جستجوی استاد راهنمای دانشگاهی

Nataša Živićمشاهده پروفایل

استاد

• Information and Coding Theory
• Cryptography
• Information Security
• +۱۱ مورد دیگر

Prof. Dr. Nataša Živić is a Professor of Information and Coding Theory and Real-Time Image Processing at the Faculty of Digital Transformation , HTWK Leipzig . Her work bridges theoretical foundations in coding, cryptography, and signal processing with practical applications in IoT, smart grids, automotive systems, and blockchain technologies. Academic background: Dr.-Ing. habil. and Dr.-Ing. in Electrical Engineering, University of Siegen; Magister and Dipl.-Ing. from University of Belgrade. Professional journey: Founder of Secutanta GmbH; Senior Technical Security Engineer at Volkswagen (Cariad); academic roles at University of Siegen. Her research focuses on secure and robust communication , including noise-tolerant data authentication, joint channel coding and cryptography, and real-time image processing. She explores probabilistic AI algorithms and distributed ledger technologies for industrial applications in mobility and energy systems. The 15 most recent publications highlight her leadership in applying blockchain and DLT to automotive and IoT contexts, advancing secure communication in smart metering and charging infrastructure, and integrating machine learning for environmental and financial prediction. Her work spans IEEE, MDPI, and Springer venues, reflecting strong academic impact. Scientific Awards: Best Paper and Innovation Award, IEEE SEGE 2015 Best Paper Award, UBICOMM 2010 She has led DAAD-funded international projects with universities in the USA, Korea, and China, and contributes to standardization as a member of DIN and ISO committees on security (SC 27) and AI (SC 42). She is also an IEEE Senior Member and active reviewer for top journals. Her research addresses real-world challenges in automotive security, smart energy, and digital transformation.

Harshit Jitendra Motwaniمشاهده پروفایل

پژوهشگر ارشد

• Computational Algebraic Geometry
• Formal Verification
• Tensors
• +۵ مورد دیگر

Harshit Jitendra Motwani is a Postdoctoral Researcher at the Max Planck Institute for Software Systems (MPI-SWS) , Germany. Previously, he was a Postdoctoral Fellow at the Hong Kong University of Science and Technology (HKUST) (2023-2024), following doctoral research at Ghent University (2021-2023) and earlier engagements at HKUST, Ghent University, and the University of Bristol . Education : PhD in Mathematics: Algebra and Geometry from Ghent University (2021-2023), Thesis on Algebro-Geometric Algorithms for Program Synthesis; Integrated BSc and MSc in Mathematics and Computing from IIT Kharagpur (2015-2020). Research Focus : Integrates mathematics into computational domains, particularly Computational Algebraic Geometry , Formal Verification , and Tensors . His work spans program synthesis , tensor networks , conditional independence models , and control theory applications . Publications : Recent contributions include algorithmic advancements in formal methods (FM, AAAI, LAGOS), algebraic statistics (IMRN), and quantum computing (SIGMA). His research combines symbolic computation with practical software engineering challenges. Awards & Honors : IEEE Computer Society Larson Best Paper Award (2023) ACM SIGPLAN Distinguished Paper Award (2023) Young Researcher of the Heidelberg Laureate Forum (2023) INSPIRE Scholarship (2015-2020) Teaching : Guest Lecturer at HKUST, Teaching Assistant at RPTU Kaiserslautern-Landau and IIT Kharagpur. Mentored multiple bachelor's projects at Ghent University and IIT Kharagpur.

Anjalie Fieldمشاهده پروفایل

استادیار

• Ethics in Artificial Intelligence
• Computational Social Science
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Center for Language and Speech Processing (CLSP), focusing on the intersection of ethics, social science, and natural language processing. Her research examines: Developing language models to address societal challenges like discrimination and propaganda Critical evaluation of AI pipelines Applications of NLP in high-stakes domains (child welfare, police accountability) Interdisciplinary collaborations across social sciences, astronomy, and public policy Notable scientific achievements include the 2022 Wikimedia Foundation Research Award of the Year . Her publications demonstrate expertise in bias detection, ethical AI, and computational social science methods.

• Data Science
• Network Science
• Computational Social Science
• +۵ مورد دیگر

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

• Microbial Ecology
• Bioinformatics
• Environmental Biotechnology
• +۵ مورد دیگر

Felipe Borim Corrêa is a Research Associate in the Department of Applied Microbial Ecology at the Helmholtz Center for Environmental Research (UFZ), Leipzig, Germany. His work focuses on virus-microbe interactions in terrestrial ecosystems and bioinformatics tool development. Education: PhD in Environmental Microbiology (2017-2022), UFZ MSc in Bioinformatics (2015-2017), Federal University of Minas Gerais, Brazil BSc in Biology (2010-2013), State University of the North of Paraná, Brazil His research spans microbial ecology , viral impacts , and bioinformatics tool development . Key projects include the Collaborative Research Centre AquaDiva and TerrestrialMetagenomeDB curation. Recent publications highlight his expertise in metagenomic analysis of antibiotic resistomes (2025) virus-driven soil biogeochemistry (2024) microbial community dynamics in benzene degradation (2022) and software tools like StandEnA (2023). He contributes to interdisciplinary initiatives such as the InCeTo PhD college, focusing on ionic chemical toxicity mechanisms, and participates in soil virology workshops.

• Computer Vision
• Biometrics
• Deep Learning
• +۱۲ مورد دیگر

Sambit Bakshi is an active academic researcher in the field of computer science, affiliated with the Department of Computer Science and Engineering at the National Institute of Technology Rourkela, India. His research spans computer vision, biometrics, deep learning, and intelligent systems, with a strong emphasis on real-world applications in surveillance, healthcare, agriculture, and IoT. His primary research interests include biometrics (especially ear and periocular recognition), person re-identification , UAV-based image analysis , multimedia forensics , and fog/edge computing . He has contributed significantly to the development of deep learning models for object segmentation, anomaly detection, and secure biometric systems. The recent publications demonstrate a consistent trend in applying deep neural networks , attention mechanisms , and lightweight architectures to solve complex problems in visual recognition and data analytics. His work often appears in high-impact IEEE and ACM journals, reflecting strong technical rigor and relevance. Scientific Contributions: Co-edited special issues in IEEE Access and Image and Vision Computing Contributed to benchmark challenges such as the Unconstrained Ear Recognition Challenge 2019 Developed novel methods in ear biometrics, periocular recognition, and UAV-based segmentation Active in editorial roles and collaborative research across international teams Advising and Collaborative Research: Frequent collaborator with researchers from NIT Rourkela, University of Beira Interior (Portugal), and other international institutions Co-authored over 120 publications, indicating active supervision and mentoring of students Involved in interdisciplinary projects spanning AI, security, and healthcare Labs and Research Groups: Though not explicitly stated, his work suggests involvement in a computer vision and biometrics lab at NIT Rourkela, focusing on AI-driven solutions for societal and industrial applications.

Yossi Azarمشاهده پروفایل

استاد

• ON-LINE ALGORITHMS
• APPROXIMATION METHODS
• LOAD BALANCING
• +۳ مورد دیگر

Prof. Yossi Azar is a faculty member at the Blavatnik School of Computer Science, Tel-Aviv University, where he holds the academic rank of Professor. He is actively involved in research and teaching in theoretical computer science, particularly in online algorithms, approximation methods, and algorithmic game theory. University: Tel-Aviv University School: Blavatnik School of Computer Science Academic Rank: Professor Email: azar@tau.ac.il His research focuses on ON-LINE ALGORITHMS, APPROXIMATION METHODS, LOAD BALANCING, ROUTING SCHEMES, PROBABILISTIC METHODS, and ALGORITHMIC GAME THEORY . He has made significant contributions to the design and analysis of algorithms for scheduling, routing, and resource allocation under uncertainty and competitive settings. The recent articles reflect a strong trend in theoretical computer science, particularly in online and approximation algorithms, algorithmic game theory, and network optimization. His work often involves competitive analysis, mechanism design, and performance guarantees in scheduling and routing problems. The research spans subfields such as load balancing, unsplittable flow, truthful mechanisms, and network design, with applications in distributed systems and communication networks. Prof. Azar has supervised numerous PhD and MSc students, including prominent researchers such as Leah Epstein and Oded Regev. He teaches advanced courses like Online and Approximation Algorithms and Algorithmic Methods , and has served on program committees of top conferences including SODA, STOC, FOCS, and ESA, often as chair. PhD Students: Leah Epstein, Oded Regev, Yossi Richter, Amir Epstein, Shai Gutner, Iftah Gamzu, Ilan Cohen, Adi Vardi, Danny Vainstein, Noam Touitou, Shahar Lewkowicz, Or Vardi MSc Students: Includes many of his PhD advisees and additional names such as Amitai Armon, Nir Avrahami, Arik Litichevskey, and others. He is an active participant in the academic community, serving as a journal editor for Algorithmica and organizing the Advanced Algorithms Seminar at Tel-Aviv University, which fosters collaboration and dissemination of cutting-edge research in algorithms.

Sami Daviesمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +۵ مورد دیگر

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

Heather Newmanمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Combinatorial Optimization
• Approximation Algorithms
• +۷ مورد دیگر

Heather Newman is an Assistant Professor in the Department of Computer Science at Vassar College. She earned her PhD from the interdisciplinary Algorithms, Combinatorics, and Optimization (ACO) program at Carnegie Mellon University through the Mathematics Department, advised by Ben Moseley. She received her A.B. in Mathematics with a minor in Computer Science from Princeton University in 2019 and an M.Sc. in Mathematical Sciences from Oxford in 2020. Her research lies at the intersection of discrete mathematics, theoretical computer science, and operations research, focusing on approximation and online algorithms for combinatorial optimization problems. She is particularly interested in clustering, scheduling, and developing new beyond-worst-case models for online algorithms with pessimistic lower bounds. The recent publications reflect a strong trend in algorithmic design for optimization problems, especially in clustering (e.g., correlation clustering, k-median), scheduling , and graph-based optimization . Her work emphasizes theoretical rigor with practical implications, often involving novel algorithmic frameworks and combinatorial techniques. She frequently publishes in top-tier theoretical venues such as ICALP, APPROX, SPAA, ICML, and SIGMETRICS. Scientific Awards No awards listed in the provided text. Advising and Grants There is no explicit mention of graduate students or PhD advisees in the provided information. Heather Newman does not list any specific grants or funded projects, but her research output suggests active engagement in theoretical computer science research. She was involved with Moon Duchin’s Metric Geometry and Gerrymandering Group, which applies mathematical methods to redistricting and social justice issues, indicating interdisciplinary outreach and potential collaborative funding. Labs and Research Teams While no formal lab is mentioned, Heather Newman has been affiliated with interdisciplinary research groups, notably the Metric Geometry and Gerrymandering Group led by Moon Duchin. This group brings together mathematicians and computer scientists to analyze gerrymandering through geometric and algorithmic tools. Her primary research collaborations appear to be with leading figures in theoretical computer science, including Ben Moseley, Kirk Pruhs, and Anupam Gupta, suggesting active participation in a broader research network focused on algorithm design and analysis.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.

Armando Solar-Lezamaمشاهده پروفایل

دانشیار

• Software Synthesis
• High-Performance Computing
• Information Flow Security
• +۳ مورد دیگر

Armando Solar-Lezama is an Associate Professor at the Massachusetts Institute of Technology (MIT) where he leads the Computer Aided Programming Group within CSAIL. His research bridges programming languages, formal methods, and machine learning to advance program synthesis techniques for real-world applications. His primary research focuses on software synthesis with applications spanning high-performance computing, information flow security, and probabilistic programming. He investigates how to automatically generate correct and efficient programs from specifications, with recent work emphasizing neurosymbolic approaches that combine neural networks with symbolic reasoning for complex synthesis tasks. Analysis of his publication trends (2021-2025) reveals a strategic evolution from foundational synthesis algorithms toward practical applications in probabilistic programming and security-critical systems. His work consistently integrates machine learning with formal verification, demonstrating growing emphasis on coarse-to-fine synthesis methods and knowledge compilation techniques for discrete probabilistic models. Award highlights: SIGPLAN Milner Award As leader of MIT's Computer Aided Programming Group, Solar-Lezama mentors graduate researchers and collaborates across institutions. His extensive service as Program Chair for POPL 2025 and committee roles at premier conferences (PLDI, POPL, SPLASH) demonstrates significant community leadership. While specific grant details aren't provided, his sustained publication output in top venues indicates robust funding support. He directs the Computer Aided Programming Group at MIT CSAIL, which develops tools for program synthesis, verification, and analysis. Current projects focus on neurosymbolic programming frameworks, probabilistic program synthesis, and security-aware compilation techniques that integrate information flow control with high-performance computing optimizations.

Jan Hamaekersمشاهده پروفایل

پژوهشگر

• Numerical Algorithms
• High-Dimensional Problems
• Sparse Grids
• +۷ مورد دیگر

Dr. Jan Hamaekers is a researcher at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Bonn, Germany. He is affiliated with the development of advanced numerical methods for high-dimensional problems in computational science, particularly in quantum mechanics and molecular dynamics. His work is centered on efficient algorithms such as HCFFT, ESPACK, and TREMOLO, and he collaborates on major research projects funded by DFG and EU programs. Research Interests: Development of hierarchical methods for high-dimensional problems Sparse grid and fast Fourier transform techniques Quantum mechanical and molecular mechanical (QM/MM) methods Numerical algorithms for the electronic Schrödinger equation Parallel software for nanoscale dynamics simulations His recent publications reflect a strong trend in computational materials science, multiscale modeling, and machine learning applications in chemistry. He has contributed to software packages like TREMOLO and ATK-ForceField, and his work bridges applied mathematics, physics, and engineering. He teaches practical labs in numerical simulation, indicating an active academic engagement. Scientific Projects: Molecular Dynamics Simulations for Material Science Applications CODICE - Computationally Driven design of Innovative Cement-based materials (EU FP7) Development of efficient algorithms for nanoscale dynamics simulations Multiscale QM/MM simulations of nanotubes and composites (DFG) Dr. Hamaekers has advised or collaborated with multiple researchers, as seen in his extensive co-authorship. He has not received any explicitly mentioned awards in the provided text. He is involved in software development and algorithmic innovation, with a focus on scalability and efficiency in scientific computing.

Longbing Caoمشاهده پروفایل

استاد

• Data mining
• Machine learning
• Anomaly detection
• +۷ مورد دیگر

Longbing Cao is a prominent academic researcher in data science and artificial intelligence, currently holding dual affiliations with Macquarie University's School of Computing in Sydney, Australia, and the University of Technology Sydney (UTS). He leads the Data Science Lab and has established himself as a leading authority in machine learning, data mining, and anomaly detection research with a prolific publication record spanning over two decades. Dr. Cao's research encompasses multiple critical domains within data science, with particular expertise in deep learning for anomaly detection, time series analysis, recommender systems, and behavior informatics. His scholarly work bridges theoretical foundations with practical applications across diverse industries including finance, cybersecurity, and e-commerce. He has pioneered innovative approaches in coupled behavior analysis, representation learning, and cross-domain collaborative filtering that have significantly advanced these fields. His research demonstrates a consistent trajectory from foundational data mining techniques to sophisticated deep learning methodologies. His publication record shows remarkable impact with 240 publications as of 2025 and 4,725 total citations. His highly influential survey papers have shaped research directions across multiple domains, with "Deep Learning for Anomaly Detection: A Review" (2021) accumulating over 1,600 citations and 25,586 downloads, establishing it as a seminal reference in the field. His recent work (2025) focuses on cutting-edge topics including diffusion models (SepDiff, ProgDiffusion), dynamic spectral graph analysis, and non-stationary time series modeling, reflecting his position at the forefront of AI research evolution. Data Science: A Comprehensive Overview (2017) - 298 citations, 41,677 downloads Data science: challenges and directions (2017) - 87 citations, 31,525 downloads AI in Finance: Challenges, Techniques, and Opportunities (2022) - 201 citations, 21,892 downloads A Survey on Session-based Recommender Systems (2021) - 346 citations, 4,563 downloads As a research leader, Dr. Cao has mentored numerous students and collaborators, contributing significantly to the development of next-generation data science researchers. His Data Science Lab serves as an interdisciplinary hub where computer scientists, statisticians, and domain experts collaborate to tackle complex data challenges. The lab's research spans theoretical advancements in machine learning algorithms to practical implementations with tangible real-world impact across multiple sectors.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

• Clinical Natural Language Processing
• Mental Health Informatics
• Temporal Information Extraction
• +۵ مورد دیگر

Dr. Sumithra Velupillai is a Research Fellow at King's College London's Institute of Psychiatry, Psychology & Neuroscience, Department of Psychological Medicine. With a PhD from Stockholm University completed in 2012, she has established herself as a leading researcher in clinical natural language processing with a specific focus on mental health applications. Her work bridges computer science, linguistics, and clinical medicine to extract meaningful insights from electronic health records. Dr. Velupillai's research interests center on clinical natural language processing, particularly for mental health applications. She specializes in temporal information extraction from clinical narratives, uncertainty detection in medical text, and developing NLP methods for analyzing mental health records. Her work has significant implications for improving clinical decision support systems, mental health diagnostics, and patient care through advanced text mining techniques. She has made substantial contributions to developing lexicons, annotation schemes, and computational models specifically tailored for healthcare contexts. Her recent publications demonstrate a strong trajectory in applying cutting-edge NLP techniques to mental health challenges, with particular emphasis on pain detection in mental health records, suicide risk assessment, and temporal reasoning in clinical narratives. Velupillai's work consistently addresses methodological challenges in healthcare NLP while maintaining clinical relevance. Dr. Velupillai maintains active collaborations with leading researchers in mental health informatics, including Robert Stewart, Angus Roberts, and Rina Dutta at King's College London. Her research is frequently published in top venues for biomedical informatics and computational linguistics, reflecting the interdisciplinary nature of her work.