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Reza Gharoie AhangarView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning Applications in Business
• Panel Data Econometrics
• +6 more

Dr. Reza Gharoie Ahangar is an Assistant Professor of Business Analytics at Lewis University's College of Business, specializing in AI applications, econometric modeling, and sustainability analytics. He earned a PhD in Management Science-Economics and dual MS degrees in Finance and Business Analytics from the University of North Texas. His research spans artificial intelligence in business policy , panel data econometrics , IT innovation analytics , energy economics , and supply chain optimization . His work includes electric vehicle demand management algorithms, pandemic-induced economic impact studies, and hybrid energy system designs. Recent publications demonstrate a focus on AI-driven financial decision-making , global supply chain disruptions , and smart grid optimization . He actively engages with professional organizations including INFORMS, POMS, and DSI. Dr. Ahangar teaches courses in supply chain analytics , data mining tools , and business information systems , bridging technical expertise with practical business applications.

JORGE MIRA PEREZView profile

Professor

• Electromagnetism
• Materials Physics
• Nanotechnology
• +7 more

Jorge Mira Pérez is a Professor of Electromagnetism at the University of Santiago de Compostela , within the Department of Applied Physics and Faculty of Physics . He holds affiliations with the Materials Institute (iMATUS) and the Strategic Grouping in Materials (AEMAT) . His research spans nanotechnology, materials physics, social dynamics, epidemiology, and interdisciplinary fields , with notable contributions to language competition models and pandemic forecasting. Education: PhD in Physics from USC (1995), thesis on magnetism in Gd₂CuO₄ particles. Research Interests: Electromagnetism, multiferroic materials, social systems modeling, chronobiology, and science communication. His awards include the Research Medal of the Royal Galician Academy of Sciences (2022) and multiple dissemination prizes. He has authored over 100 articles, including works on daylight saving time policy, black hole imaging, and public health strategies. Outreach: Active science communicator since the 1990s, writing for La Voz de Galicia , organizing events with Nobel laureates, and leading initiatives like the ConCiencia Program .

Kai PolstererView profile

Research Professor

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

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Tilmann GneitingView profile

Professor

• Computational Statistics
• Probability Theory
• Spatial and Spatio-Temporal Modeling
• +5 more

Prof. Tilmann Gneiting is a renowned academic holding dual roles as Group Leader of Computational Statistics at the Heidelberg Institute for Theoretical Studies (HITS) and Professor of Computational Statistics at the Karlsruhe Institute of Technology (KIT). He specializes in statistical methodology, probability theory, and forecasting applications across environmental, atmospheric, and earth sciences. His research bridges theoretical foundations with practical challenges in weather prediction, climate modeling, and uncertainty quantification. Education: PhD in Mathematics (1997, University of Bayreuth) MA in Mathematics (1992, Boston University) Diplom in Mathematics (1993, University of Bayreuth) Key Roles: Scientific Director of HITS (2023–2024) Affiliate Professor at University of Washington (since 2010) His research interests focus on spatial/spatio-temporal modeling, probabilistic forecasting, and statistical post-processing. He has contributed to advancements in ensemble forecasting, uncertainty quantification, and the evaluation of probabilistic predictions in meteorology and epidemiology. Prof. Gneiting has received prestigious awards, including the 2024 Ulf Grenander Prize in Stochastic Theory and Modeling, recognizing his seminal work in probabilistic forecasting and environmental modeling. He has also held editorial roles in top journals like the Annals of Applied Statistics and the Journal of the Royal Statistical Society. His recent work emphasizes methodological developments for forecasting evaluation, climate science applications, and interdisciplinary collaborations with ECMWF and other institutions.

• Synthetic Aperture Radar
• CubeSats
• Low Earth Orbit
• +8 more

**Jaan Praks** is an Associate Professor at the Department of Electronics and Nanoengineering, Aalto University, leading the Jaan Praks Group within the School of Electrical Engineering. His research focuses on satellite systems, space physics, remote sensing, and CubeSat mission engineering. He holds a Doctoral degree in Engineering and Technology from Aalto University (2012). **Research Interests**: Praks specializes in CubeSat-based missions for radiation belt studies, space weather monitoring, and low Earth orbit (LEO) positioning systems. His work integrates synthetic aperture radar (SAR) applications for boreal forest monitoring, snow water equivalent retrieval, and hyperspectral imaging for planetary missions. Key projects include the CORBES radiation belt survey and the FORESAIL CubeSat missions. **Awards**: Received the R2B - DIAMANT award (2024) for contributions to small satellite antenna design and space mission innovation. **Grants & Projects**: Principal investigator for multiple grants including the Centre of Excellence in Research of Sustainable Space (2023–2025) and AuroraSpace (2023–2026). Active in ESA collaborations and EU structural funds initiatives. **Labs & Teams**: Leads the Jaan Praks Group, a multidisciplinary team advancing CubeSat technology, space instrumentation, and remote sensing applications. Supervised 8 doctoral students to date, including Mayank, Marius Anger, and Kiril Cheremetiev.

Eugene RichardsonView profile

Assistant Professor

• Global Health
• Infectious Diseases
• Public Health Policy
• +3 more

Eugene Richardson, M.D., Ph.D., is an Assistant Professor of Global Health and Social Medicine at Harvard Medical School and Assistant Professor of Medicine at Brigham and Women's Hospital. He holds dual doctorates in Medicine from Cornell University Medical College and Anthropology from Stanford University, with postdoctoral training in Infectious Diseases and Geographic Medicine. His work integrates medical anthropology, epidemiology, and public health to address global health inequities and infectious disease outbreaks. Dr. Richardson's research focuses on the social and structural determinants of infectious diseases such as Ebola, SARS-CoV-2, and climate-sensitive pathogens. He has conducted fieldwork in Sierra Leone, the Democratic Republic of the Congo, and other regions, emphasizing community perspectives and the role of violence in disease transmission. His recent studies include antibody prevalence surveys in Sierra Leone, climate justice frameworks for global health, and innovative serological diagnostics using mass spectrometry. He is affiliated with the Africa CDC, Partners In Health, and the Center for Global Health Equity at Brigham and Women's Hospital. His work bridges clinical practice, anthropological insights, and policy advocacy, particularly around reparative justice in global health systems and vaccine equity. He collaborates with institutions such as UC Berkeley's Center for Social Medicine and the University of Global Health Equity. Dr. Richardson’s publications span topics like Ebola outbreak dynamics, pandemic response inequities, and the intersection of climate change and pediatric health. He actively participates in global health initiatives addressing health system strengthening in conflict zones and marginalized communities.

• Computer Science
• Environmental Engineering
• Medical Imaging
• +4 more

Remy Vandaele is a researcher affiliated with the University of Reading. His work spans interdisciplinary domains, including computer science, environmental engineering, and medical imaging. He specializes in deep learning, computer vision, and image analysis applications. Research Interests: Deep learning for hydroinformatics and flood risk management 3D anatomical landmark detection in bioimages Collaborative platforms for large-scale imaging data Key Publications focus on AI-driven river monitoring, anatomical imaging, and comparative studies of AI versus physics-based weather forecasting models.

• Algorithms
• Graph Theory
• Machine Learning
• +3 more

Ioannis Koutis is an Associate Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on algorithms, graph theory, and machine learning, with a particular emphasis on spectral methods and computational science. Koutis has been actively involved in federal grants and collaborative projects, including work on space weather forecasting, linear regression algorithms, and spectral network alignment funded by the National Science Foundation and the United States-Israel Binational Science Foundation. His research interests span spectral graph theory, combinatorial optimization, and the application of machine learning techniques to complex systems. Notable contributions include advancements in hypergraph partitioning, gene clustering in co-expression networks, and the development of efficient algorithms for large-scale graph problems. Koutis has led or co-led seven federally funded projects, emphasizing interdisciplinary applications in computer vision, bioinformatics, and computational social science. Publications highlight innovations in temporal knowledge graphs, neural network pruning, and spectral sparsification. Collaborations include partnerships with institutions like UC Irvine, Rensselaer Polytechnic Institute, and the University of Patras. His work bridges theoretical foundations with practical applications, addressing challenges in VLSI design, space weather modeling, and educational technologies. Awards and recognitions include his h-index of 13 and over 1,700 citations, reflecting significant scholarly impact. Teaching focuses on advanced topics in machine learning and deep learning, contributing to NJIT's graduate curriculum in data science and computer science.

Changjing ShangView profile

Academic

• Artificial Intelligence
• Fuzzy Systems
• Robotics
• +5 more

Dr. Changjing Shang is a Senior Research Fellow at the Department of Computer Science, Aberystwyth University. He holds a BSc from National University of Defense Technology (NUDT) and a PhD from Heriot-Watt University. His primary research focuses on fuzzy systems, rule interpolation, robotics control, and machine learning applications in computer vision and autonomous systems. Key research interests include: Fuzzy Rule Interpolation and its applications in robotics Unsupervised learning and deep learning techniques Unmanned aerial vehicle (UAV) navigation and tracking Data compression and neural representation optimization Medical imaging analysis and environmental forecasting systems Recent work emphasizes advancements in AI-driven solutions for: Dynamic fuzzy rule interpolation frameworks Person re-identification through contrastive learning UAV motion planning and active interaction control Efficient neural network compression techniques He has secured significant research funding including: Newton International Fellowship (2022-2024) Welsh Government funded projects (2017-2020) focusing on robotics control and big data applications Dr. Shang was recently elected as a Fellow of the Learned Society of Wales (2025) recognizing his contributions to computational intelligence and robotics research.

Jan KleisslView profile

Professor

• Solar Energy
• Renewable Energy
• Climate Science
• +4 more

Jan Kleissl is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego (UCSD). He serves as Director of the UCSD Center for Energy Research and co-Director of UCSD's Study Abroad program. His academic roles include leading research initiatives in solar energy, renewable resources, and urban energy efficiency. Kleissl holds a PhD in Environmental Engineering from Johns Hopkins University (2004), an M.Sc. and B.S. in Environmental Engineering from the University of Stuttgart, Germany. Research Focus: Kleissl’s work spans solar resource assessment, forecasting, and grid integration, with emphasis on mitigating urban heat island effects and improving energy efficiency. His lab develops innovative methods using sky imagers, numerical weather prediction models, and sensor networks. Key projects include solar variability modeling, microgrid design, and electric vehicle smart charging. His research has supported $154M in solar PV projects and informed California’s solar resource policies. Labs & Teams: Leads the Kleissl Solar Resource Assessment and Forecasting Lab , focusing on advancing solar forecasting technologies and their grid applications. Collaborates with national labs (NREL, Sandia) and industry partners on applied energy solutions. Grants & Collaborations: Funded by NSF, California Energy Commission, and DOE. Projects include optimizing PV inverter control, evaluating battery storage systems, and modeling climate impacts on energy systems. Teaching: Instructs courses on environmental engineering, renewable energy systems, and building energy efficiency (e.g., MAE126A, MAE255). Supervises ~15 PhD students and postdocs in energy-related research.

Yossiri AdulyasakView profile

Professor

• Supply Chain Analytics
• Optimization under Uncertainty
• Large-Scale Optimization
• +4 more

Yossiri Adulyasak is a Full Professor in the Department of Logistics and Operations Management at HEC Montréal, holding the Canada Research Chair in Supply Chain Analytics. He is also an Adjunct Professor at the University of Montreal's Department of Computer Science and Operations Research and a scientific advisor at IVADO Labs. His research focuses on supply chain optimization, data-driven decision-making, and robust optimization under uncertainty, with applications in retail, transportation, and humanitarian logistics. Education: PhD in Operations and Logistics Management (HEC Montréal), Master of Civil Engineering in Transportation Engineering (Chulalongkorn University). Research Interests : Supply chain analytics, large-scale optimization algorithms, stochastic programming, machine learning integration, and resilient supply chain design. His work emphasizes practical applications in logistics networks, inventory management, and dynamic resource allocation under uncertainty. Recent Articles : Focus on AI-driven retail analytics, robust optimization for lot-sizing and facility location, and humanitarian logistics. Key topics include pandemic response strategies, electric vehicle routing, and drone delivery systems. Awards : Recipient of the 2021 Prix de recherche Chenelière Éducation/Gaëtan Morin for excellence in scientific publications. Supervision : Directed 3 PhD dissertations and over 40 master’s and project supervisions, collaborating with companies like Bombardier, Pratt & Whitney Canada, and JDA. Active in industrial partnerships for supply chain optimization solutions. Lab/Teams : Member of GERAD (Group for Research in Decision Analysis) and IVADO Labs, contributing to cross-disciplinary AI and operations research initiatives.

Jérôme DANTANView profile

Associate Professor

• Computer Science
• Uncertainty Management
• Systems Engineering
• +3 more

Jérôme DANTAN is an Associate Professor in Computer Science affiliated with the Agrobiosciences Department, part of the Business and Engineering Sciences faculty. He is a member of the INTERACT research unit , focusing on systems engineering and decision models in uncertain contexts. His work integrates computational methods with applications in smart farming and digital agriculture. He holds a PhD in Computer Science from the Conservatoire National des Arts et Métiers (CNAM, Paris) and an engineering degree from ENSEA (Cergy-Pontoise). His research emphasizes decision-making frameworks for imperfect data, including applications in agricultural systems and environmental monitoring. Key areas include the Choquet integral for multi-factor prediction, fuzzy decision support systems (e.g., Decifarm), and the impact of digital technologies on farming business models. He co-directs the MSc Agricultural and Food Data Management program and collaborates with institutions like CNAM Paris and ISAMM Tunis. Recent projects include data visualization tools for industrial maintenance systems at Sogitec Industries (Dassault Group) and participatory innovation bootcamps focused on farmer-centered technology adoption. His work bridges computational methods, agricultural practices, and stakeholder engagement to address challenges in sustainable socio-environmental systems. Professional activities span teaching, research, and consulting. Notable partnerships include collaborations with the Institut Supérieur des Arts Multimédia de la Manouba (Tunisia) and industry engagements in distributed systems, Big Data, and machine learning applications.

João Tomé SaraivaView profile

Professor

• Power Systems
• Renewable Energy Integration
• Electricity Market Modeling
• +3 more

João Tomé Saraiva is a Professor at the Faculty of Engineering (FEUP) , University of Porto, and Research Coordinator at INESC Porto since July 15, 1985. He holds a PhD and Agregado degrees in Electrical and Computer Engineering, with 50+ MSc and 10+ PhD students supervised. His career spans EU and national projects, and consultancy work with agencies like EDP Distribuição and REN. Education: MSc (1987), PhD (1993), Agregado (2002) in Electrical and Computer Engineering from FEUP Research Interests: Focus on power systems with renewable integration, electricity market modeling, transmission expansion planning, climate change impact, AI applications in energy forecasting, and regulatory frameworks for collective self-consumption Publications: 30+ international journal papers, 120+ conference papers, and 3 co-authored books Projects: Leadership in EU/national funded initiatives and consultancy contracts with Portuguese and international TSOs Contact: +351222094230 | joao.tome.saraiva@inesctec.pt His 2024 publications address energy allocation in collective self-consumption, NECP impacts on MIBEL, battery arbitrage economics, climate-resilient transmission planning, and AI-driven hydro forecasting. These works reflect his expertise in market regulations, grid modernization, and renewable integration.

• Renewable Energy Systems
• Smart Grid Technology
• Machine Learning Applications in Energy
• +3 more

Goyal Awagan is a researcher affiliated with Northumbria University, contributing to advancements in smart grid technologies and renewable energy systems. His work focuses on hybrid microgrid protection, machine learning applications for energy systems, and distributed energy resource management. Key research themes include fault detection, resilient grid design, and stochastic optimization for virtual power plants. His notable publications explore topics such as protection frameworks for hybrid microgrids, demand response programs leveraging machine learning, and fault detection using LSTM networks. Goyal's research addresses challenges in grid reliability under weather intermittency and communication disruptions. Despite his prolific output, no specific academic awards or student advising roles are explicitly mentioned in the provided texts. His contributions are primarily centered on interdisciplinary energy systems, emphasizing both theoretical and applied aspects of modern power infrastructure.

Seebany Datta-BaruaView profile

Professor

• Space Weather
• Global Navigation Satellite Systems (GNSS)
• Ionospheric Physics
• +4 more

Seebany Datta-Barua is a Professor in the Department of Mechanical and Aerospace Engineering at Illinois Institute of Technology's Armour College of Engineering. Their research focuses on Global Navigation Satellite Systems (GNSS) for atmospheric and surface monitoring, ionospheric tomography, data assimilation, and space weather effects on navigation systems. Education: Ph.D. and M.S. in Aeronautics and Astronautics (2008), B.S. in Physics (2000), all from Stanford University. Current projects include analyzing Lagrangian coherent structures in geospace, auroral ionospheric irregularities, GNSS reflectometry for ice monitoring, and ionospheric data assimilation. Their work bridges GNSS signal analysis, atmospheric dynamics, and predictive modeling to address space weather challenges. Recent publications emphasize GNSS scintillation analysis, ice surface characterization, and multiscale ionospheric structuring. Their research often integrates observational campaigns with computational modeling to improve understanding of geospace dynamics. NSF CAREER Award (2014-2019) Per Enge Early Achievement Award (2018) Excellence in Teaching and Research Awards (2017, 2019) Outstanding Peer Review contributions (Navigation, Space Weather) Contact: sdattaba@illinoistech.edu | Office: RE-252B | Phone: 312.567.3566