Carlos Torres-Verdín is a Professor and holds the Brian James Jennings Memorial Endowed Chair and Zarrow Centennial Professorship in Petroleum Engineering at The University of Texas at Austin's Hildebrand Department of Petroleum and Geosystems Engineering, within the Jackson School of Geosciences. He earned a B.S. in Geophysical Engineering from the National Polytechnic Institute of México (1983), an M.Sc. in Electrical Engineering from UT Austin (1985), and a Ph.D. in Engineering Geoscience from UC Berkeley (1991). His research focuses on petrophysical and geophysical characterization of subsurface regions using well logging, seismic, and multi-physics data. Key areas include borehole geophysics, rock physics, reservoir characterization, and hydraulic fracturing. He has received numerous accolades, including the 2020 Virgil Kauffman Gold Medal (SEG) and the 2017 Conrad Schlumberger Award (EAGE). His work integrates advanced numerical methods and machine learning to enhance reservoir evaluation and CO2 sequestration monitoring. Torres-Verdín teaches courses such as PGE 358 (Formation Evaluation) and directs the Formation Evaluation Joint Industry Research Consortium, fostering industry-academia collaboration. Awards & Honors 2020 Virgil Kauffman Gold Medal, SEG 2019 Anthony F. Lucas Gold Medal, SPE 2017 Conrad Schlumberger Award, EAGE 2017 Lockheed Martin Excellence in Engineering Teaching Award Research & Teaching His recent studies address challenges in unconventional reservoirs, fluid dynamics in nanoporous media, and real-time geosteering. He has published over 150 peer-reviewed articles, emphasizing innovation in inversion techniques, NMR applications, and reservoir simulation.
Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.
Lili Zheng is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research focuses on statistical methodology, machine learning, and high-dimensional data analysis with applications in neuroscience and network science. Key areas of expertise include graphical models, stochastic processes, and algorithmic optimization. She collaborates extensively on projects involving functional connectivity analysis, neuronal data imputation, and interpretable machine learning frameworks. Her work bridges statistical theory and computational practice, addressing challenges in model inference, feature importance assessment, and low-rank tensor completion. Notable contributions include techniques for distribution-free inference, spectral clustering in patchwork learning, and Gaussian process parameter estimation using mini-batch stochastic gradient descent. Dr. Zheng's research emphasizes interdisciplinary applications, particularly in neuroimaging (calcium imaging, functional connectivity) and multi-modal data integration. She actively explores statistical challenges in big data contexts, emphasizing robust methodologies for real-world datasets.
Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.
Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.
Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.
Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.
Professor Mounim A. El Yacoubi holds positions at Institut Polytechnique de Paris, Institut Mines-Télécom, and Telecom SudParis. His research focuses on AI, machine learning, and deep learning applied to e-Health (neurodegenerative disease detection, diabetes management), biometrics (gait, vein, and handwriting recognition), and smart systems (agriculture, surveillance, robotics). He leads the SAMOVAR CNRS Lab and has supervised 17 PhDs and 30+ master's students. Education: PhD (1996, Université de Rennes 1), HDR (2014, Paris-Saclay University). Experience: Senior Researcher at Parascript (2001–2008), Visiting Scientist at CENPARMI (1997–1998), Associate Professor at PUCPR (1998–2001). Research Interests: AI applications in healthcare, biometrics, pattern recognition, and smart technologies. Recent work includes Alzheimer’s detection via handwriting analysis, diabetes prediction using PPG signals, and palm/vein recognition systems. Grants & Leadership: Program Chair of ICPRAI 2022, ICCPRA 2024. Editor of IEEE Access and journals on cyber-physical intelligence. Authored books on Pattern Recognition and AI.
Bruce Allen is the Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, where he also heads the Observational Relativity and Cosmology department. He holds dual academic appointments as Honorary Professor of Physics at Leibniz Universität Hannover and Adjunct Professor of Physics at the University of Wisconsin-Milwaukee, USA. His career spans over three decades in gravitational physics research, with a leadership role in the LIGO Scientific Collaboration from 1997 to 2018. Dr. Allen's research focuses on gravitational wave detection and data analysis, early universe cosmology, de Sitter space, curved-space quantum field theory, cosmic strings, inflationary models of the early universe, and gravitational radiation emission by cosmic strings. His work extends to large-scale cluster computing and public distributed computing projects like Einstein@Home, which has led to significant discoveries in gravitational wave astronomy. His recent publications demonstrate expertise in pulsar timing arrays, Hellings-Downs correlation analysis, and optimization of computational methods for gravitational wave detection. Allen's scientific contributions have been recognized with numerous prestigious awards including the Richard A. Isaacson Award (2020), the Bruno Rossi Prize (2017), the Princess of Asturias Award (2017), and the Special Breakthrough Prize (2016), all shared with the LIGO team for groundbreaking gravitational wave discoveries. He is also an Elected Fellow of both the American Physical Society and the Institute of Physics, UK. As a research leader, Allen has secured approximately $10 million in research funding from the National Science Foundation (1987-2018) and has mentored numerous students and researchers in gravitational physics. His work on Einstein@Home has engaged the public in scientific discovery through distributed computing, leading to several important astrophysical findings including gamma-ray pulsar discoveries.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
Dr. Chee Kiat Seow is an Associate Professor at the University of Glasgow's School of Computing Science. He holds a PhD from Nanyang Technological University (NTU) and an MSc from the National University of Singapore (NUS). His research focuses on cyber-physical security, wireless communication localization, and IoT systems leveraging AI/ML. He has led projects valued in the millions, winning awards like the IEEE Best Student Paper and National Instruments Engineering Impact Awards. Education: PhD (NTU), MSc (NUS) Research: Specializes in UWB positioning, spoofing detection, and IoT integration with 5G/GNSS. Teaching: Courses include Big Data, Software Engineering, and Data Analytics. His recent work addresses NLOS mitigation in indoor localization and cyber-physical security threats. Over 63 publications span journals like IEEE Transactions and conferences such as IPIN and WF-IoT. Supervised 6+ PhD/MSc students on topics like autonomous robotics and AI-driven localization. Grants: Includes $853K for 5G-X Smart Building projects and $797K for GNSS signal authentication. Awards: IEEE PIERS Best Student Paper (2019), NI Engineering Impact Awards (2015-2016). He advises on IoT and cybersecurity for organizations like ARTC and National Instruments. Active in IEEE Signal Processing and Computer Society.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
Parv Venkitasubramaniam is a Professor in the Department of Electrical & Computer Engineering at Lehigh University, affiliated with the P.C. Rossin College of Engineering. Previously, he served as a postdoctoral researcher at UC Berkeley under Prof. Venkat Anantharam. His research focuses on theoretical foundations of privacy and security in networks, leveraging statistical signal processing, information theory, and game theory. Key application areas include smart grids, transportation systems, and peer production networks. Education includes a Ph.D. and M.S. in Electrical Engineering from Cornell University, and a B.Tech from the Indian Institute of Technology. His doctoral work concentrated on wireless sensor networks, particularly distributed communication and statistical inference. Research interests span privacy-utility tradeoffs, cybersecurity in control systems, and resilient network design. He explores topics like stealthy attacks on dynamical systems, privacy-aware stochastic games, and resilient energy storage systems. Recent work emphasizes transportation system resilience and cyber-physical system security. His publications address cutting-edge challenges in anonymizing networks, detecting cyber attacks, and optimizing privacy-preserving mechanisms. Notable projects include NSF-funded research on anonymous networking and information-theoretic security frameworks.
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Vinod M. Vokkarane is a Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell, where he serves as Director of the Center for Smart Cyber-Physical Systems (SCyPS) and Director of Advanced Computer Network Labs. Previously, he was an Associate Professor at University of Massachusetts Dartmouth from 2004 to 2013 and a Visiting Scientist at MIT's Research Laboratory of Electronics from 2011 to 2014. His extensive research portfolio spans multiple domains of advanced networking and cyber-physical systems. Dr. Vokkarane earned his educational foundation with a B.S. from University of Mysore, India (1999), followed by an M.S. (2001) and Ph.D. (2004) in Computer Science from the University of Texas at Dallas. His dissertation focused on optical burst-switched networks, establishing the foundation for his future research trajectory. His research interests center on Cyber-Physical Systems, Network Optimization, Reliability, Smart Grids, and Cyber-Security, with particular expertise in the design, analysis, and modeling of architectures, protocols, and algorithms for ultra-high speed networks including Optical networks, Grid/Cloud networks, and Big-data networks. His work bridges theoretical foundations with practical implementations, often addressing critical challenges in network reliability, security, and efficiency. His research has received significant recognition through numerous best paper awards and substantial external funding. Analysis of his recent publications reveals a clear evolution toward increasingly sophisticated integration of cyber-physical systems with power infrastructure, particularly in the areas of grid resilience and observability. His work has expanded from fundamental optical networking research to address critical infrastructure challenges, with a growing emphasis on machine learning applications for network optimization and power system monitoring. The recent focus on PMU networks, disaster resilience, and cyber restoration demonstrates his strategic pivot toward addressing national security and critical infrastructure protection challenges. UMass Dartmouth Scholar of the Year Award (2011) UMass Dartmouth Chancellor's Innovation in Teaching Award (2010-11) University of Texas at Dallas Computer Science Dissertation of the Year Award (2003-04) Multiple Best Paper Awards including IEEE GLOBECOM 2005, IEEE ANTS 2010, ONDM 2015, ONDM 2016, and IEEE ANTS 2016 Texas Telecommunications Engineering Consortium Fellowship (2002-03) Dr. Vokkarane has successfully mentored numerous graduate students who have contributed significantly to his research projects, with several going on to successful careers in academia and industry. His research has been consistently supported by major funding agencies including NSF, DOE, and USMC, with recent projects totaling over $5 million in funding. Current projects include Unified Post-Disaster Restoration Planning for Cyber-Physical Power Distribution Systems (ONR, $550K), CyberCARE: Northeast University Cybersecurity Center (DOE, $3.5M), and Flexible Spectrum Allocation in Next-Generation Optical Networks (NSF, $350K). He leads the Center for Smart Cyber-Physical Systems (SCyPS) and Advanced Computer Network Labs at UMass Lowell, where his research teams work on cutting-edge problems in network architecture, cyber-physical security, and infrastructure resilience. His labs collaborate extensively with national laboratories and industry partners to translate theoretical advances into practical solutions for real-world infrastructure challenges.
