in the Department of Molecular and Cellular Biology at Scripps Research. His research focuses on linking genetic variation in human populations to protein function using machine learning tools like Gaussian Process (GP) modeling. He pioneered concepts in proteostasis and spatial covariance, exploring how genetic and environmental factors influence protein folding and disease. Education: Ph.D. in Microbiology from University of Illinois (1979) Research interests include inherited diseases (e.g., CFTR, AATD, NPC1), aging-related proteostasis collapse, and host-pathogen interactions in SARS-CoV-2. His lab develops computational platforms to model protein design and discover therapeutic interventions. Key projects involve GP-based analysis of genetic diversity, small molecule therapeutics targeting chaperone systems, and understanding viral evolution via spatial covariance. His work bridges genomics and phenomics to address disease mechanisms at atomic resolution. Grants and collaborations focus on protein-folding correction, with applications in precision medicine and climate change mitigation through RuBisCo optimization in plants.

Alireza Ramezaniمشاهده پروفایل

دانشیار

• Robotics
• Control Theory
• Nonlinear Systems
• +۳ مورد دیگر

Alireza Ramezani is an Associate Professor of Electrical and Computer Engineering at Northeastern University, leading the SiliconSynapse Lab. He focuses on bio-inspired robotics, nonlinear systems, and robot locomotion, with a particular emphasis on morphological design and control inspired by biological systems. His work integrates control theory and experimental robotics to develop robots capable of navigating confined spaces, such as caves and ducts, using mechanisms derived from bat movements. Education : PhD, Mechanical Engineering, University of Michigan (2014) MS, Mechanical Engineering, ETH Zurich (2010) BSc, Mechanical Engineering, Iran University of Science and Technology (2007) Research Interests : Design of robots with non-traditional morphologies Nonlinear feedback control systems Legged and fluidic-based locomotion Bio-inspired robotics and biology-driven engineering Awards : 2024 ASME Rising Star Award 2024 NSF CAREER Award 2022 NASA Game Changing Program Award Science Magazine Top 5% Research Output (2020) Advising & Labs : Ramezani mentors students in projects like the NASA-funded “Crater Observing Bio-inspired Rolling Articulator” and oversees the SiliconSynapse Lab, which develops robots for space exploration and confined environments. Notable advisees include Henry Noyes, a NASA Space Technology Fellow. Labs/Teams : His lab collaborates with institutions like NASA’s Jet Propulsion Lab (JPL) on projects such as the Mars Multi-modal Morphing (M4) Rover and bio-inspired snake robots for lunar crater exploration.

Mitra Taheriمشاهده پروفایل

استاد

• Materials Science
• Electron Microscopy
• Additive Manufacturing
• +۶ مورد دیگر

Mitra Taheri is a Professor in the Department of Materials Science and Engineering at Johns Hopkins University, serving as Director of the Materials Characterization and Processing (MCP) facility and a member of the Hopkins Extreme Materials Institute. She holds affiliations with the Pacific Northwest National Laboratory and the Ralph O’Connor Sustainable Energy Institute. Her research focuses on electron microscopy, particularly in-situ and operando techniques, combined with artificial intelligence to study materials under extreme conditions (e.g., high temperatures, radiation, and oxidation). She aims to accelerate materials discovery by integrating AI with microscopy for real-time analysis. Dr. Taheri earned her BS, MSE, and PhD in Materials Science and Engineering from Carnegie Mellon University. Her work spans corrosion-resistant alloys, additive manufacturing, quantum materials, and biomaterials. Research sponsors include PNNL, JHU, NSF, ARPA-E, and ONR. She leads the Dynamic Characterization Group (DCG), which develops autonomous platforms for materials analysis and explores applications in energy, aerospace, and medical systems. Key research areas include: Design of corrosion-resistant multi-principal element alloys AI-driven microscopy for real-time material behavior insights Additive manufacturing of soft magnetic composites for electric vehicles Biomedical hydrogels for tissue engineering Her team develops novel materials and tools to probe structural, functional, and biological systems across scales, with an emphasis on sustainability and extreme environment applications.