Jennifer Kaiser is an Associate Professor at Georgia Institute of Technology, affiliated with the School of Civil and Environmental Engineering and Earth and Atmospheric Sciences . Her research focuses on air pollutant formation, particularly volatile organic compounds (VOCs), and their impacts on air quality and climate. Endowed Position: Greene Early Career Professor (2024) Key Projects: Emissions from agriculture/oil-gas, biosphere-atmosphere interactions, satellite data validation Tools: CMAQ modeling, TROPOMI satellite analysis, low-cost sensor networks Her work spans instrument development to global chemistry-transport modeling, with recent emphasis on satellite-based monitoring and health risk assessment. She leads the Kaiser Group , which investigates VOC dynamics in urban and wildfire-affected regions. Scientific Awards: NOAA Grant (2021) Greene Early Career Professor (2024) Contact: jennifer.kaiser@ce.gatech.edu | Office: Ford Environmental Science & Technology Building, Room 3224
Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994
David Latulippe is a Professor in the Department of Chemical Engineering at McMaster University. He joined McMaster in 2012 after postdoctoral work at Cornell University and a PhD at Penn State University, focusing on membrane filtration for DNA purification. His industrial experience includes roles at ZENON Environmental (now GE Water) in hollow-fiber membrane design for water treatment. Research interests include Membrane science and technology Bioprocessing of therapeutic viruses Microscale systems for biological applications Environmental engineering solutions for water treatment Current projects involve collaborations with industry partners like Ceapro and Aevitas, and the development of a biomanufacturing automation lab with Sartorius. Recent publications highlight advancements in Nanofiltration and microfiltration for viral vectors Conductive membranes for electrochemical applications Microfluidic systems for DNA analysis Environmental monitoring of biocides and microplastics Scientific recognition includes the Young Membrane Scientist Award (2014). Teaching activities focus on Fluid Mechanics (CHEMENG 2O04) and Industrial Separation Processes (CHEMENG 4M03).
Professor James Im serves as Professor of Materials Science in the Departments of Earth and Environmental Engineering and Applied Physics and Applied Mathematics at Columbia University, with an office at 1106 S.W. Mudd (Mail Code 4701). His academic career spans over three decades at Columbia, where he progressed from Assistant Professor (1991-1994) to Associate Professor (1995-2002), and ultimately to full Professor (2002-present), including a tenure as Chair of the Materials Science and Engineering Program (2002-2014). His educational background includes a B.S. with Distinction in Materials Science from Cornell University (1984) and a Ph.D. in Electronic Materials from MIT (1989), followed by postdoctoral research at Caltech (1989-1991). Cornell University: B.S. Materials Science (1984) MIT: Ph.D. Electronic Materials (1989) Caltech: Postdoctoral Scholar (1989-1991) Im's research centers on ultra-rapid phase transitions in beam-irradiated thin films, specifically focusing on laser crystallization of silicon films , energy-beam-induced melting and solidification , and nucleation in discontinuous phase transitions . His work employs experimental, computational, and theoretical approaches to develop innovative semiconductor materials for advanced displays, solar cells, and integrated circuits. Notably, his invention of Sequential Lateral Solidification (SLS) technology has been licensed to major display manufacturers (Samsung, LG, Sharp) and implemented in products by Apple, Blackberry, and Nokia. Current research focuses on advancing the Spot-Beam Crystallization (SBC) platform using fiber lasers for next-generation microelectronics. His publication record spans environmental aerosol studies (2019-2024), oilfield operations technology (2002-2014), and foundational atmospheric research (1980s), reflecting interdisciplinary expertise bridging materials science, environmental engineering, and petroleum technology. The most recent works emphasize low-cost sensor development and aerosol monitoring. Professional recognition includes membership in prestigious societies: Bohmisch Physical Society Sigma Xi Alpha Sigma Mu Materials Research Society American Physical Society Im's research group maintains strong industry connections through technology licensing and collaborative projects, particularly in display manufacturing. His leadership as former department chair demonstrates administrative commitment alongside scientific innovation. The laboratory leverages state-of-the-art laser systems and beam delivery optics for materials development, with recent focus shifting toward environmental monitoring applications while maintaining core semiconductor research.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Steve Margulis is a Professor in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (UCLA). His research focuses on surface hydrology and hydrometeorology, particularly in snow-dominated mountainous regions. University: University of California, Los Angeles Department: Civil and Environmental Engineering Research Interests: His work aims to improve characterization of hydrologic states and fluxes through remote sensing and modeling, with applications to water resource management and environmental hazard mitigation. Key areas include: Land surface and atmospheric boundary layer modeling Remote sensing of snow and soil moisture Data assimilation techniques Hydroclimatology and climate change impacts Publications Trends show consistent focus on snow hydrology, remote sensing applications, and data assimilation frameworks across diverse mountain regions including the Sierra Nevada, Andes, and High Mountain Asia. Recent work emphasizes model improvements through spatially distributed precipitation bias correction and satellite data integration. Students: Mentoring includes Ph.D. candidates Yiwen Fang, Yufei Liu, Jacob Schaperow, and Manon von Kaenel. Group alumni include notable researchers at institutions like NASA, Ohio State University, and University of Colorado. Research Projects are funded by NSF, NASA, and DOE/CERC-WET. Key initiatives include: Snow reanalysis frameworks for Sierra Nevada and Andes Investigations into land-atmosphere interactions Global frameworks for SWOT data products
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.
Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.
Carine J.M. Doggen is a Full Professor in Health Technology & Services Research at the TechMed Centre, University of Twente. She also serves as Scientific Director at Rijnstate Hospital since October 2017. Her academic career includes previous positions as Senior Researcher at Leiden University Medical Center (2000-2009), University of Washington, Seattle (2002-2003), and Sanquin Blood Bank (2006-2009). Her research focuses on evaluating healthcare innovations, particularly technologies enabling the transition of care from hospitals to home settings. Dr. Doggen specializes in assessing the implementation, reliability, and effectiveness of new medical technologies including wearable sensors and remote monitoring systems. Her work examines measurement validity and incorporates perspectives from both healthcare providers and patients. Analysis of her recent publications reveals strong expertise in cardiovascular technology evaluation, digital health interventions, and quality of life assessment. Her research shows particular strength in randomized clinical trials of medical devices, health technology assessment methodologies, and post-COVID health outcomes research. Dr. Doggen has presented her work at numerous conferences, with recent oral presentations including 'Towards personalised medicine: the potential of intensive longitudinal data to assess the interplay of chronic diseases' (2024) and 'Prognostic markers for acute heart failure in Chronic Obstructive Pulmonary Disease' (2021). She has also contributed to media discussions about healthcare innovations through the Rijnstate Collegetour series.
David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.
Christopher Goyne is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia (UVA) and Director of the UVA Aerospace Research Laboratory. He holds a B.Eng. (1991) and Ph.D. (1999) in Mechanical Engineering from the University of Queensland, Australia. His research focuses on hypersonic propulsion, scramjet technology, instrumentation development, and advanced manufacturing. He leads the UVA Hypersonics Research Complex and is a key figure in the University Consortium for Applied Hypersonics. Goyne’s work includes contributions to NASA’s Hyper-X Program and the HyShot scramjet flight test program. He is an Associate Fellow of the AIAA and serves on editorial and advisory boards for journals and organizations such as the Shock Waves journal and Virginia’s Aerospace Advisory Council. Education: B.Eng. (Mechanical Engineering, University of Queensland, 1991); Ph.D. (Mechanical Engineering, University of Queensland, 1999). Research Interests: Hypersonics and scramjet propulsion Diagnostic techniques (e.g., laser-based methods, optical emission spectroscopy) Wind tunnel and flight testing Controls and adaptive systems for hypersonic flow paths Advanced manufacturing for aerospace components Awards: Recipient of the 2023 James C. McDaniel Fellow Award and 2022 Outstanding Researcher Award. Holds leadership roles in AIAA committees, including past Chair of the HyTASP Program Committee. Recognized with the Sigma Gamma Tau Outstanding Aerospace Professor Award (2006) and multiple research fellowships. Grants and Projects: Funded by NASA, the Air Force Office of Scientific Research, and industry partners. Leads UVA’s contributions to hypersonic ground and flight testing, including sensor development and combustion efficiency studies. Labs and Teams: Directs the UVA Aerospace Research Laboratory, collaborating on projects such as the UVA Hypersonics Research Complex and the University Consortium for Applied Hypersonics. Advises student chapters of AIAA and Sigma Gamma Tau.
Bärbel Finkenstädt Rand is a Senior Tutor at the Warwick Medical School , University of Warwick, with extensive research contributions at the intersection of statistics, machine learning, and biomedical sciences. Her work focuses on developing advanced methodologies for analyzing temporal and spatio-temporal data, particularly in circadian rhythms and disease dynamics. Research Themes : Bayesian inference, Hidden Markov Models, circadian rhythm stability, transcriptional bursting, and wearable sensor data analysis. Collaborations : Chronotherapy Group at Warwick, Université Paris-Saclay, and interdisciplinary teams across medicine, genetics, and computational biology. Publications reveal a strong emphasis on circadian health monitoring, gene expression dynamics, and epidemic modeling using stochastic frameworks. Her recent work prioritizes personalized medicine applications through telemonitored biomarkers and IoT platforms . Methodological Innovations include spline-based HMMs, distributed delay systems, and harmonic modeling for nonstationary time series. Applications span oncology, sleep medicine, and population ecology.
Gerard Pons-Moll is a Professor at the University of Tübingen, endowed by the Carl Zeiss Foundation, and heads the Emmy Noether independent research group 'Real Virtual Humans'. He is a core faculty member at the Tübingen AI Center, a senior researcher at the Max Planck Institute for Informatics (MPII), and faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the Saarland Informatics Campus. His research focuses on computer vision, graphics, and machine learning, particularly in creating virtual human models and analyzing human motion from video and sensor data. Education: PhD (with distinction) in 2014 from Leibniz University of Hannover, Master's in Telecommunications Engineering (Northeastern University, 2008), and B.S./M.Sc. in Telecommunications Engineering from the Technical University of Catalonia (2002–2008). Research Interests: 3D human modeling, pose estimation, human-object interaction, and applications in industry and research. His work emphasizes real-world applications like virtual avatars and motion capture systems. Awards: Emmy Noether Grant (2018), German Pattern Recognition Award (2019), Google Faculty Research Award (2019), and multiple best paper awards at top conferences (BMVC’13, Eurographics’17, 3DV'18, CVPR'20). Advising & Grants: Served as program chair of 3DV 2021, area chair for ECCV, CVPR, and IJCAI. Active in reviewing for DFG, ANR, and ISF. Supervises research in areas like neural rendering frameworks (Blendify) and synthetic data generation (STAGE). Labs/Teams: Leads the Emmy Noether group and collaborates with MPII, Tübingen AI Center, and IMPRS-IS on projects like XNect (real-time 3D motion capture) and Human 3Diffusion (avatar creation).
Jennifer Hicks is the Executive Director of the Wu Tsai Human Performance Alliance at Stanford University, focusing on collaborative research to advance understanding of human performance through biomechanical modeling and machine learning. She also serves as Director of Research for the NIH-funded Mobilize Center and Restore Center, integrating engineering tools into rehabilitation science. Her work emphasizes predictive modeling of surgical outcomes, mobile health data analysis, and exoskeleton design. Dr. Hicks leads software development for the OpenSim project, guiding its user-centric evolution and promoting open-source biomedical tools. Her research spans musculoskeletal dynamics, wearable technology, and clinical applications of AI. Key contributions include smartphone-based motion capture (OpenCap) and foundational datasets like AddBiomechanics. She co-develops training programs for interdisciplinary teams and advocates for large-scale health data utilization. Dr. Hicks' efforts bridge academia and industry, supporting translational research in neurorehabilitation, sports performance, and chronic disease management.
