Burak Kurkcu is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He previously served as an Assistant Professor at Hacettepe University and as a Senior Control System Design Engineer at Aselsan Inc. Education: Ph.D., TOBB University of Economics and Technology (2019) M.S., TOBB University of Economics and Technology (2015) B.S., Istanbul Technical University (2010) Research Interests: Dr. Kurkcu specializes in robust control systems, soft robotics, switched neural networks, and autonomous systems. His work focuses on disturbance estimation, simultaneous learning algorithms, and control of nonlinear systems. Recent Publication Trends: His research includes soft pneumatic actuator modeling, disturbance observer-based control methods, and evolutionary optimization for state-space models. Key themes involve soft robotics, autonomous control, and computational intelligence applications. Scientific Awards: IEEE Turkey Ph.D. Thesis Award (2020) Editorial Roles: Associate Editor for TIMC, Measurement and Control , and Turkish Journal of Electrical Engineering and Computer Science . Principal Investigator for defense-related control system projects.
Jeeseop Kim is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at The University of Texas at El Paso (UTEP), College of Engineering, specializing in robotics, autonomy, and control theory. His research focuses on safety-critical planning and control, with emphasis on bipedal/quadrupedal locomotion, hybrid dynamical system control, and whole-body planning and control. Education: B.S. in Mechanical and Aerospace Engineering, Seoul National University (2014) M.S. in Intelligence and Information (Robotics), Seoul National University (2017) Ph.D. in Mechanical Engineering, Virginia Tech (2022) Postdoctoral Scholar, Mechanical and Civil Engineering, Caltech (2022–2025) His research spans safety-critical control systems for legged robots, including obstacle-aware nonlinear model predictive control (MPC), control barrier functions, and distributed coordination algorithms. Recent work explores adaptive delay estimation, tactile sensing for robotic grasping, and hardware-software co-design for humanoid robots. Key article trends highlight advancements in autonomous inspection robotics, hybrid control architectures, and real-time planning for quadrupedal systems. His work integrates control theory with practical applications in industrial and healthcare domains. Awards: ASME DSCD Rudolf Kalman Best Paper Award (2022) IEEE ICRA Outstanding Paper Award (2023) Jeeseop teaches MECH 4332: Mechanical Computational Applications in Vision and Robotics (Fall 2025). He actively recruits Ph.D. students for Spring/Fall 2026 and seeks motivated undergraduates/MS students with skills in robotics kinematics, programming (C/C++, Python, MATLAB), and CAD design. The AIGIS Lab welcomes applicants with interests in robotics, controls, and autonomous systems.
Nathorn Chaiyakunapruk is a Professor in the Department of Pharmacotherapy at the University of Utah College of Pharmacy . He holds an adjunct appointment in Population Health Sciences and serves on multiple institutional committees including the Global Health Steering Committee and Health Economics Core at CTSI . His academic leadership extends to international roles with the World Health Organization and founding initiatives like the ISPOR Asia Consortium . Education : PhD in Pharmaceutical Outcomes Research, University of Washington PharmD, University of Wisconsin-Madison BS in Pharmaceutical Science, Chulalongkorn University Research Interests span health technology assessment , global health economics , and evidence synthesis . His work applies methodologies like network meta-analysis and umbrella reviews to address health equity, infectious disease modeling, and pharmaceutical policy. Recent studies focus on social determinants of health and vaccine economic value . Article Trends highlight collaborations in AI-assisted systematic reviews , vaccine rollout optimization , and health disparities . His publications frequently address cost-effectiveness and global health burden across infectious and non-communicable diseases. Scientific Awards : Senior Class (P4) Distinguished Teacher (2022) NRCT Outstanding Research Award (2019, 2012) Monash University PVC Research Award (2015) Nagai Research Foundation Awards (2006-2010) ISPOR Task Force Leadership (CHEERS 2022) Teaching & Service : Courses include Systematic Review and Meta-analysis and Global Health Policy . He chairs the Asia Pacific Evidence-based Medicine Network and advises WHO on vaccine economics and Thailand’s National Health Security Office on pharmaceutical policy.
Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.
Akash Srivastava is a Research Scientist and Principal Investigator (PI) at the MIT-IBM Watson AI Lab in Cambridge, MA, and Chief Architect of Large Language Model Alignment at IBM Research. His work focuses on generative modeling , Bayesian inference , and machine learning for constrained engineering design . He previously conducted PhD research at the University of Edinburgh under Dr. Charles Sutton and Dr. Michael U. Gutmann on variational inference for generative models using deep learning. His research spans Neuro-Symbolic AI , Language Model Alignment , and Synthetic Data Generation , with applications in 3D modeling , urban logistics , and material science . Recent publications highlight advancements in diffusion models , continual learning , and privacy-preserving data synthesis . As a PI, he collaborates with MIT faculty like Prof. Faez Ahmed and Prof. Rafael Gomez-Bombarelli on projects such as generative modeling for mechanical systems , synthetic data in decision-making , and greener delivery networks . He has received funding through a DARPA grant for machine common sense research.
Ntzoufras Ioannis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology, where he has served continuously since 2004 (promoted to Professor in 2015). Previously, he held teaching positions at the University of the Aegean (2000-2004) and completed military service (1999-2000). Education B.Sc. in Statistics and Insurance Science (1994) M.Sc. in Statistics with Application in Medicine, University of Southampton (1995, with distinction) Ph.D. in Statistics, Athens University of Economics and Business (1999) Research Focus His work centers on Bayesian and computational statistics , specializing in categorical data analysis, statistical modeling, and variable selection methodology. He develops sophisticated models for applications in medical research (clinical trials, risk estimation), psychometrics (latent variable models), and sports analytics (football/basketball modeling), with emphasis on computational efficiency and real-world implementation. Publication Trends Recent publications (2023-2025) reveal three dominant trends: (1) Advanced Bayesian variable selection methods for high-dimensional data, (2) Sports analytics applications in football (goal modeling, competitive balance) and basketball (in-play performance), and (3) Development of specialized R packages (ssifs, PEPBVS) for statistical computation. His work consistently bridges theoretical innovation with practical domain applications. Scientific Awards Lefkopouleion Prize for Greece's best statistics thesis (1999-2000) PROSE Award Honorable Mention for 'Bayesian Modeling Using WinBUGS' (2010) Academic Leadership He has supervised graduate students across AUEB's Statistics, Business Analytics, and Data Science programs, and taught postgraduate courses at the University of Athens (Biostatistics), University of the Aegean (Business Administration), and Italian institutions (University of Pavia, Universita Cattolica, University of Bicocca-Milan). As General Secretary of the Greek Statistical Institute (2006-2007), he advanced national statistical initiatives. Research Community He founded and maintains grstats (http://grstats.forumotion.net/), Greece's primary online statistics community, facilitating collaboration among 1,200+ statisticians and data scientists through forums, workshops, and resource sharing.
Tanel Alumäe is an Associate Professor of Speech Processing at Tallinn University of Technology's School of Information Technologies, Department of Software Science. With over 15 years of academic experience, he has held various research and teaching positions at the university since 2006, progressing from Research Fellow to Tenured Associate Professor. His work focuses on speech and language technologies with a particular emphasis on Estonian language applications. PhD in Information and Communication Technology (2006), Tallinn University of Technology Research Master's Degree in Informatics (2002), Tallinn Technical University MSc studies at Tallinn Technical University (1999-2002) and Universität Erlangen-Nürnberg, Germany (1999-2000) Diploma in Computer and Systems Engineering (1994-1999), Tallinn Technical University Alumäe's research spans automatic speech recognition, speaker recognition, natural language processing, and computational linguistics with a focus on Estonian language technology. His work addresses challenges in multilingual speech processing, deep learning applications for speech technologies, and developing practical systems for real-world applications including broadcast media processing and accessibility solutions. He has made significant contributions to low-resource language processing and specialized applications for children's speech and emotion recognition. His recent publications demonstrate a strong focus on cutting-edge speech processing techniques including deepfake detection, multi-speaker systems, speech-to-speech translation, and applying large language models to speech applications. The research shows a consistent pattern of addressing both theoretical challenges in speech processing and practical implementations for Estonian language technology. Award 'Keeletegu 2019' from the Ministry of Education and Research Award 'Keeletegu 2011' from Estonian Ministry of Education and Research 3rd award at the Tallinn University of Technology contest for applied scientific projects (2011) Boris Tamm stipend (2007) First prize at the national contest of students' scientific works (2007) Ustus Agur stipend of Estonian Information Technology and Telecommunications Association (2005) Alumäe has supervised postdoctoral researchers including Rena Nemoto (2012-2015) on pronunciation modeling for speech recognition. He serves in editorial and review capacities for major journals including Nature, Computer Speech & Language, and IEEE Transactions. His administrative roles include Secretary of the Northern European Association for Language Technology Board and membership on the Department of Software Science Council at TalTech. His research group at Tallinn University of Technology actively participates in international challenges (IWSLT, Interspeech, Odyssey) and collaborates with institutions worldwide. The team has developed open-source platforms for Estonian speech transcription and created systems for automatic closed captioning of Estonian broadcasts, demonstrating strong practical applications of their research.
Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.
Prof. Dr. Kirsten Jung is a faculty member at the Department of Microbiology , Faculty of Biology , Ludwig Maximilian University of Munich . Her research focuses on bacterial signal transduction, stress response mechanisms, and systems biology approaches to understand microbial regulatory networks. Key research areas include stress-dependent gene expression in bacterial populations Structural and functional analysis of membrane-integrated receptors Metabolism-based chemical communication in bacteria Integration of experimental and computational systems biology Recent publications highlight her lab's work on Escherichia coli epitranscriptomic modifications under heat stress, m 5 C rRNA dynamics, and the role of RNA methylation in host-pathogen interactions. Collaborative studies address bacterial acid stress responses and their implications for antibiotic tolerance. Her interdisciplinary work bridges microbiology with ecological studies, as evidenced by research on biodiversity conservation in forest and urban ecosystems. Publications also demonstrate expertise in advanced imaging techniques (e.g., arterial spin labeling for glioma analysis) and bioinformatics approaches. Current advisees include Gloria Gessinger and Tania P. Gonzalez-Terrazas . She can be contacted at jung@lmu.de .
Noa Pinter-Wollman is a Professor in the Department of Ecology and Evolutionary Biology at the University of California, Los Angeles, within the College of Life Sciences . Her work integrates field experiments, laboratory assays, computational modeling, and social network analysis to understand how individual variation among animals translates into emergent collective behavior, and how these dynamics intersect with conservation challenges. Research Focus: Mechanisms underlying collective decision-making in social insects (especially Argentine ants and harvester ants) Social network structure and its ecological consequences in endangered griffon vultures Interface between spatial ecology and social behavior, including impacts on disease transmission and conservation management Biomimetic insights from social animals to inform resilient human-designed systems Across 2023–2025, her team has produced a steady stream of high-impact articles that collectively advance four thematic pillars: (1) microbiome–behavior feedbacks in ants, (2) conservation technology for scavengers, (3) network-analytic methods for disentangling spatial versus social drivers of interaction, and (4) cooperative strategies that underlie invasion success in ants. The work is notable for integrating high-resolution tracking technologies with rigorous statistical modeling. Funding & Collaborations: Current NSF awards include the collaborative grant “ The causes and consequences of Higher Order Interactions (HOI) ” and prior support for “ Uncovering how links between social and spatial interactions affect ecological processes .” These grants foster interdisciplinary partnerships spanning ecology, computer science, and conservation practice. Laboratory & Team: The Pinter-Wollman Lab at UCLA houses graduate researchers, post-docs, and undergraduates who conduct integrative studies on ants, paper wasps, spiders, and vultures. The lab website ( https://pinter-wollmanlab.weebly.com ) provides protocols, data resources, and outreach materials that translate basic findings into actionable conservation guidance for wildlife managers.
Erin Strumpf is a Full Professor jointly appointed in the Department of Economics and the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. She is a founding member of McGill’s Public Policy and Population Health Observatory (3PO) and holds the distinguished William Dawson Scholar title. Her work bridges economics and population health, focusing on evaluating health and social policies through rigorous causal inference methods. Education: PhD in Health Policy and Economics, Harvard University BA, Smith College Research Interests: Prof. Strumpf’s research agenda centers on the impacts of health policies on health care delivery, population health outcomes, and health inequalities. She employs quasi-experimental designs and large-scale administrative data to assess interventions such as cancer screening programs, primary care reforms, and paid family leave policies. Her work spans multiple jurisdictions, including Canada, the United States, and France, and actively informs policymakers at provincial and national levels. Her recent projects include evaluating the cost-effectiveness of population-based cancer screening guidelines, assessing the health system impacts of integrated primary care in Quebec, and exploring how paid family leave policies reduce infant respiratory infections and promote equity. She is also a key contributor to the Canadian Institutes of Health Research’s Drug Safety and Effectiveness Network. Scientific Awards & Honors: William Dawson Scholar, McGill University Chercheur-boursier Junior 1 & 2, Fonds de Recherche du Québec – Santé Collaborations & Funding: Prof. Strumpf collaborates extensively with ministries of health and finance across Canadian provinces and with international agencies. She leads multidisciplinary teams that leverage rich administrative health data to generate actionable evidence for decision-makers. Her research is primarily aligned with the Centre on Population Dynamics’ Social and Economic Determinants of Health axis, and intersects with the Aging axis. Affiliations & Labs: She is affiliated with McGill’s Department of Equity, Ethics, and Policy, Family Medicine Department, Department of Oncology, and the Centre on Population Dynamics. Previously (2019-2022), she was an affiliated researcher with the cancer unit at l’Institut national d’excellence en santé et en services sociaux (INESSS).
Vicki H. Wysocki is Professor and Chair at Georgia Institute of Technology, leading pioneering research in mass spectrometry and structural biology. Her work focuses on developing advanced techniques to study protein complexes, proteomics, and metabolomics, with her research group maintaining an active presence at major conferences including ASMS 2024 and preparations for ASMS 2025. Her educational background includes: B.S. in Chemistry from Western Kentucky University (1982) Ph.D. in Chemistry from Purdue University (1987) Postdoctoral research at Purdue University (1987) and National Research Council/Naval Research Lab (1988-1989) Dr. Wysocki's research spans four interconnected areas: (1) development of surface-induced dissociation (SID) on commercial mass spectrometry platforms; (2) native mass spectrometry-guided structural biology for studying large protein-protein complexes; (3) multi-omics approaches integrating proteomics and metabolomics with genomics for biomarker discovery; and (4) determination of peptide structures using IR action spectroscopy. Her work bridges analytical chemistry, biochemistry, and structural biology to address fundamental protein science questions. Analysis of her recent publications reveals a strong focus on advancing native mass spectrometry techniques, particularly surface-induced dissociation, for structural characterization of protein complexes. Her work increasingly integrates multi-omics approaches to study bacterial pathogenesis, with emphasis on Salmonella infection mechanisms, while also exploring innovative instrumentation development for structural biology applications. Dr. Wysocki has received numerous prestigious awards: 2022 Thomson Medal from the International Mass Spectrometry Foundation 2022 ACS Division of Analytical Chemistry Award 2017 ACS Field and Franklin Award for Outstanding Achievement in Mass Spectrometry 2016 OSU Excellence in Biochemistry Award 2009 Distinguished Contribution to Mass Spectrometry Award from ASMS She actively mentors numerous graduate students and postdoctoral researchers, with current lab members including Kristie Baker, Yuan Gao, and Philip Lacey. Her research is supported by multiple NIH grants, enabling cutting-edge instrumentation development and biological applications. The Wysocki Group maintains strong collaborations across disciplines, particularly in microbiology and structural biology. The Wysocki Research Group operates state-of-the-art mass spectrometry facilities at Georgia Tech, including specialized instrumentation for native mass spectrometry and surface-induced dissociation. The group actively develops new methodologies and maintains the website nativems.gatech.edu as a resource for the mass spectrometry community, demonstrating continued leadership at the intersection of technology development and biological discovery.
George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.
Andrei Y. Khodakov is a Research Director (Professor equivalent) at the Unité de Catalyse et de Chimie du Solide (UCCS), UMR CNRS 8181, affiliated with University of Lille. He serves as Coordinator of the CEMOP research team (Catalysis for Energy and Synthesis of Platform Molecules) within the Heterogeneous Catalysis Department. His academic journey began with a Master's in Chemistry from Lomonosov Moscow State University (1987), followed by a PhD from the Zelinsky Institute of Organic Chemistry (1991), and a Dr. Sci. (Habilitation) from University of Sciences and Technologies of Lille (2002). Khodakov's research focuses on heterogeneous catalysis, with particular expertise in Fischer-Tropsch synthesis, syngas conversion to fuels and platform molecules, photocatalysis, CO 2 utilization, and methane valorization. His work bridges fundamental catalyst design with practical applications for sustainable energy and chemical production. He has pioneered research on nanoconfined catalysts, mobile promoters, and single-atom catalytic systems, with significant contributions to understanding reaction mechanisms and kinetics. His publication record spans over 122 papers since 2008, with recent work emphasizing CO 2 hydrogenation, photocatalytic methane conversion, and advanced catalyst design using nanoreactors and single-atom techniques. The research shows a clear trajectory toward sustainable catalytic processes for renewable feedstocks and carbon-neutral chemical production. CNRS Prize of Excellence (2011) CNRS Ph.D. and Research Supervising Bonus (2016) Special Invited Scientist of the Brazilian Government (2013-2016) Khodakov has supervised 27 PhD students and 14 post-doctoral researchers, demonstrating strong commitment to academic training. He teaches at Centrale Lille and University of Lille's Biorefinery Master's program, and organizes international summer schools for Chinese and Brazilian students. His research is supported by 4 ANR projects, 3 European projects, and over 20 industrial contracts, reflecting both academic excellence and industrial relevance. His laboratory focuses on catalyst design for sustainable chemical production, with particular emphasis on reactor engineering, in-situ characterization techniques, and development of catalysts for renewable feedstocks conversion.
Todd Millstein is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA). He served as the Computer Science Department Chair from 2022-2025 and is also an Amazon Scholar. His research focuses on making software systems more reliable through programming languages techniques, with significant contributions to network verification and probabilistic programming. Millstein received his Ph.D. from the University of Washington Department of Computer Science, where he was a member of the Cecil group led by Craig Chambers. Prior to that, he completed his undergraduate studies at Brown University under the guidance of Paris Kanellakis and Pascal Van Hentenryck. Millstein's research spans several areas of programming languages and systems with a focus on reliability. He has made significant contributions to network verification, developing the Batfish network configuration analyzer which is now managed by Amazon Web Services and forms the basis of Oracle Cloud's Network Path Analyzer. His work has been recognized with the ACM SIGCOMM Networking Systems Award in 2025. He also works on interactive program verification through lemma synthesis and scalable reasoning methods for probabilistic programming languages. His research bridges programming languages theory with practical systems challenges, as highlighted in his SPLASH/OOPSLA 2024 keynote "Everything is a Program (even if it's not)". Millstein's recent publications demonstrate a consistent focus on verification and reliability across multiple domains. His work shows a progression from foundational programming language techniques to practical applications in networking and probabilistic systems. Key themes include data-driven approaches to program analysis, synthesis of verification artifacts, and applying programming languages techniques to non-traditional domains like network configuration. Millstein's scientific achievements have been recognized with numerous prestigious awards including an NSF CAREER Award, an ACM SIGPLAN Most Influential PLDI Paper Award, an ACM SIGCOMM Networking Systems Award, IEEE Micro Top Picks selection, best-paper awards from PLDI, OOPSLA, and SIGCOMM, a Microsoft Research Outstanding Collaborator Award, an Okawa Foundation Research Grant, an IBM Faculty Award, and a Facebook Research Award. He has also received both the Northrop Grumman Excellence in Teaching Award (for junior faculty) and the Eon Instrumentation Inc. Excellence in Teaching Award (for senior faculty) from UCLA Engineering. Millstein advises several Ph.D. students including Ana Brendel, Poorva Garg (co-advised with Guy Van den Broeck), Rajdeep Mondal (co-advised with George Varghese), and Rathin Singha (co-advised with George Varghese). His research has been supported by various grants including an NSF CAREER Award, Okawa Foundation Research Grant, IBM Faculty Award, and Facebook Research Award. He has also been a Co-Founder and Chief Scientist of Intentionet, which was later acquired by Amazon Web Services. Millstein is actively involved in the Batfish project, an open-source network configuration analyzer that has had significant practical impact. Batfish is now managed by AWS, powers Oracle Cloud's Network Path Analyzer, and is used by dozens of companies. His research group continues to work on network reliability, developing techniques for scalable BGP policy verification and behavioral testing of protocol implementations.
