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Teemu RoosView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .

İsmail ÖZTELView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Image Processing
• +3 more

Dr. İsmail ÖZTEL serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. He has been a faculty member since 2019, following his tenure as a Research Assistant from 2012-2019 at the same institution. His educational background includes: Doctorate in Computer and Information Engineering (2014-2018) from Sakarya University with thesis on "Facial expression detection on partial and full face images using machine learning methods" Master's Degree in Computer and Information Engineering (2012-2014) from Sakarya University with thesis on "Driver simulator for educational purposes" Bachelor's Degree in Computer Engineering (2007-2011) from Sakarya University Dr. ÖZTEL's research focuses on artificial intelligence, deep learning, and computer vision with significant applications in healthcare, mobile technology, and public safety. His work has evolved from foundational facial expression recognition to sophisticated medical applications including skin disease classification using smartphones, monkeypox detection from skin lesions, and pandemic response systems for face mask detection. His research demonstrates strong interdisciplinary connections between computer science and healthcare. An analysis of his publication trends reveals a clear progression toward increasingly complex deep learning architectures applied to real-world problems, with recent work emphasizing medical applications using mobile technology and public health safety systems. His 2023-2025 publications show particular focus on skin disease classification, intelligent vehicle systems, and hybrid feature extraction methods for pandemic response. Dr. ÖZTEL has served as a reviewer for numerous prestigious journals including Expert Systems With Applications (multiple years), World Wide Web, Multimedia Tools and Applications, and Journal of King Saud University - Computer and Information Sciences, demonstrating his recognition in the academic community across multiple domains. His research projects include work on facial expression detection in open scientific databases (2020), performance evaluation of transfer learning approaches (2019), and current projects on brain tumor classification systems and earthquake safety education for individuals with developmental disabilities. His international research experience includes collaboration with Filiz Bunyak in 2017, indicating global engagement in his field.

• Databases
• Data Mining
• Information Retrieval
• +2 more

Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650

Mike WillisView profile

Associate Professor

• Cryospheric Science
• Geodesy
• Remote Sensing
• +3 more

Mike Willis is an Associate Professor of Cryospheric Science, Geodesy & Remote Sensing at the Department of Geosciences, Virginia Tech's College of Science. His research focuses on advancing remote sensing techniques to study Earth's cryosphere, coastal environments, and natural hazards. He leads projects on glacial dynamics, sea level rise impacts, landslide monitoring, and the application of GNSS and InSAR technologies. His work combines field instrumentation (e.g., OrangePi-GNSS systems) with satellite data analysis to address climate and geohazard challenges in polar regions and coastal communities. Education background: Not explicitly stated in provided text. Research interests include cryospheric science, geodetic monitoring, and environmental remote sensing applications. His lab group maintains a dedicated website at https://sites.google.com/view/4datvt/ . Key research themes include: Glacier and ice shelf mass balance Coastal vulnerability to sea level rise Disaster monitoring using GNSS-IR and InSAR High-resolution digital terrain modeling Recent projects highlight work in Greenland (landslide monitoring, sea level measurements), the Pacific Northwest (subduction zone hazards), and the Dominican Republic (coastal inundation modeling). His interdisciplinary approach bridges geophysical observations with computational geospatial analysis.

Nivee Pradip AminView profile

Assistant Clinical Professor

• Cardiovascular Disease
• Women's Cardiovascular Health
• Preventive Cardiology
• +3 more

Dr. Nivee Pradip Amin serves as Clinical Assistant Professor of Medicine at Weill Cornell Medical College and Assistant Attending Physician at NewYork-Presbyterian Hospital, holding leadership roles as Associate Director for Consultative Cardiology and Director of the Women's Heart Program & Preventive Cardiology. Her clinical expertise spans general cardiology, preventive cardiology, and echocardiography with focus on high-risk cardiovascular prevention. Her educational credentials include: B.A., B.S. in International Studies and Business from University of Pennsylvania (2002) M.D. from Johns Hopkins University School of Medicine (2007) M.H.S. in Clinical Investigation from Johns Hopkins Bloomberg School of Public Health (2013) Research centers on sex-based disparities in cardiovascular outcomes, women's heart health across the lifespan, and preventive strategies for coronary/valvular disease. She investigates intersections between cardiology and obstetrics/oncology, emphasizing personalized risk assessment and early intervention to improve quality of life. Analysis of her publications reveals consistent focus on women's cardiovascular biomarkers (e.g., breast arterial calcium), obstetric complications as long-term risk factors, and disparities in revascularization outcomes. Her work leverages large-scale database analysis to address gaps in preventive care for high-risk populations including cancer patients and those with cirrhosis. Dr. Amin maintains active board certifications in Internal Medicine, Cardiovascular Diseases, Nuclear Cardiology, and Echocardiography through the American Board of Internal Medicine, and holds professional memberships in the American College of Cardiology, American Society for Preventive Cardiology, and American Heart Association. She provides inpatient consultative care on cardiac telemetry units while leading the Women's Heart Program, which integrates preventive cardiology services with gender-specific treatment protocols. Fluent in Spanish, she emphasizes culturally competent care tailored to individual patient needs across diverse populations.

• Photosynthesis
• Global Change Biology
• Protected Cropping
• +4 more

Professor Oula Ghannoum is a renowned plant scientist and academic leader at the Hawkesbury Institute for the Environment (HIE), Western Sydney University. She serves as Director of the ARC Training Centre for Smart and Sustainable Horticulture and holds leadership roles, including Biological Sciences Discipline Lead and Associate Editor at Functional Plant Biology . Her research focuses on photosynthesis, global change biology, and protected cropping, aiming to enhance food security and climate resilience through crop improvement and sustainable agricultural practices. Education: BSc (Honours) in Plant Biochemistry, University of NSW (1993) PhD in Plant Physiology, Western Sydney University (1998) Research Interests: Professor Ghannoum’s work addresses global challenges like food security and climate change by exploring plant responses to environmental stress. Her lab uses advanced technologies like smart glasshouses and hyperspectral imaging to optimize crop yield and quality. Key areas include sugar signaling pathways in C3/C4 plants, water use efficiency, and heat tolerance in cereal crops. Grants & Funding: She has secured over $25M in research funding, leading projects on C4 photosynthesis, automated crop monitoring, and protected cropping systems. Notable collaborations include the ARC Centre of Excellence for Translational Photosynthesis and Future Food Systems CRC. Awards: 2024 Vice-Chancellor’s Excellence in Research Award 2023 Education and Outreach Award (Australian Society of Plant Scientists) 2001 ARC Postdoctoral Fellowship Labs & Teams: Her team develops innovative frameworks for sustainable horticulture, combining biology with AI and smart technologies. Ongoing work includes imaging-based crop monitoring and phenotyping for climate-resilient crops.

Ying CaiView profile

Associate Professor

• AI
• Machine Learning
• Data Science
• +4 more

Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.

Dino PedreschiView profile

Professor

• Big Data Analytics
• Social Network Analysis and Mining
• Analysis of Human Mobility
• +17 more

Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.

Roxana GeambasuView profile

Associate Professor

• Computer Systems
• Distributed Systems
• Security and Privacy
• +8 more

Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.

Sally GibsonView profile

Researcher

• Geochemistry
• Volatiles cycling in Earth's mantle
• Mantle and volcanic processes
• +3 more

Sally Gibson is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in mantle geodynamics and volatile cycling processes. Her work integrates field observations, geochemical analysis, and numerical modeling to investigate how deep Earth processes influence surface environments over 3.5 billion years of planetary evolution. Research focuses on volatile cycling (CO₂, H₂O, F, Cl, S) in mantle systems Key projects include mantle plume-ridge interactions with collaborators in the US and Ecuador Operates a LA-ICP-MS laboratory for high-resolution geochemical analyses Supervises PhD students in petrology, geochemistry, and numerical modeling Her research addresses fundamental questions about Earth's habitability through studies of mantle-derived volatiles critical for climate regulation and energy transition metal deposits. Fieldwork in remote regions like Antarctica, Lesotho, and the Galápagos Islands provides empirical data for her interdisciplinary approach. Recent publications highlight her expertise in mantle xenolith analysis, plume dynamics, and volatile quantification in large igneous provinces. Her group's work combines 3He/4He isotopic analysis with seismic tomography to constrain lithospheric evolution and mineral deposit formation. Students under her supervision develop expertise in petrology and geochemical modeling while engaging with environmental and societal impacts of geological research. She actively promotes scientific outreach and community engagement, fostering connections between academia and broader society.

Duminda WijesekeraView profile

Professor

• Cybersecurity
• Cyber-Physical Systems
• Digital Forensics
• +5 more

Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.

• Differential Privacy
• Machine Learning
• Privacy-Preserving Data Analysis
• +3 more

Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.

Manfred DrosteView profile

Professor

• Theoretical Computer Science
• Automata Theory
• Logic
• +6 more

Manfred Droste is a Professor at the Institute of Computer Science of the University of Leipzig, where he leads the Research Group on Automata and Formal Languages. He serves as Director of the Graduate Centre Mathematics, Computer Science and Natural Sciences and is Vice-speaker of the DFG-Research Training Group Quantitative Logics and Automata. His academic career spans decades of research and leadership in theoretical computer science and algebra. Prof. Droste's research focuses on theoretical computer science, particularly automata theory, logic, algebraic models for concurrent systems, and domain theory. In algebra, his interests include model theory, automorphism groups, and ordered algebraic structures. His work bridges theoretical foundations with practical applications in formal language theory and quantitative systems. His extensive publication record demonstrates a consistent focus on weighted automata, formal languages, and their logical characterizations. Over the years, his research has evolved to address increasingly complex quantitative models, with recent work focusing on weighted complexity classes, weighted linear dynamic logic, and decidability boundaries for weighted automata. Prof. Droste has received significant recognition including election to Academia Europaea, an honorary doctorate from Immanuel Kant Baltic Federal University, and fellowship in the Asia-Pacific Artificial Intelligence Association. These honors reflect his substantial contributions to theoretical computer science. He has supervised numerous PhD students including Dietrich Kuske, Paolo Boldi, and Karin Quaas, many of whom have become prominent researchers. His extensive grant portfolio includes multiple DFG projects on weighted automata and international collaborations through DAAD funding. Prof. Droste leads a vibrant research team including Andrea Hesse, Karin Quaas, Erik Paul, and others. He has organized the international workshop series "Weighted Automata: Theory and Applications" since 2002, fostering global collaboration in this specialized field.

Alan BovikView profile

Professor

• Image quality
• Video compression
• Streaming video
• +7 more

Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.

Jennifer WidomView profile

Professor

• Nontraditional Data Management
• Data Streams
• Crowdsourcing Quality Management
• +3 more

Jennifer Widom is the Frederick Emmons Terman Dean of Stanford University's School of Engineering and holds the Fletcher Jones Professorship in Computer Science and Electrical Engineering. She previously served as Chair of the Computer Science Department (2009–2014) and Senior Associate Dean (2014–2016). Widom earned her Ph.D. in Computer Science from Cornell University (1987) and completed her undergraduate degree in Music at Indiana University (1982). She joined Stanford in 1993 after research at IBM Almaden. Education: Ph.D., Computer Science, Cornell University, 1987 MS, Computer Science, Cornell University, 1985 MS, Computer Science, Indiana University, 1983 BS, Music, Indiana University Jacobs School of Music, 1982 Research Interests: Widom's work focuses on nontraditional data management, including data streams, uncertain databases, crowdsourcing, and query processing systems like STREAM and Deco . She has pioneered methods for managing and querying uncertain data, optimizing graph algorithms, and integrating human computation into data systems. Key Contributions: Developed the STREAM system for real-time data stream management Advanced techniques for crowdsourcing quality management Contributed to foundational work in uncertain databases and provenance tracking Awards & Recognition: ACM Fellow (2005) Member, National Academy of Engineering (2005) Edgar F. Codd Innovations Award (2007) ACM-W Athena Lecturer (2015) EPFL-WISH Erna Hamburger Prize (2018) Teaching & Leadership: Widom teaches courses on data analytics and database systems, advising students like Arnav Joshi. She has led major initiatives in computational education and institutional leadership at Stanford.