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Dean EcklesView profile

Associate Professor

• Social Influence
• Network Analysis
• Causal Inference
• +4 more

Dean Eckles is an Associate Professor of Marketing at MIT Sloan School of Management and serves as an Associate Director of the MIT Institute for Data, Systems, and Society (IDSS). He is also affiliated with the MIT Schwarzman College of Computing through the Institute for Data, Systems & Society and its Statistics and Data Science Center. Additionally, he leads the analytics research area at the Initiative on the Digital Economy and organizes the annual Conference on Digital Experimentation (CODE@MIT). His educational background includes a BA in philosophy, BS and MS in cognitive science, MS in statistics, and PhD in communication, all from Stanford University. Prior to joining MIT, Eckles worked as a scientist at Facebook, where he contributed to areas including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. He previously held research positions at Nokia and Yahoo. Eckles's research primarily focuses on social influence mediated by interactive technologies, examining how communication technologies mediate, amplify, and direct social influence. His work spans multiple specific areas including social interactions, contagion, and interventions in networks; experimental design and inference in networks; and methods for causal inference. His research often combines social science with advanced statistical methods. His notable publications include research on long ties in social networks and their relationship to economic prosperity, how network structure affects social contagions, and algorithmic transparency in social media platforms. His work has appeared in prestigious journals including PNAS and Nature Human Behaviour, and he has provided expert testimony before the US Senate on algorithmic ranking. Long ties, disruptive life events and economic prosperity (PNAS) Long ties accelerate noisy threshold-based contagions (Nature Human Behaviour) Algorithmic transparency and assessing effects of algorithmic ranking (Senate testimony) Eckles actively shares his research through social media platforms including Bluesky, Twitter, and Mastodon, as well as through his blog and contributions to the Gelman et al. blog. His work bridges academic research with practical applications in technology and policy.

Jonathan T. BarronView profile

Researcher

• Computer Vision
• Deep Learning
• Generative AI
• +3 more

Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .

Michael BaldeaView profile

Associate Professor

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +3 more

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

Junjian QiView profile

Associate Professor

• Electric power systems
• Cascading failure
• Microgrid control
• +2 more

Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.

Dylan CampbellView profile

Lecturer

• Computer Vision
• Optimization
• Machine Learning
• +3 more

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

John F. CannyView profile

Professor

• Artificial Intelligence
• Robotics
• Human-Computer Interaction
• +5 more

John F. Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). He joined the faculty in 1987 and is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and other research institutes. His research focuses on artificial intelligence, robotics, human-computer interaction, and computational geometry. Canny is renowned for developing the Canny edge detector and contributions to motion planning, privacy-preserving algorithms, and real-time simulation. Education: B.Sc. in Computer Science and Theoretical Physics (Adelaide University, 1979), B.E. (Hons) in Electrical Engineering (Adelaide University, 1980), M.S. and Ph.D. in Electrical Engineering (MIT, 1983 and 1987). Research Interests: His work spans AI, robotics (including universal planar manipulation), HCI (e.g., MultiView video conferencing), and security (privacy in collaborative filtering). He has pioneered algorithms for edge detection, non-linear FEM simulation, and computational algebra. Awards: ACM Fellow (2020), Okawa Research Grant (2003), AAAI Classic Paper Award (2002), NSF PYI (1989), Packard Fellowship (1988), and ACM Doctoral Dissertation Award (1987). Advising & Grants: Advised over 30 Ph.D. students, many of whom hold prominent roles in academia and industry. His grants include NSF and Packard funding for foundational research in robotics and computational methods. He currently teaches CS188: Introduction to Artificial Intelligence. Labs/Teams: Leads research at the Berkeley Institute of Design (BiD), focusing on RISC robotics, activity-based computing, and privacy-enhanced telepresence systems.

Xiaohui YuView profile

Professor

• Big Data Management and Analytics
• Database Systems
• Machine Learning
• +3 more

Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.

• Animal Nutrition
• Dairy Cattle
• Methane Emission
• +2 more

Jan Dijkstra is an Associate Professor at Wageningen University specializing in Animal Nutrition . His research focuses on dairy cattle nutrition, methane emissions, and nutrient efficiency. Academic Rank: Associate Professor Department: Animal Nutrition His work emphasizes: Mathematical modelling of methane production Phosphorus and nitrogen balance optimization Feed supplementation strategies for reduced emissions Genotype-environment interactions in dairy systems Recent publications highlight advancements in methane mitigation , nutrient cycling , and rumen microbiome dynamics . He serves as a promotor for multiple PhD projects and contributes to datasets on equine microbiomes and rumen metabolites. Scientific Awards American Feed Industry Association Award (2015) Publicatieprijs ASG (2013) Supervision PhD candidates: Kozorezov, Henry, Koning, Vivares Martinez EngD candidate: Holshof

James Herbert-ReadView profile

Associate Professor

• Behavioural Ecology
• Marine Biology
• Collective Animal Behaviour
• +4 more

Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration

• Water and Wastewater Treatment
• Environmental Biotechnology
• Adsorption Processes
• +5 more

Ferhan Çeçen is a Professor at the Institute of Environmental Sciences, Boğaziçi University (Istanbul, Turkey). He has held academic positions since 1990, including Professor since 1999, and has conducted research in environmental engineering and biotechnology. His expertise includes water/wastewater treatment, environmental biotechnology, and adsorption processes. Education: Ph.D. in Environmental Engineering, Istanbul Technical University (1990) M.S. in Environmental Engineering, Istanbul Technical University (1993) B.S. in Chemical Engineering, Boğaziçi University (1984) Research Interests: Prof. Çeçen focuses on advanced water treatment technologies, including nanosilver effects on biological systems, biodegradation of pharmaceuticals, and adsorption using activated carbon. His work emphasizes practical solutions for hazardous pollutant mitigation in biological treatment systems. Key areas include: Environmental biotechnology applications Biodegradation kinetics and modeling Activated carbon integration in wastewater systems Toxicology of nanomaterials in water treatment Recent Projects (2015–2023): He leads projects funded by Boğaziçi University BAP and TÜBİTAK, including studies on micropollutant removal via granular activated carbon, nanosilver effects on biological systems, and biodegradation of pharmaceuticals. These projects address emerging contaminants and sustainable treatment methods. Grants and Advising: His grants include BAP-funded research on micropollutant adsorption and TÜBİTAK support for microbial product inhibition studies. He has advised numerous graduate students on environmental engineering topics, though specific student names are not listed in the provided texts. Labs and Teams: His research group collaborates on experimental and computational studies, focusing on lab/pilot-scale testing of water treatment innovations. Key facilities include Boğaziçi University's environmental engineering labs and partnerships with institutions like Munich Technical University.

• Digital Agriculture
• Machine Systems Engineering
• Data Science
• +4 more

Dr. Dennis Buckmaster is a Professor in Agricultural & Biological Engineering at Purdue University, serving as Dean's Fellow for Digital Agriculture. He holds a B.S. from Purdue University and M.S./Ph.D. from Michigan State University. His research focuses on digital agriculture, machine systems engineering, and data science applications in farming. He co-coordinates the Agricultural Systems Management program and teaches courses like Computing Technology with Applications and Ag Tech and Innovation. He leads the Open Ag Technology and Systems Center (OATS Center), advancing open-source solutions for agriculture through platforms like ISOBlue and OADA. His work integrates IoT, robotics, and machine learning to optimize crop production, livestock management, and farm decision-making. He has authored over 150 publications on precision agriculture technologies. Professional memberships include American Society of Agricultural and Biological Engineers and Fluid Power Society. He emphasizes data interoperability, edge computing, and bridging engineering with agricultural practices through collaborative frameworks like LATTICE and Meta Ag.

• Antenna Design
• Wireless Communication
• RFID Technology
• +3 more

Jari Holopainen is a Senior Lecturer at the Department of Electronics and Nanoengineering , Aalto University. His work focuses on advanced antenna systems, wireless communication, and RFID technologies, with significant contributions to broadband, tunable, and wearable antenna designs. Current affiliation: Aalto University Academic role: Senior Lecturer Research interests span antenna design for mobile terminals, microwave engineering, and machine learning applications in RF systems. His publications highlight innovations in: Bluetooth antennas for metallic smartwatches and jewelry Wideband and dual-polarized antenna arrays RFID transponders with beam steering Machine learning-driven load optimization 3D-printed and capacitive-coupling antenna structures Wave propagation and scattering analysis Scientific contributions include: 15+ peer-reviewed articles (2025-2020) Collaborations with leading researchers in electromagnetics (e.g., Ville Viikari, Pertti Vainikainen)

Zackary JohnsonView profile

Associate Professor

• Marine Microbial Ecology
• Biological Oceanography
• Algal Biotechnology
• +4 more

Zackary Johnson , the Juli Plant Grainger Associate Professor of Biological Oceanography and Marine Biotechnology at Duke University, leads interdisciplinary research at the intersection of marine microbiology and biogeochemical innovation. Affiliated with the Nicholas School of the Environment and based at the Duke Marine Laboratory , his work spans microbial ecology, algal biotechnology, and climate mitigation strategies. Education: Ph.D. in Marine Science (Duke University, 2004), B.S. in Biology (MIT, 1994) Research Interests focus on marine microbial communities, particularly the model phytoplankton Prochlorococcus , algal cultivation for sustainable bioproducts, and the ecological impacts of ocean acidification. His lab investigates microbial interactions across diverse environments—from coastal estuaries to open-ocean gyres—and develops technologies for carbon-negative aquaculture systems. Publications highlight expertise in microbial biogeography, algal biofuels, and climate-resilient marine food webs. Recent work explores drone-based ocean color sensing, Gulf Stream eddy microbiomes, and the role of Labyrinthulomycetes protists in carbon export. Scientific Awards: Juli Plant Grainger Associate Professorship DOE and NSF-funded projects Grants include high-profile initiatives like the Marine Algae Industrialization Consortium (MAGIC) and REU Site program for coastal research training. His lab maintains a dedicated research site for studying microbial dynamics and sustainable algal cultivation.

Fan ZhangView profile

Assistant Professor

• Computer Security
• Applied Cryptography
• Decentralized Systems
• +4 more

Fan Zhang is an Assistant Professor in the Department of Computer Science at Yale University. He holds a Ph.D. from Cornell University, advised by Ari Juels, and a B.S. from Tsinghua University. His research focuses on computer security, applied cryptography, decentralized systems, blockchains, and trusted execution environments (TEEs). He leads the Decentralized Systems Group at Yale and is affiliated with the IC3, CDCC, and CADMY centers. He teaches courses on blockchain and real-world cryptography. Key research interests include blockchain decentralization, privacy-preserving protocols, and secure distributed systems. Notable contributions include the Town Crier oracle system (acquired by Chainlink), the DECO TLS protocol, and foundational work on transaction order fairness and MEV mitigation. Awards include the Ethereum Foundation Academic Grant (2022) and the IBM PhD Fellowship (2018-2020). His work has been published in top venues like CCS, S&P, CRYPTO, and USENIX Security.

Sajid AlaviView profile

Professor

• Food Engineering
• Extrusion Processing
• Pet Food and Feed Applications
• +4 more

Dr. Sajid Alavi is a Professor in the Department of Grain Science and Industry at Kansas State University. He joined the faculty in 2002 after earning his Ph.D. in Food Science/Food Engineering from Cornell University (2002), M.S. in Agricultural and Biological Engineering from Penn State (1997), and B.S. in Agricultural Engineering from IIT (1995). His research focuses on extrusion processing in food, pet food, and feed applications, with expertise in rheology, food microstructure imaging, and process sustainability. He leads global projects in Africa, Brazil, India, and beyond, emphasizing sustainable food technologies and AI-driven processing innovations. Dr. Alavi is a recipient of the 2010 Young Research Scientist Award from the Cereals & Grains Association. He teaches GRSC 620 (Intro to Extrusion Processing) and GRSC 820 (Advanced Extrusion Processing), and has trained over 1,000 industry leaders through his renowned 'Extrusion Processing: Technology and Commercialization' short course. His work bridges food science and engineering, addressing challenges in plant-based meat analogs, nutrient bioavailability, and food aid product development. Key facilities associated with his work include the BIVAP Feed Quality Assurance Lab and Hal Ross Flour Mill. His research spans sensory analysis of meat alternatives, fiber utilization in pet food, and sustainability assessments of novel crops like intermediate wheatgrass. Recent studies explore insect protein in pet food, AI-driven extrusion optimization, and iron bioavailability in fortified foods. Dr. Alavi’s contributions span academic, industrial, and global food security domains, reflecting a commitment to innovative, scalable food solutions.