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Shaileshh Bojja VenkatakrishnanView profile

Assistant Professor

• Blockchain Networking
• Cryptocurrency Algorithms
• Network Systems
• +5 more

Shaileshh Bojja Venkatakrishnan is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. His research spans multiple domains at the intersection of networking, distributed systems, and machine learning with a particular focus on blockchain technologies and cryptocurrency networks. Educational Background: PhD in Computer Science from University of Illinois Urbana-Champaign (UIUC), 2017 Bachelor's degree from Indian Institute of Technology Madras, 2012 Dr. Bojja Venkatakrishnan's research interests center around fundamental algorithmic questions at the networking layer of Bitcoin and other cryptocurrency networks. He is particularly interested in applying machine learning techniques for developing algorithms over networks and graphs, with emphasis on 'first-principles' thinking to solve complex distributed systems problems. His work extends to scheduling algorithms for data center networks, peer-to-peer networks, and information theory. The breadth of his research is evident in his publications spanning blockchain networking, payment channel networks, decentralized applications, and machine learning applications for network optimization. Research Impact: Recipient of the Joan and Lalit Bahl fellowship at UIUC His publication record demonstrates significant contributions to the field, with papers appearing in top venues including SIGCOMM, NSDI, SIGMETRICS, and IEEE Transactions. His recent work focuses on enhancing blockchain scalability through peer sampling techniques, transaction reordering mitigation, and efficient peer-to-peer network design for decentralized applications. The trajectory of his research shows a consistent focus on addressing fundamental challenges in distributed systems with practical implications for real-world blockchain implementations and network architectures.

Marta ZanioloView profile

Assistant Professor

• Water Resources Planning and Management
• Drought Modeling
• Climate Change Adaptation
• +5 more

Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.

Alessandro Lameiras KoerichView profile

Professor

• Machine Learning
• Computer Vision
• Pattern Recognition
• +6 more

Alessandro Lameiras Koerich is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ÉTS), part of the Université du Québec network in Montreal, Canada. He is affiliated with the LIVIA (Imaging, Vision and Artificial Intelligence Laboratory), where he conducts research in machine learning, computer vision, and pattern recognition. His work focuses on developing AI solutions for real-world applications in healthcare, security, and multimedia analysis. His research interests span multiple domains of artificial intelligence, with particular emphasis on multimodal machine learning, affective computing, and trusted machine learning systems. Dr. Koerich's work explores how AI can interpret human emotions through facial expressions and audio analysis, develop robust models resistant to adversarial attacks, and create efficient learning systems that can adapt to changing data streams. His research bridges theoretical advances with practical applications in healthcare monitoring, security systems, and multimedia content analysis. Analysis of his recent publications reveals a strong focus on multimodal emotion recognition, with increasing emphasis on transformer architectures, knowledge distillation techniques, and addressing challenges in real-world settings (in-the-wild recognition). His work consistently combines computer vision with audio processing, demonstrating expertise in both visual and auditory modalities for affective computing applications. Recent trends show growing interest in large language models for audio classification and addressing challenges of concept drift in text stream mining. Dr. Koerich actively supervises numerous graduate students across various research projects, with a particular focus on multimodal expression recognition, music genre classification, and robust machine learning systems. His collaborations span multiple institutions and research groups, reflecting the interdisciplinary nature of his work in artificial intelligence. He is a key member of the LIVIA laboratory, which has established ÉTS as a leading institution in computer vision research in Canada (ranked 6th according to CSRankings). The laboratory focuses on solving real-life problems through AI engineering, particularly in developing complex deep learning models with massive amounts of data that have incomplete annotations.

Tracey RogersView profile

Professor

• Marine mammals
• Ecology
• Behavioural Ecology
• +8 more

Professor Tracey Rogers is a distinguished academic at the University of New South Wales, Faculty of Science, School of Biological, Earth and Environmental Sciences. Her research focuses on marine mammals and their adaptation to changing environments, with particular expertise in Antarctic wildlife, predator-prey interactions, and wildlife communication. Her research interests span marine mammal ecology, behavioral ecology, evolutionary biology, and animal behavior. Professor Rogers leads the MammalLab at UNSW, conducting cutting-edge research on various marine mammal species including whales, seals, and dolphins. Her work combines field observations, bioacoustic analysis, and physiological studies to understand how mammals overcome environmental challenges. Professor Rogers's publication record shows a strong focus on marine mammal vocalizations, Antarctic ecosystems, and conservation biology. Her recent work has increasingly incorporated advanced analytical techniques for studying large audio datasets and examining physiological responses to environmental change. 2015: Finalist Australian Academy Science, Nancy Millis Medal 2011: Finalist Eureka Prize People's Choice Science Award 2011: Finalist Eureka Prize Animal Protection, Research & Innovation 2008-2010: Ambassador, Australian Institute of Policy & Science Tall Poppy Program 2009: F.G. Wood Memorial Prize, International Society Marine Mammalogy 2007: Research Award, Australasian Association of Zoological Parks & Aquaria 2005: NSW Tall Poppy Award Professor Rogers has successfully supervised over 20 PhD students to completion, with many now holding prominent positions globally in academia, government agencies, and conservation organizations. She currently supervises five doctoral candidates working on diverse projects related to marine mammal acoustics and ecology. She is an active participant in Scientists in Schools and conducts workshops at the Powerhouse Museum and Taronga Conservation Society Australia.

Hideyuki SuzukiView profile

Professor

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +5 more

Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

Antonio Contreras de VillarView profile

Associate Professor

• Coastal Engineering
• Hydraulic Engineering
• Sediment Transport
• +3 more

Antonio Contreras de Villar is an Associate Professor at the University of Cádiz, affiliated with the Department of Industrial Engineering and Civil Engineering. A member of the RNM912 Coastal Engineering research group, his work focuses on hydraulic engineering, sediment transport, and coastal resilience. Education: PhD in Coastal Engineering from University of Cádiz (2017), with thesis on beach regeneration parameters under tidal conditions. His research spans advanced technologies like UAV-SfM photogrammetry for coastal defense monitoring, climate change impacts on coastal zones, and innovative sediment analysis methods. He also contributes to engineering pedagogy through virtual resources and laboratory practice improvements. Key scientific trends include sediment transport dynamics, climate adaptation strategies, and integration of geospatial technologies in coastal studies. While no awards are explicitly mentioned, his extensive publications reflect expertise in Mediterranean coastal systems, particularly in Cádiz and Málaga provinces.

Justine E. Vanden HeuvelView profile

Professor

• Sustainable viticulture production
• Ecophysiological factors affecting fruit and wine composition
• Computational tools for vineyard management
• +5 more

Justine E. Vanden Heuvel is a Professor in the Horticulture Section at Cornell University's School of Integrative Plant Science . Her work bridges viticulture research and undergraduate education . Education: Doctorate (2002) from the University of Guelph M.Sc. in Plant Agriculture (1999) from the University of Guelph B.Sc. (1996) from the University of Guelph Research Interests: Focus on digital viticulture for precision management, flavor/aroma optimization in wine grapes, and ecophysiological factors affecting fruit-wine composition. Implements computational tools like NDVI imaging and computer vision for sustainable practices in cool climates. Publication Trends: Research spans soil-plant interactions , climate adaptation , and vineyard technology . Key areas include under-vine vegetation for environmental sustainability, microbial composition studies , and canopy management to improve grape quality. Outreach Activities: Actively engages through @thegrapeprof YouTube content and media contributions to outlets like the New York Times Well blog and HuffPost Science . Teaches courses such as The Science of Grapevines (VIEN 3300) , Advanced Viticulture (VIEN 4460) , and Wine Culture (VIEN 2810) . Contact: G14 Plant Science Building, Ithaca, NY 14853

• Quality of Experience
• Video Quality Assessment
• Augmented Reality
• +7 more

Kjell Brunnström serves as an Adjunct Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, based in Sundsvall. He maintains active affiliation with the STC Research Centre, contributing to cutting-edge research in multimedia quality assessment and immersive technologies through extensive international collaborations. His research centers on Quality of Experience (QoE) with specialized focus on 3D video systems , virtual reality environments , and augmented teleoperation interfaces . He pioneers methodologies for subjective and objective video quality evaluation, particularly examining human factors in sports broadcasting (Video Assistant Referee systems), mining applications, and automotive displays. His work bridges theoretical psychophysical models with industrial implementations through standards development like ITU-T Rec. P.919. Analysis of his 15 most recent publications reveals consistent investigation into latency effects, distortion impacts, and viewing condition variables across emerging media formats. Key application domains include sports video refereeing, 360-degree video systems, VR simulators for remote machinery control, and adaptive streaming protocols, demonstrating strong industry-academia translation. Scientific Awards: No awards documented in source material. Advising and Grants: Student supervision and grant funding details were not disclosed in available documentation. Labs and Teams: Brunnström operates within Mid Sweden University's STC Research Centre framework, collaborating with global institutions including Royal Institute of Technology (Sweden), University of Surrey (UK), and multiple European research consortia. His work directly informs ITU standardization efforts and industrial implementations in broadcast and automotive sectors.

Alia DietschView profile

Associate Professor

• Human cognition and behavior
• Nature-based recreation
• Noncompliance
• +7 more

Dr. Alia Dietsch serves as an Associate Professor in Parks, Protected Areas, and Natural Resources Management at The Ohio State University's School of Environment and Natural Resources. Her work bridges social science theory with practical conservation applications, focusing on human dimensions of natural resource management. She maintains an active research program collaborating with state and federal natural resource agencies to inform planning, management, and communication efforts. Dr. Dietsch employs social psychological approaches to understand how human cognition influences individual behaviors across various conservation contexts. Her research spans human cognition and behavior, nature-based recreation, noncompliance issues, equity and justice in conservation, public lands management, and human-wildlife interactions, with particular expertise in carnivore-related conflicts. She utilizes advanced methodologies including multilevel modeling, survey sampling, and geospatial analysis to investigate complex human-nature relationships. Her publication record demonstrates consistent scholarly output across high-impact conservation journals, with recent work examining trauma in nature-based leisure, trust in conservation partnerships, wildlife value orientations, and human dimensions of wildlife management. Her research increasingly addresses equity, justice, and power dynamics in conservation decision-making, reflecting evolving societal values toward wildlife. Dr. Dietsch leads the Dietsch Team which has worked extensively on human-nature interactions, carnivore conservation, wildlife management, nature-based experiences, and recreation management on public lands. She directs significant projects including The National Visitor Survey in partnership with the U.S. Fish and Wildlife Service, which has surveyed over 15,000 visitors across 110+ national wildlife refuges to inform management decisions. She also contributes to America's Wildlife Values project and the Environmental and Social Sustainability Lab. As an educator, Dr. Dietsch teaches ENR 2300 (Society and Natural Resources), ENR 3600 (Recreation Management on Public Lands), and ENR 6510 (Research Design for Environmental Social Sciences), mentoring numerous graduate students who appear as co-authors on her publications. Her work has practical applications for wildlife managers seeking to understand public perspectives and improve conservation outcomes through evidence-based approaches.

Amy G FroelichView profile

Associate Professor

• Statistics Education
• Psychometrics
• Educational Assessment
• +3 more

Amy G Froelich serves as Associate Professor and Director of Undergraduate Studies in the Department of Statistics at Iowa State University, where she has taught over 3,000 students since joining in 2000. Her academic leadership includes curriculum development for undergraduate statistics programs and oversight of degree requirements. Ph.D. in Statistics, University of Illinois, Urbana-Champaign (2000) B.S. in Secondary Mathematics Education, University of Illinois, Urbana-Champaign (1994) Dr. Froelich specializes in statistics education, psychometrics, and educational assessment with particular expertise in categorical data analysis and probability instruction. Her research develops innovative teaching methodologies for introductory statistics courses and focuses on training future secondary mathematics teachers through specialized curriculum design. She investigates how electronic assessments and resampling techniques enhance student learning outcomes in statistical reasoning. Her publication record (2008-2018) demonstrates consistent contributions to statistics pedagogy, emphasizing practical classroom applications, assessment design, and teacher preparation. Key themes include the development of engaging activities (e.g., M&M's weight analysis, iPod randomness testing), psychometric methods for educational measurement, and curriculum reforms for mathematics educators. Waller Education Award from the American Statistical Association (2010) Multiple teaching and advising awards at Iowa State University Dr. Froelich has secured significant research funding from the National Science Foundation, U.S. Department of Agriculture, and U.S. Department of Education to support her statistics education initiatives. As Director of Undergraduate Studies since 2018, she mentors students through curriculum development projects and advises on academic pathways in statistics. Her co-authorship of Business Statistics and Analytics in Practice (9th ed., 2019) extends her impact to textbook development.

Debabrota BasuView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +6 more

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Explainable AI
• Counterfactual Explanations
• Water Distribution Networks
• +3 more

Dr. André Artelt is a researcher at the University of Bielefeld within the Faculty of Engineering and affiliated with the Machine Learning Group at the Center for Cognitive Interaction Technology (CITEC). His work focuses on Explainable AI (XAI), particularly counterfactual explanations, and their applications in critical infrastructure like water distribution networks. Current Research: Explainable AI Counterfactual explanations Water network monitoring Physics-informed graph neural networks Scientific Contributions: His recent publications explore reinforcement learning for water pump scheduling, scalable graph neural networks for water systems, and benchmark frameworks like EPyT-Flow. He investigates how training data affects explanation quality and develops tools for robust counterfactual reasoning. Awards: Project Lamarr Fellowship

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Alexander GoettkerView profile

Researcher

• Saccadic and pursuit eye movements
• Integration of retinal and extra-retinal information
• Control of arm and eye movements

Dr. Alexander Goettker is a postdoctoral researcher at Justus Liebig University Giessen's Department of Psychology and Sports Science, specializing in oculomotor behavior and sensorimotor integration. He contributes to the Collaborative Research Center "Cardinal Mechanisms of Perception" and the International Research Training Group "The Brain in Action". Ph.D. in Psychology (2020) with thesis on saccadic-pursuit eye movement interactions M.Sc. in Psychology (2016), including predoc program in visual neuroscience B.Sc. in Psychology (2014) Key research areas: Saccadic and pursuit eye movement coordination Integration of retinal and extra-retinal signals Eye-hand movement interactions Naturalistic visual processing in sports contexts Publication trends show focus on sensorimotor integration, visual processing during eye movements, and the interplay between perceptual judgments and motor control. His work spans journals like Current Biology , PNAS , and Scientific Reports .

Ayan MukhopadhyayView profile

Researcher

• Multi-Agent Systems
• Cyber-Physical Systems
• Smart Cities
• +5 more

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research spans critical domains in smart infrastructure systems with particular focus on: Developing robust decision-making frameworks for cyber-physical systems under uncertainty Creating multi-agent solutions for emergency response optimization Designing machine learning approaches for urban mobility and energy management Building proactive incident detection pipelines using heterogeneous data sources Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration. Award highlights include: CARS Post-Doctoral Fellowship (2019) Best Paper Award at ICLR's AI for Social Good Workshop Victor Lesser Distinguished Dissertation Award Nomination (2020) Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.