جستجوی استاد راهنمای دانشگاهی

Risto Miikkulainenمشاهده پروفایل

استاد

• Neuroevolution
• Evolutionary Computation
• Deep Learning
• +۷ مورد دیگر

Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.

• Inverse Problems
• Applied Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.

• Algebra
• Combinatorics
• Number Theory
• +۳ مورد دیگر

Gábor Somlai is a mathematics researcher at Eötvös Loránd University's Faculty of Science, affiliated with the Department of Algebra and Number Theory since 2006. He is a core member of the HUN-REN-ELTE Geometric and Algebraic Combinatorics Research Group (previously MTA-ELTE until 2023) and participates in the Asymptotic Group Theory ERC project. His institutional roles include contributions to the Mathematics Doctoral School and the Institute of Mathematics. His research spans Algebra , Combinatorics , and Number Theory , with emphasis on spectral sets, tiling problems, group theory, and finite geometry. Recent work explores cyclotomic polynomials, the generalized trifference problem, and connections between tiling and spectral properties in finite abelian groups. His publications reveal a strong focus on combinatorial number theory and algebraic methods in discrete geometry. Somlai's publication trends show sustained contributions to theoretical foundations of tiling and spectral sets (testing Fuglede's conjecture in cyclic groups) Cayley graph properties and CI-group classifications polynomial methods in finite geometry and direction problems His work bridges pure algebra with combinatorial applications, often resolving long-standing conjectures in specialized domains. As part of the HUN-REN-ELTE research group, he collaborates on geometric and algebraic combinatorics projects, leveraging ERC funding for asymptotic group theory investigations. His research infrastructure includes access to the Alfréd Rényi Institute of Mathematics (http://www.renyi.hu) resources.

Matilde Marcolliمشاهده پروفایل

استاد

• Noncommutative Geometry
• Mathematical Physics
• Number Theory
• +۵ مورد دیگر

Matilde Marcolli is the Robert F. Christy Professor of Mathematics and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds joint appointments in the Division of Physics, Mathematics, and Astronomy (PMA) and the Division of Engineering and Applied Sciences (EAS). Her research spans noncommutative geometry, mathematical physics, number theory, and mathematical linguistics. She has been recognized with prestigious awards such as the Sofja Kovalevskaya Award (2001) and the Heinz Maier Leibnitz Prize (2001). Marcolli has advised numerous PhD students and contributed to over 300 publications. Her work bridges abstract mathematics with applications in cosmology, quantum field theory, and computational linguistics. Education : PhD in Mathematics, University of Chicago, 1997 M.Sc., University of Chicago, 1994 Laurea in Mathematics, University of Pavia, 1993 Research Interests : Marcolli’s research explores the intersection of geometry, number theory, and physics. Key areas include noncommutative geometry models of particle physics and cosmology, motives in quantum field theory, and algebraic models of generative linguistics. She applies advanced techniques such as Feynman integrals, spectral action principles, and Hopf algebras to interdisciplinary problems. Grants & Awards : NSF grants DMS-2104330, DMS-1707882, and others NSERC Discovery Grant RGPIN-2018-04937 FQXi grant FQXi-RFP-1804 Collaborations & Labs : Marcolli collaborates with institutions like the Perimeter Institute and Florida State University. She leads research groups on topics such as quantum statistical mechanics, holography, and neural information networks. Her work on syntax-semantics interfaces and quantum gravity models has been pivotal in interdisciplinary studies.

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +۷ مورد دیگر

Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).

Pierre Baldiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

Pierre Baldi is a Distinguished Professor of Computer Science and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine (UCI). He is affiliated with the Donald Bren School of Information and Computer Sciences. His research spans artificial intelligence, machine learning, bioinformatics, and communication networks, with notable projects in protein structure prediction, gene expression modeling, and neutrino physics collaborations like DUNE. Baldi’s work bridges theoretical foundations (e.g., neural network theory) and applied domains, including medical imaging and fusion technology. Key research interests include AI-driven biomedical applications, neural network theory, and interdisciplinary projects such as the DUNE neutrino experiment. His contributions to neural network engineering were recognized with the 2023 INNS Dennis Gabor Award, highlighting his paradigm-changing impact on computational neuroscience and physics. Baldi’s academic leadership includes directing UCI’s Institute for Genomics and Bioinformatics, fostering collaborations in computational biology and AI. His recent work explores AI’s role in healthcare, climate modeling (e.g., ClimSim-Online), and fundamental physics challenges like neutrino oscillation studies.

• Phonology
• Phonetics
• Automatic Speech Recognition
• +۲ مورد دیگر

Dr. Gábor Pintér holds dual academic appointments as Assistant Professor at Károli Gáspár Reformed University's Faculty of Humanities and Social Sciences (Department of Japanology) and as Associate Professor at Kobe University's School of Languages and Communication. Born in Hungary in 1977, he earned his MA (2005) and PhD (2008) in Linguistics from Kobe University, Japan, with a dissertation on asymmetrical segment distributions in Japanese. His research focuses on three interconnected domains: phonological theory (especially Japanese phonology), experimental phonetics, and applications of automatic speech recognition in language education. Specific interests include vowel devoicing patterns, prosodic perception in L2 learners, phonotactic constraints, and diachronic sound changes in Japanese. Pintér actively contributes to international research collaborations, including the JSPS-funded project 'Stochastic & theoretical phonological research' and serves as chair of the Phonology Association in Kansai (PAIK). His 15 most recent publications demonstrate methodological diversity, combining theoretical linguistics with computational approaches and experimental studies, primarily focused on Japanese phonetics and speech technology applications. Professional memberships include board positions in The Phonological Society of Japan and memberships in the International Speech Communication Association and Association for Laboratory Phonology.

Gabor Viragمشاهده پروفایل

دانشیار

• Economic Theory
• Auction Theory
• Search Theory
• +۵ مورد دیگر

Gabor Virag is an Associate Professor of Economic Policy Analysis at the University of Toronto, Mississauga , with a cross-appointment to the Rotman School of Management . He serves as the PhD Coordinator for the Economic Analysis and Policy area. His research focuses on market dynamics, auction theory, and search theory, with a particular interest in decentralized market interactions and information policy. Education : PhD in Economics from Princeton University, MA from Central European University, BA from Budapest University of Technology and Economics. Research Interests : Economic Theory, Industrial Organization, Game Theory, and their applications to dynamic contests, patent markets, and labor economics. Publications : His work appears in top-tier journals such as the American Economic Journal, Games and Economic Behavior, and Review of Economic Studies, emphasizing auctions with resale, innovation prizes, and wage inequality. Collaborations : Co-author of studies with scholars from Bocconi University, Claremont McKenna College, and the Hungarian Academy of Sciences.

Gabor Oroszمشاهده پروفایل

استاد

• Nonlinear dynamics and control
• Time delay systems
• Connected and automated vehicles
• +۶ مورد دیگر

Gabor Orosz is a Professor at the University of Michigan in both the Department of Mechanical Engineering and Department of Civil and Environmental Engineering . His work bridges nonlinear dynamics and control , time delay systems , and connected automated vehicles , with a focus on traffic flow optimization and vehicle safety . Education: PhD in Engineering Mathematics, University of Bristol, UK (2006) MSc in Engineering Physics, Budapest University of Technology and Economics, Hungary (2002) Research Focus : Orosz's research explores the intersection of vehicle automation , connectivity , and nonlinear dynamics . He investigates time delay effects in teleoperation , intent-sharing protocols for cooperative maneuvering , and control barrier functions for safety-critical systems . His work spans theoretical analysis, numerical validation, and real-world experimentation. Article Trends : Recent publications highlight advancements in latency mitigation for remote driving , nonholonomic vehicle control , intent-sharing frameworks , and energy-efficient strategies for connected vehicle systems . Themes include delayed feedback , stochastic communication , and safety-guaranteed control . Awards & Appointments : NSF CAREER Award (2014) Fulbright Scholar at Budapest University of Technology (2023-2024) Editorial roles in Vehicle System Dynamics (2020) and Time Delay Systems (2017) Student Mentorship : Orosz has advised numerous PhD students, including Anil Alan (2024, TU Delft), Chaozhe He (2018, University at Buffalo), and Tamás Molnár (2020, Wichita State University). His alumni work at institutions like Toyota Research Institute , Ford Motor Co. , and Zoox . Labs & Teams : He leads research at the University of Michigan's Mechanical Engineering Department and collaborates with international institutions such as Caltech and Budapest University of Technology . His team focuses on experimental validation of connected vehicle systems and delay-tolerant control .

Levente Buttyanمشاهده پروفایل

استاد

• Cyber Security
• Networked Embedded Systems
• Internet of Things (IoT) Security
• +۷ مورد دیگر

Roles: Full Professor at Budapest University of Technology and Economics (BME), leading the Laboratory of Cryptography and Systems Security (CrySyS Lab) . Specializes in cyber security, IoT security, and privacy technologies. Served as Associate Editor for IEEE Transactions on Mobile Computing and Elsevier Computer Communications. Education: M.Sc. in Computer Science, BME (1995) Ph.D. in Computer Science, Swiss Federal Institute of Technology Lausanne (EPFL, 2002) Habilitation at BME (2013) Doctor of Science, Hungarian Academy of Sciences (2021) Research Interests: Focuses on malware detection on embedded systems, security of industrial control systems, and privacy-preserving AI. Current projects include DOSS (IoT supply chain security), SECURED (health data security), and SPAM (AI and cybersecurity). Published over 150 papers and co-authored books on wireless network security and cryptographic obfuscation. Grants & Awards: Awarded Dennis Gabor Award (2024), Bolyai Fellowship (2008-2011), and led EU projects like SEVECOM and WSAN4CIP. Current grants include H2020 DOSS and OTKA-funded research on federated learning incentives. Advising: Supervised 13 PhD students, including current faculty members (e.g., András Gazdag, Dorottya Papp). Active in mentoring CTF teams like !SpamAndHex (DEFCON qualifier). Labs & Teams: Director of CrySyS Lab, leading research in embedded device security, vehicle cyber defense, and industrial IoT resilience. Active in EDIH cybersecurity consulting for SMEs.

• Physical Chemistry
• Inorganic Chemistry
• Spectroscopy
• +۵ مورد دیگر

Gábor Magyarfalvi is an Assistant Professor and Lecturer at Eötvös Loránd University, affiliated with both the Institute of Chemistry and the Department of Inorganic Chemistry. His office is located at 1117 Budapest, Pázmány Péter sétány 1/a. (Room 542), and he can be contacted via email at gmagyarf@elte.hu or phone extension 6587. His research focuses on physical and inorganic chemistry, with specialization in spectroscopy, astrochemistry, and computational methods. Key areas include matrix isolation techniques for studying interstellar molecule formation (e.g., H 2 catalysis via polyaromatic hydrocarbons), photochemical generation of reactive intermediates, and conformational dynamics of biomolecules. His work extensively employs low-temperature matrix isolation coupled with laser spectroscopy and quantum chemical calculations. Magyarfalvi's publications demonstrate consistent themes: 60% focus on low-temperature photochemistry and spectroscopy of small molecules (e.g., nitrogen/sulfur compounds, amino acids), 30% on peptide/protein conformational analysis using vibrational circular dichroism (VCD) and NMR, and 10% on methodological developments in computational chemistry. Recent works increasingly explore astrochemistry and quantum tunneling phenomena.

Pierre F Baldiمشاهده پروفایل

استاد

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

Pierre Baldi is a Distinguished Professor at the University of California, Irvine (UCI), holding appointments in the School of Information and Computer Sciences (ICS), the Institute for Genomics and Bioinformatics (IGB), and the Center for Machine Learning and Intelligent Systems (CML). His research focuses on AI, machine learning, and their applications in natural sciences, including physics, chemistry, and biology. He leads projects on neural networks, probabilistic modeling, and bioinformatics. Key roles include Director of the IGB and AI in Science Institute. Awards include the Dennis Gabor Award and Fellowships from AAAI, AAAS, and IEEE. His work spans interdisciplinary collaborations, such as the DUNE neutrino experiment and AI-driven drug discovery. Teaches courses like Probabilistic Modeling of Biological Data and Neural Networks. Active in conferences, including workshops on deep learning and its implications in science.

• Machine Learning
• Signal Processing
• Biomedical Engineering
• +۷ مورد دیگر

Professor Danilo Mandic is a leading academic in Machine Intelligence and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering. He holds roles including President of the International Neural Network Society and Distinguished Lecturer for IEEE Computational Intelligence and Signal Processing Societies. His research spans Statistical Learning, Wearable Sensing (Hearables), Financial Signal Processing, and Tensor Networks for Big Data. Key contributions include pioneering in-ear physiological sensing and developing quaternion-based adaptive filters. He has authored over 600 publications, including seminal monographs on neural networks and complex-valued signal processing. Education: PhD in Nonlinear Adaptive Signal Processing from Imperial College (1999). Professional accolades include the 2019 Dennis Gabor Award and multiple IEEE Best Paper Awards. His labs include the Financial Signal Processing & Machine Learning Lab and collaborations with the Centre for Neurotechnology. He advises numerous students and leads projects on AI ethics, graph signal processing, and biomedical applications. His work emphasizes translating research into educational curricula via participatory sensor-based learning.

Jonathan Pakianathanمشاهده پروفایل

استاد

• Algebraic Topology
• Cohomology of Groups
• Lie Algebras
• +۳ مورد دیگر

Jonathan Pakianathan is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences, Department of Mathematics. He holds a PhD from Princeton University (1997) and a BS in Mathematics and Physics from Caltech (1992). His research focuses on algebraic topology, cohomology of groups and Lie algebras, geometric combinatorics, and finite fields. He has held leadership roles, including Director of Graduate Studies (2014–2020) and Director of Undergraduate Studies (2003–2010). Notable awards include the Goergen Award for Excellence in Teaching (2014) and the Sloan Foundation Doctoral Dissertation Fellowship (1996). His research explores applications of topology and algebra in discrete geometry, often collaborating with Alex Iosevich and students. Recent work addresses topics like Fuglede's conjecture over finite fields, geometric configurations, and probabilistic methods in combinatorics. His articles frequently intersect harmonic analysis, group theory, and number theory, reflecting interdisciplinary strengths. Education : PhD, Princeton University, 1997 BS in Mathematics and Physics, Caltech, 1992 Awards : Goergen Award for Excellence in Undergraduate Teaching, 2014 Sloan Foundation Doctoral Dissertation Fellowship, 1996 H. J. Ryser Scholarship, 1991 Grants include an NSA Mathematical Sciences Grant (2016–2017, $110,000 total) with A. Iosevich. He advises numerous PhD students, many of whom now hold academic or research positions. His teaching spans undergraduate to graduate courses, including algebra, topology, and financial mathematics. Research groups and collaborations extend to geometric combinatorics, algebraic topology, and applications in physics and data science. Ongoing projects explore topological methods in discrete geometry and probabilistic structures over finite fields.

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis
• +۲ مورد دیگر

Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.