جست‌وجوی استاد و پژوهشگر

Oleg Zendelمشاهده پروفایل

پژوهشگر ارشد

• Information systems
• Data management and data science
• Information retrieval and web search
• +۱ مورد دیگر

Dr. Oleg Zendel is a Research Fellow at RMIT University's Research & Innovation department, based at the City Campus in Australia. His academic focus lies in advanced information systems and data science, with expertise in query performance prediction, human information interaction, and large language models (LLMs) applied to information retrieval. He holds a Ph.D. in Computer Science from RMIT University (2020–2024). Research Interests: His work bridges theoretical and applied aspects of information retrieval, emphasizing practical frameworks for evaluating query effectiveness and leveraging LLMs to enhance interactive systems. Key areas include conversational AI, automated annotation, and data-driven methodologies for IR optimization. Collaborations: Open to collaborative projects, industry partnerships, and supervising Masters/PhD students in his research domain. He actively participates in organizing workshops like the Search-Oriented Conversational Artificial Intelligence (SCAI) and contributes to events such as ACM SIGIR and ECIR conferences. Publications: Recent work explores LLM evaluation validity, entropy-based neural IR systems, and simplified query performance prediction tools like QPPTK. His publications reflect a trend toward integrating AI advancements into scalable, user-centric IR solutions.

• Music Processing
• Music Information Retrieval
• Audio Signal Processing
• +۱ مورد دیگر

Prof. Dr. Meinard Müller is a Professor at Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), leading the Chair of Semantic Audio Signal Processing within the International Audio Laboratories Erlangen (a joint institute with Fraunhofer IIS). He specializes in Music Processing, Music Information Retrieval, Audio Signal Processing, and Motion Processing. His work bridges fundamental research and practical applications in music technology, including audio decomposition, source separation, and computational musicology. Research Interests: Focus on algorithmic methods for music analysis, audio feature extraction, and real-time audio processing. Teaching: Supervises a large group of doctoral and master’s students, offering courses on audio processing and music technology. Awards: Recognized as an IEEE Fellow (2020) and ACM Senior Member (2020). His research group develops open-source tools like the FMP Notebooks and libfmp, and maintains datasets such as the Wagner Ring Dataset and Dagstuhl ChoirSet. He actively contributes to conferences like ISMIR and ICASSP, with over 200 publications in top-tier journals and conferences.

Pooja Raniمشاهده پروفایل

پژوهشگر ارشد

• Empirical Software Engineering
• Green Software Engineering
• Natural Language Processing
• +۴ مورد دیگر

Pooja Rani is a Senior Researcher at the Software Evolution and Architecture Lab (SEAL), Department of Informatics, University of Zurich. She holds a PhD from the University of Bern (2022) and an M.E. in Software Systems from BITS Pilani (2017). With industry experience at Samsung, VMware, and People Interactive Ltd., she bridges theoretical and practical software engineering. Her research focuses on: Empirical Software Engineering, particularly developer practices in code comprehension and maintenance. Green Software Engineering, including energy anti-patterns and sustainability tooling. NLP and machine learning applications in software evolution, documentation quality, and polyglot environments. AI-driven approaches for debugging, testing, and optimization. Her publications emphasize energy efficiency in software systems, AI-assisted development tools, code comment analysis, and metamorphic testing. Recent work explores LLMs for sustainable coding, object-centric debugging, and autonomous system validation. She actively advises students, with ongoing supervision of 15+ BSc/MSc theses on topics like comment quality, energy anti-patterns, and polyglot development. She collaborates internationally with institutions including TU Delft, Inria, and the University of Victoria.

Martin Szummerمشاهده پروفایل

پژوهشگر

• Deep Learning
• Probabilistic Models
• Reinforcement Learning
• +۹ مورد دیگر

Martin Szummer is a Researcher at the University of Cambridge, UK, and part of the Spoken Dialogue Systems group. Previously, he worked at Microsoft Research in Cambridge (Machine Learning and Perception Group). He holds a Ph.D. in Machine Learning from MIT (2002) and an M.S. from the MIT Media Lab. Research Focus: Szummer’s work spans probabilistic models, deep learning, reinforcement learning, and natural language processing. He explores methods to improve interactions with users through dialogue systems and develops techniques to manage complex data via Bayesian inference, semi-supervised learning, and structured prediction. His contributions include probabilistic programming frameworks, big data processing on cloud clusters, and applications in text mining, image recognition, and computational advertising. Notable Achievements: Recipient of the SIGDIAL 2013 Best Paper Award for dialogue system adaptation research. Developed the Temporal Texture database, foundational for video analysis and synthesis. Collaborations & Mentoring: Advised prominent students like Percy Liang (Stanford) and Volodymyr Mnih (DeepMind). His work at Microsoft Research led to innovations in POMDP-based dialogue systems and semi-supervised learning. Technical Contributions: Publications on dialogue systems, probabilistic modeling, and web-scale data mining. Authored datasets like the Temporal Texture collection, widely used in computer vision research.

Balder ten Cateمشاهده پروفایل

دانشیار

• Logic in computer science
• Data management
• Knowledge representation
• +۵ مورد دیگر

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

• Computer Vision
• Vision and Language
• 3D Human Motion Synthesis
• +۵ مورد دیگر

Gül Varol is an Associate Professor (permanent researcher) in the IMAGINE group at École des Ponts ParisTech. She holds an ELLIS Scholar distinction and collaborates with MPI. Previously, she was a postdoc at the University of Oxford’s Visual Geometry Group (VGG). Her PhD from Inria Paris/ENS under Ivan Laptev and Cordelia Schmid earned her ELLIS and AFRIF awards. She earned BS/MS degrees from Boğaziçi University. Research Focus: Her work bridges computer vision and language, emphasizing video understanding, human motion synthesis, and sign language technologies. Notable contributions include the BOBSL dataset (BSL), SLRTP challenge, and methods for sign language translation/contextual retrieval. She also pioneers text-driven 3D motion editing (MotionFix) and video chaptering with LLMs (Chapter-Llama). Awards & Service: Received Google Research Scholar Award (2023), ECCV 2024 Program Chair, IJCV associate editor, and leadership in workshops like NeurIPS’20 Preregistration and CVPR’25 Sign Language. Organized over 15 conferences/workshops since 2017. Labs & Teams: Core member of IMAGINE (LIGM lab), collaborates with MPI and industry (Google/Adobe). Active in outreach initiatives like Women in CV and Girls Who Code.

• Irrigation Engineering
• Irrigation Water Management
• Machine Learning in Irrigation Management
• +۷ مورد دیگر

Sumon Datta is an Assistant Professor and Irrigation Engineer in the Department of Biosystems and Agricultural Engineering at Oklahoma State University. His work focuses on smart-engineered technologies for irrigation water management, particularly for major crops in Oklahoma. He is actively involved in the Master Irrigator Program, conducts irrigation uniformity tests, and organizes the Oklahoma Irrigation Conference. He collaborates with the Oklahoma Water Resources Center and producer focus groups to promote efficient water use. Education: PhD, Biosystems Engineering, Oklahoma State University (2017–2020) MS, Agricultural and Biosystems Engineering, North Dakota State University (2015–2016) BS, Agricultural Engineering, Bangladesh Agricultural University (2010–2014) Dr. Datta's research interests include irrigation engineering, water conservation, machine learning in irrigation management, and hydrological impacts of irrigation. He emphasizes the development of packaged irrigation management modules to improve water use efficiency. His scholarly work spans irrigation scheduling, soil water sensors, remote sensing, and groundwater sustainability. The recent publications highlight a strong trend toward data-driven modeling, sensor performance evaluation, and region-specific irrigation practices, particularly in the Southern High Plains and Oklahoma. He has received funding from the U.S. Department of Agriculture, U.S. Geological Survey, Cotton Incorporated, and Kansas State University for projects on sustainable irrigation, climate adaptation, and smart water conservation technologies. His work supports Sustainable Development Goals related to clean water, zero hunger, climate action, and quality education. Professional Affiliations: Member, American Society of Agricultural and Biological Engineers (ASABE) Member, American Society of Civil Engineers (ASCE) Member, Evapotranspiration, Irrigation, and Hydrology Committee (since 2020) Dr. Datta teaches courses such as Agricultural Water Management and Advanced Techniques in Irrigation Water Management. He supervises master's and doctoral research, guiding students in irrigation technology and water conservation. He also contributes to extension publications and outreach, enhancing farmer education and adoption of best practices.

Markus Schedlمشاهده پروفایل

استاد

• Recommender Systems
• Information Retrieval
• Algorithmic Fairness
• +۶ مورد دیگر

Markus Schedl is a Full Professor at Johannes Kepler University Linz , Austria, where he leads the Multimedia Mining and Search (MMS) group within the Institute of Computational Perception . He also heads the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab . Education: Computer Science (TU Wien), PhD (JKU Linz), Master's in International Business Administration (WU Wien, University of Gothenburg) Research Interests span recommender systems , information retrieval , algorithmic fairness , user modeling , and machine learning . His recent publications focus on: Multimodal and hybrid AI for human-centric personalization Context-aware music recommendation and emotion recognition Graph neural networks for session-based recommendation Technical and regulatory compliance in fair, transparent, and privacy-preserving systems Projects are funded by the Austrian Science Fund (FWF) , Austrian Research Promotion Agency (FFG) , and European Commission (EC) . He maintains industry collaborations with Siemens, Spotify, and Deezer. Teaching includes courses like Introduction to Machine Learning and Social Media Mining and Analysis at JKU, with guest lecturing experiences at Pompeu Fabra, Queen Mary London, and KTH Stockholm. Labs & Teams: MMS group at JKU's Institute of Computational Perception and HCAI group at LIT AI Lab.

Loïc Lannelongueمشاهده پروفایل

پژوهشگر ارشد

• Environmentally sustainable computing
• Biomedical data science
• Carbon footprint of computational research
• +۳ مورد دیگر

Loïc Lannelongue is a Senior Research Associate at the University of Cambridge, affiliated with the Cardiovascular Epidemiology Unit in the Department of Public Health and Primary Care (School of Clinical Medicine) and the VPD Heart and Lung Research Institute. He serves as a Postdoctoral Associate at Jesus College and is a visiting scientist at EMBL-EBI, leading research at the critical intersection of sustainable computing and biomedical data science. His academic foundation includes: BSc from ENSAE Paris (2017) MSc (Diplôme d’Ingénieur) from ENSAE Paris (2018), majoring in mathematics and statistics with minors in theoretical physics and economics MSc in Statistics and Machine Learning from the University of Oxford (2018) PhD in Health Data Science from the University of Cambridge (2022), supervised by Prof Michael Inouye under MRC-DTP funding Lannelongue’s pioneering work centers on environmentally sustainable computational science , developing tools like the Green Algorithms calculator to quantify and reduce computing’s carbon footprint. His research bridges hardware infrastructure limitations with scientific practice, focusing on AI ethics, carbon accounting methodologies, and policy frameworks like the GREENER Principles. Concurrently, he integrates machine learning with genomics and medical imaging to decode cardiovascular disease mechanisms, exemplified by his Nature-published multi-omic trait atlas. His publication portfolio reveals a decisive shift toward sustainability metrics in computational research, with 2023 marking a peak in high-impact policy-oriented work (Nature journals, Heidelberg Agreement). Key trends include standardizing carbon footprint methodologies, advocating for green procurement in research funding, and translating environmental impact data into actionable researcher guidelines through the Green DiSC certification. Accolades recognizing his dual impact: Susannah Boddie Impact of the Year Award (Health Data Research UK, 2024) for sustainability leadership Software Sustainability Institute Fellowship (2023) Cambridge University Sport’s Outstanding Contribution Award (2022) As an educator, he supervises Computer Science tripos courses in statistics, AI, and bioinformatics while mentoring through the Green Algorithms Initiative. His research is supported by MRC-DTP funding during his PhD and ongoing institutional backing for the Lannelongue Group, which collaborates with European funders to embed sustainability criteria in grant frameworks. He directs the Lannelongue Group—a hub for Environmentally Sustainable Computational Science—managing the Green Algorithms Initiative (open-source carbon calculators), Green DiSC certification scheme, and ESCS Community. The group’s work bridges hardware engineers and domain scientists to operationalize sustainability, evidenced by tools adopted globally and policy agreements like the Heidelberg Agreement signed by major European research funders.

William W. Cohenمشاهده پروفایل

استاد مهمان

• Machine Learning
• Natural Language Processing
• Neuro-Symbolic Reasoning
• +۵ مورد دیگر

William W. Cohen is a Visiting Professor at Carnegie Mellon University's Machine Learning Department and holds a 20%-time appointment at Google. He earned his PhD in Computer Science from Rutgers University in 1990 and has held roles at AT&T Bell Labs, Whizbang Labs, and CMU. His research focuses on machine learning, NLP, neuro-symbolic reasoning, and knowledge representation. Education: B.S. (Duke, 1984), PhD (Rutgers, 1990). Research Interests: Cohen's work spans question answering, NLP tasks, and neuro-symbolic systems. He emphasizes scalable reasoning methods and has contributed to systems like Never-Ending Learning (NELL) and knowledge graph construction. Recent efforts include improving large language model evaluation and retrieval-augmented generation. Awards: AAAI Fellow (2006), 2008 SIGMOD Test of Time Award, 2014 SIGIR Test of Time Award, 2023 Semantic Web Science Ten-Year Award. His work on subtopic retrieval (SIGIR 2003) and data integration (SIGMOD 1998) are foundational. Grants & Advising: Supervised over 50 students, including notable figures like Bhuwan Dhingra (now at Duke) and Zhilin Yang. Active in funding projects related to AI ethics, knowledge graphs, and scalable learning systems. Labs & Teams: Core contributor to the NELL project and collaborator on initiatives like the Knowledge Vault and Open Information Extraction systems. Currently involved in developing robust AI evaluation frameworks and multimodal reasoning systems.

Alaa Nehmeمشاهده پروفایل

استادیار

• Information Security
• IT Threats
• Cybersecurity
• +۳ مورد دیگر

Alaa Nehme is an Assistant Professor of Information Systems at Mississippi State University's College of Business. He holds a Ph.D. from Iowa State University and an MBA and B.S. in Computer Science from Lebanese American University. His research focuses on information security, IT threats, and IS/social issues, with publications in top journals like JMIS and Computers & Security. He teaches Data Analytics, Cybersecurity, and Management Information Systems at both graduate and doctoral levels. Dr. Nehme's scholarly contributions include work on password manager adoption, cybersecurity communication fatigue, and smart home security. He actively serves as an associate editor for ECIS 2025 and has chaired tracks at MENA and Mediterranean conferences. His teaching evaluations highlight his effectiveness in applying real-world examples and project-based learning. He has reviewed for major IS journals/conferences and contributed to NSF grant advisory boards. His service includes program committee roles for EuroUSEC, HICSS, and others. He maintains a personal website at https://www.alaanehme.com/.

Max Panditمشاهده پروفایل

مدرس ارشد

• Machine Learning
• AI in Computer Games
• Biomedical Signal Processing
• +۳ مورد دیگر

Dr. Max Pandit is a Senior Lecturer in Computer Game Design at Teesside University's Department of Computing & Games. He holds a PhD in applied artificial intelligence for biomedical signal processing from Northumbria University (2017). His expertise spans machine learning, AI in games, evolutionary optimization, and biomedical applications. He specializes in integrating Unreal Engine and visual scripting for game development. Research Interests include Machine Learning , AI-driven Game Development , Biomedical Signal Processing , and Deep Learning . His work explores applications such as brain-computer interfaces and ensemble models for medical signal analysis. Recent projects include: Visual Machine Learning Plugin for Unreal Engine 4 (2024, ongoing) Play2Secure: AI-enabled cybersecurity awareness game (2019) HackEscape : Cybersecurity awareness using serious games (2021) Collaborations focus on virtual training environments, digital twins, and procurement optimization in industry. He has led or co-led 5 funded research projects totaling over £1.5M in external funding.

Svjetlana Curcicمشاهده پروفایل

دانشیار

• Special Education
• Literacy Education
• Technology Integration
• +۳ مورد دیگر

Svjetlana Curcic is an Associate Professor of Special Education and instructor in the Elementary Education program at the University of Mississippi's School of Education, Department of Teacher Education. She holds a Ph.D. in Special Education from Michigan State University and an Ed.D. in International and Multicultural Education from the University of San Francisco. Her research focuses on literacy and technology integration to support students with learning disabilities, second language learners, and marginalized populations like Roma students. She has supervised doctoral students and taught courses such as Special Education Law & Procedures and Strategies for the Inclusive Classroom. Her education includes B.A. degrees in English Literature and Journalism from the University of Zagreb (1980), an M.A. in Communicating Arts from the University of Wisconsin-Superior (1981), and advanced degrees in multicultural and special education. Her work emphasizes inclusive practices, policy analysis, and digital tools for diverse learners. Her articles explore topics like preservice teacher writing interventions, Roma education policy, and cross-cultural technology adoption in K-12 settings. She is based at the Tupelo Campus and her office is 333A. Curcic’s research trends span special education pedagogy, multicultural curriculum design, and leveraging technology to address educational disparities. Her recent work highlights collaborative approaches between teacher education programs and writing centers, as well as systemic challenges in inclusive education policies. She has authored over 20 peer-reviewed publications and book chapters in journals like TESOL Quarterly and International Journal of Inclusive Education .

Zhenkai Zhangمشاهده پروفایل

استادیار

• Computer Systems Security
• Cyber-Physical Systems
• Hardware Security
• +۴ مورد دیگر

Zhenkai Zhang is an Assistant Professor at the School of Computing, Clemson University. Previously, he held an academic position at Texas Tech University (2019–2021). His research focuses on computer systems security, cyber-physical systems, and hardware vulnerabilities. He holds a Ph.D. from Vanderbilt University (supervised by Prof. Xenofon Koutsoukos), an M.S. from the University of Chinese Academy of Sciences, and a B.S. from Beijing Institute of Technology. His research interests include GPU security, side-channel attacks, memory corruption exploits, and defensive techniques leveraging physical layer information. Recent work involves GPU cache attacks, DNN backdoor insertion, and electromagnetic side-channel exploitation. Received "Distinguished Reviewer" award at CCS'24 . Notable contributions include Graphics Peeping Unit (Oakland 2022), Leveraging EM for Rowhammer Detection (Oakland 2020), and BitJabber (HOST 2020). Current Ph.D. advisees include Hongyue Jin and Fatemeh Moradihaghighi. Alumni Zihao Zhan and Sisheng Liang now hold academic positions. Active grants include NSF projects on rowhammer defenses and secure CPS control. Professional activities include TPC roles at CCS, Oakland, and EuroS&P, plus editorial and organizational roles in security conferences.

• Database Systems
• Big Data Analytics
• XML Processing
• +۷ مورد دیگر

Leonidas Fegaras is an Associate Professor in the Computer Science and Engineering Department at the University of Texas at Arlington's College of Engineering, where he has been employed since 1996, initially as Assistant Professor until 2002 when he was promoted to Associate Professor. His academic journey began with a BEE in Engineering from the National Technical University (1902), followed by an MS in Electrical & Computer Engineering from the University of Massachusetts Amherst (1903), and culminated with a PhD in Computer Science from the same institution in 1992. His educational background includes: PhD in Computer Science, University of Massachusetts Amherst, 1992 MS in Electrical & Computer Engineering, University of Massachusetts Amherst, 1903 BEE in Engineering, National Technical University, 1902 Fegaras's research spans multiple domains within computer science, with a strong emphasis on database systems and big data analytics. His work focuses on developing innovative frameworks for processing large-scale data, particularly in XML, array-based computations, and graph analytics. He has pioneered approaches for query optimization in distributed environments, stream processing, and translation of high-level programming constructs to efficient distributed execution. More recently, his research has expanded into healthcare applications, applying data analytics techniques to clinical data for improved patient outcomes and healthcare decision-making. His interdisciplinary work bridges computer science with healthcare, criminal justice, and environmental science domains. Analysis of his recent publications reveals a clear evolution in his research focus from traditional database systems and XML processing toward modern big data analytics frameworks. His work now heavily emphasizes distributed computing models, particularly leveraging Spark and SQL-based distributed processing. A significant portion of his recent work applies these techniques to healthcare analytics, demonstrating his ability to translate theoretical database concepts into practical applications that address real-world problems in medicine and social sciences. His research shows consistent innovation in query processing techniques across evolving data paradigms. Fegaras has been actively involved in mentoring the next generation of computer scientists, serving as dissertation committee chair for numerous PhD students and supervising many master's theses. His research has been supported by substantial funding from federal agencies including the National Science Foundation and the U.S. Department of Education, with projects totaling over $2 million. Notable grants include the GAANN Doctoral Fellowships in Computer Science and Engineering and several NSF-funded projects focused on big data analytics and database systems. As an educator, Fegaras teaches advanced courses in compilers, web data management, and cloud computing & big data. He has held significant administrative roles including Chairperson of the CSE Graduate Studies Committee since 2009 and membership on the University Graduate Curriculum Committee. His service extends to professional program committees and conference organization, demonstrating his active engagement with the broader computer science research community.