Tobias Oechtering is a Professor at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. His research focuses on information theory, privacy-preserving technologies, statistical signal processing, machine learning, and smart grid systems. He has held academic positions at KTH since 2008, advancing from Post-Doctoral Researcher to Assistant Professor (2010–2013), Associate Professor (2013–2018), and Professor (2018-present). He has supervised over 20 PhD students and contributed to numerous postdoctoral programs. Research Interests: - Network information theory and physical-layer security - Privacy mechanisms with provable guarantees - Distributed statistical inference and sensor calibration - Reinforcement learning and privacy-aware machine learning - Smart grid privacy and energy management - Wireless communication algorithms and signal processing - Networked control systems and stability analysis He currently supervises 7 PhD students and hosts 3 postdocs. His work has led to over 150 peer-reviewed publications, with recent contributions in privacy-preserving smart grid strategies, adversarial inference control, and information-theoretic security. He has served as editor for IEEE Transactions on Information Forensics and Security and held leadership roles in KTH's Digitalisation Research Platform.
Aziz Amoozegar is a Professor in the Department of Crop and Soil Sciences at NC State University's College of Agriculture and Life Sciences. His research focuses on environmental soil physics, specifically investigating water and pollutant movement through soils, phosphorus dynamics, and soil characterization for agricultural and engineering applications. Research programs include evaluating soil-water interactions in contaminated environments, assessing fertilizer-enhanced phosphorus transport, and developing soil analysis techniques. Key areas of study involve pollutant migration through saprolite, cadmium release kinetics in amended soils, and hydraulic functioning of compacted soils. His work bridges environmental science, agricultural engineering, and soil chemistry. Recent publications cover topics such as phosphate mobility under unsaturated conditions, cadmium release in contaminated soils, and bioenergy grass residue effects on evaporation. He has contributed to methodological advancements in soil testing and remediation strategies for heavy metals like lead. Dr. Amoozegar's research emphasizes practical applications for sustainable agriculture and environmental protection. No specific awards or grants are listed in the provided information, but his involvement in USDA and EPA-related programs suggests active collaborative research efforts.
Tancredi Caruso is an Associate Professor at the School of Biology and Environmental Science, University College Dublin. He holds a PhD in Ecology from the University of Siena (2006) and a Postgraduate Certificate in Higher Education Teaching (2015). His career includes roles as Reader and Lecturer at Queen's University Belfast, and research fellowships at Freie Universität Berlin and the University of Siena. Research Interests : Caruso's work focuses on biodiversity processes, aboveground-belowground linkages, ecological networks, and ecosystem responses to perturbations. His research has led to over 100 peer-reviewed publications and grants from the Alexander von Humboldt Foundation, NERC, EU Marie Sklodowska-Curie program, and others. Teaching & Professional Activities : Coordinates modules on environmental science, soil ecology, and conservation. Active in professional committees, including the IEA Committee. Languages: Italian, English, German (fluent), and French (reading). Grants & Collaborations : Leads projects like the ReEcoNet initiative. Recent grants include funding for wheat genetic diversity (EU), soil-microbe interactions (Leverhulme Trust), and climate-land use impacts (NERC). Key Contributions : Pioneered studies on mycorrhizal networks, soil microbial responses to drought, and the ecological role of oribatid mites. His work bridges theoretical ecology and applied conservation, emphasizing stochastic processes and biodiversity resilience.
Priya Narasimhan is a Professor of Electrical & Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. Her research focuses on dependable distributed systems, fault-tolerance, embedded systems, mobile systems, and sports technology. She leads the Intel Science and Technology Center in Embedded Computing (ISTC-EC) and founded YinzCam, a CMU spin-off providing mobile live streaming to sports venues. She holds multiple awards, including the Sloan Fellowship and NSF CAREER Award. Education: Ph.D. and M.S. in Electrical & Computer Engineering from UC Santa Barbara. Notable roles include former CTO of Eternal Systems, Director of Intel Labs Pittsburgh, and Director of CMU's CyLab Mobility Research Center. Research spans failure diagnosis in distributed systems, live upgrades, mobile cloud computing, football technology, assistive tech for the blind (Trinetra), and civic tech (iBurgh). Over 30+ students advised across Ph.D., M.S., and undergraduate programs. Active in entrepreneurship, teaching (courses like 18-349 Embedded Systems), and industry collaborations.
Saleh Taghvaeian is an Associate Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln and a Faculty Fellow at the Daugherty Water for Food Global Institute. His research focuses on precision irrigation systems, water resource management, and climate-resilient agricultural practices. He holds a Ph.D. in Irrigation Engineering from Utah State University and academic degrees from Ferdowsi University of Mashhad, Iran. Education: Ph.D., Irrigation Engineering, Utah State University M.S., Irrigation Engineering, Ferdowsi University of Mashhad, Iran B.S., Irrigation Engineering, Ferdowsi University of Mashhad, Iran Research Interests: His work integrates remote sensing, hydrological modeling, and machine learning to address challenges in irrigation efficiency, drought response, and sustainable water use. Key areas include soil moisture dynamics, crop water productivity, and the application of big data in agricultural systems. Publications Trends: Recent articles emphasize GRACE data downscaled analysis for water storage monitoring, AquaCrop modeling for crop performance under water stress, and machine learning applications in precision agriculture. His work spans transboundary water basins (Indus), U.S. High Plains, and Oklahoma Panhandle regions. Awards: 2021 Oklahoma State University Distinguished Early Career Faculty Award 2019 ASABE Larry W. Turner Young Extension Professional Award 2018 Oklahoma Cooperative Extension Service Honor Award Grants and Advising: Leads research projects on irrigation energy conservation, soil salinity mitigation, and agricultural drought resilience. Collaborates with institutions like USDA, UCOWR, and international partners on transboundary water management. Labs/Teams: Active in Nebraska's Water for Food Institute, leading interdisciplinary teams developing decision-support tools for irrigators and policymakers. Engaged in extension programs promoting smart irrigation technologies and water policy advocacy.
Dr. Donna Harris is an Assistant Professor in the Department of Plant Sciences at the University of Wyoming, based at the Sheridan Research & Extension Center. Her academic career includes roles as Senior Scientist at BASF Vegetable Seeds (2015–2020), Research Professional III at the University of Georgia (2007–2015), and prior industry experience with Monsanto and Pioneer Hi-Bred International. She holds a PhD (2014) and MS (2001) in Crop Science from the University of Georgia, alongside a BS (1998) in the same field. Her research focuses on plant breeding and genetics, addressing crop needs for Wyoming producers and consumers. Key areas include soybean rust resistance, quantitative trait loci (QTL) analysis, and germplasm evaluation for disease resistance. She has contributed significantly to soybean breeding programs, identifying novel resistance genes and developing resistant cultivars. Dr. Harris has published extensively on soybean pathology and genetics, with work appearing in Theoretical and Applied Genetics , Molecular Breeding , and Crop Science . Her publications emphasize molecular mapping of resistance genes, germplasm screening, and disease management strategies. While no awards are explicitly listed, her impactful research has shaped soybean improvement efforts in the U.S. and globally. Her professional experience spans academic and industrial settings, with expertise in both theoretical genetics and applied breeding. Current research integrates field-based evaluations with genomic approaches to enhance crop resilience against biotic stresses.
Lara Gautier is a Professor at the School of Public Health, Université de Montréal, affiliated with the Department of Management, Evaluation, and Health Policy. She holds research positions at the Centre de recherche en santé publique, InterActions, and SHERPA Institute. Her expertise lies in participatory evaluation of health services, qualitative and mixed-method research, and structural determinants of migrant health. She has led projects on intersectoral initiatives for vulnerable migrants and pandemic responses in Canada and Europe. Education: PhD in Economics (Université de Paris, 2019), PhD in Public Health (Université de Montréal, 2019), Master's in Health Economics (Université Paris-Dauphine, 2010), and Political Science diploma (Sciences Po Rennes). Research focuses on migrant health systems, intersectoral collaboration, and pandemic preparedness. Key projects include evaluations of health services for precarious migrants and climate change impacts on temporary agricultural workers. She has secured grants from Canadian Institutes of Health Research and Fond de Recherche du Québec. Awards include the Governor General's Gold Medal (2020) and ADESAQ Best Thesis Award (2020). She supervises Master's and PhD students in public health, health administration, and pharmacy programs. Active on international editorial boards (PLOS Global Public Health, Journal of Migration and Health). Leadership roles include co-chairing the Intersectoral Research Colloquium and serving on Université de Montréal's Indigenous Affairs committee. Her work emphasizes culturally responsive evaluation methodologies and health equity in crisis contexts.
Per Bruheim is a Professor at the Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU). He leads the Bruheim research group, focusing on Deep Phenotyping of prokaryotic and eukaryotic systems using advanced metabolomics, fluxomics, and lipidomics techniques. He is the scientific leader of the NV faculty Mass Spectrometry lab and has developed targeted/non-targeted MS methods applied to diverse biological systems. Teaching roles include leading the 2-year and 5-year Biotechnology programs (MSBIOTECH and MBIOT5). He teaches courses such as TBT4140 Biochemical Engineering, TBT4110 Microbiology, and TKP4170 Process Design. Current research projects include targeting antibiotic resistance via bacterial stress responses (Trond Mohn Foundation), chemically defined cell culture media development, and peptide drug inhibition of translesion synthesis mechanisms. Research interests center on metabolomics and systems biology approaches to study microbial physiology and antibiotic resistance, with applications in bioprocessing and sustainable protein production. His lab employs mass spectrometry for precise metabolic profiling and collaborates internationally on projects involving lipidomics and flux analysis. Advising and grants involve mentoring over a dozen master’s theses on topics like microbial fermentation optimization, single-cell protein production, and recombinant protein expression. He actively pursues funding for projects addressing AMR mechanisms and bioprocess innovation. The Bruheim group also collaborates with industry on sustainable raw material utilization for microbial cultures. Labs/Teams: Leads the Mass Spectrometry facility at NTNU and directs the Bruheim research group within the Institute of Biotechnology (IBT). Collaborates with national/international partners in microbiology, metabolomics, and bioprocessing.
Stefano Caselli is a Full Professor of Banking and Finance at Bocconi University and holds the Algebris Chair in Long-Term Investment and Absolute Return. He serves as the Dean of the SDA Bocconi School of Management and previously served as Vice Rector for International Affairs at Bocconi University (2012–2022). He has been a member of the SDA Bocconi School of Management board since 2006. His academic career includes teaching roles in the MSc in Finance, MSc in International Management, and CEMS programs, focusing on courses such as Investment Banking, International Finance, and Private Equity & Venture Capital. His research explores the interplay between financial systems and industrial sectors, emphasizing banking regulation, corporate finance, private equity, and venture capital. Education: MSc in Business Administration from the University of Genoa and a Ph.D. in Financial Markets and Institutions from the University of Siena. Research Interests: Caselli’s work spans financial stability, banking structure, corporate finance, and the role of venture capital in innovation. He has published extensively in journals like Journal of Financial Intermediation and Journal of Financial Stability , and authored textbooks such as Corporate and Investment Banking: A Hands-On Approach and Private Equity and Venture Capital in Europe . Labs and Initiatives: Co-founded the “Equita Research Lab in Capital Markets” (2013) and the “Algorand Fintech Lab” (2021) at Bocconi’s Baffi-Carefin Research Center. These labs focus on financial market dynamics and fintech innovations.
Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.
Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)
Srinivas Sridhar is a University Distinguished Professor of Physics, Biomedical Engineering, and Chemical Engineering at Northeastern University, with a secondary appointment as Lecturer on Radiation Oncology at Harvard Medical School. He previously served as Vice Provost for Research at Northeastern University (2004–2008), overseeing its research portfolio. As an elected Fellow of the American Physical Society and the American Institute of Medical and Biological Engineering, his research spans nanomedicine, neurotechnology, drug delivery, and quantitative MRI, with over 450 publications and patents. He founded the Nanomedicine Innovation Center and directs major NIH/NSF programs like CaNCURE and IGERT, focusing on undergraduate and graduate training in nanomedicine, particularly for underrepresented communities. His research interests include Nanomedicine Neurotechnology Quantitative MRI Drug Delivery Systems Metamaterials and Nanophotonics Quantum Chaos Superconductivity . Recent work involves machine learning-enhanced diagnostics for glaucoma, engineered nanoparticles for BRCA-deficient cancers, and portable neuro-ophthalmic devices. His publications from 2025–2017 reflect interdisciplinary applications in oncology, neurology, and materials science, with a focus on therapeutic and diagnostic innovation. Scientific accolades include the 2016 Biomedical Engineering Society Diversity Award University Distinguished Professorship . As an educator and entrepreneur, he has trained over 120 researchers, developed first-of-their-kind nanomedicine courses, and founded companies commercializing technologies like QUTE-CE MRI. His lab leads projects on cancer nanomedicine, quantitative imaging, and nanoscale magnetism, supported by grants from NIH, NSF, DoD, and private foundations.
Yintong Huo is a tenure-track Assistant Professor in the Department of Computer Science at Singapore Management University (SMU), School of Computing and Information Systems. He joined SMU in early 2024 after completing his PhD at The Chinese University of Hong Kong (CUHK) under Prof. Michael R. Lyu. His academic journey includes a Bachelor's degree from the University of Electronic Science and Technology of China. Education: PhD in Computer Science and Engineering, The Chinese University of Hong Kong (2024) Bachelor's degree, University of Electronic Science and Technology of China Huo's research focuses on intelligent software engineering , particularly empowering AI models (especially LLMs) for software development, testing, and operations. His work spans AI4SE, LLM4SE, AIOps, code intelligence, and multimodal software engineering . Two flagship projects define his current research: LogPAI - an open-source AI platform for automated log analysis adopted by leading tech companies, and WebPAI - a multimodal intelligence project for automatic webpage development. His research addresses critical challenges in software reliability, log analysis, and UI code generation through innovative applications of AI. His recent publications reveal a strong trend toward multimodal approaches in software engineering , combining vision and language models for UI code generation, and increasingly sophisticated applications of LLMs for log analysis and software reliability. Huo's work demonstrates exceptional impact, with multiple papers accepted at top-tier venues including ASE, ICSE, and FSE with high acceptance rates (e.g., 9.5% for ASE'25). Scientific Awards: ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022, for LogPAI with 3k+ GitHub stars and 70k+ downloads) ACM SIGSOFT CAPS Travel Grants (ASE'23, ICSE'24, FSE'24) Nomination for Best Teaching Assistant Award (2022) National Scholarship (2019) Huo actively mentors students at multiple levels, currently supervising PhD students Shi Ying Chang and Dan Huang (co-supervised with Prof. David Lo), research engineer Minxing Wang, and visiting students including Shiwen Shan. His undergraduate mentee Truong Hai Dang will intern at Apple Inc. He maintains strong industry connections, with his LogPAI project adopted by world-leading tech companies. Huo serves on program committees for major conferences including ASE'25, ICSE'26, and FSE'26, and is recruiting fully-funded PhD students and research assistants for projects in AI4SE and multimodal software engineering. Huo leads the LogPAI and WebPAI research initiatives, which have evolved into substantial open-source projects with significant industry adoption. His team focuses on practical applications of AI in software engineering, with particular emphasis on reliability and usability in real-world systems. The research environment benefits from SMU's strong position in software engineering research, where the university ranks No. 2 globally in Software Engineering according to CSRankings (2020-2025).
Lisa Yan serves as a Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, appointed in Spring 2022. She teaches core computer science education courses including CS 195 (Social Implications of Computer Technology), CS H195 (Honors variant), CS 294-189 (Teaching Process Design), and CS 375 (Teaching Techniques), holding regular office hours in Soda Hall for student engagement. Her academic credentials include: PhD in Electrical Engineering from Stanford University (2019) MS in Electrical Engineering from Stanford University (2015) BS in Electrical Engineering and Computer Science from UC Berkeley (2013) Dr. Yan's research centers on data-driven analysis of student learning in large-scale computer science courses, with significant contributions to computing ethics pedagogy and teaching assistant development programs. Her work develops innovative methodologies for assessing student earnestness in interactive lectures, creating flexible learning extensions, and designing integrity-focused assessments. Earlier research focused on software-defined networking and network switch performance optimization, demonstrating technical depth before her pivot to educational innovation. Current projects emphasize scalable teaching techniques and mastery learning frameworks that address challenges in modern CS education. Analysis of her 14 publications (2013-2024) reveals a strategic shift from computer networking (pre-2018) to computer science education research (2018-present). Recent work (2020-2024) dominates in venues like SIGCSE, featuring tools such as Otter-Grader for Jupyter notebook grading and the Earnest Insight Toolkit for lecture participation analysis. This evolution highlights her commitment to solving practical educational challenges through data analysis and tool development, particularly for large undergraduate courses. She received recognition through: The Faculty Award for Outstanding Mentorship of GSIs (2024) Lisa actively mentors Graduate Student Instructors and collaborates with educational technology initiatives. Her research team includes dedicated support staff like Taylor Kaserman (taylor.kase@berkeley.edu), reflecting structured collaboration in developing teaching innovations. She contributes to curriculum design committees within EECS, focusing on assessment integrity and scalable pedagogical methods for growing student populations. Her work operates through the EECS department's educational infrastructure, utilizing Soda Hall resources for both teaching coordination and research development, with strong connections to Berkeley's broader computing education ecosystem.
Chris Peikert is a Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He received his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory in 2006 under the supervision of Silvio Micali. Peikert is a leading researcher in cryptography, particularly known for his foundational work in lattice-based cryptography. His research interests span cryptography, lattices, coding theory, algorithms, and computational complexity, with a particular focus on cryptographic schemes whose security can be based on the apparent intractability of lattice problems. Peikert has made significant contributions to the development and analysis of lattice-based cryptographic primitives, including ring-LWE, fully homomorphic encryption, and zero-knowledge proofs. Peikert's recent work demonstrates continued leadership in post-quantum cryptography, with publications in top venues like CRYPTO, EUROCRYPT, and STOC. His research spans theoretical foundations of lattice problems to practical implementations of lattice-based cryptographic systems, including hardware acceleration for fully homomorphic encryption. IACR Fellow (2024) Test-of-Time Award from Crypto 2008 (2023) TCC Test-of-Time Award (2017) Patrick C. Fischer Development Professor of Theoretical Computer Science (2017) Best Paper Award at Eurocrypt 2010 Best Paper Award at STOC 2009 Alfred P. Sloan Foundation Fellowship Google Research Award Peikert has been actively involved in the cryptographic research community, serving on program committees for major conferences including CRYPTO, EUROCRYPT, FOCS, and TCC (where he was program co-chair in 2021). He has also developed educational resources, including extensive lecture materials on lattice-based cryptography and teaching courses on cryptography and theoretical computer science at both the undergraduate and graduate levels.
