Tanya P. Garcia, PhD is an Associate Professor of Biostatistics at the Gillings School of Public Health and Research Faculty in the UNC Neurology Huntington Disease Program at the University of North Carolina at Chapel Hill . She leads the Methods for INcomplete Data (MIND) Lab , focusing on statistical methods for handling censored, missing, or incomplete data in neurodegenerative disease progression studies. Education: PhD in Statistics, Texas A&M University MS in Statistics, University of Western Ohio MS in Industrial Engineering and Operations Research, UC Berkeley Research Interests: Specializing in High-Dimensional Variable Selection , Longitudinal Data Analysis , and Neurodegenerative Disease Modeling , her work develops reproducible statistical methods for Huntington's disease progression, improving clinical trial design and biomarker identification. Scientific Awards: American Statistical Association Fellow (2024) Landis Award for Outstanding Mentorship (2024) Roy R. Kuebler Award (2024) Gertrude M. Cox Award (2024) Leadership & Mentorship: Director of the MIND Lab, Chair-Elect of the Biometrics Section of ASA, and Tyson Academic Leadership Fellow (2023–2024). Her lab alumni have secured prestigious positions at institutions like Wake Forest University and Baylor University.
Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.
Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence
Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.
Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .
Dr. Jeremy Guest is the Levenick Professor in Civil and Environmental Engineering (and courtesy appointment in Chemical & Biomolecular Engineering) at the University of Illinois Urbana-Champaign (UIUC). He serves as Associate Director for Research at the Institute for Sustainability, Energy, and Environment (iSEE) and leads the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). His research focuses on advancing circular bioeconomies through sustainable sanitation systems, resource recovery from wastewater, and biofuel/bioproduct development from lignocellulosic biomass. Guest's work emphasizes Quantitative Sustainable Design (QSD) methodologies to bridge engineering innovation with policy and investment decisions. Education: PhD in Environmental Engineering (University of Michigan, 2012); MS (Virginia Tech, 2007); BS (Bucknell University, 2005). Research interests include wastewater treatment innovation, nutrient recovery, bioenergy systems design, and sustainable agriculture practices. Key contributions include the EcoRecover phosphorus recovery process, the QSDsan modeling platform, and leadership in CABBI's $262.5M DOE-funded initiative. Guest has secured funding from NSF, US EPA, USDA, and the Bill & Melinda Gates Foundation. Recognitions include the NSF CAREER Award, 2021 James J. Morgan Early Career Award, and the Paul L. Busch Award. He advises over 15 graduate students and leads multiple high-impact projects like the NEWgenerator sanitation system and oilcane biorefinery integration. Lab/Group: Guest Research Group focuses on experimental, computational, and policy-oriented research to solve global sustainability challenges. Current projects address algae-based wastewater treatment, fecal sludge management in low-income regions, and precision fermentation systems development.
Dr. John Francis Clinton is the Director of Seismic Networks and Head of the Earthquake Monitoring Section at the Swiss Seismological Service (SED), ETH Zurich. He leads the Marsquake Service for NASA's InSight mission and oversees Switzerland's broadband and strong-motion seismic networks. His expertise spans earthquake early warning systems, seismic instrumentation, and glacial seismology. Clinton is also a Co-Investigator on the Mars InSight mission and involved in international projects like EPOS and RAMSIS. Education PhD in Civil Engineering (Minor in Geophysics), California Institute of Technology (2004) MSc in Civil Engineering, California Institute of Technology (1998) BEng in Civil Engineering, University College Dublin (1997) Research Interests Dr. Clinton focuses on real-time seismology, seismic instrument design, structural health monitoring, and glacial seismology. His work bridges engineering applications with geophysical data analysis, particularly in earthquake early warning systems and induced seismicity studies. He collaborates internationally on projects such as the Valais Near Fault Observatory and Nicaragua’s Earthquake Early Warning development. Publications Overview His 15 most recent papers (2011–2015) highlight advancements in seismic network optimization, Marsquake detection algorithms, and glacial icequake mechanisms. Key themes include improving early warning accuracy, understanding subglacial dynamics, and validating high-rate GPS for structural monitoring. Awards & Memberships Member, Swiss Academy of Sciences (since 2008) Member, IRIS Quality Assurance Advisory Committee (since 2013) Chair, European Integrated Data Archives (EIDA) (2013–2015) Labs & Teams Clinton directs the SED’s Earthquake Monitoring team and collaborates with ETH Zurich’s Geophysics Masters Program. His group manages Switzerland’s seismic networks and leads the Marsquake Service, which analyzes InSight lander data for Martian seismic events.
Eduardo Azevedo is the John M. Bendheim and Thomas L. Bendheim Professor of Business Economics and Public Policy at the Wharton School , University of Pennsylvania. He holds a courtesy appointment as Professor of Economics and was awarded the 2016 Sloan Foundation Fellowship. His research integrates economic theory with practical applications across science and business domains. His research interests include: Market design Selection markets Social science genetics Experimental economics Game theory Recent publication trends focus on: Economic theory applications to healthcare and digital markets Empirical Bayes methods in A/B testing Adverse selection in insurance markets Strategic behavior in two-sided matching Evolutionary behavioral economics He serves as an instructor for BEPP2500 - Managerial Economics , emphasizing real-world application of microeconomic theory to business problems. His work also involves software development for economic research, including MATLAB-based empirical Bayes tools for analyzing treatment effects in large-scale experiments. Scientific awards : Sloan Foundation Fellow (2016)
Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.
Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.
Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.
Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.
Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.
Dr. Aria Fallah, MD, is an Assistant Professor in Neurosurgery at the University of California, Los Angeles (UCLA), with secondary appointments in Health Policy and Management. His research focuses on Evidence-Based Surgery , Clinical Trials , and Meta-Analyses for pediatric epilepsy surgery outcomes, including prognostic tools and comparative effectiveness studies . He actively participates in international collaborations like the CTSI and has published extensively in journals such as Epilepsia , Neurology , and Clinical Neurophysiology . University: University of California, Los Angeles Department: Neurosurgery Email: afallah@mednet.ucla.edu Dr. Fallah’s research integrates machine learning and deep learning to analyze high-frequency oscillations in epilepsy, aiming to refine diagnostic and therapeutic strategies. His recent work includes developing tools like PyHFO for oscillation detection and HOPS calculator for predicting seizure freedom after hemispherectomy. His publications highlight collaborations across institutions in pediatric epilepsy surgery , hemimegalencephaly , and thalamic neuromodulation . While no explicit awards or student advisement are listed, his contributions span clinical trials , neuroimmunology , and global neurosurgery initiatives , including work in Haiti.
Professor Michael C.L. CHAU serves as Professor and Deputy Area Head of Innovation and Information Management at The University of Hong Kong's Faculty of Business and Economics. He also holds the position of Associate Director at the HKU HKJC Centre for Suicide Research and Prevention, and serves on the HKU Senate and Court. His academic journey includes a Ph.D. in Management Information Systems from the University of Arizona and a B.Sc. in Computer Science (Information Systems) from the University of Hong Kong. Dr. Chau's research spans business analytics, artificial intelligence, web mining, fintech, smart health, and security informatics. His work focuses on applying data/text/web mining techniques to business, education, and social domains. He leads the Artificial Intelligence Research Group at HKU Business School, which has secured significant funding from the Hong Kong Research Grants Council, Food and Health Bureau, and other agencies for projects including 'The Invisible Hand in Crowdfunding' and 'Factors Moderating the Predictive Power of Social Media Sentiment on Stock Returns'. His publication portfolio includes over 150 articles in premier journals like MIS Quarterly, JMIS, and IEEE Transactions, with recent work exploring large language models, hate speech detection, and disaster-related social media analysis. His research demonstrates strong interdisciplinary connections across computer science, information systems, and business applications. INFORMS ISS Design Science Award (2020) IEEE ITSS Leadership Award in Intelligence and Security Informatics (2020) AIS Sandra Slaughter Service Award (2016) HKU Outstanding Young Researcher Award (2014) Faculty Research Postgraduate Supervision Award (2020 & 2025) Cited over 9,400 times (h-index = 47) Dr. Chau actively mentors doctoral students and has supervised numerous PhD graduates who now hold academic positions at institutions including Fudan University, Xi'an Jiaotong-Liverpool University, and the University of Maryland. His research grants portfolio demonstrates sustained funding success across diverse areas including blockchain, mental health applications, and social media analytics. The Artificial Intelligence Research Group he leads maintains strong industry connections through projects with Hong Kong Red Cross and the Hospital Authority.
