جستجوی استاد راهنمای دانشگاهی

• Brain-Computer Interface
• Neuroscience
• Functional Magnetic Resonance Imaging
• +۷ مورد دیگر

David Steyrl is an active researcher in the Faculty of Psychology, Department of Cognition, Emotion, and Methods in Psychology. His academic credentials include Dipl.-Ing. Dr.techn., indicating a technical doctorate. With 73 publications spanning from 2012 to 2025, his research output shows consistent productivity with recent activity including 8 publications in 2024 and 7 publications in 2025. Steyrl's research interests center on the intersection of neuroscience, psychology, and computational methods. His work prominently features brain-computer interfaces, neuroimaging techniques (particularly fNIRS and EEG), and the application of machine learning to psychological questions. His research fingerprint reveals strong expertise in Brain-Computer Interface (100% Neuroscience), Functional Magnetic Resonance Imaging (91% Psychology), Neurofeedback (90% Psychology), and Random Decision Forests (54% Computer Science). His recent publications demonstrate a clear trend toward applying machine learning methods to complex neuroscience and psychological questions, with particular focus on PTSD, interbrain synchrony, climate-related behaviors, and relationship quality in sexual minority populations. Steyrl has also been active in presenting his work, with recent talks on interpretable machine learning frameworks for hypothesis testing. Steyrl has participated in research projects including 'Jointly Optimized Allocation of Functionality in Systems of Systems' (2021-2024), indicating collaboration with researchers like Scharnowski and Melinšcak. His work contributes to UN Sustainable Development Goals, particularly in areas related to health and well-being.

Szemerédi Endreمشاهده پروفایل

استاد

• Discrete mathematics
• Combinatorics
• Theoretical computer science
• +۱ مورد دیگر

Endre Szemerédi is a Hungarian mathematician and computer scientist, currently serving as a Permanent Research Fellow at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences) and holding the New Jersey Professor of Computer Science position at Rutgers University since 1986. His career spans prestigious institutions including Stanford, Caltech, and the Institute for Advanced Study. Expertise: Discrete mathematics, combinatorics, theoretical computer science, and number theory. Education: M.Sc. (1965, Eötvös Loránd University), Ph.D. (1970, Moscow State University under Israel Gelfand). Research legacy encompasses foundational contributions to combinatorics, graph theory, and computational complexity. His work on Hamiltonian cycles in random graphs , parallel sorting networks , and the Blow-up Lemma revolutionized extremal combinatorics and algorithm design. Scientific honors include: 2012 Abel Prize and Széchenyi Prize 2008 Rolf Schock Prize and Steele Prize 1982 Member of Hungarian Academy of Sciences , 2010 Member of US National Academy of Sciences 1975 Pólya Prize , 1973 Rényi Prize , multiple Grünwald Prizes .

Chellappa Ramaمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Rama Chellappa is the Bloomberg Distinguished Professor at Johns Hopkins University (JHU), holding primary appointments in the departments of Electrical and Computer Engineering and Biomedical Engineering, under the Whiting School of Engineering and School of Medicine, respectively. He is affiliated with the Center for Imaging Science, Center for Language and Speech Processing, Institute for Assured Autonomy, and Mathematical Institute for Data Science. Previously, he spent 29 years at the University of Maryland as a College Park Professor and held roles at the University of Southern California and Purdue University. His research focuses on computer vision, machine learning, artificial intelligence, pattern recognition, and biometrics, with applications in smart cars, forensics, 2D/3D facial modeling, and medical diagnostics. Notable contributions include work on Markov random fields, 3D structure recovery, deep learning for face recognition, and gait recognition. He has published over 900 papers, achieving an h-index of 140. Rama Chellappa has been honored with prestigious awards, including the US National Academy of Engineering membership (2023), IEEE Jack S. Kilby Medal (2020), and K.S. Fu Prize (2012). He is a Fellow of multiple organizations, including IEEE, IAPR, and AAAS. His current collaborations at JHU School of Medicine involve applying computer vision to analyze facial expressions for monitoring stroke/dementia patients, autism detection in children, and digital pathology.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +۳ مورد دیگر

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.

• Machine Learning
• Computer Vision
• Robust Learning
• +۷ مورد دیگر

Christoph H. Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Machine Learning and Computer Vision Group. His research focuses on creating robust, fair, and verifiable machine learning systems with strong theoretical foundations. Academic Rank: Professor (ISTA) Research Focus: Machine Learning, Computer Vision, Robustness, Fairness, Formal Verification Editorial Roles: Action Editor (JMLR), Former Editor (IJCV), Associate Editor-in-Chief (TPAMI) His recent publications explore robust deep learning architectures, formal verification of neural networks, and fairness in multi-source learning environments. Research keywords span neural network design, algorithmic accountability, and structured data modeling. Scientific achievements include: DARPA Disruptive Ideas award (2023) ISTA Alumni Award (2023) He has mentored numerous PhD students including: Bernd Prach (2022 thesis: Robust image classification with 1-Lipschitz networks) Egor Zverev, Nikita Kalinin, Hossein (Qualifying Exam passed 2023-2025) Alex Peste (2023 thesis: Robustness and Fairness in Machine Learning) Mary Phuong (2021 thesis: Underspecification in Deep Learning) Amelie Royer (2020 thesis: Computer Vision applications) Alexander Kolesnikov (2018 thesis: Weakly-Supervised Segmentation) Alex Zimin (2018 thesis: Dependent data learning)

Gerhard Popovskiمشاهده پروفایل

مدرس ارشد

• Small-Angle Scattering
• Colloidal Science
• Polymer Physics
• +۳ مورد دیگر

Dr. Gerhard Popovski serves as Senior Lecturer at the Institute of Physics, University of Leoben, specializing in advanced scattering methodologies for soft matter characterization. His expertise bridges physical chemistry and materials science with extensive instrumentation experience at international facilities including ILL, NIST, and HASYLAB. Education: Chemistry studies at Karl-Franzens University Graz (1991-1997), Diploma thesis on fluid system characterization via scattering techniques PhD in Chemistry (1997-2000) evaluating scattering data of interacting systems Habilitation in Physical Chemistry (2009) titled "Characterization of Liquids and Polymers by Scattering" Research Focus: Pioneering work in small-angle scattering (SAXS/SANS) for colloidal systems, polymer physics, and soft matter. Developed generalized indirect Fourier transformation (GIFT) methods for analyzing charged particle interactions and dense colloidal systems. Investigates micelle formation, vesicle structures, emulsion dynamics, and rheological properties of complex fluids through combined scattering and rheological techniques. Publication Trends: Recent work (2006-2009) demonstrates methodological innovation in scattering data interpretation for block copolymers, colloidal interactions, and organic liquid structures. Key contributions include computational frameworks for Monte Carlo scattering simulations, determination of pair correlation functions, and stimuli-responsive polymer micelle characterization using advanced scattering techniques. Research Infrastructure: Extensive utilization of neutron (ILL, PSI) and synchrotron (HASYLAB) facilities for SANS, GISAXS, and rheo-SANS experiments. Collaborative networks span BASF AG, University of Delaware, CNRS Bordeaux, and University of Ljubljana for interdisciplinary soft matter research.

• Cryptography
• Cybersecurity
• Blockchain Technology
• +۳ مورد دیگر

Elena Andreeva is a tenure-track Assistant Professor in the Security and Privacy department at TU Wien. Her research focuses on symmetric cryptography, including authenticated encryption, block ciphers, hash functions, privacy-friendly protocols, and blockchain technologies. She coordinates the Double-Degree Program in IT Security and actively participates in the Faculty Council. Her work emphasizes both theoretical foundations and practical cryptographic solutions for secure data communication and storage. Research interests include designing and analyzing cryptographic algorithms with applications in IoT-to-cloud secure computation, lightweight encryption schemes, and quantum-resistant primitives. Her projects, such as SFB SPyCoDe (2023–2026), explore efficient compression functions and expanding PRFs for arithmetization-oriented hashing. Articles Overview : Recent publications emphasize authenticated encryption schemes (e.g., COLM), pseudorandom number generators (TPRF-based), and novel cryptographic designs like Forkcipher. These contributions address both theoretical security models and practical efficiency in resource-constrained environments. She advises students on master/bachelor theses and internships, fostering the next generation of cybersecurity researchers. Her involvement in curriculum development and interdisciplinary programs underscores her commitment to advancing both academic and applied cryptography.

Monika Henzingerمشاهده پروفایل

استاد

• Combinatorial algorithms
• Data structures
• Algorithmic game theory
• +۱ مورد دیگر

Monika Henzinger is a Full Professor of Computer Science at the University of Vienna since 2009. Previously, she held faculty positions at the Ecole Polytechnique Federale de Lausanne (2005-2009) and the University of the Saarland (1999-2005). She has also worked as a Director of Research at Google Inc and as a Research Staff member at Digital Equipment Corporation . Education: Ph.D., Computer Science, Princeton University (1993) Diploma, Computer Science, University of the Saarland (1989) Her research focuses on combinatorial algorithms , data structures , algorithmic game theory , and web information retrieval , with significant contributions to dynamic graph algorithms and probabilistic verification. Her work addresses problems in sponsored search auctions, web mining, and formal verification of stochastic systems. Key trends in her publications include web algorithmics , dynamic graph processing , probabilistic verification , and online optimization . Her research has been recognized through prestigious grants like the ERC European Young Investigator Award and the NSF CAREER Award . Scientific Awards: ERC Advanced Grant (2013) Honorary Doctorate, Technical University Dortmund (2013) European Young Investigator Award (2004) NSF CAREER Award (1995) She has advised PhD students at EPFL and the University of Vienna , including Paul Dütting and Veronika Loitzenbauer . Her editorial roles include serving as Editor of EATCS Monographs in Theoretical Computer Science and on the ACM Research Highlights board. She has led projects such as the Doctoral School Computational Science and Dynamic Graph Algorithms in Directed Graphs .

Richard Houlstonمشاهده پروفایل

استاد

• Cancer genetics
• Precision oncology
• Genetic discovery
• +۱۰ مورد دیگر

Professor Richard Houlston is a distinguished cancer genomics expert at The Institute of Cancer Research (ICR) , serving as Head of the Division of Genetics and Epidemiology since 2019. His career spans academic and clinical roles, including positions at King's College London and Southampton University. Education: MD, PhD, DSc in genetics from the University of London Research Focus: Houlston's work bridges cancer genetics and genomics, with groundbreaking contributions to understanding hereditary cancer risks, regulatory genomics, and the 3D structure of the genome in malignancies. His research has translated genetic discoveries into diagnostic tools. Publications: Recent articles highlight his expertise in genome-wide association studies (GWAS), non-coding mutations, and mechanisms of cancer susceptibility across multiple myeloma, leukemias, and colorectal/renal cancers. Honors & Awards: Fellow of the Royal Society (FRS) 2017 Fellow of the Academy of Medical Sciences (FMedSci) 2010 Fellow of the European Academy of Cancer Sciences (FCanSci) 2015 Fellow of the Royal College of Pathologists (FRCPath) 1997 Fellow of the Royal College of Physicians, UK (FRCP) 1998 Fellow of the Royal Society of Biology (FRSB) 2011

Marek M. Karpinskiمشاهده پروفایل

استاد

• Theory of computation
• Design of efficient algorithms
• Computational complexity
• +۱۰ مورد دیگر

Marek M. Karpinski is a Chair Professor of Computer Science at the University of Bonn and a founding member of the Hausdorff Center for Mathematics . He has held visiting or professorial positions at institutions such as Princeton University, Carnegie-Mellon University, and the University of Edinburgh. His affiliations also include the B-IT Research School on Applied Informatics and the Lab for Foundations of Computing . His research spans efficient algorithms , combinatorial optimization , computational complexity , randomized approximation techniques , and applications in network design , quantum computation , and molecular biology . Recent work focuses on approximation hardness for NP-hard problems, graph algorithms , and algebraic computational complexity . His scientific contributions include polynomial time approximation schemes for dense NP-hard problems and key publications in randomized algorithms , VC dimension , and network optimization . He has advised numerous researchers and received honors such as the Humboldt Research Award and the Max Planck Research Prize .

• Theoretical Computer Science
• Mathematical Logic
• Automatic Structures
• +۶ مورد دیگر

Bakhodyr Khoussainov is a Professor and the Head of the Algorithms and Logic Lab at the University of Electronic Science and Technology of China (UESTC) , since 2021. He has also been a Professor at the University of Auckland (1996–present), with prior roles including Visiting Professor at institutions like Kyoto University, National University of Singapore, and Cornell University. His work spans Theoretical Computer Science and Mathematical Logic , focusing on automatic structures, algorithmic randomness, and computability. Education: PhD in Mathematics from Novosibirsk University (1988), Diploma in Mathematics (1984), BSc in Mathematics (1981). His research interests include Automatic structures , Computable model theory , Algorithmic randomness , and Games on graphs . He has contributed to the theory of automatic groups and solved problems related to algorithmically random structures. The trends in his recent publications highlight advancements in Parity Games (2022), Infinite Strings (2022), and Quasi-polynomial time complexity (2017). His work often bridges Computability , Automata Theory , and Game Theory , with interdisciplinary applications. Scientific Awards: Research Excellence Award, New Zealand Mathematical Society (2002) Humboldt Fellowship (2002) JSPS Invitation Fellowships (2001, 2012, 2016) Fellow of The Royal Society of New Zealand (2005) Aitken Lectureship (2019) Humboldt Research Award (2019) Humboldt Prize (2020) EATCS Nerode Prize co-winner (2021) He supervises numerous PhD and Master's students and has secured significant grants, including Marsden Fund grants (2000–2016), NSFC grant (2022–2026) , and MoE Singapore grant (2016–2020) . He served as Editor of the Journal of Symbolic Logic (2014–2021) and contributed to major conferences like LICS and ICALP.

Michael Aizenmanمشاهده پروفایل

استاد

• Mathematical Physics
• Statistical Mechanics
• Probability Theory
• +۳ مورد دیگر

Michael Aizenman is a Professor of Physics and Mathematics at Princeton University, holding the Thomas D. Jones Professorship of Mathematical Physics since 2016. Previously, he was at NYU’s Courant Institute (1987–1990), Rutgers University (1982–1987), and Princeton as an Assistant Professor (1977–1982). His research focuses on mathematical physics, statistical mechanics, and probability, addressing critical phenomena, disorder effects, and random operator dynamics. Education: BA from Hebrew University (Jerusalem), PhD from Yeshiva University (advised by Joel L. Leibowitz). Postdoctoral training included visits at Courant Institute and Princeton under Elliot H. Lieb. Research interests emphasize quantum spin chains, critical behavior analysis, φ₄ field theory, and stochastic geometric structures in statistical mechanics. He has been recognized with major awards including the Dannie Heineman Prize, Brouwer Medal, and Norbert Wiener Prize. Professional service includes editorial leadership as Editor-in-Chief of Communications in Mathematical Physics (2000–2012) and roles in the International Association of Mathematical Physics.

• Distributed Computing
• Graph Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Yannic Maus is a Professor at Graz University of Technology, affiliated with the Institute of Algorithms and Theory and the Institute of Software Engineering and Artificial Intelligence. His research focuses on distributed computing, graph algorithms, and theoretical computer science. He holds a PhD and multiple bachelor’s and master’s degrees in Computer Science. His work emphasizes distributed graph coloring, locality in algorithms, and massively parallel computing. He has contributed to foundational results in distributed algorithms, including optimal edge coloring and coloring hyperbolic random graphs. His research bridges theoretical insights with practical distributed systems challenges. Education: PhD in Natural Sciences (Dr.rer.nat.), B.Sc. and M.Sc. in Computer Science Key research interests include distributed algorithms for graphs, Lovász Local Lemma applications, and algorithmic efficiency in dynamic networks. His publications explore topics like ruling sets in trees, exponential speedups in MPC models, and adaptive coloring techniques for sparse graphs. He has also investigated the algorithmic small-world phenomenon and connectivity in forests using deterministic approaches. Yannic Maus’s work often addresses theoretical lower bounds and upper limits in distributed computing, with applications to real-world networked systems. His contributions span conferences like DISC and SoCG, focusing on both foundational problems and their algorithmic solutions.

• Randomized Algorithms
• Networking
• Social Networks
• +۷ مورد دیگر

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Wojciech Szpankowskiمشاهده پروفایل

استاد

• Analysis of Algorithms
• Information Theory
• Analytic Combinatorics
• +۶ مورد دیگر

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University. He holds concurrent positions as a Guest Professor at ETH Zurich and a Professor at Jagiellonian University, Krakow. His research focuses on analysis of algorithms, information theory, analytic combinatorics, and random structures. He has directed major initiatives like the NSF Science & Technology Center on Science of Information (CSoI), a $50M 10-year project. Szpankowski is a Fellow of the IEEE and has received prestigious awards including the Flajolet Prize (2020) and Humboldt Research Award (2010). He has held visiting roles at institutions worldwide, including Stanford University and the Newton Institute, Cambridge. His academic journey includes tenure as Full Professor at Purdue (since 1992), Assistant Professorships at McGill University (1984–1992) and Technical University of Gdańsk (1980–1984). His scholarly contributions span monographs like "Average Case Analysis of Algorithms on Sequences" (2001) and "Analytic Pattern Matching" (2015), with a forthcoming work on Analytic Information Theory (2022). He actively contributes to scientific boards, including HIIT Helsinki and NeuroMat Brazil. Szpankowski’s publications emphasize interdisciplinary research at the intersection of computer science, mathematics, and biology. His work on privacy-preserving data analysis, dynamic network inference, and protein superfamily evolution showcases his broad impact. Keynote talks at major conferences (e.g., SODA 2019, AofA 2016) reflect his leadership in theoretical computer science. His grants and recognitions underscore institutional trust: the NSF Science & Technology Center (2010) and Arden L. Bement Jr. Award (2015) highlight his pioneering role in redefining information science. Collaborations span academia and industry, with affiliations at Hewlett-Packard and INRIA.