Hi, I'm Zia Uddin, Co-President of MCAP Acquisition Corporation, a SPAC sponsored by Monroe Capital LLC. I'm pleased to announce the merger of AdTheorent with MCAP Acquisition Corporation. A few words about the sponsor. Monroe Capital is one of the largest U.S. middle market asset managers. We manage over $10 billion and are currently invested in over 475 middle market companies. As most of you know, this is our third SPAC as a sponsor or member of the sponsor group. Our first, Thunder Bridge I/REPAY, is a top 10 SPAC performer, and we've used a similar playbook here. A critical part of our investment process is using our front row seat at over 475 companies to gain unique insights into industry and business trends and gain an informational advantage. Technology and software is one of Monroe Capital's largest industry exposures, with over 300 investments since 2004, including several ad tech names. Monroe Capital has been invested in AdTheorent since 2016, so we have a long running relationship with management and deep knowledge of the business. For the De-SPAC process, we engaged PricewaterhouseCoopers, Winterberry, and a variety of top-tier third parties to assist in diligence, including topics like technology, industry, financial, tax, and market positioning trends. Our extensive diligence confirms that AdTheorent is uniquely positioned to capitalize on a once in a generational opportunity for ad tech companies, since it is not dependent on Google or Facebook or personalized user information. AdTheorent is an important part of the ad tech ecosystem, specifically programmatic advertising, which is where ad placements are bought and sold digitally. On page two, you see that the programmatic advertising industry contributes about $90 billion of the $171 billion spent annually on digital media advertising. This market is growing at 17.6% annually. AdTheorent is projected to grow even faster. We're excited about AdTheorent for many reasons. First, there is a large and rapidly growing addressable market with strong, sustainable growth driven by digital transformation. COVID accelerated the transformation. It's a permanent shift in ad spend away from traditional media into digital. Second, AdTheorent was built on privacy-forward, machine learning-based solutions. It has an advantage over its ad tech peers, who will soon no longer be able to rely on third-party cookies and other one-to-one methods to target ads due to the increasing privacy regulations. Third, the company is delivering strong ROIs to customers, generating high retention and driving AdTheorent's attractive financial profile. AdTheorent is already a Rule of 50 company, and the net proceeds from this partnership is expected to be used to accelerate sales and marketing and pursue international expansion and strategic M&A to capture more of the growth opportunity. As an established company with strong profitability and cash flow characteristics, the AdTheorent business is rare as a De-SPAC in that the net proceeds will be used to accelerate growth initiatives as opposed to funding operating losses. A few details on the deal. We have reached an agreement that values AdTheorent today at $775 million enterprise value, and the combined business is expected to have a pro forma market capitalization of approximately $1 billion. We have raised $121.5 million, fully committed, oversubscribed, and upsized common stock PIPE, priced at $10 per share and anchored by top tier institutional and strategic investors, including Hana Financial Group, Palantir Technologies, and affiliates of Monroe Capital. The transaction is to provide as much as $213.5 million, but no less than $100 million of net proceeds to the company. This merger is expected to close in the Q4 of 2021. While we are excited about how this deal was structured, including being priced at a meaningful discount to the peer group, we are most excited about the opportunity to partner with a pioneer in programmatic advertising sector with the opportunity to experience accelerated growth over and above an already attractive growing market. With that, I'll hand it over to Jim Lawson, AdTheorent's CEO. Thank you, Zia. It's been great to partner with Monroe for the past five years, I'm very excited to talk about AdTheorent and the next chapter of our growth. AdTheorent has built and scaled a highly differentiated technology platform premised on machine learning, which solves a number of advertising industry problems. Most important, we execute digital ad campaigns which deliver measurable and provable business value to the world's most sophisticated digital advertisers. We do so using data science and machine learning without relying on sensitive or individualized personal data for targeting in order to drive tangible business outcomes for our customers as defined by them. Regulatory and industry changes which disfavor individualized and user profile-based advertising are further accelerating demand for our privacy forward solutions and creating sustained strategic advantages for AdTheorent. Before explaining how we are different, let's take a step back. In the programmatic advertising ecosystem, we occupy the demand side platform or DSP silo, as shown in this graphic on page four. We have reimagined what that means and created a new method for ad targeting. We call it predictive advertising. At a high level, a demand side platform or DSP connects sellers of digital media publishers with buyers of that digital media advertisers. Digital publishers desire to monetize their digital real estate by selling ads. Advertisers compete to buy the specific digital ad opportunities or digital impressions that are most likely to yield engagement with or business conversions by their customers. For example, a retailer seeking to drive online sales or a financial institution seeking credit card applications or insurance quotes. A real-time auction system allows the parties to buy and sell ad opportunities in microseconds, and that is the environment in which we work. AdTheorent enables digital advertisers to purchase media ad impressions in real time, one at a time. We have transformed how that is done. Our advertiser clients value us because we drive business conversions for them on their terms. Our publisher partners benefit from AdTheorent because our intelligent and privacy-forward targeting capabilities make their ad slots more valuable. On page five, we highlight the two most common ad targeting methods used by competing DSPs, cookie-based retargeting and segment-based audience targeting. At AdTheorent, we believe that these methods alone are limited and deficient, representing a strategic opportunity for our more advanced and holistic data-driven methods. The most prevalent method used today in programmatic advertising is cookie-based retargeting, which relies on users' web browsing histories. This method is very personalized. It recycles prospective customer pools rather than expands them, and we expect its usefulness to decline as a result of Google's and Apple's respective initiatives to make it more difficult for advertisers to leverage cookie IDs and device IDs for ad targeting. The second most prevalent method is segment-based audiences. These are essentially third-party licensed pools of IDs associated with perceived or historic user interests. These licensed audience segments often rely on data whose source, age, and reliability is not known or provable. In our view, it is often not accurate, and it yields significantly lower conversions or advertiser ROI than AdTheorent's ML-powered predictive advertising. AdTheorent predictive advertising is transformative and better. We are aware of no other programmatic media buying platform that uses machine learning and data science as the core method of ad targeting and campaign optimization at the impression level. Simply put, our platform uses machine learning and data science to identify ad impressions with the highest likelihood of converting on a client's desired action, whether an online action or a physical world action, such as store visitation. AdTheorent predictive advertising is not reliant upon third-party data licenses, cookies, device IDs, or any of the new unified or individualized IDs being discussed in the market right now. Instead, our platform ingests statistical and non-individualized data attributes in each bid request and machine learning models inform our real-time media buying decisions. If our customer's KPI is an online insurance quote application, our platform will identify data correlations which exist in the historic insurance quote conversion activity. In other words, our platform identifies the data attributes or combinations thereof, which are present most often when there is a conversion. This may be device type, operating system, one or more keywords in the URL, keywords in the page content, geographic data, time, or one of close to 200 other data attributes that are available to inform our machine learning models. Using historic conversion data, we can determine the likelihood that each specific bid request will drive the online or real-world actions our clients desire. Our platform assigns higher predictive scores to impressions with data attributes that correlate with historic conversions, and we look to buy those higher-scored impressions for our customers because they perform better. Our scale is virtually limitless. Our platform evaluates and assigns predictive scores to over 1 million impressions per second or 87 billion impressions per day. Driving digital conversions based on client-specified KPIs is like searching for a needle in a haystack, and machine learning and data science make this possible. Only computers powered by ML can do this. We bid on less than 1/10 of 1% of impressions that we score, and as our machine learning platform ingests more conversion data, it learns and optimizes campaign delivery, driving both conversion performance and cost efficiencies. Our privacy-friendly ad targeting gives AdTheorent a huge strategic advantage as industry privacy regulation increases. GDPR in Europe, CCPA in California, with more regulation coming. As noted, Apple and Google are also making it difficult to access and use individual user IDs. Several of our public industry peers are working to create alternative privacy-friendly digital IDs for use in digital ad targeting. We support those efforts because we believe those IDs can be useful, and we believe that consumers benefit from an open and free Internet subsidized by responsible advertising. We don't rely on individualized IDs to target ads. We rely on statistics and machine learning models. That is a notable and growing advantage. We are not aware of any other DSP that uses custom machine learning models to evaluate and predictively score individual impressions. Our data scientists make sure campaigns perform and continually feed learnings into our machine learning models, compounding our advantages. We also leverage advanced machine learning and data science to drive platform efficiencies by optimizing against ad impressions, which represent a greater risk of IVT fraud, poor viewability, and brand safety, or impressions that may not be measurable by third-party measurement providers. Taken together, this is how we win. Advertisers choose AdTheorent for the superior performance we drive relative to their scorecard, which is return on ad spend. Our predictive targeting drives performance, which in turn drives repeat business with the most sophisticated and discerning advertising customers in the world. We serve Fortune 500 customers across diverse and attractive industry verticals, including healthcare and pharmaceuticals, banking, financial services and insurance, government, education and nonprofit, retail, dining and QSR, and travel and hospitality. A number of our multi-year, multi-million dollar clients started with $100,000 pilots. A quick example. A Fortune 500 global pharmaceutical brand's KPI was new patient starts. We leveraged a two-pronged approach using predictive targeting and third-party pharmaceutical audience segments, developing custom machine learning models that identified anonymized condition sufferers with the highest likelihood of completing various actions on the brand site. Our results outperformed our client's cost per action benchmarks by 4x, driving 5,000 incremental conversions and exceeding the expectations of our customer, fueling revenue growth of nearly 2,000% over five years. There are many examples like this. I'd also like to share our high-level point of view about the competitive landscape for DSPs. We believe that AdTheorent predictive advertising is the future, that other DSPs are relying on the tools and methods of the past. Like other DSPs, for as long as the data is available, we can execute retargeting strategies, we can target client-desired third-party audiences. We make those types of campaign executions better by adding machine learning optimizations. Geo-targeting and geo-fencing enhanced by AdTheorent machine learning drives greater performance. Creative optimizations powered by machine learning drive superior user experience and engagement. AdTheorent contextual advertising is more than targeting categorized publisher content. It is better and more effective when you use machine learning-powered natural language processing and keyword-driven optimizations. We've doubled down on complex KPI achievement and ROI. We don't rely on selling clicks or video completes. We sell our ability to use ML-powered digital advertising to drive actual business outcomes, and we prove to customers with reporting and analysis that our ads drove business results. This is what makes AdTheorent special. AdTheorent was founded in 2012 based on the thesis that mobile advertising would be big, and ad targeting in this medium needed to be executed without cookies. We quickly learned that statistics-powered machine learning could drive superior performance across desktop and all other screens. We are now truly omni-channel and execute campaigns across all media types, digital display, rich media, video, CTV, and others. We are already succeeding and operating at a high level. Our 2021 year-over-year growth outlook is 80% for brands directly, 60% partnership commitment increases, 50% bookings growth, 300% CTV growth, and 40% video growth. Demand for our services has never been greater. I'll briefly highlight three large growth opportunities. CTV, or connected TV, is a major growth opportunity for AdTheorent. Streaming services like Hulu are the fastest-growing sector as traditional TV dollars shift to digital. Close to $45 billion in 2021, $53 billion in 2022. AdTheorent applies machine learning to CTV to drive performance-based outcomes. With limited investment, our 2021 CTV revenues are anticipated to be $40 million, which represents approximately 300% growth year-over-year. With additional investment, we believe we can continue to materially outpace segment growth. Our platform is also available for a customer's direct use, offering a flexible method of transacting with clients and extending our addressable market. AdTheorent Direct Access lets advertisers, both brands and agencies, drive the AdTheorent platform themselves with an enterprise or SaaS subscription, supplemented by AdTheorent data science as a service. We also continue to pursue our successful land and expand program, which involves continuing to scale healthcare, pharma, and banking, financial services, and insurance solutions, capitalizing on unique advantages related to AdTheorent privacy-friendly data practices and targeting and modeling protocols, which comply with industry regulations and brand model governance. Our dedicated vertical teams will also deliver more unique solutions to expand growing verticals such as auto, entertainment, and CPG. In addition to those three, we are also seeing exciting avenues for growth through international expansion and M&A. We expect the net proceeds from the transaction to support these inorganic growth opportunities, which have not historically been a core focus. To summarize, we have a multi-year track record of disciplined operational and financial success. Our core technology platform and products are highly differentiated and value-adding from the perspective of clients. We have several strategic advantages accelerating demand for our offerings. Industry and regulatory changes make our approach to data and digital ad targeting generally the future for ad tech. Accessing the public market now will allow us to pursue our massive growth opportunity more aggressively. We believe there has never been a better time to be an AdTheorent stakeholder, and we are excited about the opportunity to perform on this bigger stage. Now I'll turn it over to our CFO, Chuck Jordan, to walk through some financial highlights. Jim, thank you. We have a long-established history of operating efficiently, delivering top-line and margin growth, and strong cash flows. We have organically built a strong balance sheet, which this transaction will significantly strengthen and serve as a foundation to accelerate our investment in growth opportunities. Our projections are conservative and reflect organic growth only. Potential benefits from international expansion or M&A are excluded from the figures we are presenting today. Like others in the industry, we present both revenues and revenues ex TAC, or traffic acquisition costs, which are variable campaign-driven costs. Revenue ex TAC is a non-GAAP metric we and our peers monitor and is conceptually similar to contribution margin. When we present EBITDA margin, we are doing so using adjusted EBITDA, and it is calculated as a percentage of revenue ex TAC. On page 19, the top chart here highlights our revenue growth trajectory, the second, the margins we had and will continue to deliver alongside this revenue growth. The momentum we established in the back half of 2020 set the foundation for a strong 2021. Revenues grew 34% year-over-year in the Q1 of 2021, and the company expects growth in excess of 70% in Q2, demonstrating strong increase across segments for the H1 of 2021. We are projecting $102 million revenue ex TAC for 2021 and expect continued strong growth in 2022. We will continue managing operating leverage effectively, and we are on track to deliver 30% adjusted EBITDA margins in 2021. We have a long history of growing revenues and margins, and we will continue to do so at a greater scale. We're currently a Rule 50 company. Moving on to page 20, you can see we doubled our revenues from 2017 to 2021, and by 2023, we expect to double again. We do expect temporary adjusted EBITDA margin compression in 2021 and 2022 as we absorb incremental public company costs and invest to drive growth. Margins are projected to be back in the 30% range in 2023 as the business scales and recover these costs. I think all the pieces are in place here for us to scale our operations and grow both top and bottom lines. We're looking forward to executing against this plan. I'll turn the digital floor back to Zia, who will talk about comps and valuation. Thanks, Chuck. We believe the company compares favorably to the competitor group in terms of revenue growth and adjusted EBITDA margin. We were purposeful in pricing the transaction at a discount to the public comps. We're purchasing AdTheorent at under 6x 2022 revenue ex TAC versus comps at nearly 10x as of July 23, 2021. On an adjusted EBITDA basis, we're looking at a purchase price of approximately 21x versus comps at approximately 40x, a nearly 50% discount. AdTheorent is an opportunity to buy into a uniquely differentiated company in a rapidly growing industry for a meaningful discount to its peers. Thank you.