Deepak Ganesan is a Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on low-power sensing and communication, networked systems, and machine learning applied to pervasive health monitoring and societal challenges. PhD, Computer Science, University of California, Los Angeles (2004) MS, Computer Science, University of California, Los Angeles (2000) BTech, Computer Science, Indian Institute of Technology, Madras (1998) Ganesan's work bridges wireless sensor networks, smart textiles, and healthcare applications. He designs ultra-low-power wearable devices for tracking health signals like drug use, smoking, and cognitive performance, often integrating machine learning for robust detection. His research emphasizes societal impact, particularly in aging and Alzheimer's care through the Massachusetts AI and Technology Center for Connected Care (MassAITC) and the Center for Personalized Health Monitoring (CPHM). Recent publications highlight innovations in edge-cloud collaboration, fabric-based sensors, and longitudinal health analytics. His NIH-funded MD2K Center for Excellence and affiliations with the Center for Data Science and Computational Social Science Institute further underscore his interdisciplinary approach. ACM Fellow NSF CAREER Award (2006) IBM Faculty Award (2008) UMass Junior Faculty Fellow (2008) UMass Lilly Teaching Fellow (2009) Best Paper at CHI 2013 Best Paper Runner-up at Mobicom 2014 Honorable Mentions at Ubicomp 2013 Ganesan leads the SENSORS: Wireless Sensor Networks Group and contributes to global initiatives like the Internet of Battlefield Things. His work spans academic research, industry partnerships, and policy development in AgeTech and digital health.
Samarpita Roy is an Assistant Professor at TU Delft's Faculty of Applied Sciences, leading the Environmental Biotechnology department's Samarpita Roy Group. Her research focuses on microbial ecology in engineered bioprocesses, integrating metagenomics and quantitative physiology to study microbial metabolisms and community interactions. Key projects include exploring phototrophic and polyphosphate-accumulating organisms in wastewater treatment for nutrient/resource recovery. She actively seeks industrial collaborations and is hiring PhD candidates in metagenomics/microbial ecology. Research emphasizes understanding microbial community dynamics under fluctuating conditions, developing sequencing/data analysis workflows, and applying findings to enhance bioprocess efficiency. Her work aims to advance circular bioeconomy solutions through innovative biotechnology approaches.
Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.
Steffi Haag is a Professor of Digital Innovation and Entrepreneurship at the Institute of Computer Science , Heinrich Heine University Düsseldorf (HHU) . She bridges the Faculty of Mathematics and Natural Sciences and Faculty of Business Administration and Economics , collaborating with the Center for Entrepreneurship Düsseldorf (CEDUS) to inspire tech startups. Her research focuses on sustainable information systems, digital experiences, and business models. Steffi Haag’s research integrates Shadow IT , Usable Cybersecurity , and Digital Idea Management . Her work emphasizes theoretical , quantitative , qualitative , and mixed-methods research , including experimental and survey methodologies. Steffi Haag’s publications (2018–2024) span Information Systems , Cybersecurity , Digital Twins , and Sustainable Design . Common themes include Shadow IT dynamics , user behavior in security , and innovation management . Hermann Gutmann Award for special scientific achievements (2023) Schöller Fellow (2020) HMD Best Paper Award (2018) Research Award in Data Protection and Data Security (2017) Dissertation Prize (2017) Steffi Haag has secured grants such as the TU Darmstadt Postdoctoral Fellowship for Female Researchers (2016–2018) and Deloitte Foundation Fellowship (2009–2011). She actively moderates conferences and serves as Associate Editor of Business & Information Systems Engineering .
Iona Cheng is a Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF), where she conducts groundbreaking research in cancer epidemiology. She serves as co-Investigator of the SEER Greater Bay Area Cancer Registry and is Principal Investigator of multiple NIH- and foundation-funded projects examining genetics, lifestyle factors, and neighborhood characteristics in relation to cancer risk. Dr. Cheng has developed an extensive research program focused on racial/ethnic differences in cancer risk and leads population-based cancer surveillance studies that document variations in cancer incidence and mortality patterns across diverse racial and ethnic groups. University of California, Davis, BS, 1990–1994, Physiology Yale University, MPH, 1999–2001, Chronic Disease Epidemiology University of Southern California, PhD, 2001–2005, Epidemiology University of California, San Francisco, Postdoc, 2006–2008, Genetic and Molecular Epidemiology Dr. Cheng's research spans multiple disciplines within cancer epidemiology, with particular emphasis on understanding how environmental exposures, genetic factors, and social determinants interact to influence cancer risk and outcomes across different racial and ethnic populations. Her work frequently examines the impact of air pollution, endocrine-disrupting chemicals, and neighborhood characteristics on cancer development and survival. She has made significant contributions to understanding cancer disparities among Asian American, Native Hawaiian, and Pacific Islander populations, bringing attention to the unique cancer risks and outcomes within these understudied groups. Her research often leverages the Multiethnic Cohort Study, one of the largest prospective studies of cancer incidence and mortality across diverse racial/ethnic populations. Analysis of Dr. Cheng's recent publications reveals a consistent focus on environmental and social determinants of cancer risk across multiple organ sites. Her work demonstrates a sophisticated integration of epidemiological methods with environmental exposure assessment, genetic analysis, and health disparities research. Many of her studies examine the intersection of environmental exposures and racial/ethnic disparities in cancer outcomes, particularly regarding breast cancer, lung cancer, and other malignancies. She has published extensively on the impact of air pollution on cancer risk and survival, as well as the effects of endocrine-disrupting chemicals like bisphenol A, parabens, and phthalates. American Association for Cancer Research Scholar-in-Training Award (2007) National Institutes of Health Loan Repayment Award (2007) National Institutes of Health Loan Repayment Renewal Award (2009) American Association for Cancer Research Faculty Scholar Award (2011) National Institutes of Health Loan Repayment Renewal Award (2011) National Institutes of Health Loan Repayment Renewal Award (2013) American Journal of Epidemiology/Society of Epidemiology Research Top 10 manuscripts (2014) Cancer Prevention Institute of California Mentoring Award (2015) American Society of Human Genetics Top poster As Principal Investigator of multiple NIH-funded projects, Dr. Cheng oversees substantial research grants focused on cancer epidemiology and health disparities. Her work often involves large interdisciplinary collaborations with researchers across multiple institutions, including the Multiethnic Cohort Study which follows over 200,000 participants from diverse racial/ethnic backgrounds. She has demonstrated leadership in mentoring junior researchers, particularly those from underrepresented backgrounds in science, as evidenced by her Cancer Prevention Institute of California Mentoring Award. Her research program integrates data from cancer registries, electronic health records, and geospatial information to provide comprehensive insights into cancer patterns and risk factors. Dr. Cheng's research is closely connected to the UCSF Helen Diller Family Comprehensive Cancer Center and leverages collaborations with Lawrence Berkeley National Laboratory, which provides advanced technological resources for cancer research. Her work benefits from access to extensive cohort data, sophisticated exposure assessment methods, and interdisciplinary expertise in genetics, environmental science, and computational biology available through these institutional partnerships. She frequently collaborates with researchers studying the genetic and environmental determinants of cancer across multiple organ systems, contributing to a more comprehensive understanding of cancer etiology and prevention strategies.
Sudha Ram is the Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation at the Eller College of Management, University of Arizona. She holds joint faculty appointments as Professor of Computer Science and is a member of the BIO5 Institute and the Institute for the Environment. She is also the Director of INSITE: Center for Business Intelligence and Analytics, a leading research center in data-driven decision-making. Her research focuses on Big Data Analytics , Business Intelligence , Large Scale Network Science , and Machine Learning , with applications in healthcare, smart cities, environmental policy, and social media. She has pioneered methods in explainable AI, conceptual modeling, and multimodal data fusion, integrating statistical, ontological, and machine learning approaches. Recent publications demonstrate a strong trend in healthcare analytics (e.g., asthma, diabetes, fracture prediction), explainable AI (ROLEX, argumentation-based models), and urban/smart systems (mobility, wearables, environmental impact). Her work consistently appears in top-tier journals and conferences, reflecting sustained scholarly impact. AIS Fellow (2018) INFORMS ISS Distinguished Fellow IBM Faculty Award Peter Chen Award Best Paper Award, IEEE Smart Cities (2016) Best Paper Award, ACM Digital Health (2016) Woman of Impact Award, University of Arizona (2023) Dr. Ram has secured over $70 million in research funding from agencies like NSF, NASA, CIA, and corporations including IBM, Intel, and SAP. She has mentored numerous students and leads a multidisciplinary research team at INSITE. She has held editorial leadership roles in Information Systems Research , Journal of AIS , and is founding co-editor of the Journal of Business Analytics . She directs the INSITE Center, which fosters collaboration across business, computer science, and health domains, enabling large-scale data synthesis and knowledge discovery. The center supports projects in healthcare innovation, smart cities, and environmental policy analytics.
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.
Nasir Gharaibeh serves as a Professor in the Zachry Department of Civil & Environmental Engineering at Texas A&M University, specializing in infrastructure systems performance, resilience, and management decision-support systems. His academic credentials include: Ph.D. in Civil Engineering from the University of Illinois, Urbana-Champaign (1997) M.S. in Civil Engineering from Jordan University of Science and Technology (1991) B.S. in Civil Engineering from Jordan University of Science and Technology (1990) Dr. Gharaibeh's research program addresses critical infrastructure challenges through computational methodologies for mitigating gradual deterioration and disaster-induced damage. His work spans pavement, bridge, and stormwater systems with pioneering contributions to citizen-science infrastructure monitoring, enabling community-driven data collection and decision-making processes. He develops decision-support tools for resource allocation, maintenance planning, and project selection while advancing sustainable community resilience frameworks through hazard informatics and infrastructure management systems. No information regarding scientific awards was provided in the source material. Details about graduate student supervision and research grants are not specified in the available documentation. Dr. Gharaibeh leads an active research team focused on computational infrastructure modeling and community-engaged monitoring systems, with ongoing projects targeting real-world implementation of resilience strategies.
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.
Ioannis Athanasiadis is a Full Professor and Chair of Artificial Intelligence at Wageningen University & Research (The Netherlands). He leads the Artificial Intelligence (AIN) group, focusing on advancing AI methods for global challenges in agriculture, ecology, and sustainability. Previously, he was faculty at the Dalle Molle Institute for Artificial Intelligence (IDSIA, Switzerland) and the Democritus University of Thrace (Greece). He holds a PhD (2005, cum laude) in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research integrates machine learning, knowledge engineering, and environmental modeling to address food security, climate adaptation, and ecosystem services. He leads initiatives like AgML (AgMIP's machine learning benchmarking effort) and coordinates European grants such as LTER-LIFE and CYBELE . Prof. Athanasiadis has supervised over 40 PhD/postdoc researchers and serves as Editor of Environmental Modelling and Software . He collaborates internationally on projects involving AI for crop modeling, digital twins, and sustainable agriculture. His team develops frameworks like Crop2ML and PyCrop2ML to enhance interoperability between process-based models and machine learning systems.
Claudia Berger is a Visiting Assistant Professor at the School of Information, Pratt Institute, and serves as the Digital Humanities Librarian at Sarah Lawrence College. They are actively engaged in digital scholarship, critical making, and interdisciplinary research at the intersection of libraries, technology, and the humanities. Education: MSLIS and Advanced Certificate in Digital Humanities, School of Information, Pratt Institute MA in Classics & Ancient History, University of Exeter BA in Electronic Media & Art and Classics, Bard College at Simon’s Rock Their research focuses on innovative methodologies in digital humanities, particularly data physicalization, digital environmental humanities, and critical engagement with data through craft and tactile experiences. They explore how digital tools can be reimagined through embodied and inclusive practices, often integrating queer perspectives and ecological awareness into scholarly work. Their recent publications reflect a strong trend toward interdisciplinary making, environmental justice, and pedagogical innovation in library and information science. Themes include countermapping, plant-human relations, craft-based digital scholarship, and inclusive professional identity formation. Their work bridges technical practice with critical theory, emphasizing accessibility and material engagement. Professional Service: Editor, dh+lib and dh+lib Review Deputy Secretary, Association for Computers and the Humanities (ACH) Former Librarian, The Mellon Foundation (supporting research in higher learning, arts and culture, public knowledge, and humanities in place) Claudia Berger is deeply involved in mentoring and shaping the future of information professionals through curriculum analysis, editorial work, and collaborative projects. While no formal grants or advising relationships are listed, their leadership in digital humanities education and community-building initiatives demonstrates significant scholarly impact. They maintain an active public research presence through a Zotero repository on physical data visualization.
Svetlana Stanišić is an Associate Professor at Singidunum University's Faculty of Informatics and Computer Science, Department of Applied Artificial Intelligence. She holds a dental degree from the University of Belgrade's Dental Faculty (1998-2004) and a PhD in Physical Chemistry from the University of Belgrade's Faculty of Physical Chemistry (2007-2011). Her interdisciplinary research bridges environmental science, artificial intelligence, and public health. Her research interests focus on environmental science, air pollution modeling, and artificial intelligence applications . She investigates the atmospheric fate of pollutants using advanced machine learning techniques, with particular emphasis on polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and particulate matter. Her work combines environmental chemistry, computational modeling, and public health impact assessment to address urban air quality challenges. Analysis of her recent publications reveals a clear trend toward explainable AI applications in environmental science . She has pioneered the use of SHAP (SHapley Additive exPlanations), XGBoost, and metaheuristic optimization for pollutant fate prediction and source apportionment. Her research spans indoor and outdoor environments, with particular attention to health implications of air pollution exposure in urban settings like Belgrade. Dr. Stanišić leads significant research projects including "crAIRsis" (2024-2026) , which characterizes crisis-caused air pollution alternations using AI frameworks, and "ATLAS" , focusing on artificial intelligence theoretical foundations for spatio-temporal modeling. She has also authored influential books including "Ako je hrana Vaš porok" (2024) and "Ishrana i zdravlje" (2018). Her research group focuses on environmental informatics , developing computational tools to understand pollutant behavior in complex urban environments. The team combines atmospheric chemistry measurements with advanced machine learning techniques to create predictive models with practical applications for urban air quality management and public health protection.
Jonas Anderegg is a Lecturer at the Department of Environmental Systems Science, ETH Zurich. He holds a PhD and MSc from ETH Zurich in Agricultural Sciences. His research focuses on crop disease phenotyping, precision agriculture, and host-pathogen interactions. Current projects include automated image analysis for disease detection and quantification of Zymoseptoria tritici under field conditions. He has held postdoc positions in Plant Pathology (Institute of Integrative Biology) and Crop Science (Institute of Agricultural Sciences) at ETH Zurich. His work involves developing scalable imaging and deep learning methods for field phenotyping, with applications in quantitative resistance assessment and crop disease management. Education: PhD, Group of Crop Science, ETH Zurich (2016-2020) MSc in Agricultural Sciences, ETH Zurich (2015) Research interests emphasize high-throughput phenotyping, automated disease detection, and integrating imaging technologies into agricultural practices. Key contributions include datasets like SYMPATHIQUE and FIP 1.0, which provide foundational resources for crop disease research. Grants and advising: While no formal student advisees are listed, his postdoc roles and collaborative projects likely involve mentoring junior researchers. His work aligns with ETH Zurich's focus on sustainable agriculture and technological innovation in plant sciences. Labs and teams: Active member of the Crop Disease Phenotyping group, leveraging UAVs, thermal imaging, and deep learning for field-scale monitoring of crop health and senescence dynamics.
Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.
