جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Fuzzy Logic
• +۳ مورد دیگر

Carlos Andrés Pena is a Professor and Director of the Institute of Information and Communication Technologies (IICT) at HEIG-VD. His research focuses on Machine Learning, Artificial Intelligence, and Bioinformatics, with applications in healthcare, environmental science, and computational biology. He leads projects like ImpTox and EXPLaiN, addressing challenges in nanotoxicology, phage therapy, and ethical AI. His work bridges AI with real-world problems, including antimicrobial resistance and soil health monitoring. Education: Not explicitly listed in the provided text. Research Interests: Pena’s research integrates AI with interdisciplinary domains. Key areas include: Machine learning for healthcare diagnostics (e.g., diabetic retinopathy, perinatal health) Phage genome engineering using deep learning to combat antibiotic resistance Protist bioindicators for soil quality assessment using metabarcoding Fuzzy logic systems for biomarker discovery and disease classification Grants & Projects: He has secured over CHF 1M in funding, including EU Horizon 2020 projects and Swiss National Science Foundation grants. Notable projects include: ImpTox (2021–2025): Investigating nanoplastics’ toxicity on allergies PERPHECT (2019–2021): Engineering phages via LSTM networks HES-XPLAIN (2023–2024): Open platform for explainable AI Labs/Teams: Directs the IICT lab, collaborating with institutions like CNRS and University of Fribourg on bioinformatics and environmental studies.

• Digital Signal Processing
• Power Quality
• Motor Current Signature Analysis
• +۵ مورد دیگر

Prof. Levent EREN is a Professor and Vice Rector at İzmir University of Economics, leading the Faculty of Engineering's Electrical and Electronics Engineering department. He holds a Ph.D. from the University of Missouri (2002) and has extensive experience in academia and industry, including roles as Vice Dean (2014–2019) and faculty member at Bahçeşehir University (2003–2012). His research focuses on motor fault diagnosis, power quality, and digital signal processing, with over 30 journal and conference publications. Prof. EREN’s work emphasizes innovative applications of neural networks and wavelet transforms in machinery condition monitoring. He has contributed to EU-funded projects like Future Education and Training in Computing (2013–2014). His administrative roles include managing institutional research and academic policies as Vice Rector since 2019. His teaching spans courses on electrical energy conversion, digital signal processing, and senior engineering projects. He has no explicitly listed awards but is recognized for his technical contributions in motor current signature analysis and bearing fault detection.

Ali Jaliliمشاهده پروفایل

مدرس ارشد

• Sustainable Energy & Net-Zero Technologies
• Plasma Gas Conversion
• Plasma-Electrocatalysis
• +۷ مورد دیگر

Ali Jalili is a Senior Lecturer and Australian Research Council (ARC) Future Fellow at the School of Chemistry, UNSW Sydney. He earned his PhD from the University of Wollongong (2013). His research focuses on sustainable energy technologies, plasma gas conversion, and plasma-electrocatalysis for green ammonia and hydrogen production. Key contributions include advancing renewable energy integration, scalability of chemical processes, and environmental technologies. Jalili has secured over AUD 2.8 million in grants, including ARC DECRA, Future Fellowship, and NSW Environmental Trust funding. His work emphasizes techno-economic analysis and AI-driven modeling to bridge lab-to-industry gaps. He has published 65+ peer-reviewed articles (H-index 45, 7,500+ citations), holds four patents, and actively engages in industry partnerships through PlasmaLeap Technologies. Education: PhD in Chemistry (University of Wollongong, 2013). Research interests span plasma-catalyzed nitrogen fixation, additive manufacturing of electrocatalysts, and CO₂ conversion to value-added chemicals. Supervision focuses on AI-driven catalyst discovery, plasma-electrocatalysis, and sustainable technology commercialization. Awards include ARC Future Fellowship (2023), ARC DECRA (2017), and international conference recognitions. Labs/Teams: Leads plasma research activities at UNSW, collaborating with industry on pilot systems for on-demand ammonia synthesis. Grants include AUD 1.5M NSW Environmental Trust grant (2024), ARC Discovery grants (2023, 2022), and international partnerships through UNSW-SJTU collaborations. Awards emphasize his leadership in sustainable chemical production and decarbonization strategies. His research bridges academia and industry, addressing global challenges like emission reduction and renewable energy storage.

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.

• Power Electronics
• Vehicle Electrification
• Renewable Energy Integration
• +۷ مورد دیگر

Nathan Miles Ellis is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His research focuses on power electronics, with applications in energy sustainability, electric vehicles, renewable integration, data centers, aerospace, and biomedical devices. Education: B.S. in Electrical and Electronic Engineering, University College Cork, Ireland, 2013 M.S. in Electrical and Computer Engineering, University of California, Davis, 2017 Ph.D. in Electrical and Computer Engineering, University of California, Davis, 2020 Dr. Ellis specializes in hybrid-switched-capacitor (HSC) power converters, advanced control techniques, and analog & mixed-signal IC design. His work enables efficient power conversion for next-generation clean energy systems. He has extensive experience in GaN-based converters, resonant topologies, and high-density power delivery architectures. His recent publications highlight innovations in split-phase control, resonant gate drivers, regenerative snubbing, and ZVS optimization in GaN circuits—reflecting strong trends in high-efficiency, high-frequency power conversion for data centers and electric vehicles. Scientific Awards: Best Graduate Researcher, UC Davis Industrial Affiliates, 2017 Analog Devices Outstanding Student Designer, ISSCC, 2020 Best Technical Lecturer, APEC, 2021 and 2024 Co-author of two IEEE prize papers Dr. Ellis has been funded by the U.S. Department of Energy, NASA JPL, NSF, DARPA, and industrial partners including Texas Instruments. He co-advised multiple graduate students during his tenure at UC Berkeley’s Power and Energy Center (BPEC). His research promotes rapid adoption of cutting-edge power electronics in real-world clean energy applications. He is actively involved in research and mentoring within the Power Electronics group at UC Santa Cruz, collaborating closely with industry and national labs to advance next-generation energy technologies.

Vasileios Paschalidisمشاهده پروفایل

استادیار

• Gravitational Physics
• Theoretical Astrophysics
• Numerical Relativity
• +۵ مورد دیگر

Vasileios Paschalidis is an Assistant Professor in the Department of Astronomy and Department of Physics at The University of Arizona . He serves as one of the 5 core faculty members in the Theoretical Astrophysics Program and leads research at the intersection of gravitational physics and theoretical astrophysics , with a focus on compact object binaries and multimessenger astronomy . Department of Astronomy, The University of Arizona Department of Physics, The University of Arizona Theoretical Astrophysics Program, The University of Arizona His research investigates strong-field gravitation through simulations of black hole-black hole , neutron star-neutron star , black hole-neutron star , and white dwarf-neutron star binaries. Key areas include gravitational wave modeling , electromagnetic signatures , neutrino emissions , and equation of state constraints for nuclear matter. Recent work explores eccentric mergers in globular clusters , spin-flip mechanisms in X-shaped radio galaxies, and planet formation around pulsars . The 15 most recent publications highlight his expertise in numerical relativity , general relativistic MHD , and radiation transport . These works examine charged black hole mergers , ultrarelativistic particle dynamics , accretion disk variability , and novel periodicities in jet power from binary black hole systems. His simulations underpin gravitational wave detection by LIGO/Virgo/KAGRA and future missions like eLISA .

Kaitlyn Cookمشاهده پروفایل

استادیار

• Biostatistics
• Infectious Disease Statistics
• Missing Data Analysis
• +۷ مورد دیگر

Kaitlyn Cook serves as Assistant Professor of Statistical & Data Sciences at Smith College, where she maintains an active research program at the intersection of biostatistics and medical applications. Her office is located in McConnell Hall 210, and she holds regular office hours on Tuesdays 9–11 a.m. during Spring 2025. Her educational background includes advanced degrees from prestigious institutions: Ph.D. and A.M. from Harvard University B.A. from Carleton College Dr. Cook's research program demonstrates dual focus: methodological innovation in statistical techniques and substantive medical collaborations. She develops robust statistical methods for infectious disease treatment and prevention trials, drawing on expertise in missing data literature, clustered and longitudinal data analysis, survival analysis, and computational statistics. Her substantive collaborations span multiple medical domains including pediatric sleep medicine (particularly snoring and sleep apnea in children), pregnancy outcome prediction, cancer diagnostics, and statistical genetics. Analysis of her recent publications reveals consistent engagement with NIH-funded clinical trials like the Pediatric Adenotonsillectomy for Snoring (PATS) study. Her scholarly output shows significant methodological contributions to handling complex data structures (particularly clustered interval-censored data) while maintaining strong ties to clinical applications. The publications demonstrate expertise in translating statistical innovations into practical medical research tools, with particular emphasis on pediatric sleep disorders and cancer diagnostics. Dr. Cook actively engages in interdisciplinary research collaborations that address pressing medical questions through sophisticated statistical approaches. Her work on the PATS clinical trial demonstrates leadership in designing and analyzing studies that evaluate surgical interventions for pediatric sleep disorders. She also contributes to developing novel diagnostic tools for cancer through work on extracellular vesicle profiling from blood plasma.

Chenchen Liuمشاهده پروفایل

استادیار

• High-performance computing for machine learning
• Brain-inspired computing
• Hardware-software co-design
• +۴ مورد دیگر

Dr. Chenchen Liu is an Assistant Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She is affiliated with the Computing Compass Laboratory and holds a Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) under the focus group Enabling Neuromorphic Computing for Multi-Tenant AI. Her work bridges hardware-software co-design with neuromorphic computing. Ph.D., Electrical and Computer Engineering, University of Pittsburgh (2017) M.S., Electrical and Computer Engineering, Peking University (2013) Her research focuses on high-performance computing for machine learning through novel computer architecture and system designs, brain-inspired computing, machine learning security, non-volatile memory, and VLSI design. Key areas include: Neuromorphic hardware resilience and optimization Memristor-based neural network architectures Runtime scheduling for multi-tenant AI Security in neuromorphic computing Energy-efficient memory systems Recent publications explore memristor defect tolerance, ReRAM-based CNN training efficiency, and spiking network quantization. The work emphasizes hardware-software co-design for AI acceleration. NSF Career Award (2023) Best Poster Award, Machine Learning and Systems Conference (2022) Best Paper Award, IEEE Symposium on VLSI (2014) She contributes to academic service as TPC Chair/Track Chair for DAC, GLVLSI, Cloud Summit conferences and serves as Associate Editor for IEEE Transactions on Circuits and Systems (TCAS-1) and Neurocomputing journal.

Xianming Daiمشاهده پروفایل

دانشیار

• Heat transfer
• Thermal management
• Water sustainability
• +۷ مورد دیگر

Xianming Dai is an Associate Professor in the Department of Mechanical Engineering at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. He also holds an affiliated faculty position in Bioengineering. He leads the Dai Lab, which conducts interdisciplinary research in thermal science and sustainable technologies. Post-Doc: Mechanical Engineering, The Pennsylvania State University, 2016 PhD: Mechanical Engineering, University of South Carolina, 2013 M.S.: Thermal Engineering, Huazhong University of Science and Technology, 2009 B.S.: Thermal Energy and Power Engineering, Chongqing University, 2007 Dr. Dai’s research focuses on heat transfer, surface science, microfluidics, and water sustainability . His lab develops innovative surfaces for water harvesting, condensation enhancement, and thermal management , with applications in energy systems and electronics cooling. He combines nanofabrication, biomimetics, and fluid dynamics to engineer next-generation thermal interfaces. His recent publications (2015–2025) reveal a strong trend in bioinspired surfaces and droplet manipulation , particularly for atmospheric water harvesting and high-efficiency condensation. Key themes include quasi-liquid surfaces, SLIPS, and coalescence phenomena, published in journals like Science Advances , PNAS , Joule , and Advanced Functional Materials . Dr. Dai has received several prestigious awards, including: DARPA Young Faculty Award (2023) National Science Foundation CAREER Award (2021) Army Research Office Young Investigator Award (2019) UT Dallas Jonsson School Assistant Professor Research Award (2022) Outstanding Early Career Award (2021) JALA Top Ten Breakthrough Award (2016) Fellow, Eugene McDermott Distinguished Professor (2025) He actively advises PhD students and leads significant research initiatives, including a $3M Department of Energy award for energy sustainability and a $500K DARPA Director’s Fellowship. He was appointed Associate Editor of the journal Droplet in 2025. His lab, the Dai Lab, fosters interdisciplinary collaboration and recruits students from diverse backgrounds. The Dai Lab is at the forefront of thermal science innovation, focusing on passive water harvesting, anti-icing, and high-performance heat transfer . It has secured major funding and produced breakthroughs recognized by Nature Materials and other leading outlets.

Jen Schradieمشاهده پروفایل

دانشیار

• Digital democracy
• Digital activism
• Digital inequality
• +۱۲ مورد دیگر

Jen Schradie is an Associate Professor at Sciences Po, affiliated with the Centre for Research on Social Inequalities (CRIS), where her research focuses on digital democracy, digital activism, and digital inequality. She holds a PhD in Sociology from the University of California, Berkeley, and her work integrates perspectives from communication, labor studies, and political sociology. Her research interests include: Digital democracy and political participation Digital activism and social movements Class, race, and gender disparities in digital access and production Technology and media Work, labor, and digital unionizing Disinformation and online hate speech Her recent publications reveal a consistent theme: the internet does not democratize political participation but rather reproduces and amplifies existing social inequalities. Her work examines how class, institutional resources, and political ideology shape who participates in digital spaces and how. She has conducted comparative research in the U.S. and France, particularly on the startup economy and the gilets jaunes movement. Her influential book The Revolution That Wasn’t (2019) argues that conservative groups often have greater digital advantage due to resource disparities. Her scientific contributions are recognized through affiliations with major academic networks: Chair, Scientific Panel on AI and Economic Inclusion, International Panel on the Information Environment (IPIE) Co-founder, European Network on Digital Labour (ENDL/INDL) Affiliate, Center on Digital Culture and Society, University of Pennsylvania Advisory Committee Member, National Academies of Sciences – Computational Social Science Expert List Member, International Communication Association – Activism, Communication and Social Justice Jen Schradie has advised multiple collaborative research projects and grants related to digital inequality and social movements, particularly during the pandemic through the CoCo project. She has also led comparative studies on gender and class in the startup economy and online hate speech against Muslims. Her research combines qualitative and quantitative methods, drawing on both online and offline data to contextualize digital participation. She is actively involved in academic discourse through recent presentations on Web3, digital governance, and content moderation at venues such as the McCourt Institute and The Shed in New York.

Gavin J. Kilduffمشاهده پروفایل

استاد

• Organizational Behavior
• Rivalry and Competition
• Status and Power Dynamics
• +۷ مورد دیگر

Gavin J. Kilduff is a Professor of Management and Organizations at the Leonard N. Stern School of Business, New York University. He is based at the Kaufman Management Center in New York, NY, and teaches courses such as 'Collaboration, Conflict and Negotiation,' 'Negotiation and Consensus Building,' and 'Advanced Research in Organizational Behavior.' His educational background includes a B.S. in Computer Science from Pennsylvania State University and a Ph.D. in Business Administration from the University of California, Berkeley. Professor Kilduff's research centers on rivalry and competition, status dynamics, social intelligence, and the role of play in organizations. He investigates how rivalries form and influence decision-making, how individuals navigate organizational hierarchies, and how non-serious play can enhance team and organizational outcomes. His work bridges organizational behavior and social psychology, emphasizing behavioral mechanisms in competitive contexts. The trends in his recent publications reveal a strong focus on rivalry, status perception, and competitive motivation. His research consistently explores how psychological states and interpersonal dynamics affect leadership, performance, and ethical behavior in organizational settings. He frequently publishes in top-tier journals such as the Academy of Management Journal , Journal of Personality and Social Psychology , and Journal of Applied Psychology . His research has been widely recognized in the media, with features in The Wall Street Journal , TIME , Scientific American , ESPN , and Harvard Business Review . He has also contributed op-eds to the Wall Street Journal and Harvard Business Review , discussing rivalry, leadership, and team dynamics. Professor Kilduff advises on organizational behavior topics and has received significant media and academic attention for his research. While specific grant details are not listed, his frequent publication in high-impact journals and media engagement suggest sustained research support and influence. He is actively involved in advancing theory and practice in management and organizational psychology. He is affiliated with the Department of Management and Organizations at NYU Stern, contributing to research initiatives and academic programs focused on leadership, negotiation, and team performance.

Yin Chenمشاهده پروفایل

مدرس

• Algebra
• Representation Theory
• Finite Fields
• +۳ مورد دیگر

Yin Chen is a Lecturer at the Edwards School of Business, University of Saskatchewan. Their research focuses on advanced algebraic structures, including Lie algebras, invariant theory, and finite field applications. Notable work includes studies on modular invariants, cohomology of color algebras, and quantum code enumerators. Publications span topics such as Yang-Baxter matrix equations and deformations of left-symmetric algebras. No awards or grants are explicitly listed in the provided materials. Research interests prominently feature algebraic geometry, representation theory, and structural analysis of mathematical objects. Recent work emphasizes applications in coding theory and quantum computing. No advising or lab affiliations are mentioned.

Edward Gebaraمشاهده پروفایل

دانشیار

• Radiation-tolerant electronics
• Extreme temperature integrated circuits
• Self-healing integrated circuits
• +۴ مورد دیگر

Dr. Edward Gebara is an Associate Professor of Electrical and Computer Engineering at Michigan State University's College of Engineering. His expertise spans radiation-tolerant electronics, extreme-temperature integrated circuits, and self-healing mixed-signal techniques. He holds B.S., M.S., and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology (1996–2003). Research Interests: Radiation-tolerant electronics for aerospace Extreme temperature environment circuits Self-healing and reconfigurable ICs Wireless cancellation and signal integrity solutions Professional Activities: Former Director of Engineering at Electromagnetic Sensor Technologies Inc. (2011–2023) Adjunct Assistant Professor at Georgia Tech (2010–2011) IEEE MTT-S Symposium roles: Vice Chair (2010–2012), Technical Committee Chair (2018–2021), and Workshop Chair (2021) Awards: Best Paper Award at IEEE International Microwave Symposia (2000) Grants & Advising: Directed Ph.D. students in mixed-signal and microwave systems Led industry-academia collaborations in GaAs/GaN technologies Labs & Teams: Mixed-Signal Research Group at Georgia Tech (2003–2011) Electromagnetic Sensor Technologies R&D team

• Modeling and Simulation
• Energy System Analysis
• Vehicle Grid Integration
• +۳ مورد دیگر

Prof. Dr.-Ing. Marco Pruckner leads the Chair of Communication Networks at the University of Würzburg since 2022, following his assistant professorship in Energy Informatics at Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022). His research focuses on energy system analysis, vehicle grid integration, and future mobility systems using modeling and simulation methodologies. University of Würzburg (2022–present) Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022) UC Berkeley (Visiting Scholar, 2020) Key research areas: Modeling and simulation of integrated energy systems Digital twin development for local energy systems Optimization of electric vehicle charging strategies Water-energy nexus analysis and sustainability metrics Recent publications (2022–2025) demonstrate expertise in: Smart charging algorithms for vehicle grid integration Heat pump electrification and seasonal performance modeling Battery state-of-health estimation techniques Multi-objective optimization for data centers and energy systems His team at University of Würzburg includes Daniel Bayer, Paul Benz, Jonas Schiller, and Leo Strobel. Methodologies combine system dynamics, discrete-event simulation, and machine learning for energy and mobility system analysis.

• Machine Learning Security
• Adversarial Machine Learning
• AI for Healthcare
• +۵ مورد دیگر

Xiaojun Xu is a Professor at the School of Computer Science, Beijing Institute of Technology, with a prolific research career spanning machine learning security, medical AI, and robotics. Their work demonstrates strong interdisciplinary collaboration across computer science, healthcare, and engineering domains. Institution: Beijing Institute of Technology, School of Computer Science Research Focus: AI security, medical imaging, robotics, and remote sensing applications Collaborations: Extensive work with Bo Li (29 papers), Dawn Song (11 papers), and medical researchers Xu's research interests center on adversarial machine learning, with significant contributions to model security, backdoor detection, and LLM unlearning. They've pioneered techniques like Meta Neural Analysis for Trojan detection and developed frameworks for certified robustness. Their medical imaging work focuses on quantitative susceptibility mapping for neurodegenerative diseases, particularly Parkinson's and Alzheimer's. In robotics, they've advanced control systems for quadruped and amphibious vehicles. Recent publications reveal a strong trend toward large language model security, with multiple 2024-2025 papers on machine unlearning and watermarking techniques. Their work bridges theoretical security with practical healthcare applications, particularly in medical image analysis where they've developed tools for subcortical nucleus segmentation and brain age prediction. Key venues: NeurIPS, CCS, IEEE S&P, NeuroImage, IEEE Transactions Research impact: High citation count with consistent top-tier publication record Xu has secured significant research funding, evidenced by the volume and diversity of publications across multiple domains. Their work on blockchain-enabled IoT systems and RAFT-based private blockchain demonstrates expertise in distributed systems. The medical imaging research shows strong hospital collaborations, particularly in developing tools for Parkinson's diagnosis. Current projects appear focused on LLM security challenges and multimodal medical AI systems with potential clinical applications.